bioRxiv Subject Collection: Neuroscience's Journal

Toward Crosstalk-free All-optical Interrogation of Neural Circuits
All-optical interrogation, based on high-resolution two-photon stimulation and imaging, has emerged as a potentially transformative approach in neuroscience, allowing for the simultaneous precise manipulation and monitoring of neuronal activity across various model organisms. However, the unintended excitation of light-gated ion channels such as channelrhodopsin (ChR) during two-photon calcium imaging with genetically encoded calcium indicators (GECIs) introduces artifactual neuronal perturbation and contaminates neural activity measurements. In this study, we propose an active pixel power control (APPC) approach, which dynamically adjusts the imaging laser power at each scanning pixel, to address the challenge. We aim to achieve simultaneous two-photon optogenetic manipulation and calcium imaging with a single femtosecond laser, while minimizing the crosstalk between manipulation and imaging. To study this technology's capabilities, we applied it to the larval zebrafish brain in vivo. Our results demonstrate that the APPC approach preserves GECI signal quality while suppressing optogenetic artifacts significantly. This enhances the accuracy of neural circuit dissection and advances the precision of all-optical interrogation, offering a robust framework for probing neural circuit dynamics and causality in vivo with high fidelity, potentially across various model organisms. Importantly, this technology can be seamlessly integrated with commonly used two-photon microscope systems in laboratories worldwide.

TNF-α-induced type I IFN signalling decreases neurogenesis and drives T cell chemotaxis
Adult hippocampal neurogenesis (AHN) is essential for learning, memory, and mood regulation, and its disruption is implicated in ageing, neurodegeneration, and mood disorders. However, the mechanisms linking inflammation to AHN impairment remain unclear. Here, we identify chronic tumour necrosis factor-alpha (TNF-) signalling as a key driver of neurogenic dysregulation via a previously unrecognized type I interferon (IFN) autocrine/paracrine loop in human hippocampal progenitor cells (HPCs). Using a human in vitro neurogenesis model, single-cell RNA sequencing, and functional T cell migration assays, we show that TNF- induces a robust type I IFN response in HPCs, promoting chemokine and CXCR3-dependent T cell recruitment and suppressing neurogenesis. This inflammatory signalling cascade drives a fate switch in HPCs from a neurogenic trajectory towards an immune-defensive phenotype, with critical implications for infectious and inflammatory disease pathogenesis. These findings uncover a key inflammatory checkpoint regulating human AHN and highlight potential therapeutic targets to restore neurogenesis in chronic inflammatory states.

Predictive modeling of TMS-evoked responses: Unraveling instantaneous excitability states
Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) and electromyography (EMG) provides a unique window into instantaneous cortical and corticospinal excitability states. We investigated 50 healthy participants to determine how fluctuations in pre-stimulus brain activity influence single-trial TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs). We developed a novel automated source-level TEP extraction method using individualized spatiotemporal priors that is robust against poor single trial signal-to-noise ratios (SNRs) and ongoing oscillations. TEP and MEP amplitudes were predicted with linear mixed-effects models based on pre-stimulation EEG band-powers (theta to gamma), while accounting for temporal drifts (within-session trends), coil control, and inter-subject differences. We found that higher pre-stimulus sensorimotor alpha, beta, and gamma power were each associated with larger TEPs, indicating a more excitable cortical state. Increases in alpha and gamma power immediately before stimulation specifically predicted larger MEPs, reflecting increased corticospinal excitability. These results reveal relationships between ongoing oscillatory brain states and TMS response amplitudes, identifying EEG biomarkers of high- and low-excitability states. In conclusion, our study demonstrates the feasibility of single-trial source-level TMS-EEG analysis and shows that spontaneous alpha-, beta-, and gamma-band oscillations modulate motor cortical and corticospinal responsiveness. These findings pave the way for EEG-informed, brain-state-dependent TMS protocols to optimize neuromodulatory interventions in clinical and research applications.

Modular Inter-brain Synchrony Network Associated with Social Difficulty in Autism Spectrum Disorder: a Graph Neural Network-Driven Hyperscanning Study
Understanding social difficulties in Autism Spectrum Disorder (ASD) remains challenging due to its neurobiological heterogeneity and the limited ecological validity of conventional neuroimaging methods in capturing dynamic social interactions. Hyperscanning analysis based on functional near-infrared spectroscopy (fNIRS), which measures inter-brain synchrony (IBS) during dyadic interaction, offers a novel avenue to address these challenges. However, prior studies on ASD have reported inconsistent findings, primarily focusing on intra-regional synchronization while overlooking cross-regional network dynamics. To bridge this gap, we proposed an interpretable graph neural network (GNN) model to systematically identify ASD-specific IBS modular network between child-caregiver dyads during naturalistic cooperative puzzle-solving and free-talking tasks. We identified distinctive key IBS sub-networks for the cooperative puzzle-solving task and free-talking task, with the frontal eye field (FEF) of caregivers, the dorsal lateral prefrontal cortex (DLPFC) and the motor region of children highlighted. Furthermore, the key IBS sub-networks were found to be able to predict multiple domains of the core ASD symptoms. By integrating hyperscanning with GNN-driven analysis, this work uncovers task-dependent inter-brain neural mechanisms underlying social difficulties in ASD. These findings advance the field by proposing a data-driven framework to identify IBS biomarkers tied to clinical profiles, paving the way for personalized interventions that integrate computational neuroscience with clinical practice.

Comparing Brain-Score and ImageNet performance with responses to the scintillating grid illusion
Perceptual illusions are widely used to study brain processing, and are essential for elucidating underlying function. Successful brain models should then also be able to reproduce these illusions. Some of the most successful models for vision are several variants of Deep Neural Networks (DNNs). These models can classify images with human-level accuracy, and many behavioral and activation measurements correlate well with humans and animals. For several networks it was also shown that they can reproduce some human illusions. However, this was typically done for a limited number of networks. In addition, it remains unclear whether the presence of illusions is linked to either how accurate or brain-like the DNNs are. Here, we consider the scintillating grid illusion, to which two DNNs have been shown to respond as if they are impacted by the illusion. We develop a measure for measuring Illusion Strength based on model activation correlations, which takes into account the difference in Illusion Strength between illusion and control images. We then compare the Illusion Strength to both model performance (top-1 ImageNet), and how well the model explains brain activity (Brain-score). We show that the illusion was measurable in a wide variety of networks (41 out of 51). However, we do not find a strong correlation between Illusion Strength and Brain-Score, nor performance. Some models have strong illusion scores but not Brain-Score, or vice-versa, but no model does both well. Finally, this differs strongly between model types, particularly between convolutional and transformer-based architectures, with transformers having low illusion scores. Overall, our work shows that Illusion Strength measures an important metric to consider for assessing brain models, and that some models could still be missing out on some processing important for brain functioning.

Botulinum Neurotoxin A1 Signaling in Pain Modulation within Human Sensory Neurons
Botulinum neurotoxin type A1 (BoNT/A1) is an effective treatment for chronic migraine, but its direct mechanism of action on human sensory neurons has not been fully elucidated. While rodent studies on dorsal root ganglion (DRG) and trigeminal ganglion (TG) show that BoNT/A1 inhibits neurotransmission, including calcitonin gene-related peptide (CGRP) release, by cleaving SNAP-25, only one previous study has assessed its effect on human DRG neurons. The objective of this study was to understand the mechanism of action of BoNT/A1 in cultured human sensory neurons and assess, using RNA sequencing, the transcriptomic consequences of BoNT/A1 treatment. Using DRGs obtained from organ donors the expression of key targets, including SNAP25, SV2C, & CALCA, was validated by mining existing transcriptomic datasets as well as immunohistochemistry. Cultured dissociated human DRG neurons treated with BoNT/A1 were used to examine cleavage of SNAP25, release of CGRP and transcriptomic changes after BoNT/A1 treatment. SV2C was found to be widely expressed in human DRG neurons in a pattern that completely overlapped with CGRP expression. Consistent with this finding, BoNT/A1 disrupted SNARE protein complexes in human DRG neurons as demonstrated by SNAP-25 cleavage in most somatosensory neurons and a reduction in capsaicin-evoked CGRP release, indicating impaired vesicle fusion. Moreover, Bulk RNA sequencing experiments revealed downregulated expression of a large subset of genes responsible for neurotransmitter and neuropeptide release from neurons suggesting a novel mechanism through which BoNT/A regulates neurotransmission. These results provide new insight into the molecular mechanisms by which BoNT/A may exert its pain-relieving effects in humans.

Neural Predictors of Functional Genomic Responses to Negative Social Evaluation in Adolescent Females
Background Social stress, particularly when experienced during adolescence, can have a lasting impact on health and well-being. Among other key biological pathways, inflammatory and innate immune signaling appear to play important roles in linking stress to physical and mental health problems. Individual differences in sensitivity to social threats may leave certain people more vulnerable to stress and its harmful sequelae than others, and a growing body of research has found that stress sensitivity is reflected in neural activity throughout the threat network. However, few studies have investigated whether heightened neural sensitivity to social threats is related to acute changes in immune and neuroendocrine pathways relevant to health, particularly among those for whom the effects of stress may be especially impactful. Method In the current research, 52 adolescent females (MAge = 14.90, SD = 1.35) participated in a functional magnetic resonance imaging study to examine brain activity and functional connectivity during a social evaluation task. Nearly half of the sample (n = 22) were identified as having a maternal history of depression. Blood samples were collected prior to the task, as well as 35 and 65 min. after the task began, and were used for transcriptional profiling. Results The primary analyses tested whether threat network activity and connectivity predicted the magnitude of change in gene expression from baseline to the follow-up time points. Results revealed robust shifts in expression of genes in innate immune pathways in response to the task (e.g., hypoxia inducible factor-1, interferon signaling). Although activity across the entire threat network was related to individual differences in gene expression, anterior cingulate cortex-insula and insula-ventromedial prefrontal cortex connectivity were most consistently related to up- and down-regulation of immune pathways, respectively. These patterns were further moderated by differences in maternal depression history. Conclusion Results demonstrate that individual differences in threat network activity may have important implications for biological responses to social threat among adolescent females. In turn, these findings both provide insights into neural signatures of social stress vulnerability and the biological pathways that may contribute to poorer health outcome among those most vulnerable to stress.

Benefits of dual-tasking on implicit sensorimotor adaptation
Attention plays a crucial role in maintaining precision and effectiveness in goal-directed actions. Although there is evidence that dividing attention across tasks impairs performance in various domains, the impact of attention on sensorimotor adaptation remains inconclusive, with some studies reporting deficits and others showing no effects. Because sensorimotor adaptation arises from the interaction of explicit and implicit processes, this discrepancy may reflect differential effects of attention on each process. Here, we investigate how divided attention influences implicit sensorimotor adaptation using an error-clamp paradigm, coupled with a random dot kinematogram (RDK) motion coherence discrimination task. We also assessed whether the timing of the secondary task affects error processing during sensorimotor adaptation by presenting the RDK either during the outward movement (coinciding with error feedback), or the inward movement (following error feedback). We observed that attentional manipulation influenced implicit sensorimotor adaptation only when the RDK was presented on the outward movement, not the inward movement. Remarkably, implicit sensorimotor adaptation was enhanced when attention was divided, compared to when attention was focused entirely on the adaptation task. This suggests that implicit sensorimotor adaptation is sensitive to attentional demand, particularly during the time window where error feedback is received.

Xylazine exacerbates fentanyl-induced respiratory depression and prevents rescue by naloxone in mice
Xylazine is a veterinary sedative and widespread adulterant of illicit opioids, where it is commonly combined with the highly potent synthetic mu opioid receptor (MOR) agonist fentanyl. Xylazine adulteration of fentanyl is associated with increased risk of lethal overdose and decreased efficacy of reversal by the MOR antagonist naloxone. Here we use whole body plethysmography in mice to show that xylazine produces profound respiratory depression at subanesthetic doses. Xylazine rapidly and dose-dependently suppressed minute ventilation, tidal volume, and respiratory frequency. These effects were dependent on alpha-2 adrenergic receptors and were fully blocked by coadministration of the alpha-2 adrenergic antagonist atipamezole. Atipamezole, administered alone, produced only modest reversal of fentanyl-induced respiratory depression. Xylazine, when combined with a dose of fentanyl with modest respiratory effects, suppressed breathing with greater efficacy than when administered alone. Strikingly, doses of naloxone sufficient to completely reverse fentanyl-induced respiratory depression were ineffective in reversing the respiratory suppression induced by xylazine-adulterated fentanyl. By contrast, combinations of naloxone with atipamezole rapidly and fully reversed the suppression of breathing induced by xylazine-adulterated fentanyl. Our results show that xylazine suppresses breathing via activation of MORs, an effect enhanced by coadministration with the MOR agonist fentanyl. Respiratory suppression inflicted by the mixture of xylazine and fentanyl resisted reversal by naloxone but was fully reversible by subsequent coadministration of both naloxone and atipamezole. These observations have profound implications for the current opioid epidemic.

The cost of cognition: Measuring the energy consumption of non-equilibrium computation
In biological systems, survival is predicated on an animal being able to perform computations quickly on a minimal energy budget. What is the energy consumption of non-equilibrium brain computation, i.e., what is the cost of cognition? Previous literature has estimated the metabolic cost using neuroimaging measures of glucose consumption but complementary to these findings, here we directly estimate the computational costs by combining the new field of stochastic thermodynamics with whole-brain modelling. We developed the COCO (COst of COgnition) framework using an analytical expression quantifying the links between energy cost, non-equilibrium and information processing for any given brain state measured with neuroimaging. Importantly, this key relationship also holds at the level of individual brain regions. We used this to quantify the benefits of information processing on the highly anatomically, interconnected hierarchical systems of the brain. Crucially, in empirical neuroimaging data we demonstrate that the human brain uses significantly less energy overall than other mammals (including non-human primates and mice), suggestive of an evolutionary optimisation of the effectiveness of computation. Focusing on the cost of cognition, using large-scale human neuroimaging data of 970 healthy human participants, we show that the resting state uses significantly less energy that seven different cognitive tasks. Furthermore, different kinds of tasks require different amounts of non-equilibrium, information processing and energy consumption. We found that tasks requiring more distributed computation also use more energy. Overall, these results directly quantify the cost of cognition, i.e., the non-equilibrium and energetic demands of information processing, allowing a deeper understanding of how the brain compute in a way that is far more energy efficient than current generations of digital computers and artificial intelligence.

Stimulus effects dwarf task effects in human visual cortex
Task context affects stimulus representations in human visual cortex, suggesting that visual representations are flexible. However, this interpretation is at odds with a major computational goal of the human visual system: creating a perceptually stable representation of the external visual environment. How does the visual system balance stability and flexibility? Here, human participants (71 percent females) categorized object images and written words according to different task rules, while brain responses were measured with fMRI. Using an ANOVA-based modeling strategy, we precisely quantified the relative contributions of stimulus, task, and their interaction in explaining representational variance across the cortical hierarchy. Our results show that stimulus effects account for the overwhelming majority of explainable representational variance across the ventral visual system: > 95 percent in V1 and V2, and > 90 percent in higher-level visual cortex. In prefrontal cortex, the relative contributions reverse: task effects dominate stimulus effects, accounting for 80 percent of explainable representational variance. In parietal cortex, contributions of stimulus and task are approximately equal. Our findings suggest that population coding in sensory cortex is optimized for representational stability to allow a consistent interpretation of the external environment. Population coding in parietal and frontal multiple-demand cortex, by contrast, is optimized for representational flexibility to accommodate changing behavioral goals and support flexible cognition and action.

Significance statementStimulus representations in human visual cortex are affected by behavioral goals and are therefore thought to be flexible. However, this view is inconsistent with a major computational goal of the human visual system: creating a perceptually stable representation of the external environment.

Here, we show that modulatory effects of behavioral goals on stimulus representations in visual cortex are surprisingly small. In contrast, behavioral goals strongly affect representations in parietal and frontal multiple-demand cortex. Our findings suggest that population coding in sensory cortex is optimized for stable perception, while population coding in parietal and frontal multiple-demand cortex is optimized for flexible cognition.

Comparative Analysis of Neonatal Hypomyelination Models Using Spatial Transcriptomics
White matter injury (WMI) is a major cause of morbidity in premature infants, contributing to 5% to 10% of cerebral palsy cases and up to 50% of cognitive and behavioral deficits in the United States. Two commonly used preclinical models, intermittent hypoxia (IH) and hypoxia-ischemia (HI) are widely employed to investigate the effects of WMI. The internal capsule (IC) and corpus callosum (CC) are major white matter tracts undergoing active myelination during the neonatal period, making them particularly vulnerable to hypoxic insults. This study aims to compare the effects of IH and HI models on myelination as well as the involvement of inflammatory cells in the IC and CC. We evaluated five oligodendrocyte (OL) subtypes, along with astrocytes, microglia, and activated microglia in IC and CC at postnatal day 12 (P12) and day 20 (P20) using spatial transcriptomics (CosMx, Novogene). For the HI model, C57BL/6 mice at P10 underwent permanent ligation of the left carotid artery followed by 45 minutes of hypoxia (8% O2 / 92% N2). For the IH model, P3 mice were exposed to 5% O2 / 95% N2, twice daily for five consecutive days. Animals were euthanized at P12 and P20, perfused transcardially, and brains were post-fixed in 4% paraformaldehyde, dehydrated in an ethanol series, embedded in paraffin, and coronally sectioned at 7 m. Slides were submitted for CosMx spatial transcriptomic analysis (NanoString Technologies), and data analysis was performed using the Seurat package in RStudio. Our results demonstrate that IH and HI models affect OL populations differently, and these effects vary by brain region. In the IC, the IH model caused earlier and more pronounced changes in OL differentiation-related gene expression compared to HI. In contrast, the CC was more affected by HI. Moreover, in the HI group, mature OL s in both regions showed reduced expression of myelination-associated genes. This was accompanied by greater activation of inflammatory cells and increased intercellular communication between these cells and mature OLs, potentially contributing to the observed hypomyelination. Overall, our study provides critical insights into how each model of neonatal hypoxia differentially impacts white matter development. This knowledge can help refine preclinical strategies and guide therapeutic research tailored to the underlying pathology of each model.

Gradual sensory maturation promotes abstract representation learning
Human infants begin life with limited visual capacities, such as low acuity and poor color sensitivity, due to gradual sensory maturation. In contrast, machine learning models are trained on high-fidelity inputs from the outset, often leading to shortcut learning and overfitting to spurious correlations. Here, we show that early sensory immaturity plays a critical role in shaping bias-resistant, abstract visual representations that conventional models struggle to develop. Using neural network simulations and human psychophysics experiments, we demonstrate that gradual sensory development supports the emergence of robust and generalizable internal representations, reduces reliance on superficial cues, and promotes disentangled representations that enable compositional reconstruction and visual imagination. Comparative analyses of human and model behavior reveal shared patterns of bias resistance and adaptive generalization, including resilience to misleading information. Our findings suggest that gradual sensory maturation is not merely a developmental constraint, but rather a key mechanism that enables abstract representation learning.

Maternal singing synchronizes the preterm infants' brain
Singing to infants is a universal human practice that has beneficial effects on infant's cognitive and affective development. Children born preterm have impaired brain development, and their perception of maternal speech is known to be affected by the atypical hospital auditory environment. Understanding how preterm infants perceive maternal singing is of critical importance, yet it remains largely unexplored. Using high-density EEG, we examined neural responses to the same melody presented through maternal singing, stranger singing, and instrument, and compared the responses in 12 preterm infants. Moreover, to examine their processing of spatialisation, auditory stimuli were presented under monaural and binaural conditions. Preterm newborns are able to discriminate the same melody when sung by their mother, a stranger, or played by an instrument. When presented monaurally, the mother's singing voice enhances widespread brain synchrony across the entire scalp. In contrast, this synchrony diminishes with binaural spatialization. These findings suggest that maternal singing constitutes a highly salient auditory stimulus for preterm newborns, eliciting a distinct neural signature. Given that brain synchrony is a critical component of healthy brain function and development, harnessing maternal singing may offer a promising, natural intervention to support neurodevelopment-particularly in vulnerable populations such as preterm infants.

Spatially and non-spatially tuned hippocampal neurons are linear perceptual and nonlinear memory encoders
The hippocampus has long been regarded as a neural map of physical space, with its neurons categorized as spatially or non-spatially tuned according to their response selectivity. However, growing evidence suggests that this dichotomy oversimplifies the complex roles hippocampal neurons play in integrating spatial and non-spatial information. Through computational modeling and in-vivo electrophysiology in macaques, we show that neurons classified as spatially tuned primarily encode linear combinations of immediate behaviorally relevant factors, while those labeled as non-spatially tuned rely on nonlinear mechanisms to integrate temporally distant experiences. Furthermore, our findings reveal a temporal gradient in the primate CA3 region, where spatial selectivity diminishes as neurons encode increasingly distant past events. Finally, using artificial neural networks, we demonstrate that nonlinear recurrent connections are crucial for capturing the response dynamics of non-spatially tuned neurons, particularly those encoding memory-related information. These findings challenge the traditional dichotomy of spatial versus non-spatial representations and instead suggest a continuum of linear and nonlinear computations that underpin hippocampal function. This framework provides new insights into how the hippocampus bridges perception and memory, informing on its role in episodic memory, spatial navigation, and associative learning.

Single Cell Proteomics in the Developing Human Brain
Proteins are the functional effectors of virtually all biological processes, and accurately measuring their abundance and dynamics is essential for understanding development and disease. Although mRNA levels have historically been used as proxies for protein expression, growing evidence, especially from studies of the human cerebral cortex, has revealed widespread discordance between transcript and protein abundance. To directly address this limitation, we developed a rigorously optimized workflow that combines single-cell mass spectrometry with precise sample preparation to resolve, for the first time, quantitative proteomes of individual cells from the developing human brain. Our platform achieved deep proteomic coverage (~800 proteins per cell) even in immature prenatal human neurons (5-10um diameter, ~100pg of protein per cell), capturing major brain cell types and enabling proteome-wide characterization at single-cell resolution. This approach revealed extensive transcriptome-proteome discordance across cell types, with particularly strong discrepancies in genes associated with neurodevelopmental disorders, a finding validated through orthogonal experiments. Proteins exhibited markedly higher cell-type specificity than their mRNA counterparts, underscoring the importance of proteomic-level analysis for resolving cellular identity and function. By reconstructing developmental trajectories from radial glia to excitatory neurons at the proteomic level, we identified dynamic stage-specific protein co-expression modules and pinpointed the intermediate progenitor-to-neuron transition as a molecularly vulnerable phase linked to autism. Altogether, by enabling single cell proteomics, this study establishes a foundational resource and technological advance for developmental neuroscience. It demonstrates that single-cell proteomics can capture critical developmental events and disease mechanisms that are undetectable at the transcript level. As this technology continues to improve in sensitivity and scalability, single-cell proteomics will become an indispensable tool for uncovering the molecular logic of brain development and for illuminating pathophysiological processes underlying neurodevelopmental disorders.

Hierarchical systems in the default mode network when reasoning about self and other mental states
Humans spend time contemplating the minds of others. But this ability is not limited to external agents - we also turn the lens for reading minds inward, reflecting on our own thoughts, emotions, and sense of self. Some processes involved in reasoning about minds may rely on shared mechanisms, while others may be specific to the agent under consideration. We developed a paradigm where participants performed either a mental state inference task or a control task targeting either another person presented onscreen or their own mind. Using fMRI and multi-voxel pattern analysis, we replicate a well-established self-other axis along the medial wall of prefrontal cortex: ventral regions selectively decoded mental state inference patterns for self, but not other, whereas more dorsal regions decoded mental state inference for both self and other, compared to control conditions. Posterior cingulate cortex, on the other hand, differentiated the target of mental state inference. Using a cross-classification analysis, we also found patterns in the dorsomedial prefrontal cortex, ventromedial prefrontal cortex, and right temporoparietal junction were sensitive to mental state reasoning in general, regardless of the target agent. These findings highlight one process reflecting reasoning specific to the agent and another reflecting the reasoning process itself.

Repetition-related gamma plasticity in macaque V1 and V2 is highly stimulus specific and robust to stimulus set size
When a visual stimulus is repeated, the cortex has the opportunity to adjust its processing. Indeed, repeated stimuli induce reduced neuronal spike rates and increased neuronal gamma-band synchronization. Previous studies found the repetition-related gamma increase to occur both in human and non-human primates, for artificial and natural stimuli, to persist for minutes and to not transfer between strongly differing stimuli. Here, we further investigated the repetition-related effects using laminar recordings of multi-unit activity and local field potentials from awake macaque areas V1 and V2. We find that the effects on spike rate and gamma occur in all laminar compartments of V1 and V2. We quantify the degree of stimulus specificity with oriented gratings and find that the repetition-related gamma increase does not transfer to gratings differing by merely 10 degrees, the smallest difference tested. Furthermore, we find that the repetition-related effects are robust to stimulus set size, occurring both when one stimulus was repeated and when eighteen different interleaved stimuli were repeated. Finally, we show that alpha-beta activity increases and remains elevated when a stimulus is repeated, and decreases sharply when an unexpected stimulus is presented. These results suggest that repetition-related plasticity leads to changes in spike rates and rhythmic neuronal synchronization in different frequency bands that adjust the cortical processing of repeated stimuli.

Astrocyte CB1 receptors drive blood-brain barrier disruption in CNS inflammatory disease
Reactive astrocytes shape central nervous system (CNS) inflammation and participate in myelin damage and repair mechanisms in multiple sclerosis (MS). Through the activation of cannabinoid CB1 receptors (CB1R) expressed by neurons and oligodendrocyte lineage cells, endocannabinoid signaling restricts neurodegeneration and promote remyelination in preclinical MS models. However, despite accumulating evidence that supports a crucial role for these receptor populations in brain physiology and pathology, the implications of astrocyte CB1R signaling in MS initiation and progression remain uncertain. Using complementary in vivo disease models, here we investigated the effects of targeted genetic deletion of astrocytes CB1R on the expression of MS-like pathology in mice. Interestingly, astrocyte-specific deletion of CB1R reduced demyelinating neuropathology, attenuated astrocyte reactivity and improved clinical deficits during the time-course of experimental autoimmune encephalomyelitis (EAE). Mice with astrocyte CB1R inactivation displayed unaltered oligodendrocyte populations both in EAE lesions and in lysolecithin-induced remyelinating spinal cord lesions, likely excluding that astrocyte CB1R modulate myelin repair processes. Conversely, inactivation of CB1R in astroglial cells restricted humoral and leukocyte parenchymal infiltration and reduced the expression of vascular effectors in EAE lesions. Finally, loss of blood-brain barrier (BBB) function induced by cortical microinjection of VEGF-A was less severe in GFAP-CB1R-KO mice. These results show that astrocyte CB1R signaling constitutes a significant pro-inflammatory mechanism in MS and bring to light a deleterious role for endocannabinoid-mediated modulation of astroglial cells with potential implications in the etiopathology and therapy of neuroinflammatory disorders.

Reactivation of threat conditioning memory in humans: disentangling the effects on emotional memory and cognitive biases.
Learning to detect and respond to threats is fundamental for survival and is often modeled through threat conditioning (TC) paradigms. While these paradigms reliably produce implicit memories that elicit physiological and behavioral responses to conditioned stimuli (CS), less is explored about how TC influences cognitive and emotional biases, particularly those implicated in anxiety disorders, such as threat overestimation and negative stimulus representation. In this study, we investigated the dynamic interaction between the reactivation of the implicit threat memory and these cognitive biases using a validated TC paradigm in humans. In Experiment 1, participants underwent TC on Day 1, followed by a memory reactivation session (incomplete reminder: one unreinforced CS+) and a highly demanding working memory (HWM) task, used as an amnesic manipulation, or a control condition on Day 2. On Day 3, memory retention was tested using a simplified, single-trial protocol (one CS+, one CS-, and one neutral CS), followed by tasks assessing threat valuation and representation. Results indicated that the HWM task administered post-reactivation significantly reduced skin conductance responses (SCRs) and attenuated cognitive biases, without altering expectancy of the unconditioned stimulus (US). In Experiment 2, we evaluated the effect of varying reactivation frequency (none, one, or two reminders) on implicit memory and cognitive biases. While repeated reactivations generalized the conditioned response to other stimuli, cognitive and emotional biases remained stable, suggesting a dissociation between memory generalization and evaluative processing. These findings demonstrate that implicit threat memories can be selectively modified through post-reactivation interventions, affecting both physiological and cognitive-emotional domains. Importantly, the distinct effects of memory reactivation and reconsolidation on physiological versus cognitive outcomes support the existence of temporally and functionally dissociable mechanisms. This research highlights the need to consider cognitive biases alongside physiological responses when evaluating memory-based interventions and offers novel insight into mechanisms underlying anxiety maintenance and treatment.

Identification of Chlamydia pneumoniae and NLRP3 inflammasome activation in Alzheimer's disease retina
Chlamydia pneumoniae (Cp), an obligate intracellular bacterium, has been implicated in Alzheimer's disease (AD), yet its role in retinal pathology remains unexplored. We analyzed postmortem tissues from 95 human donors and found 2.9-4.1-fold increases in Cp inclusions in AD retinas and brains, with no significant elevation in mild cognitive impairment (MCI). Proteomics revealed dysregulation of retinal and brain bacterial infection-related proteins and NLRP3 inflammasome pathways. NLRP3 expression was markedly elevated in MCI and AD retinas, and its activation was evident by increased N-terminal gasdermin D (NGSDMD) and mature interleukin-1{beta}. Retinal Cp strongly correlated with A{beta}42 and NLRP3 inflammasome components, which tightly linked to cleaved caspase-3-apoptotic and NGSDMD-pyroptotic cell death. Although retinal microgliosis was elevated in AD, Cp-associated microglia were reduced by 62%, suggesting impaired Cp phagocytosis. Higher retinal Cp burden correlated with APOE{varepsilon}4, Braak stage, and cognitive deficit. Machine learning identified retinal Cp or NLRP3 combined with A{beta}42 as strong predictors of AD diagnosis, staging, and cognitive impairment. Our findings suggest that Cp infection contributes to AD dementia but not initiating pathology, whereas early NLRP3 activation may promote disease development, warranting studies on Cp's role in AD pathogenesis and early antibiotic or inflammasome-targeted therapies.

Spectral imprint of structural embedding in effective connectivity
Neural fluctuations exhibit rich spectral profiles that reflects both local dynamics and structural (or anatomical) embedding. Yet, standard models of resting-state effective connectivity neglect structural embedding and assume uniformity in the timescales of regions' endogenous fluctuations. We introduce a chromatic dynamic causal model (DCM) in which structural valency (or degree) modulates the spectral 'color' of endogenous fluctuations. Specifically, we assume a linear mapping between regional structural valency and the spectral exponent of scale-free auto-spectra. Simulations show this mapping can emerge as a generic consequence of structural embedding under minimal coupling in a non-equilibrium regime. We show chromatic DCM reliably recovers ground-truth parameters across network sizes and noise conditions, outperforming standard spectral DCM. Applied to empirical data, chromatic DCM reveals that valency-exponent mappings vary across a cortical hierarchy, and that its parameters are conserved across a homologous network in humans, macaques, marmosets, and mice. These findings advance a generative account of structure-function coupling and expand the repertoire of biophysical mechanisms available for inference in effective connectivity modeling.

Rapid Computation of High-Level Visual Surprise
Predictive processing theories propose that the brain continuously generates expectations about incoming sensory information. Discrepancies between these predictions and actual inputs, sensory prediction errors, guide perceptual inference. A fundamental yet largely unresolved question is which stimulus features the brain predicts, and therefore, what kind of surprise drives neural responses. Here, we investigated this question using EEG and computational modelling based on deep neural networks (DNNs). Participants viewed object images whose identity was probabilistically predicted by preceding cues. We then quantified trial-by-trial surprise at both low-level (early DNN layers) and high-level (late DNN layers) visual feature representations. Results showed that stimulus-evoked responses around 200ms post-stimulus onset over parieto-occipital electrodes were increased by high-level, but not by low-level visual surprise. These findings demonstrate that high-level visual predictions are rapidly integrated into perceptual inference, suggesting that the brain's predictive machinery is finely tuned to utilize expectations abstracted away from low-level sensory details to facilitate perception.

The synergistic interactions of low-dimensional brain modes
Neuroimaging techniques produce vast amounts of data, capturing brain activity in a high-dimensional space. However, brain dynamics are consistently shown to reside in a rather lower-dimensional space, which contains relevant information for cognition and behavior. This dimensionality reduction reflects distinct types of interactions between brain regions, such as redundancy -shared neural information distributed across regions- and synergy, where information emerges only when regions are considered collectively. Significant efforts have been devoted to developing linear and non-linear algorithms to reveal these low-dimensional dynamics, often termed "brain modes." Here, we apply various dimensionality reduction techniques to resting-state functional magnetic resonance imaging (fMRI) data from 100 healthy participants to examine how synergistic interactions in brain dynamics are preserved by these techniques. We first demonstrate that biologically informed brain parcellation modulates and preserves synergy-dominated interactions. Next, we show that synergy among low-dimensional modes enhances functional-connectivity reconstruction: nonlinear autoencoders not only achieve the lowest reconstruction error but also maximally preserve synergy, outperforming principal component analysis, diffusion maps, and Laplacian eigenmodes. Finally, we confirm previous results suggesting that global signal regression helps to identify synergistic interactions between regions. Our findings establish synergy preservation as a complementary criterion to reconstruction accuracy, highlighting autoencoders as a nonlinear tool for uncovering synergistic low-dimensional brain modes from high-dimensional neuroimaging data.

Towards Optimizing Target Engagement in Non-Invasive Trigeminal Nerve Stimulation: Anatomical Characterization and Computational Modeling of the Human Trigeminal Nerve
Objective: Cranial nerve stimulation (CNS) uses electric current to modulate higher-order brain activity and organ function via nerves, including the vagus and trigeminal, with applications in migraine, epilepsy, and pediatric ADHD. The trigeminal nerve is an emerging target for non-invasive neuromodulation due to the superficial trajectory of its branches, the supraorbital (SON), infraorbital (ION), and mental nerves (MN), and the predominantly sensory composition of the SON and ION. However, the parameters and outcomes of trigeminal nerve stimulation (TNS) remain varied. Approach: This study characterizes the anatomical course, tissue composition, and activation profiles of the SON, ION, and MN using five human donors. CT imaging was utilized to localize each nerve's exit foramen and distance to midline. Microdissections quantified nerve circumference and depth relative to the skin surface. Histological analysis described the number of fascicles and fascicular tissue area. Nerve depths were incorporated into computational models to illustrate the activation function across tissue layers, comparing expected nociceptor and nerve trunk activation functions as a measure of neural engagement. Main Results: The SON was found to be significantly more superficial than the ION and MN and had a higher nerve-to-connective tissue ratio relative to the MN. Computational modeling demonstrated that the activation function at the depths of nociceptors was orders of magnitude greater than within the main nerve trunks, suggesting preferential recruitment of cutaneous nociceptors, dependent on nociceptor density. Significance: The SON presents the most accessible and anatomically favorable target for transcutaneous trigeminal nerve stimulation among the branches examined due to its superficial location. However, preferential activation of low-threshold nociceptors compared to nerve trunks may lead to treatment-limiting off-target side effects, favoring strategies that target fibers of interest within the skin. These findings offer an anatomically informed framework to guide further computational modeling and electrode design for targeted trigeminal neuromodulation.

Disrupted Energy Landscape in Individuals with Mild Cognitive Impairment: Insights from Network Control Theory
Introduction: Patients with mild cognitive impairment (MCI) have shown disruptions in both brain structure and function, often studied separately. However, understanding the relationship between brain structure and function can provide valuable insights into this early stage of cognitive decline for better treatment strategies to avoid its progression. Network Control Theory (NCT) is a multi-modal approach that captures the alterations in the brain's energetic landscape by combining the brain's functional activity and the structural connectome. Our study aims to explore the differences in the brain's energetic landscape between people with MCI and healthy controls (HC). Methods: Four hundred ninety-nine HC and 55 MCI patients were included. First, k-means was applied to functional MRI (fMRI) time series to identify commonly recurring brain activity states. Second, NCT was used to calculate the minimum energy required to transition between these brain activity states, otherwise known as transition energy (TE). The entropy of the fMRI time series as well as PET-derived amyloid beta (A{beta}) and tau deposition were measured for each brain region. The TE and entropy were compared between MCI and HC at the network, regional, and global levels using linear models where age, sex, and intracranial volume were added as covariates. The association of TE and entropy with A {beta} and tau deposition was investigated in MCI patients using linear models where age, sex, and intracranial volume were controlled. Results: Commonly recurring brain activity states included those with high and low amplitude activity in visual (+/-), default mode (+/-), and dorsal attention (+/-) networks. Compared to HC, MCI patients required lower transition energy in the limbic network (adjusted p = 0.028). Decreased global entropy was observed in MCI patients compared to HC (p = 7.29e-7). There was a positive association between TE and entropy in the frontoparietal network (p = 7.03e-3). Increased global A{beta} was associated with higher global entropy in MCI patients (rho = 0.632, p = 0.041). Conclusion: Lower TE in the limbic network in MCI patients may indicate either neurodegeneration-related neural loss and atrophy or a potential functional upregulation mechanism in this early stage of cognitive impairment. Future studies that include people with AD are needed to better characterize the changes in the energetic landscape in the later stages of cognitive impairment.

Independence-based causal discovery analysis reveals statistically non-significant regions to be functionally significant
Background and Hypothesis: Traditional fMRI analyses often ignore regions that fail to reach statistical significance, assuming they are biologically unimportant. We tested the accuracy of this assumption using causal discovery based-analysis that go beyond associations/correlations to test the causality of influence of one region over the other. We hypothesized that the network of statistically significant (active network, AN) and non-significant regions (silent network, SN) causally interact and their features will causally influence psychopathology severity and working memory performance. Study Design: We examined AN and SN during N-BACK task on 25 FHR and 37 controls. Clusters with significantly different activations were juxtaposed to 360 Glasser atlas parcellations. The PC algorithm for causal discovery was implemented. Connectivity of regions with the highest alpha-centrality (HAC) were examined. Results: Seventy-seven Glasser regions were in the AN and the rest were silent nodes. Two regions showed HAC for FHR and HC. Among controls, one HAC region was silent (auditory association cortex) and the other one was active (insula). Among FHR, both were silent nodes (early auditory cortex). These HAC regions in both groups had bidirectional directed edges between each other forming a reciprocal circuit whose edge-weights causally increased magical ideation severity. Conclusion: Causal connectivity between SN and AN suggests that the statistically non-significant and significant regions influence each other. Our findings question the merit of ignoring statistically non-significant regions and exclusively including statistically significant regions in the pathophysiological models. Our study suggests that causality analysis should receive greater attention.

ASC contributes to sustained glial reactivity and mild cognitive impairment after closed-head injury
Mild brain trauma from closed-head injuries (CHI) can lead to prevalent neuropsychiatric disorders, including an increased risk for neurodegenerative diseases and dementia. Inflammasomes are molecular complexes crucial for neuroinflammation and secondary damage after trauma, however their role in mild CHI is poorly understood. In this study, we investigate the cellular expression of inflammasome-related genes and their functional significance after a CHI models. Analyzing single-cell RNA sequencing data from cortical cells from a model of CHI, we found that Pycard (Asc), the gene encoding for a common inflammasome adaptor apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), is expressed particularly in microglial clusters. Sustained upregulation of inflammasome-related proteins persisted up to 21 days in a model for mild CHI, with this pattern significantly reduced in Asc-/- mice. Importantly, mild cognitive impairment induced after mild CHI was largely abrogated in Asc-/- mice. These findings suggest that ASC, as the primary inflammasome adaptor, plays a critical role in sustaining neuroinflammation and contributes to cognitive deficits after mild CHI. This study provides insights into the molecular neuroinflammatory mechanisms underlying CHI, potentially informing future therapeutic strategies.

Protein kinase CK2 alpha prime as a dual modulator of immune signaling and synaptic dysfunction in Tauopathy
Tauopathies are a group of neurodegenerative diseases characterized by tau accumulation, neuroinflammation, and synaptic dysfunction, yet effective treatments remain elusive. Protein Kinase CK2 has been previously associated with different aspects of tau pathology but genetic evidence for the contribution of CK2 to tauopathy remained lacking. Here, we show CK2', one of the two catalytic subunits of CK2, as a novel regulator of tau-mediated neurodegeneration. We found that CK2' expression is elevated in the hippocampus of PS19 tauopathy mice and in postmortem brains of dementia patients, particularly in neurons and microglia. Using genetic haploinsufficiency in PS19 mice, we demonstrated that reduced CK2' levels significantly decrease phosphorylated tau and total tau burden in the hippocampus and cortex. CK2' depletion also enhanced synaptic gene expression, synaptic density, and LTP, while attenuating microglial activation, synaptic engulfment, and pro-inflammatory cytokine levels. Importantly, CK2' depletion rescued cognitive deficits assessed in the Barnes maze. These effects appear to be mediated through both neuronal and glial functions and may involve CK2'-dependent modulation of tau-associated phosphorylation and neuroinflammatory and immune signaling pathways. Our findings highlight CK2' as a key node at the intersection of tau pathology, synaptic dysfunction, and neuroimmune signaling. Targeting CK2' may offer a novel and selective therapeutic strategy for modifying disease progression in tauopathies.

Controlling a simple model of bipedal walking to adapt to a wide range of target step lengths and step frequencies
We tested whether the same principles of actuation and control that support steady-state walking are sufficient for robust, rapid gait adaptation over a wide range of step lengths and frequencies. We begin by demonstrating that periodic limit cycle gaits exist at combinations of step frequency and step length that span the full range of gaits achievable by humans. We demonstrate that open-loop local stability is not enough to rapidly transition to target gaits because some gaits in the gait space are unstable and the stable gaits have slow convergence rates. Next, we show that actuating with only one push-off and one hip spring of fixed stiffness cannot fully control the walker in the entire gait space. We solve this by adding a second hip spring with an independent stiffness with respect to the first one to actuate the second half of the swing phase. This allowed us to design local feedback controllers that provided rapid convergence to target gaits by making once-per-step adjustments to push-off and hip spring stiffnesses. To adapt to a range of target gaits that vary over time, we interpolated between local controllers. This policy performs well, accurately tracking rapidly varying combinations of target step length and step frequency with human-like response times across a wide range of human achievable gaits. To test whether this policy is biologically plausible, we use it with supervised learning to train an artificial neural network to perform nearly identical control.

Identifiability of Bayesian Models of Cognition
Inferring the underlying computational processes from behavioral measurements is a fundamental approach in cognitive science and neuroscience. Although Bayesian decision theory has become a major normative framework for modeling cognition, it is unclear to what extent its modeling components (i.e., prior belief, likelihood function, and the loss function) can be recovered from behavioral data. Here, we systematically investigated the problem of inferring such Bayesian models from behavioral tasks. In contrast to a pessimistic picture often painted in previous research, our analytical results guarantee in-principle identifiability under broadly applicable conditions, without any a priori knowledge of prior or encoding. Simulations and applications on the basis of behavioral datasets validate that the predictions of this theory apply in realistic settings. Importantly, our results demonstrate that reliable recovery of the model often requires having data from multiple noise levels. This is a crucial insight that will guide future experimental design.

Deceptive safety? The impact of costly pain avoidance on the modulation and extinction of visceral pain-related fear
Along the gut-brain axis, visceral pain demonstrably evokes emotional learning and memory processes shaping behavior in clinically relevant ways. Avoidance motivated by learned fear may constitute a major obstacle to treatment success in extinction-based interventions. However, the effects of avoidance on visceral pain-related fear extinction remain poorly understood. By implementing an ecologically valid experimental protocol, we investigated how costly avoidance affects the modulation and extinction of visceral pain-related fear. Thirty-three healthy volunteers underwent conditioning with visual cues (conditioned stimuli; CS+,CS-) consistently followed by visceral pain or remaining unpaired. During avoidance, participants decided to avoid or receive pain upon confronting CS+. Avoidance decisions resulted in pain omission in some trials, while in others, participants experienced unpredictable pain. During extinction, CS were presented unpaired. CS valence, fear, and trial-by-trial decisions were analyzed. Avoidance decisions depended on prior experiences, with the highest probability of avoidance following successful pain omission. Negative CS+ valence and fear remained elevated across avoidance and extinction. Learned fear and more avoidance decisions explained 57% variance in sustained CS+ fear. Our findings indicate that avoidance, which provides short-term absence of pain even when followed by unpredictable pain, motivates its maintenance. However, it perpetuates pain-related fear and may impede extinction, with implications for persisting symptoms and therapeutic outcomes in chronic visceral pain.

In vivo cartography of state-dependent signal flow hierarchy in the human cerebral cortex
Understanding the principle of information flow across distributed brain networks is of paramount importance in neuroscience. Here, we introduce a novel neuroimaging framework, leveraging integrated effective connectivity (iEC) and unconstrained signal flow mapping for data-driven discovery of the human cerebral functional hierarchy. Simulation and empirical validation demonstrated the high fidelity of iEC in recovering connectome directionality and its potential relationship with histologically defined feedforward and feedback pathways. Notably, the iEC-derived hierarchy revealed a monotonically increasing level along the axis where the sensorimotor, association, and paralimbic areas are sequentially ordered -- a pattern supported by the Structural Model of laminar connectivity. This hierarchy was further demonstrated to flexibly reorganize across brain states: flattening during an externally oriented condition, evidenced by a reduced slope in the hierarchy, and steepening during an internally focused condition, reflecting heightened engagement of interoceptive regions. Our study highlights the unique role of macroscale directed functional connectivity in uncovering a biologically interpretable state-dependent signal flow hierarchy.

Disassociating cerebral vasomotion from low frequency spontaneous neurovascular coupling
Vasomotion, vascular oscillations at ~0.1 Hz, may serve as a biomarker and therapeutic target for neurodegenerative diseases, but its origins, structure across brain vasculature, and correlation with neural activity remain unclear. This study examined the spatiotemporal characteristics of cerebral vasomotion and its relationship to neural activity in anaesthetised Hooded Lister rats using simultaneous recordings of neuronal activity and haemodynamics in motor and whisker barrel cortices. In a subset of rats, tissue oxygen was also measured. Blood pressure was pharmacologically modulated to alter vascular oscillations. We found that vasomotion was driven by the arterial tree. Two prominent activity patterns emerged: global vasomotion across the entire hemisphere and phasic vasomotion seen as a travelling wave running through the surface arteries. Moreover, vasomotion was associated with low tissue oxygen and was largely independent of spontaneous neural activity and therefore not a product of neurovascular coupling.

Altered therapeutic capacities of olfactory ensheathing cells caused by a lesion in an autologous transplantation model for the treatment of spinal cord injury.
Spinal cord injury (SCI) causes irreversible loss of motor, sensory, and autonomic functions and currently has no cure. Beyond local damage, SCI induces systemic inflammation, including cerebral inflammation that impairs neurogenesis. While cell therapies show promising effects in animal models, such as scar reduction and neuroprotection, their benefits in humans remain limited. One key difference lies in the transplantation strategy: animals receive healthy donor cells, whereas humans require autologous transplants. This led us to investigate how the lesion context affects the neuro-reparative potential of olfactory ensheathing cells (OECs) harvested from olfactory bulbs. To this end, we cultured OECs from healthy animals and from animals that had undergone SCI one week earlier. We then transplanted both types of OECs into recipient animals after SCI for therapeutic purposes. Using functional sensory-motor studies, histological and gene expression analyses, we were able to demonstrate for the first time that the lesion negatively affects the therapeutic properties of cells used to treat SCI. Indeed, transplantation of cells from previously injured animals does not modulate the fibrotic and glial scar, or the demyelinated areas at the lesion site, and therefore fails to improve functional recovery; unlike cells derived from healthy donors. Moreover, our in vitro studies show that cells derived from SCI animals secrete pro-inflammatory molecules that promote the polarization of microglia toward a pro-inflammatory phenotype. Altogether, these innovative findings provide new insights into the potential of cell transplantation in the context of autologous therapy after SCI.

Medial Frontal Theta Reduction Impairs Rule Switching via Prediction Error
Cognitive flexibility, the ability to switch behavior in response to changing rules in an uncertain environment, is crucial for adaptive decision making. Prior research has hypothesized a key role of prediction error and theta oscillations in medial frontal cortex in this process. However, the causal link between such processes remains to be established. To address this, we combined neural stimulation, EEG, behavioral measurement, and computational modelling. Specifically, we applied high-definition transcranial direct current stimulation (HD-tDCS) to modulate theta oscillations as measured via EEG followed by a probabilistic reversal learning task. We find that anodal stimulation reduces theta power and rule prediction error, and it increases the number of trials needed to reliably switch between rules. These findings support the role of rule prediction error signaling as a key mechanism linking neural oscillations to behavioral adaptation and highlight the importance of theta power and rule prediction error for cognitive flexibility. Key words: Cognitive flexibility; Medial frontal cortex (MFC); Rule switching; Theta oscillations; Prediction error; HD-tDCS.

Theory of Temporal Pattern Learning in Echo State Networks
Echo state networks are well-known for their ability to learn temporal patterns through simple feedback to a large recurrent network with random connections. However, the learning process itself remains poorly understood. We develop a quantitative theory that explains learning in a regime where the network dynamics is stable and the feedback is weak. We show that the dynamics is governed by a finite number of master modes whose nonlinear interactions can be described by a normal form. This formulation provides a simple picture of learning as a Fourier decomposition of the target pattern with amplitudes determined by nonlinear interactions that, remarkably, become independent of the network randomness in the limit of large network size. We further show that the description extends to moderate feedback and recurrent networks with multiple unstable modes.

TBRS-associated DNMT3A mutations disrupt cortical interneuron differentiation and neuronal networks
Pathogenic DNMT3A mutations cause Tatton-Brown-Rahman Syndrome (TBRS), a disorder characterized by intellectual disability and overgrowth of multiple somatic tissues including the brain. However, the functions of DNMT3A during human cortical development remain poorly understood. Here, we utilized newly developed human pluripotent stem cell models of TBRS-associated DNMT3A mutation to define DNMT3A requirements and consequences of mutation during human cortical neuron development. Profiling changes to epigenetic gene regulation across both GABAergic and glutamatergic neuron development, we identified GABAergic cortical interneurons as particularly sensitive to TBRS-associated mutation. During GABAergic neuron development, TBRS-associated DNMT3A mutations resulted in reduced DNA methylation and were associated with concomitant de-repression of gene expression, causing precocious neuronal differentiation. By contrast, the consequences of DNMT3A mutation on glutamatergic neuron development were less pronounced, due in part to compensatory repressive histone methylation, and resulted in increased expression of early neurodevelopmental genes during glutamatergic neuron differentiation. Assessing the consequences of these molecular phenotypes by patch-clamp electrophysiology, we found that DNMT3A deficient GABAergic neurons were hyperactive, while glutamatergic neuron function was largely unaffected by these DNMT3A loss of function mutations. Finally, we used both low density and high density multi electrode array techniques in conjunction with glutamatergic-GABAergic neuron co-cultures to assess how TBRS-associated GABAergic neuron hyperactivity affected the emergence and development of neuronal networks. We found that TBRS GABAergic neuron hyperactivity was sufficient to drive abnormal neuronal network development, increasing the neuronal activity consolidated into neuronal bursting and networks. Ultimately, this work elucidated new roles for DNMT3A-mediated repression in human cortical development, identifying critical requirements in regulating neuronal and synaptic gene expression during GABAergic differentiation, with these TBRS-associated molecular changes driving alterations of neuronal network function likely to contribute to TBRS etiology.

Evidence for divergent cortical organisation in Parkinsons disease and Lewy Body Dementia
Dementia is a defining feature of Lewy body disease: its timing and onset distinguish different clinical diagnoses, and its effect on quality of life is profound. However, it remains unclear whether processes leading to cognitive and motor symptoms in Lewy body disease differ. To clarify this, we used in-vivo neuroimaging to assess spatial gradients of inter-regional differences in structural and functional connectivity in 108 people across the Lewy body disease spectrum (46 Parkinsons with normal cognition (PD-NC), 62 Lewy body dementia (LBD)) and 23 controls. We found divergent structural gradient differences with cognitive impairment: PD-NC showed increased inter-regional differentiation, whilst LBD showed overall gradient distribution similar to controls despite widespread organisational differences at the regional level. We then assessed cellular and molecular underpinnings of these organisational changes. We reveal similarities and also important differences in the drivers of cortical organisation between LBD and PD-NC, particularly in layer 4 excitatory neurons.

Decision-making components and times revealed by the single-trial electro-encephalogram
Decision-making stems from a sequence of information processing steps between the choice onset and the response. Despite extensive research, uncertainty remains about the actual cognitive sequence involved in the reaction time. Using the hidden multivariate pattern method we modeled the single-trial electroencephalogram of participants performing a decision task as a sequence of an unknown number of events estimated as trial-recurrent, time-varying, stable topographies. We provide evidence for three events occurring during participant's decision making, respectively representing encoding, attention orientation, and decision. This interpretation is supported by the observation that a targeted manipulation of stimulus intensity yields Pieron's law in the interval between encoding and attention orientation, and Fechner's law in the interval between attention orientation and decision. This final, decision-related, event is represented in the brain as a positive burst in parietal areas whose timing, amplitude and build-up predict the participants' decision accuracy.

ALE Meta-Analysis Reveals Neural Substrates for the Impact of Prematurity on Executive Functioning in Children and Adults
Premature birth has known impacts on brain development, leading to sustained differences in cognitive function throughout the lifespan. Despite known deficits in executive functioning (EF) within individuals born premature, the extent to which neural engagement during executive functioning tasks differs between those born preterm and full-term is not fully understood. Additionally, it is unknown whether regions of differential engagement are the same in children and adults. This meta-analysis synthesizes fMRI results of activation differences between preterm and full-term subjects during executive functioning tasks in adult and child groups separately. Our results indicate that differences in neural engagement during EF tasks differ between pre-term (PT) and full term (FT) individuals in both age groups. Moreover, the regions affected contribute to well-known brain networks, including the fronto-striatal circuitry, the default mode network (DMN), and the salience network, all of which subserve broad EF capabilities. We found no differences between child and adult maps in a direct contrast, suggesting that effects of prematurity on executive functioning may persist from childhood into adulthood, although these findings should be interpreted in context of methodological limitations and potential confounding factors. This meta-analysis provides greater insight into the neural mechanisms behind EF disruption following premature birth.

Lysosomal Enhancement Prevents Infection with PrPSc, α-Synuclein & Tau Prions
Prion diseases are fatal neurodegenerative diseases of humans and other mammals with no current treatment options. Here, we describe the characterization of a novel anti-prion compound, elacridar (GW120918), which has sub-micromolar activity in assays of prion infection, propagation and toxicity. Elacridar acts at an early step in the prion infection process, enhancing degradation of newly formed PrPSc. The lysosome is the likely site of elacridars anti-prion effects, based on transcriptomic analysis and the use of functional lysosomal probes. Elacridar alters gene expression networks controlling lysosomal sterol and lipid metabolism but, unlike other lysosomotropic drugs, it prominently upregulates genes that control lysosomal pH. Surprisingly, these effects occur independently of TFEB nuclear translocation, suggesting novel regulatory mechanisms. The anti-prion effects of elacridar extend to -synuclein and tau prions, highlighting lysosomal enhancement as a general strategy for the treatment of protein misfolding neurodegenerative diseases.

Vitamin D attenuates TNF-α-mediated neurotoxicity and improves functional recovery in experimental intracerebral haemorrhage
While the neuroprotective effects of vitamin D (Vit-D) have been demonstrated pre-clinically in a wide range of neurologic conditions, its potential use in the treatment of spontaneous intracerebral hemorrhage (ICH) has not been fully explored. We previously reported that Vit-D could expedite hematoma clearance in experimental ICH by inducing the conversion of M1 to M2 macrophage to enhance erythrophagocytosis1,2. Here, we provide new evidence on the dose-dependent effects of Vit-D on neuronal survival and functional recovery, lending further support for the clinical testing of Vit-D in the management of ICH.

Expectations and uncertainty shape pain perception during learning
Pain perception is modulated by expectations and learning processes, but the influence of uncertainty in this relationship is not well established. We aimed to examine the relationship between uncertainty, pain learning and perception using hierarchical Bayesian modeling. In an aversive learning task, fifty participants learned contingencies between auditory cues and painful stimulations under changing levels of uncertainty, to create periods of stability and volatility. Model-free analysis of our data suggested unexpected trials resulted in reduced accuracy and greater response times. In unexpected trials, high pain perception was reduced, while low pain perception was increased, in line with documented effects of expectations on pain perception. Computational model fitting revealed participants learning was best described by a two-level hierarchical gaussian filter model, suggesting participants adapted their beliefs at multiple levels during the task. Uncertainty influenced pain perception in opposite patterns for high and low pain stimulations : high pain perception was greater under high levels of uncertainty while there was a non-significant trend for low pain perception to be reduced. Analyses of individual differences suggested depressive symptoms were associated with a reduced learning rate throughout the task. These results shed light on processes involved in pain learning in changing environments. They also suggest a possible relationship between learning alterations and psychological traits commonly found in chronic pain, such as depressive symptoms.

Mosaic human cortical organoids model mTOR-related focal cortical dysplasia through DEPDC5 loss-of-function
Focal cortical dysplasia type II (FCDII), a leading cause of pediatric drug-resistant focal epilepsy, results from brain somatic variants in genes of the mTOR pathway, including germline and somatic second-hit loss-of-function variants in the mTOR repressor DEPDC5. Here, we investigated the effects of mosaic DEPDC5 two-hit variants on cortical development and neuronal activity using patient-derived human cortical organoids (hCOs). Mosaic hCOs displayed increased mTOR activity and altered neural rosette densities, which were both rescued by treatment with the mTOR inhibitor rapamycin. In addition, mosaic hCOs presented dysmorphic-like neurons and increased neuronal excitability, recapitulating FCDII pathology. Longitudinal single-cell transcriptomics at three developmental stages revealed altered neuronal differentiation, dysregulated expression of genes associated with the Notch and Wnt pathways in neural progenitors, and of synaptic- and epilepsy-associated genes in excitatory neurons. We further identified cell-autonomous alterations in metabolism and translation in mosaic two-hit hCOs. This study provides novel insights into the consequences of mosaic biallelic DEPDC5 deficiency on corticogenesis in the context of FCDII, highlighting both autonomous and non-cell autonomous effects.

EEG-GAN: A Generative EEG Augmentation Toolkit for Enhancing Neural Classification
Electroencephalography (EEG) is a widely applied method for decoding neural activity, offering insights into cognitive function and driving advancements in neurotechnology. However, decoding EEG data remains challenging, as classification algorithms typically require large datasets that are expensive and time-consuming to collect. Recent advances in generative artificial intelligence have enabled the creation of realistic synthetic EEG data, yet no method has consistently demonstrated that such synthetic data can lead to improvements in EEG decodability across diverse datasets. Here, we introduce EEG-GAN, an open-source generative adversarial network (GAN) designed to augment EEG data. In the most comprehensive evaluation study to date, we assessed its capacity to generate realistic EEG samples and enhance classification performance across four datasets, five classifiers, and seven sample sizes, while benchmarking it against six established augmentation techniques. We found that EEG-GAN, when trained to generate raw single-trial EEG signals, produced signals that reproduce grand-averaged waveforms and time-frequency patterns of the original data. Furthermore, training classifiers on additional synthetic data improved their ability to decode held-out empirical data. EEG-GAN achieved up to a 16% improvement in decoding accuracy, with enhancements consistent across datasets but varying among classifiers. Data augmentations were particularly effective for smaller sample sizes (30 and below), significantly improving 70% of these classification analyses and only significantly impairing 4% of analyses. Moreover, EEG-GAN significantly outperformed all benchmark techniques in 69% of the comparisons across datasets, classifiers, and sample sizes and was only significantly outperformed in 3% of comparisons. These findings establish EEG-GAN as a robust toolkit for generating realistic EEG data, which can effectively reduce the costs associated with real-world EEG data collection for neural decoding tasks.

Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering
It has been suggested that humans and other animals are driven by a fundamental desire to acquire information about opportunities available in their environments. Not only might such a desire explain pathological behaviors, but it may be needed to account for how everyday decisions are resolved. Here, we combine artificial neural networks (ANNs) with symbolic regression to extract an expressive yet interpretable model that specifies how human participants evaluate decision-relevant information during choice. This model accounts for behavior in our own data and in previous work, outperforming existing accounts of information sampling such as the Upper Confidence Bound heuristic. This modelling approach has broad potential for uncovering novel patterns in behavior and cognitive processes, while also specifying them in human-interpretable formats. We then used the value of information derived by our model, together with ultra-high field neuroimaging, to examine activity across a suite of subcortical neuromodulatory nuclei and two cortical regions that influence these nuclei. This established roles for midbrain dopaminergic nuclei, anterior cingulate cortex, and anterior insula in mediating the influence of value of information on behavior.

Oscillatory Control of Cortical Space as a Computational Dimension
Flexible cognition depends on the ability to represent and apply context, allowing the brain to interpret sensory input and guide behavior in a context-dependent manner. Recent work has proposed Spatial Computing as a mechanism for this flexibility, suggesting that contextual signals organize information processing through spatial patterns of oscillatory activity across the cortical surface. These patterns act as "inhibitory stencils" that constrain where information (the "content" of cognition) can be expressed in spiking activity. Here, we provide a comprehensive empirical test of Spatial Computing Theory using multi-electrode recordings from the lateral prefrontal cortex in non-human primates performing a range of cognitive tasks (object working memory, sequence working memory, categorization). We found that alpha/beta oscillations encoded contextual information, reorganized their spatial patterns with context and task demands, and spatially gated the expression of content-related spiking activity. Furthermore, we found that alpha/beta oscillations reflected misattributions of task context and correlated with subjects' trial-by-trial decisions. These findings validate core predictions of Spatial Computing by showing that oscillatory dynamics not only gate information in time but also shape where in the cortex cognitive content is represented. This framework offers a unifying principle for understanding how the brain flexibly coordinates cognition through structured population dynamics.

Genetically encoded nAChR upregulation is neuroprotective in female parkinsonian mice
Parkinsons disease is projected to rise to pandemic proportions by 2050, which has resulted in an urgent need for disease-modifying treatments. In this regard, we previously showed that in a mouse model of parkinsonism with unilateral 6-hydroxydopamine (6-OHDA) injection into the dorsolateral striatum (DLS), low doses of the neuronal nicotinic acetylcholine receptor (nAChR) partial agonist and smoking cessation drug, cytisine exerts sex-specific neuroprotection in substantia nigra pars compacta (SNc) dopaminergic (DA) neurons of only female mice by reducing apoptotic endoplasmic reticulum (ER) stress. Although these data suggest that neuroprotection might occur via cytisine-mediated upregulation of {beta}2 subunit-containing ({beta}2*) nAChRs in SNc DA neurons, there is no direct evidence to support this idea. Therefore, this study asks the critical question of whether upregulation of {beta}2* nAChRs in SNc DA neurons alone is sufficient to reduce apoptotic ER stress and exert neuroprotection in a preclinical unilateral DLS mouse model of 6-OHDA-induced parkinsonism. To address this question, we generate and characterize a novel {beta}2-upregulated transgenic mouse line. These transgenic mice possess mutations in the M3-M4 intracytoplasmic loop of {beta} subunits that cause constitutive upregulation of {beta}2* nAChRs without nicotinic ligands. Surprisingly, when compared to wild-type littermates, only female {beta}2-upregulated transgenic mice demonstrate upregulation of {beta}2* nAChRs in SNc DA neurons as assessed by significant increases in Sec24D-containing ER exit sites (Sec24D-ERES). Using the optogenetic calcium and dopamine sensors, GCaMP6f and GRABDA respectively, we found significant increases in dihydro-beta-erythroidine (Dh{beta}E)-sensitive {beta}2* nAChR-mediated calcium influx in SNc DA neuron dendrites and Dh{beta}E-sensitive acetylcholine (ACh)-evoked dopamine release at SNc DA neuron terminals of the DLS in female transgenic mice. We then used four independent readouts to assess neuroprotection of SNc DA neurons following unilateral 6-OHDA injection into the DLS, viz., contralateral apomorphine-induced rotations, preservation of SNc DA neurons, inhibition of a major proapoptotic ER stress protein, C/EBP homologous protein (CHOP) and glial fibrillary acid protein (GFAP) expression in SNc astrocytes. In all four readouts, female {beta}2-upregulated transgenic mice showed significant neuroprotection. From a clinical perspective, this study shows that upregulation without nicotinic ligand-mediated activation of {beta}2* nAChRs in SNc DA neurons can be a translationally viable disease-modifying strategy for Parkinsons disease. In addition, we envision that the novel transgenic {beta}2-upregulated mice created in this study will provide a valuable tool for understanding the role of nAChR upregulation in major neurological disorders such as addiction, anxiety, depression and dementia.

T-401 binding to monoacylglycerol lipase (MAGL) in the brains of patients and mice with temporal lobe epilepsy
Monoacylglycerol lipase (MAGL) inhibitors are considered as drug candidates for epilepsy. In order to determine the level of MAGL and evaluate changes in the epileptic brain, we have validated and used autoradiography and the MAGL radiotracer [3H]T-401 on resected temporal neocortex specimens obtained from patients with temporal lobe epilepsy and in brains from mice with chronic reoccurring seizures. Saturation experiments revealed a KD around 4 nM for the human temporal cortex and 7 nM for the mouse brain. In the human brain, binding of [3H]T-401 was detected mostly in the grey matter, and in the subcortical white matter in lower amounts. The levels were strongly correlated in the two cortical compartments. The level of [3H]T-401 binding in the human temporal cortex varied about a 4-fold among the patients, but was not correlated to either epilepsy duration or the age of the patients. In the epileptic mouse brain, a significant reduction was observed bilaterally in the hippocampus, as well as in other cortical regions, including the temporal cortex. Interestingly, a highly significant negative correlation was seen between MAGL and binding to the translocator protein 18 kDa (TSPO) expressed in glia. These data support the presence of MAGL in neuronal and non-neuronal cells, and indicate that MAGL levels in the brains of either patients with epilepsy or mice after intra-hippocampal kainite injection are reduced not only in the epileptic zone in the hippocampus, but more widespread in the brain.

Self-inactivating AAV-CRISPR at different ages enables sustained amelioration of Huntington's disease deficits in BAC226Q mice
Huntington's disease (HD) is a monogenic autosomal dominant neurodegenerative disorder caused by a CAG repeat expansion in the first exon of the HTT gene, yielding a gain-of-toxic-function mutant Huntingtin protein mHTT. CRISPR/Cas9 is a potentially powerful therapeutic tool for treating HD by eliminating mutant HTT (mHTT) gene. We developed a specific SaCas9 guide RNA to target human mHTT, and a self-inactivating gene editing system that abolishes SaCas9 after a short transient expression for high gene editing efficiency and maximal safety to prevent off-target effects. Both conventional and the new self-inactivating gene editing systems achieved successful elimination of mHTT gene, 60-90% mHTT protein and 90% of mHTT aggregation in BAC226Q HD mouse brains, which resulted in significant long-term rescue of neural pathology, motor deficits, weight loss and shortened lifespan. These beneficial effects were observed when gene editing was applied before, at and well after the on-set of pathological and behavioral abnormalities. These proof-of-concept data demonstrate that gene editing can be a highly effective therapeutic approach for HD and other inherited neurodegenerative diseases.

Spatial transcriptomic profiling of human paravertebral sympathetic chain ganglia reveals diabetes-induced neuroplasticity
The paravertebral sympathetic chain ganglia (SCG) are autonomic ganglia critical for regulating the fight-or-flight response. Symptoms of sympathetic dysfunction are prevalent in diabetes, affecting up to 90% of patients. The molecular and cellular composition of the human SCG and its alteration in diabetes remains poorly defined. To address this gap, we performed spatial transcriptomic profiling of lumbar SCGs from diabetic and non-diabetic organ donors. We identified 3 three distinct neuronal populations, two noradrenergic (NA1 and NA2) and one cholinergic (CHO), based on tyrosine hydroxylase (TH) and SLC18A3 expression, respectively. We also characterized 9 non-neuronal populations consisting of Schwann cells, immune cells, fibroblasts, adipocytes, and endothelial cells. In diabetic SCGs, we observed a significant loss of myelinating Schwann cells and a phenotypic shift of cholinergic neurons toward a noradrenergic identity. Additionally, diabetes was associated with a significant reduction in the transcripts of vasodilatory neuropeptides, such as VIP and CALCA, suggesting a mechanism for impaired vascular control. Upstream regulator analysis highlighted altered neurotrophic signaling in diabetes, with enhanced NGF/TRKA and diminished BDNF/TRKB activity, potentially driven by target-derived cues. Comparison between SCG and dorsal root ganglia (DRG) neurons revealed ganglia-specific genes, like SCN3A and NPY (SCG) versus SCN10A and GPX1 (DRG), offering specific therapeutic targets for autonomic dysfunction or pain. Our findings provide a transcriptomic characterization of human SCG, revealing molecular signatures that underlie diabetic autonomic dysfunction. This work lays a foundation for the development of therapies to restore sympathetic function and avoid unintended autonomic effects in the development of analgesics.

Cortical Serotonin Type 2A Receptor Activation Shields Episodic-like Memories from Retroactive Interference in Rodents
The acquisition of temporally proximate information can impair the brain ability to consolidate earlier experiences, resulting in retroactive interference (RI). Recognition-based behavioral paradigms are well-suited for investigating RI in rodents, particularly those involving sequential learning episodes. The medial prefrontal cortex (mPFC) integrates multimodal information relevant to the regulation of memory interference and is strongly modulated by the serotonergic system. Serotonin 2A receptors (5-HT2AR), which are densely expressed in the mPFC, have been shown to influence the retrieval of competing object-recognition memories. However, their role in other phases of memory processing, particularly in modulating RI, remains unclear. Using a novel object recognition task designed to induce RI, combined with pharmacological manipulation of 5-HT2AR, we demonstrate that RI specifically impairs the object-related component of memory. Moreover, serotonin signaling through 5-HT2AR is necessary to prevent RI. Strikingly, the activation of 5-HT2AR before retrieval can rescue the expression of memories affected by RI, suggesting that RI may not erase memory traces but rather hinder access to them.

Smaller stepping thresholds in older adults might be related to reduced ability to suppress conflicting sensory information
Aging leads to alterations in the sensorimotor system and balance control but it is not well understood how changes in sensorimotor function affect how people respond to postural disturbances. Elucidating the relationships between balance control and sensorimotor function is crucial for developing effective rehabilitations. Here, we compared the kinematic responses to platform translations and rotations during standing in 10 young and 30 older adults and explored relationships between sensorimotor function and balance responses. We found that older adults were less able to withstand perturbations without stepping, not because their non-stepping strategies were less effective but because they chose to step at smaller deviations of the extrapolated center of mass. Older adults performed worse than young adults on measures of sensory and motor function but lower stepping thresholds were associated with susceptibility to unreliable visual information and not with reduced sensory acuity or reduced strength. Poor sensory reweighting may contribute to and combine with age-related cognitive rigidity, leading to a higher priority on safer strategies. Older adults may resort to stepping, even if a step is not necessary, rather than rely on potentially inaccurate sensory signals to inform a corrective response. Our results provide initial evidence that sensory reweighting could be a potential target for fall prevention methods.

Single-Cell and Population-Level Neuromodulation Dynamics in Dual-Electrode Intracortical Stimulation
In neuroprosthetics, intracortical microstimulation (ICMS) recruits cortical networks to evoke brain responses and sensory perceptions. However, multi-electrode ICMS often generates suboptimal percepts compared to single-electrode ICMS, suggesting nonlinear neuromodulation rather than simple summation by multi-electrode ICMS. Yet, the factors and mechanisms underlying this modulation remain poorly understood. To investigate multi-electrode ICMS, we combined two-photon calcium imaging with a well-controlled dual-electrode ICMS in the mouse visual cortex to investigate how neurons integrate converging ICMS inputs at varying intensities. We found that stimulation intensity significantly shapes neuromodulation at both single-cell and population levels. Specifically, low intensities (5-7 A) have a minimal effect on neural responses. At intermediate intensities (10-15 A), we observed diverse, nonlinear bipolar modulation--both enhancement and attenuation--at the single-cell level. However, we achieved net enhancement at the population level. At higher intensities (15-20 A), although the proportion of modulated neurons increased in both enhancement and attenuation directions, the net effect at the population level was neutral (zero modulation). Furthermore, neurons strongly responsive to single-electrode ICMS were more likely to be attenuated, while weaker responding cells exhibited enhanced modulation. The strongest neuromodulatory effects occur at intermediate spatial distances in between the two electrodes. Computational modeling based on spiking neural network composed of adaptive exponential integrate-and-field neurons implicated the importance of inhibitory network dynamics and network variability as key mechanisms. Our experimental data was used to train an advanced deep learning approach, which successfully predicted the neuromodulation patterns induced by dual-electrode ICMS. Our findings reveal intensity- and spatial-dependent rules of neuromodulation by ICMS, providing necessary insights to optimize multi-electrode ICMS for neuroprosthetic applications.

Tracing infant sleep neurophysiology longitudinally from 3 to 6 months: EEG insights into brain development
Sleep is critical for brain plasticity during early development, yet the individual maturation of sleep neurophysiology in infancy remains poorly characterized. In particular, slow wave activity (SWA) has emerged as a key marker of both cortical maturation and experience-dependent plasticity. Understanding the regional dynamics of sleep neurophysiology early in life could yield critical insights into neurodevelopmental health. We conducted a longitudinal high-density EEG study in 11 healthy infants (3-6 months) assessing non-rapid eye movement (NREM) sleep. We analyzed the maturation of SWA (0.75-4.25 Hz), theta power (4.5-7.5 Hz), and sigma power (9.75-14.75 Hz) across scalp regions and examined their association with behavioral development. From 3 to 6 months, SWA increased maximally in occipital regions, while theta power exhibited a global increase. Sigma power, initially concentrated centrally, dispersed towards frontal regions. Greater power increases over frontal regions correlated with higher motor (theta) and personal-social skill scores (sigma) at 6 months. These findings establish a framework for typical infant sleep EEG maturation, highlighting frequency-specific and regionally distinct developmental patterns. This study provides the first longitudinal evidence that early changes in sleep EEG topography reflect individual developmental trajectories, supporting its utility as a non-invasive and yet precise biomarker for early identification of atypical neurodevelopment at preverbal ages.

Altered cognitive processes shape tactile perception in autism.
Altered sensory perception is a hallmark of autism and determines how autistic individuals engage with their environment. These sensory differences are shaped by top-down cognitive processes, such as categorization, attention, and priors, which themselves exhibit characteristic atypicalities in the condition. Among sensory modalities, tactile perception is particularly critical for daily functioning and social interactions. However, the dynamic interplay between tactile and cognitive processes remains poorly understood. In this study, we investigated the influence of top-down cognitive processes on tactile perception in the Fmr1-/y genetic mouse model of autism. We developed a translational, forepaw-based decision-making task designed to dissociate stimulus-driven tactile responses from those modulated by cognitive factors. This approach enabled us to assess multiple aspects of perceptual processing, including perceptual learning, stimulus categorization and discrimination, as well as the influence of prior experience and attention. Mice were initially trained to distinguish between high- and low-salience stimuli and were subsequently tested with a continuum of intermediate stimulus intensities. Our results revealed salience-dependent cognitive alterations that significantly influenced sensory performance. During the training phase, Fmr1-/y mice exhibited an increased choice consistency bias in low-salience trials, resulting in impaired perceptual learning. In the testing phase, Fmr1-/y mice demonstrated enhanced tactile discrimination under low-salience conditions, driven by a reduced influence of categorization. Moreover, under conditions of high cognitive load, Fmr1-/y mice displayed attentional deficits that were dissociable from their enhanced tactile sensitivity. Together, our findings reveal that cognitive context critically shapes sensory phenotypes in autism. They advocate for a shift beyond traditional sensory-cognitive dichotomies to better capture the dynamic interplay between perceptual and cognitive alterations in autism.

Peroxisomal import is circadian in glia and regulates sleep and lipid metabolism
Peroxisomes are critical organelles that detoxify wastes while also catabolizing and anabolizing lipids. How peroxisomes coordinate protein import and support metabolic functions across complex tissues and timescales remains poorly understood in vivo. Using the Drosophila brain, we discover a striking enrichment of peroxisomes in the neuronal soma and the cortex glia that enwrap them. Unexpectedly, import of peroxisomal proteins into cortex glia, but not neurons, dramatically oscillated across time and peaked in the early morning. Rhythmic peroxisomal import in cortex glia autonomously required the circadian clock and Peroxin 5 (Pex5; peroxisomal biogenesis factor 5 homolog), with endogenous Pex5 protein peaking in the morning. Notably, removing Pex5 in cortex glia severely reduced sleep while concomitantly causing aberrant lipid metabolism characterized by ectopic lipid droplets and increases across multiple lipid families. Thus, the circadian import of peroxisomal proteins via Pex5 in cortex glia is essential for lipid homeostasis and organismal behavior.

Generative Phenomenology of form perception: Perceptograms and cortical models for amblyopic form distortions
We introduce Generative Phenomenology: making viewable images of perceptions (Perceptograms) and generating the images from neural models, as a powerful technique for understanding the neural bases of perception. Amblyopia, a disorder of spatial vision, provides a perfect case because signals from the two eyes go through partly different cortical neurons, and many amblyopes report form distortions when viewing sinusoidal gratings through their amblyopic eye (AE) but not through the fellow eye (FE). Using a dichoptic display, we acquired high-fidelity perceptograms for 24 gratings shown to AE while sums-of-gratings plaids were shown to FE with contrast, frequency, phase, and orientation of the plaid gratings adjusted to match the two percepts exactly. Plaids provided exact matches to 92.6% of distortions. A formal equation that the signals generated in visual cortex by the test gratings seen through AE match the signals generated by their matched perceptograms seen through FE for each observer, was used to analytically derive cortical filters processing AE signals as linear transforms of standard steerable filters modeling normal V1 neurons for FE. Passing gratings through AE filters accurately generated the measured perceptograms. The filter transformations reflected complex changes in V1 receptive fields and possibly in cross-correlations. The AE filters also explained amblyopic deficits in perceiving sinusoidally modulated circular contours and were consistent with orientation perceptive fields estimated from reverse-correlation experiments. Changes in neuronal receptive fields thus have profound effects on perception, to the extent that observers can see more features than are present in the viewed stimulus.

Multiomics analysis identifies VPA-induced changes in neural progenitor cells, ventricular-like regions, and cellular microenvironment in dorsal forebrain organoids
Pharmaceutical agents, such as antiepileptic medications, can cross fetal barriers and affect the developing brain. Prenatal exposure to the antiepileptic drug valproate (VPA) is associated with an increased risk of neurodevelopmental disorders, including congenital malformations and autism spectrum disorder. In animal models and neural organoids, VPA has been shown to alter signaling pathways, such as Wnt pathway, providing insights into VPA-induced neurodevelopmental defects. Here, we exposed dorsal forebrain organoids to VPA for 30 days and examined effects at the tissue, cellular, and molecular level. VPA treatment disrupted ventricular-like regions, indicating defects in cell-cell and cell-matrix interactions. Transcriptomics analysis confirmed altered expression of extracellular matrix (ECM) genes and single cell RNA sequencing analysis identified genes involved in microenvironment sensing, such as cellular mechanosensing and Hippo-YAP/TAZ signaling pathway. Finally, proteomics analysis corroborated that VPA alters the microenvironment of the human dorsal forebrain organoids by disrupting the secretion of ECM proteins. Altogether, our study suggests that VPA-treated dorsal forebrain organoids serve as a model to investigate the role of extracellular processes in brain development and to understand how their disruptions might contribute to neurodevelopmental disorders.

Kappa opioid receptors control a stress-sensitive brain circuit and drive cocaine seeking
Stress is a potent trigger for drug-seeking behaviors in both rodents and humans with a history of substance use. Kappa opioid receptors (kORs) play a critical role in mediating stress responses. Our previous studies in the ventral tegmental area (VTA) demonstrated that acute stress activates kORs to block long-term potentiation at GABAA synapses on dopamine neurons (LTPGABA) and triggers stress-induced reinstatement of cocaine seeking. Here we identify the specific GABAergic afferents affected by stress, the precise localization of kORs within the VTA, and show that VTA kOR activation is sufficient to drive reinstatement. We optogenetically activated specific GABAergic afferents and found that nucleus accumbens (NAc)-to-VTA, but not lateral hypothalamus (LH)-to-VTA projections, exhibit stress-sensitive LTPGABA. Using a conditional knock-out approach, we found that selectively deleting kORs from NAc neurons but not from dopamine cells prevents stress-induced block of LTPGABA. Selectively activating dynorphin-containing NAc neurons with an excitatory DREADD mimics acute stress, preventing LTPGABA at VTA synapses. We furthermore demonstrated that without acute stress, microinjection of a selective kOR agonist directly into the VTA facilitates cocaine reinstatement without similarly affecting sucrose-motivated responding, demonstrating the critical role of kORs in stress-induced cocaine reinstatement. Our results show that kORs on GABAergic NAc nerve terminals in the VTA underlie loss of LTPGABA that may drive stress-induced addiction-related behaviors. Our work highlights the importance of inhibitory inputs for controlling dopamine neuron excitability in the context of addiction and contributes to defining the circuit involved in stress-induced drug reinstatement.

Evolutionary Perspectives on Anxiety: Telencephalic Circuitry and the Anxiogenic Role of TrkB Signaling in Tuberous Sclerosis Complex
Tuberous Sclerosis Complex (TSC) is a genetic disease which manifests as a range of neurological symptoms, including benign brain tumors, epilepsy, and TSC-associated neuropsychiatric disorders (TANDs). Among the latter, according to recent reports, anxiety and mood disorders affect over 50% of patients. We have previously demonstrated anxiety-like behavioral symptoms in the zebrafish model of TSC, which were rescued by treatment with the TrkB antagonist ANA-12. Here, we aimed to investigate the mechanism of how ANA-12 regulates behavior by analyzing brain activity in the telencephalon of TSC zebrafish larvae, and we identified the affected regions as corresponding to the known mammalian circuitry involved in anxiety processing. Due to differences in development, the identification of telencephalic territories that are homologous between zebrafish and mammals remains challenging, particularly at early, dynamic stages of development. However, we were able to identify populations of neurons in the zebrafish habenula and ventral subpallium whose involvement in anxiety parallels that of mammals. Those regions were dysregulated in the TSC mutant. This dysregulation correlated with aberrant anxiety behavior and was rescued by treatment with ANA-12. Our results suggest that hyperactivation of TrkB in those regions is a major contributor to anxiety-like behavior as seen in TSC fish, and that those mechanisms could be evolutionarily conserved between zebrafish and mammals.

Early life food insecurity impairs memory function during adulthood
Approximately 14% of U.S. households are estimated to be food insecure. The neurocognitive and metabolic impacts of unpredictable food access during early-life periods of development are poorly understood. To address these gaps we devised a novel rat model of food insecurity to control the timing, type, and quantity of accessible food using programmable feeders. Male rats were divided into 3 groups: Secure-chow (SC), a control group given 100% of daily caloric needs, distributed evenly across 4 daily meals of standard chow at set mealtimes; Secure-mixed (SM), a 2nd control group identical to the SC group except that the food type predictably alternated daily between chow and a high-fat, high-sugar diet (HFHS); and Insecure-mixed (IM), the experimental group given randomly alternating daily access to either chow or HFHS at either 85% or 115% of daily caloric needs, distributed evenly across 3 daily meals with unpredictable mealtimes. These feeding schedules were implemented from postnatal days (PNs) 26-45, after which all groups received chow ad libitum. Metabolic assessments performed in adulthood revealed no group differences in caloric intake, body weight, or body composition when maintained on either chow (PN46-149) or a cafeteria diet (PN150-174). Behavioral measures (PN66-126) revealed no group differences in anxiety-like, exploratory, or impulsive behavior (zero maze, open field, differential reinforcement of low rates of responding procedures). However, the IM group exhibited hippocampus-dependent memory impairments compared to both control groups in the novel location recognition test. These findings suggest that early-life food insecurity may contribute to long-term impairments in memory function.

Cerebral vascular tortuosity and aneurysm formation and rupture: a novel vessel tortuosity scale
Cerebral aneurysm (CA) rupture is the most common cause of nontraumatic subarachnoid hemorrhage. Recent data suggests that tortuosity is associated with aneurysm formation and rupture risk. We aimed to determine if tortuosity correlates with CA development and rupture in a mouse CA model and to develop a novel tortuosity scale to be used for in vivo CA studies. A highly validated, elastase-mouse CA model was used to assess cerebral vessel tortuosity with CA formation and rupture in sham and elastase groups. A 4-point ordinal scale was created to evaluate predictive capacity for vessel tortuosity level and CA formation and rupture. Nearly all sham animals (92%) had little to no vessel tortuosity on the visual scale (median, IQR: 1, [1-2]), compared to 24% in the elastase groups (2, [2-3]) (p=0.001). Sham cohorts had zero animals with highly tortuous vessels, while 3.5mU and 35mU cohorts had >35% of animals with significant visual tortuosity, p=0.003 and p<0.000, respectively. CA formation and rupture was higher in the elastase groups compared to the sham group (p=0.002). Both the visual scale and tortuosity index significantly predicted CA formation (p<0.001) and rupture (p<0.001). A novel tortuosity scale is highly predictive of CA formation and rupture in vivo. It may offer a new measurement to better understand vessel stress in the pathogenesis and progression of CAs.

Profiling Lysosomal and Mitochondrial Dysfunction in Neurodegenerative Diseases Using Human Fibroblasts for Translational Therapeutic Screening
Lysosomal dysfunction and mitochondrial health are intricately connected, playing essential roles in cellular homeostasis. Lysosomes are acidic membrane-bound organelles responsible for degrading and recycling cellular waste, while mitochondria generate the energy required for cellular functions. Growing evidence implicates roles for lysosomal and mitochondrial dysfunction in neurodegenerative diseases, including Alzheimer's and Parkinson's disease. With novel therapeutics targeting both the lysosomal and mitochondrial functions, robust assays for compound screening are becoming critical to evaluate modulation of both organelles in disease-relevant cellular models. Here, we investigated human fibroblasts derived from healthy donors, as well as patients with Alzheimer's and Parkinson's disease, to assess their capacity to model key aspects of lysosomal and mitochondrial dysfunction. Lysosomal function was evaluated using various assays, including quantification of lysosomal proteins (TMEM175 and LAMP1), LysoTracker staining, measurement of lysosomal pH, and lysosomal enzymatic activity. Autophagic flux was assessed by measuring p62 levels as a marker of autophagy. Mitochondrial function was investigated by measuring mitochondrial calcium levels, membrane potential, oxidative stress, and mitochondrial content using MitoTracker. To explore the potential of using human fibroblasts for in vitro compound screening, we validated these assays in a 384-well high-throughput format using compounds such as chloroquine and ammonium chloride. Our findings demonstrate that human fibroblasts faithfully recapitulate lysosomal and mitochondrial dysfunctions characteristic of neurodegenerative diseases. Moreover, the use of robust assays positions these cells as a valuable platform for high-throughput screening to identify novel therapeutics targeting lysosomal and mitochondrial pathways.

Neural mechanisms of training in Brain-Computer Interface: ABiophysical modeling approach
Brain-computer interface (BCI) is a system that translates neural activity into commands, allowing direct communication between the brain and external devices. Despite its clinical application, BCI systems fail to robustly capture the intent of subjects due to a limited understanding of the neural mechanisms underlying BCI control. To address this issue, we introduce a biophysical modeling approach that leverages a linear neural mass model to investigate the associated neural mechanisms of motor imagery-based BCI experiments. We tailor this model to simulate both motor imagery task and resting state. We apply this approach to a cohort of 19 healthy subjects trained along four sessions where magnetoencephralography(MEG) and electroencephalography (EEG) signals were simultaneously recorded. The neural synaptic gain and time scale of the modeled excitatory and inhibitory neural mass populations capture changes in neural activity across conditions and sessions. Those changes appear in important areas of the sensorimotor cortex, relevant for motor imagery tasks. We observed these effects in both EEG and MEG modalities. These findings provide insights into the underlying neural mechanisms in a motor imagery task in BCI, paving the way to tailored BCI training protocols.

Optogenetic activation of entorhinal projection neurons alters the target recognition and circuit development without enhancing axon regeneration after axotomy in organotypic slices.
The central nervous system (CNS) has a limited intrinsic capacity for axonal regeneration, making functional recovery after injury extremely challenging. Numerous strategies have been explored to overcome this blockade, among others, molecular interventions or modulation of the inhibitory extracellular environment. Despite some advances, effective regeneration remains elusive, particularly in adult CNS neurons. To investigate these mechanisms in a controlled and reproducible setting, we employ organotypic slice cultures (OSCs), which retain key structural and cellular features of the intact brain while allowing for long-term in vitro experimentation. In particular, the entorhino-hippocampal (EH) co-culture model preserves the anatomical and functional connectivity of the perforant pathway, providing an excellent platform for studying axonal degeneration and regeneration. This model reproduces laminar specificity, axonal myelination, and inhibitory signaling after axotomy, closely mimicking in vivo conditions. Furthermore, EH co-cultures facilitate the application of optogenetic tools to monitor and manipulate neuronal activity. Our study explores whether enhancing activity in entorhinal cortex neurons can promote axonal regeneration after a EH lesion. Our results show that increased activity in entorhinal neurons alters the development of the EH connection and fails to enhance the regrowth of injured mature entorhinal axons. These findings suggest that both extrinsic and intrinsic factors shape the regenerative response and highlight the utility of EH OSCs as a versatile model for testing future pro-regenerative interventions.

Bidirectional regulation of glycoprotein nonmetastatic melanoma protein B by β-glucocerebrosidase deficiency in GBA1 isogenic dopaminergic neurons from a patient with Gaucher disease and parkinsonism
Variants in GBA1 are common genetic risk factors for several synucleinopathies. The increased risk has been attributed to the toxic effects of misfolded glucocerebrosidase (GCase) (gain-of-function), and the accumulation of lipid substrates due to reduced enzyme activity (loss-of-function). To delineate GBA1 pathogenicity, an iPSC line was generated from a patient with both type 1 Gaucher disease (GBA1: N370S/N370S; p.N409S/p.N409S) and Parkinson disease (PD). From this line, we created a reverted wild-type (WT) line and a GBA1 knockout (KO) line to eliminate misfolded GCase and intensify lipid accumulation. N370S/N370S and KO dopaminergic neurons (DANs) exhibited decreasing GCase levels and progressive accumulation of lipid substrates compared to WT DANs. Notably, the expression of GPNMB, whose levels correlate with PD risk, was upregulated by the mild lipid accumulation in N370S/N370S DANs but disrupted in KO DANs. These findings refine the loss-of-function mechanism by associating PD risk levels of GPNMB with lipid levels specific to GBA1 risk variants.

β-bursting as a sensitive neural marker of inhibitory control in healthy older adults: a linear mixed-effects modelling and threshold-free cluster approach
Inhibitory control is essential for adaptive behaviour and declines with age, yet the underlying neural dynamics remain poorly understood. The {beta}-rhythm (15 to 29 Hz) is thought to reflect inhibitory signalling within the fronto-basal ganglia network. Recent evidence suggests that transient {beta}-bursts support inhibitory performance, often masked by conventional analyses of trial-averaged {beta}-power. To reveal the link between trial-by-trial {beta}-bursting and inhibition, we applied a recently developed analysis framework combining linear mixed-effects modelling (LMM) with threshold-free cluster enhancement (TFCE) during response inhibition and initiation in older adults. Twenty healthy older adults performed a bimanual anticipatory response inhibition task, while electroencephalography and electromyography were recorded to capture {beta}-activity ({beta}-burst rate/duration; averaged {beta}-power) and muscle bursting dynamics, respectively. Our analysis revealed distinct {beta}-bursting signatures absent in averaged {beta}-power data. Following the stop-signal, parieto-occipital {beta}-bursting presented before a temporal cascade from attentional to inhibitory processes. In addition to expected right fronto-central and bilateral sensorimotor activity, we observed left prefrontal {beta}-bursting, indexing broader inhibitory network engagement during bimanual response inhibition. Moreover, we established a functional link between right sensorimotor {beta}-bursting and muscle bursts during stopping, indicating rapid cortical suppression of initiated motor output. These results help clarify the mechanistic role of {beta}-oscillations and underscore the sensitivity of {beta}-bursting to both the timing and context of inhibitory demands in healthy older adults. Future research will help establish the potential of {beta}-bursting, combined with LMM-TFCE analysis, as a clinically relevant marker of impulse control dysfunction.

Mitochondrial Energy Transformation Capacity Influences Brain Activation During Sensory, Affective, and Cognitive Tasks
Brain function relies on energy supplied by mitochondrial energy transformation, but how cellular energetics constrain neurological function and cognition remains poorly understood. Genetic defects in mitochondrial DNA cause rare mitochondrial diseases (MitoD) that offer a unique window into the energetic foundations of cognition, shedding light on the neural processes that are most energetically constrained. In this study, we assessed functional magnetic resonance imaging (fMRI) on 29 participants with MitoD and 62 matched controls during resting state and tasks probing cognitive (N-back task), affective (cold pain), and sensory (multisensory visual and auditory perception) functions. MitoD individuals exhibited significant cognitive deficits across a range of functions, including executive function and working memory, mental and physical fatigability, low exercise tolerance, and low mood. These deficits were accompanied by markedly elevated blood levels of metabolic stress markers, including GDF15 and FGF21. Surprisingly, overall BOLD fMRI activity and connectivity were largely intact across all tasks in MitoD individuals. However, those with more severe cognitive impairment and higher GDF15 levels showed reduced working memory-related activity, which in turn mediated poorer task performance. Conversely, individuals with relatively preserved cognitive function exhibited hyperactivation in working memory regions and working memory performance compared to controls, suggesting compensatory engagement of cortical systems in high-functioning MitoD individuals. These effects were weaker in the sensory domain and absent during affective (cold pain) processing, suggesting an energy hierarchy in the brain that prioritizes essential functions such as affective responses while downregulating more energy-demanding, complex cognitive processes when resources are limited.

Disentangling the Functional Roles of Pre-Stimulus Oscillations in Crossmodal Associative Memory Formation via Sensory Entrainment
The state of neural dynamics prior to the presentation of an external stimulus significantly influences its subsequent processing. This neural preparatory mechanism might be of particular importance for crossmodal memory formation. The integration of stimuli across different sensory modalities is a fundamental mechanism underlying the formation of episodic memories. However, the causal role of pre-stimulus neural activity in this process remains largely unclear. In this preregistered study, we investigate the direct relationship between transient brain states induced by sensory entrainment and crossmodal memory encoding. Participants (n = 105) received rhythmic visual stimuli at theta (5 Hz) or alpha (9 Hz) frequencies to evoke specific brain states. EEG recordings confirmed successful entrainment, with sustained increases in neural activity within the stimulated frequency bands persisting until stimulus onset. Notably, induced alpha oscillatory activity enhanced recognition memory performance reflected by increased sensitivity, and suggesting that alpha oscillations prepare the brain for optimal multisensory integration. These findings highlight the functional significance of distinct oscillatory brain states in facilitating memory encoding by increasing cortical excitability before stimulus presentation. Overall, our results emphasize the importance of pre-stimulus brain states in shaping the efficiency of memory formation across sensory modalities and shed light on how dynamic neural preparations support learning.

Multiple groups of neurons in the superior colliculus convert value signals into saccadic vigor.
Eye movements directed to high-valued objects in the environment are executed with greater vigor. Superior Colliculus (SC) - a subcortical structure that controls eye movements - contains multiple subtypes of neurons that have distinct functional roles in generating saccades. How does value-related information processed in other parts of the brain affect the responses of these different subtypes of SC neurons to facilitate faster saccades? To test this, we recorded four subtypes of neurons simultaneously while the monkey made saccades to objects they had been extensively trained to associate with large or small rewards (i.e., good or bad). In three subtypes of neurons (visual, visuomotor, and motor), the good objects elicited more spikes than bad objects. More importantly, using a bootstrapping procedure, we identified three separable phases of activity: 1) early visual response (EVIS), 2) late visual response (LVIS), and 3) pre-saccadic (PreSAC) motor response in these neuronal subtypes. In each subtype of neurons, the value of objects (good vs. bad) was positively correlated with the activity in the LVIS and PreSAC phases but not the EVIS phase. These data suggest that the value information from other brain regions modulates the visual (LVIS) and the motor (PreSAC) responses of visual, visuomotor, and motor neurons. This enhanced activation facilitates the faster initiation and execution of the saccade based on the value of each object. In addition, we found a novel class of tonically active neurons that decrease their activity in response to object onset and remain inhibited till the end of the saccade. We suggest that these tonic neurons facilitate the saccade to objects by disinhibiting the interactions between the other three SC neurons.

Fine-grained alignment of cortical signals to smartphone touchscreen temporal pattern
Smartphone use varies ranging from rapid, rhythmic tapping (e.g., texting) to slower, irregular scrolling (e.g., browsing), resulting in diverse temporal patterns of inter-touch intervals. The underlying brain processes may dynamically align to these behaviors. We investigated population neural signals captured by using EEG during hour long smartphone use sessions (n = 53 subjects, accumulating 136869 interactions). We grouped the brain signals according to the transition patterns between consecutive touchscreen intervals (next-interval statistics), resulting in a matrix of EEG signals. Using data-driven dimensionality reduction on this matrix, we identified low-dimensional neuro-behavioral clusters that captured brain signal features associated with specific next-interval statistics. These neuro-behavioral clusters were found for diverse cortical locations spanning occipital, parietal and frontal cortices, suggesting a cortex-wide alignment to the next-interval statistics. Notably, these clusters were observed predominantly before rather than after the touchscreen interactions and they varied across individuals, suggesting personalized strategies for planning and executing smartphone use. Our findings indicate that the brain tracks and adapts to the fine-grained temporal patterns in touchscreen behavior, likely to support efficient smartphone interactions. More broadly, this work demonstrates how naturalistic smartphone use can reveal dynamic, individualized cortical adaptations to real-world temporal structure.

Gut microbial diversity and inferred capacity to produce butyrate modulate cortisol reactivity following acute stress in healthy adults
Acute stress triggers the release of stress hormones such as cortisol, increasing stress reactivity and aiding post-stress recovery. Prior work in rodents revealed the modulating role of the gut microbiota in stress reactivity, but whether this is also the case in humans is unclear. Additionally, to what degree stress reactivity is tied to one's capacity to produce microbial metabolites such as short-chain fatty acids (SCFAs) is untested. To close this gap, we invited 80 healthy human adults to the laboratory who were either exposed to a well-established, standardized intervention that induced acute stress or to a non-stressful control condition (n = 40 per group). Changes in stress hormones were assessed from repeated saliva sampling. Stool samples were obtained at baseline, and the gut microbiota were characterized through 16S rRNA gene amplicon sequencing. We found that higher gut microbiota diversity was associated with lower cortisol stress reactivity and lower levels of subjectively experienced stress, but not faster post-stress recovery, across the individuals of the stress group. Moreover, lower cortisol stress reactivity was associated with a higher relative abundance of taxa that encode metabolic pathways for the production of butyrate, a key SCFA. These results are the first to highlight the role of gut microbial diversity and inferred butyrate production capacity in modulating stress reactivity in healthy adults, underscoring the microbiota's potential to buffer against the detrimental effects of acute stress.

The impact of contingency awareness on the neurocircuitry underlying pain-related fear and safety learning
Visceral pain-related fear, shaped by associative learning, drives maladaptive emotional reactions and may contribute to the chronicity of pain in disorders of gut-brain interaction. However, the role of contingency awareness remains unclear. In a translational model of pain-related conditioning, we investigated the brain-behavior relationships underlying contingency awareness in shaping the neural circuitry involved in visceral pain-related fear and safety learning. Data from 75 healthy individuals undergoing differential conditioning were acquired in two functional magnetic resonance imaging studies. Visceral pain as unconditioned stimulus (US) was paired with a visual cue as conditioned stimulus (CS+) while another cue (CS-) remained unpaired. Differential neural responses to predictive cues were analyzed using a full factorial model and regression analyses to evaluate the predictive value of neural activation patterns based on contingency awareness. Analyses revealed a significant interaction between CS-type and contingency awareness involving dorsolateral prefrontal cortex (dlPFC) and parahippocampus, driven by an enhanced CS+>CS- differentiation in highly aware participants. The reverse contrast revealed widespread activation in fronto-parietal and limbic networks, more pronounced in the highly aware group. Regression analyses showed that enhanced CS--related were associated with increased contingency awareness and CS- valence change, while no activation clusters predictive of behavioral responses were found for CS+. The recruitment of emotional arousal and executive control networks as a function of contingency awareness highlights its relevance in shaping pain- and, particularly, safety-predictive cue properties. These results suggest distinct processes for fear acquisition and inhibition, with significant implications for exposure-based treatments of disorders of gut-brain interaction.

Similar Dynamic Frontal Cortex Representations of Auditory Stimuli Cueing Opposite Actions and Rewards
Frontal Cortex (FC) plays a pivotal role in controlling actions and their dynamics in response to incoming sensory stimuli. We explored FC representations of the same stimuli when signifying diametrically opposite behavioral meanings depending on task context. Two groups of ferrets performed Go-NoGo auditory categorization tasks with opposite contingencies and rewards, and varied stimuli. Remarkably, despite the opposite stimulus-action associations, single-unit responses were similar across all tasks, being more sustained and stronger to (Target) sounds signaling a change in action, than to (Reference) sounds indicating maintenance of ongoing actions, especially during task engagement. Three major dynamic response profiles were extracted from the overall responses, and their combination defined separate neuronal clusters that exhibited different roles in relation to task events. Decoding based on the temporal structure of the population responses revealed distinct decoders that were aligned to different task events. Furthermore, the {beta}-band power, extracted from the FC local field potentials, was similarly and strongly modulated during Target stimuli in all tasks despite opposite behavioral actions. Based on these findings, we propose a model of pathway-specific functional projections from the tripartite FC neuronal clusters to the basal ganglia that is consistent with previous evidence for the conjoint roles of the FC and striatum in adaptive motor control.

Ocular delivery of different VCP inhibitory formulations prevents retinal degeneration in rhodopsin 255 isoleucine deletion mice
Rhodopsin-mediated autosomal dominant retinitis pigmentosa (RHO-adRP) is a progressive inherited retinal degenerative disorder currently lacking effective treatments. A recurrent 3-base pair deletion in the RHO gene, resulting in the loss of isoleucine at codon 255 or 256 (RHO{triangleup}I255 or RHO{triangleup}I256), has been identified in patients from the United Kingdom, Germany, Belgium, China, and Korea, suggesting a broad geographic distribution. This mutation leads to rhodopsin (RHO) misfolding, its retention in the endoplasmic reticulum (ER), and aggregation with wild-type (WT) RHO, ultimately triggering ER stress and photoreceptor degeneration. These aggregates are primarily cleared via the ER-associated degradation (ERAD) pathway, with valosin-containing protein (VCP) playing a key role in their retrotranslocation and proteasomal degradation. Pharmacological or genetic inhibition of VCP has shown neuroprotective effects in other models of adRP, but the poor aqueous solubility of VCP inhibitors and challenges in retinal drug delivery hinders clinical translation. To overcome these limitations, we evaluated and compared three VCP-targeted therapeutic strategies in Rho{triangleup}I255 knock-in mouse retinae: (1) small-molecule inhibitors (ML240, NMS-873) solubilized in DMSO, (2) ML240 encapsulated in monomethoxy-polyethylene glycol (mPEG)-cholane nanoparticles, and (3) small interfering RNA (siRNA) targeting VCP, delivered via magnetic nanoparticles. Neuroprotective effects were assessed in vitro in retinal explants and in vivo following intravitreal injection. Our findings provide the first evidence that VCP inhibition restores RHO trafficking to the outer segments and prevents photoreceptor cell death in the Rho{triangleup}I255 model. Among the three approaches, nanocarrier-encapsulated ML240 exhibited superior efficacy, enabling sustained drug delivery and enhanced photoreceptor protection. These results establish a preclinical proof-of-concept for nanocarrier-mediated VCP inhibition as a promising therapeutic strategy for RHO-adRP and potentially other ER-stress-related retinal degenerations.

Perceptual processes as charting operators
Sensory operators are classically modelled using small circuits involving canonical computations, such as energy extraction and gain control. Notwithstanding their utility, circuit models do not provide a unified framework encompassing the variety of effects observed experimentally. We develop a novel, alternative framework that recasts sensory operators in the language of intrinsic geometry. We start from a plausible representation of perceptual processes that is akin to measuring distances over a sensory manifold. We show that this representation is sufficiently expressive to capture a wide range of empirical effects associated with elementary sensory computations. The resulting geometrical framework offers a new perspective on state-of-the-art empirical descriptors of sensory behavior, such as first-order and second-order perceptual kernels. For example, it relates these descriptors to notions of flatness and curvature in perceptual space.

A Deep Learning Framework for Predicting Functional Visual Performance in Bionic Eye Users
Efforts to restore vision via neural implants have outpaced the ability to predict what users will perceive, leaving patients and clinicians without reliable tools for surgical planning or device selection. To bridge this critical gap, we introduce a computational virtual patient (CVP) pipeline that integrates anatomically grounded phosphene simulation with task-optimized deep neural networks (DNNs) to forecast patient perceptual capabilities across diverse prosthetic designs and tasks. We evaluate performance across six visual tasks, six electrode configurations, and two artificial vision models, positioning our CVP approach as a scalable pre-implantation method. Several chosen tasks align with the Functional Low-Vision Observer Rated Assessment (FLORA), revealing correspondence between model-predicted difficulty and real-world patient outcomes. Further, DNNs exhibited strong correspondence with psychophysical data collected from normally sighted subjects viewing phosphene simulations, capturing both overall task difficulty and performance variation across implant configurations. While performance was generally aligned, DNNs sometimes diverged from humans in which specific stimuli were misclassified, reflecting differences in underlying decision strategies between artificial agents and human observers. The findings position CVP as a scientific tool for probing perception under prosthetic vision, an engine to inform device development, and a clinically relevant framework for pre-surgical forecasting.

Healthy human eyes misaligned optical components: Binocular Listings law
The healthy human eyes optical components are misaligned. Although important in studying vision quality, it has been overlooked in research on binocular and oculomotor vision. This study presents the construction of ocular torsion in the binocular system that incorporates the fovea displaced from the posterior pole and the lens tilted away from the eyes optical axis. When the eyes binocular posture changes, each eyes torsional position transformations, computed in the framework of Rodrigues vector, are visualized in GeoGebra simulations. Listings law, important in oculomotor control by constraining a single eye redundant torsional degree of freedom, is ab initio formulated for bifoveal fixations in the binocular system with misaligned optical components for the fixed upright head. It leads to the configuration space of binocularly constrained eyes rotations, including the noncommutativity rule. This formulation modifies the Listing plane of the straight-ahead eyes primary position by replacing it with the binocular eyes posture corresponding to the empirical horopters abathic distance fixation, a unique bifoveal fixation for which the longitudinal horopter is a straight frontal line. Notably, it corresponds to the eye muscles natural tonus resting position, which serves as a zero-reference level for convergence effort. Supported by ophthalmology studies, it revises the elusive neurophysiological significance of the Listing plane. Furthermore, the binocular constraints couple 3D changes in the eyes orientation and, hence, torsional positions during simulations with GeoGebras dynamic geometry. The binocular Listings law developed here can support this coupling, which is important in oculomotor control. The results obtained in this study should be a part of the answers to the questions posted in the literature on the relevance of Listings law to clinical practices.

Testing Sensorimotor Timing in a Living Laboratory: Behavioral Signatures of a Neural Oscillator
Rhythmic ability has been studied for more than a century in laboratory settings testing timed finger taps. While robust results emerged, it remains unclear whether these findings reflect behavioral limitations in realistic scenarios. This study tested the synchronization-continuation task in a museum with 455 visitors of a wide variety of ages (5-74yrs), musical experiences (0-40yrs) and educational and cultural backgrounds. Adopting a dynamic systems perspective, three metronome pacing periods were anchored around each individuals preferred tempo, and 20% faster and 20% slower. Key laboratory findings were replicated and extended: timing error and variability decreased during childhood and increased in older adults and were lower, even with moderate musical experience. Consistent with an oscillator perspective, timing at non-preferred tempi drifted toward their preferred rate. Overall, these findings demonstrate that timing limitations may reflect attractor properties of a neural oscillator and its signature is still present even in noisy, naturalistic settings.

Oscillatory markers of interoceptive attention: beta suppression as a neural signature of heartbeat processing
Interoceptive attention - the ability to selectively focus on internal bodily signals - has been linked to distinct neural responses, yet the contribution of oscillatory dynamics to this process remains underexplored. This study investigates the neural mechanisms underlying interoceptive attention by examining beta-band power suppression during heartbeat and auditory discrimination tasks. Fifty-one healthy participants engaged in interoceptive (heartbeat detection) and exteroceptive (auditory discrimination) tasks while their brain activity was measured using magnetoencephalography (MEG). The results revealed significant beta suppression time-locked to the R-peak in the somatosensory cortex, anterior cingulate cortex, mid-cingulate cortex, and dorsolateral prefrontal cortex from 310 to 530 ms post-R-peak. Beta suppression was more pronounced during interoceptive attention, correlating positively with interoceptive accuracy. The findings support the notion that beta suppression in fronto-cingulo-somatosensory network may serve as a neural marker of interoceptive processing, contributing to predictive coding models of interoception. This study highlights the potential for using beta suppression as an objective measure of interoceptive accuracy and suggests that neural oscillations play a critical role in the brain's regulation of heartbeat-related information. Furthermore, the study proposes that interoceptive attention involves a top-down mechanism that dynamically adjusts the brain's response to cardiac afferent signals, enhancing the precision of interoceptive processing. These findings have implications for understanding how the brain integrates interoceptive signals and may provide insights into clinical applications targeting interoceptive dysfunctions.

Autophagy activators normalize aberrant Tau proteostasis and rescue synapses in human familial Alzheimer's disease iPSC-derived cortical organoids
Alzheimer's disease (AD) is the most common form of dementia worldwide. Despite extensive progress, the cellular and molecular mechanisms of AD remain incompletely understood, partially due to inadequate disease models. To illuminate the earliest changes in hereditary (familial) Alzheimer's disease, we developed an isogenic AD cerebrocortical organoid (CO) model. Our refined methodology produces COs containing excitatory and inhibitory neurons alongside glial cells, utilizing established isogenic wild-type and diseased human induced pluripotent stem cells (hiPSCs) carrying heterozygous familial AD mutations, namely PSEN1 {Delta}E9/WT, PSEN1M146V/WT, or APPswe/WT. Our CO model reveals time-progressive accumulation of amyloid beta (A{beta}) species, loss of monomeric Tau, and accumulation of aggregated high-molecular-weight (HMW) phospho(p)-Tau. This is accompanied by neuronal hyperexcitability, as observed in early human AD cases on electroencephalography (EEG), and synapse loss. Single-cell RNA-sequencing analyses reveal significant differences in molecular abnormalities in excitatory vs. inhibitory neurons, helping explain AD clinical phenotypes. Finally, we show that chronic dosing with autophagy activators, including a novel CNS-penetrant mTOR inhibitor-independent drug candidate, normalizes pathologic accumulation of A{beta} and HMW p-Tau, normalizes hyperexcitability, and rescues synaptic loss in COs. Collectively, our results demonstrate these COs are a useful human AD model suitable for assessing early features of familial AD etiology and for testing drug candidates that ameliorate or prevent molecular AD phenotypes.

Safety processing shifts from hippocampal to network engagement across adolescence
Adolescence is a critical period that requires balancing exploration of uncertain and novel environments while maintaining safety. This task requires sophisticated neural integration of threat and safety cues to guide behavior. Yet little work has been conducted on threat and safety processing outside of conditioning paradigms, which, while valuable, lack the complexity to identify how the adolescent brain supports distinguishing threat from safety when both are present and as task contingencies change. In the current study, we take an approach that expands on elements of differential conditioning as well as conditioned inhibition. We recorded brain responses to external threat and self-oriented protection cues to examine how the adolescent brain supports threat-safety discrimination using 7-Tesla functional magnetic resonance imaging (fMRI). Our findings reveal an adolescent transition in the neural mechanisms supporting accurate threat-safety discrimination, with younger adolescents (12-14 years) relying predominantly on the hippocampus and older adolescents (15-17 years) utilizing a more integrated circuit involving the hippocampus and anterior ventromedial prefrontal cortex (vmPFC) connectivity. Our results clarify how competition between threat and safety cues is resolved within the visual cortex, demonstrating enhanced perceptual sensitivity to protection that is independent of threat. By examining the dynamic encoding of safety to different stimuli, the current study advances our understanding of adolescent neurodevelopment and provides valuable insights into threat-safety discrimination beyond conventional conditioning models.

Social Jetlag Has Detrimental Effects on Hallmark Characteristics of Adolescent Brain Structure, Circuit Organization and Intrinsic Dynamics
Study Objectives: To investigate associations between social jetlag and developing brain circuits and structures in adolescents. Methods: N = 3507 youth (median (IQR) age = 12.0 (1.1) years; 50.9% females) from the Adolescent Brain Cognitive Development (ABCD) cohort were studied. Social jetlag (adjusted for sleep debt (SJLSC) versus non-adjusted (SJL)), topological properties and intrinsic dynamics of resting-state networks, and morphometric characteristics were analyzed. Results: Over 35% of participants had SJLSC [≥]2.0 h. Boys, Hispanic and Black non-Hispanic youth, and/or those at later pubertal stages had longer SJLSC ({beta}=0.06 to 0.68, CI=[0.02, 0.83], p[≤]0.02), which was also associated with higher BMI ({beta}=0.13, CI=[0.08, 0.18], p<0.01). SJLSC and SJL were associated with weaker thalamic projections ({beta}=-0.22, CI=[-0.39, -0.05], p=0.03), potentially reflecting a disrupted sleep-wake cycle. Longer SJLSC was also associated with less topologically resilient and weakly connected salience network ({beta}=-0.04, CI=[-0.08, -0.01], p=0.04), and lower thickness and/or volume of cortical and subcortical structures overlapping with this and other networks supporting emotional and reward processing and regulation, and social function ({beta}=-0.08 to -0.05, CI=[-0.12, -0.01], p<0.05). SJLSC and SJL were associated with alterations in spontaneous brain activity and coordination that indicate disrupted neural maturation and plasticity. SJL was associated with lower information transfer between regions supporting sensorimotor integration, social function and emotion regulation ({beta}=-0.07 to -0.05, CI=[-0.12, -0.01], p<0.04). Conclusions: Misaligned sleep may have detrimental effects on adolescent brain circuit organization and dynamics, and structural characteristics of regions that play critical roles in cognitive function and regulation of fundamental biological processes.

MEGaNorm: Normative Modeling of MEG Brain Oscillations Across the Human Lifespan
Normative modeling provides a principled framework for quantifying individual deviations from typical brain development and is increasingly used to study heterogeneity in neuropsychiatric conditions. While widely applied to structural phenotypes, functional normative models remain underdeveloped. Here, we introduce MEGaNorm, the first normative modeling framework for charting lifespan trajectories of resting-state magnetoencephalography (MEG) brain oscillations. Using a large, multi-site dataset comprising 1,846 individuals aged 6-88 and spanning three MEG systems, we model relative oscillatory power in canonical frequency bands using hierarchical Bayesian regression, accounting for age, sex, and site effects. To support interpretation at multiple scales, we introduce Neuro-Oscillo Charts, visual tools that summarize normative trajectories at the population level and quantify individual-level deviations, enabling personalized assessment of functional brain dynamics. Applying this framework to a Parkinson's disease cohort (n = 160), we show that normative deviation scores reveal disease-related abnormalities and uncover a continuum of patients in theta-beta deviation space. This work provides the first lifespan-encompassing normative reference for MEG oscillations, enabling population-level characterization and individualized benchmarking. All models and tools are openly available and designed for federated, continual adaptation as new data become available, offering a scalable resource for precision neuropsychiatry.

Beyond Pairwise Interactions: Charting Higher-Order Models of Brain Function
Traditional models of brain connectivity have primarily focused on pairwise interactions, overlooking the rich dynamics that emerge from simultaneous interactions among multiple brain regions. Although a plethora of higher-order interaction (HOI) metrics have been proposed, a systematic evaluation of their comparative properties and utility is missing. Here, we present the first large-scale analysis of information-theoretic and topological HOI metrics, applied to both resting-state and task fMRI data from 100 unrelated subjects of the Human Connectome Project. We identify a clear taxonomy of HOI metrics - redundant, synergistic, and topological-, with the latter acting as bridges along the redundancy-synergy continuum. Despite methodological differences, all HOI metrics align with the brain's overarching unimodal-to-transmodal functional hierarchy. However, certain metrics show specific associations with the neurotransmitter receptor architecture. HOI metrics outperform traditional pairwise models in brain fingerprinting and perform comparably in task decoding, underscoring their value for characterizing individual functional profiles. Finally, multivariate analysis reveals that - among all HOI metrics - topological descriptors are key to linking brain function with behavioral variability, positioning them as valuable tools for linking neural architecture and cognitive function. Overall, our findings establish HOIs as a powerful framework for capturing the brain's multidimensional dynamics, providing a conceptual map to guide their application across cognitive and clinical neuroscience.

Contralesional grey matter volume as an index of macrostructural plasticity in patients with brain tumors
This research challenges the traditional localizationist view that brain tumors affect only regions directly associated with the lesion, by examining whether they also induce macrostructural alterations in the contralesional hemisphere. We applied Voxel-Based Morphometry, linear regression, and Principal Component Analysis (PCA) to a cohort of 107 adults, including patients with gliomas in the language-dominant left hemisphere and healthy participants. Unlike previous studies, a subset of the clinical population was followed longitudinally for up to four months after oncological treatment, allowing us to describe the temporal progression of structural grey matter changes. Interestingly, a principal component model based on anomaly detection enabled robust differentiation between patients and controls. Patients exhibited significantly greater grey matter volume in the contralesional hemisphere compared to healthy participants, and these structural differences evolved over time, improving the model's AUC-ROC metrics. Although exploratory, a correlation analysis revealed that these structural changes were negatively associated with postsurgical cognitive performance. Together with the PCA findings, these results suggest that brain tumors induce extensive and dynamic adaptive mechanisms in the contralateral, unaffected hemisphere, likely reflecting altered patterns of structural covariance rather than simple regional volume increases. Understanding whether these changes could represent potential predictors of postoperative cognitive recovery is crucial for developing comprehensive clinical strategies.

Spatially resolved mapping of histones reveal selective neuronal response in Rett syndrome
Rett Syndrome (RTT), a severe neurological disorder caused by loss-of-function mutations in the X-linked MECP2 gene, results in profound life-long neurological dysfunction. RTT patients live an apparently normal initial life until 12-18 months of age following which, a progressive accumulation of a wide range of phenotypic manifestations sets in. While MeCP2 is known to regulate chromatin, its impact on global histone composition and dynamics remains poorly understood. Here, we combine mass spectrometry imaging (MSI) and laser capture microdissection (LCM) coupled to LC-MS/MS to systematically profile histone proteoforms in three key brain regions: the dentate gyrus (DG) and cornu ammonis (CA) of the hippocampus, and the cerebellum (Cb). Our analysis reveals striking neuron-specific differences in histone composition between Mecp2-deficient and wildtype (WT) mice. Interestingly, the expression of a pathogenic Mecp2 missense mutant (Y120D) results in subtler changes in histone composition that are distinct from the null mutations. This study provides the first spatially resolved epigenetic atlas of histone proteoforms in RTT and suggests that Mecp2 loss perturbs chromatin homeostasis in a neuron- and mutation-dependent manner. Our findings underscore the critical need for cell-type-resolved analyses to unravel the mechanistic underpinnings of RTT and emphasize the importance of personalised therapeutic strategies that consider both the affected cell-type and particular Mecp2 mutation.

Regulation of Adult Zebrafish Retinal Regeneration by Lamβ1b-Chain-Containing Laminins
Retinal degenerative diseases are a major cause of blindness in humans that often result in permanent and progressive loss of vision. Unlike humans, zebrafish possess the remarkable ability to regenerate lost retinal neurons through Muller glia (MG) reprogramming and asymmetric cell division to produce multipotent retinal progenitor cells (RPCs). While most studies on the molecular mechanisms underlying this regeneration process have focused on intracellular mechanisms, the role of the microenvironment surrounding retinal cells, the extracellular matrix (ECM), has been understudied. Laminins are heterotrimeric glycoproteins, are principal components of the ECM basement membrane, and play important roles in vertebrate retinal development. Here, we examine the role of {beta}1b chain-containing laminins in the regenerative response of the zebrafish retina. We found that the zebrafish lamb1b gene is differentially expressed during MG reprogramming and MG and NPC proliferation during retinal regeneration. Further, we found that {beta}1b-containing laminins play important roles in regulating MG and NPC proliferation and neuroprotection of photoreceptors in light-damaged zebrafish retinas. Finally, Lam{beta}1b plays an important role in regulating the expression of integrin receptors and other laminin genes during the regeneration response. Taken together, Lam{beta}1b, and likely other ECM components, play a critical role in the MG-dependent neuronal regeneration response in the zebrafish retina.

Blocking somatic repeat expansion and lowering huntingtin via RNA interference synergize to prevent Huntingtons disease pathogenesis in mice
Huntingtons disease (HD) is a progressive neurodegenerative disorder with no approved therapies. Two major molecular drivers, somatic expansion of inherited CAG repeats and toxic mutant HTT (mHTT) variants, lead to neuronal dysfunction. Despite multiple trials, HTT-lowering strategies have not shown meaningful clinical benefit. Using therapeutic divalent siRNAs, we assessed the long-term impact of silencing MSH3 (a key regulator of somatic expansion), HTT, or both. In Q111 HD mice (>110 CAGs), which exhibit robust expansion, mHTT inclusions, and transcriptional dysregulation by 12 months, long-term MSH3 silencing blocked expansion, reduced inclusions, and reversed gene expression changes. HTT silencing alone had limited effect, but combined MSH3/HTT targeting synergistically eliminated inclusions and restored transcriptomic profiles. Parallel treatment in wild-type mice showed no toxicity, supporting the safety of long-term intervention. These findings position somatic expansion as a promising therapeutic target and demonstrate the potential of RNAi-based co-silencing of MSH3 and HTT as a disease-modifying strategy for HD.

Humans Optimally Integrate Cutaneous and Proprioceptive Cues In Haptic Size Perception
Sensory perception often relies on the brain's integration of multiple noisy inputs (cues), a process known as cue combination. Cue combination within the sense of touch has been understudied. Here, we investigated whether humans optimally combine haptic cutaneous and hand configuration cues when discerning the size (e.g., diameter) of a disk held edge-on between the thumb and index fingers. When these two fingers span the diameter of a disk to contact its perimeter, a hand configuration cue (relating to the perceived distance between the fingers) provides information about the disk's size. Less obviously, cutaneous cues to disk size may be provided simultaneously from the indentation of the skin caused by the curvature of the disk (smaller disks cause greater indentation). It is unknown whether humans make use of all these cues when perceiving the size of the held object, and if so, whether they integrate the cues optimally. We considered three hypotheses for how humans might use these cues: they might rely solely on the least noisy cue (Winner-Take-All Model, WTA), combine cues based on a simple arithmetic average (Average-Measurement Model, AVG), or combine cues via an optimal weighted average (Optimally-Weighted Model, OPT). In three experiments involving 34 participants, we measured the reliabilities of these cues and compared participant performance to the predictions of the three models. Each experiment tested participants using a two-interval forced-choice (2IFC) paradigm with 3D printed disk stimuli. On each trial, under occluded vision, participants felt two disks sequentially and responded which felt larger. Participants were tested with each finger's cutaneous cue alone, the configuration cue alone, and all three cues together. In two experiments, the disks presented were circular. In a third experiment, unknown to participants, some of the presented disks were oval-like cue-conflict stimuli. The improvement of accuracy observed in multi-cue conditions over single-cue conditions, and the Point of Subjective Equality (PSE) shifts observed in cue-conflict conditions, were consistent with optimal cue combination. We conclude that humans are capable of combining haptic cutaneous and configuration cues optimally to judge the sizes of held objects.

Creativity Potential Networks: Brain Markers for Novelty and Feasibility of Upcoming Divergent Thinking Solutions
The brain's resting-state activity can serve as an indicator of cognitive flexibility and predict the likelihood of an upcoming Aha experience. This suggests that spontaneous neural dynamics reflect a person's readiness for creative insight and underscore the potential of resting-state measures as biomarkers for anticipating creative breakthroughs. However, solutions accompanied by an Aha experience are not always truly creative, so it may be more valuable to identify biomarkers specifically linked to novelty and usefulness--two key dimensions of creative performance. To achieve this, we recruit 49 participants to complete the Alternative Uses Test, in which unconventional uses for everyday items are generated. We evaluate the responses for both novelty and feasibility using automated GPT-based methods and analyze resting-state EEG prior to the test. We find that creative performance is better predicted by interactions between different brain areas than by the activation of individual regions. Specifically, the degree centrality of theta-band functional connectivity in the right parietal and occipital areas correlates with novelty, while connectivity in the right middle and inferior frontal areas is associated with more feasible answers. These findings highlight distinct resting-state brain networks underlying the ''creative potential'' for novelty and feasibility, which could be leveraged to monitor and enhance brain flexibility.

Harnessing cell-encapsulated hydrogels to study astrocyte mechanoresponse in 4D
In glaucoma, the optic nerve head (ONH) is exposed to increased biomechanical strain, impacting the resident astrocytes that maintain neural homeostasis. After injury, astrocytes exhibit morphologic and metabolic shifts; however, the specific impact of glaucoma-related biomechanical strains on astrocyte behavior remains poorly understood. To address this, we utilized our previously established 3D cell-encapsulated ECM hydrogel to elucidate ONH astrocyte transcriptomic and cellular responses to varying biomechanical strain levels over time. Murine ONH astrocyte-encapsulated hydrogels were subjected to 0, 3, or 10% cyclic strain for 4h and 24h. Using confocal reflectance microscopy, we observed that hydrogel porosity was adequate for nutrient supplementation, while bulk hydrogel stiffness and cell viability remained unchanged after biomechanical strain. Mechanotranscriptional responses were robustly altered within 4h in a hydrogel region-, strain-, and time-dependent manner. RNA sequencing revealed changes in gene expression related to cell morphology, division, senescence, hypoxia, metabolism, and ECM regulation. Morphometric analyses of strained ONH astrocytes showed reduced F-actin area coverage, increased GFAP, HIF-1, fibronectin, and collagen fibril reorganization. Our findings demonstrate that ONH astrocyte transcriptional responses are highly dependent on duration/magnitude of biomechanical strain and surrounding ECM density, corresponding with altered cell morphology, hypoxia, and ECM modification. This ONH astrocyte-encapsulated hydrogel provides a valuable platform for nuanced future manipulation of porosity, ECM composition, and cellularity to study the impact of biomechanical strain on ONH pathophysiology.

Novel mouse model of cerebral microbleeds created by Crispr/Cas9-mediated Col4a1 deletion in adult brain microvessels
Cerebral small vessel disease is a leading cause of cognitive decline and stroke in the elderly, with cerebral microbleeds (CMBs) as one of the key imaging biomarkers. Our understanding of its pathophysiology remains limited due to the lack of appropriate animal models. We report a novel mouse CMB model created by disrupting collagen IV, a core component of the vascular basement membrane (BM), specifically within brain microvessels. Targeted deletion of Col4a1 was achieved in adult mice using brain endothelial-specific AAV vectors with CRISPR/Cas9. MRI revealed numerous CMBs with distributions similar to those of human CMBs. CMB burden increased progressively over six months following Col4a1 deletion in a dose-dependent manner, accompanied by cognitive decline and motor incoordination. Histological examination revealed hemosiderin deposits corresponding to MRI-detected CMBs without evidence of macroscopic hemorrhage or white matter lesions, while ultrastructural analysis demonstrated significant BM thinning in Col4a1-depleted microvessels. Analysis of human MRI and genomic data identified significant associations between CMB susceptibility and genetic variants in TIMP2, an endogenous inhibitor of the matrix-degrading enzyme MMP2, underscoring the clinical relevance of our model. These findings establish a direct causal relationship between microvessel COL4A1 and CMB, suggesting that dysregulated collagen IV homeostasis in BM underlies CMB development.

Activation of Infralimbic cortex neurons projecting to the nucleus accumbens shell suppresses discriminative stimulus-triggered relapse to cocaine seeking in rats
Cocaine addiction is marked by high relapse rates, often triggered by drug-associated cues. These cues can be conditioned stimuli (CSs), which occur after drug intake and are paired with drug effects, and discriminative stimuli (DSs), which signal drug availability, regardless of ongoing drug-seeking behaviour. While projections from the infralimbic cortex (IL) to the nucleus accumbens (NAc) shell are known to regulate CS-induced cocaine relapse, their role in DS-triggered relapse is not known. To investigate this, we examined how activating IL[->]NAc shell projections influences relapse driven by DSs and CSs during abstinence from intermittent cocaine use. Female Sprague-Dawley rats received viral-mediated gene expression of excitatory designer receptors exclusively activated by designer drugs in the IL. Rats then self-administered cocaine during 12 intermittent-access sessions (5-min cocaine ON/25-min cocaine OFF, 4h/day). A discrete light (DS+) signalled drug-available periods, while a different light (DS-) signalled drug non-availability. During each DS+ period, cocaine infusions were paired with a compound light-tone (CS+). Four weeks later, rats were tested for cue-induced cocaine seeking following response-independent presentation of DS+, CS+ or both. Immediately prior to testing, rats received intra-NAc shell clozapine N-oxide or aCSF to activate IL terminals. DS+ alone and DS+/CS+ combined triggered greater cocaine seeking than did the CS+. Activation of IL[->]NAc shell projections suppressed relapse behaviour in DS+ and DS+/CS+ conditions. These findings highlight the distinct and powerful influence of DSs on relapse and identify the IL[->]NAc shell circuit as a promising target for relapse prevention.

Synergistic effects of APOE ϵ4 and Alzheimer's pathology on the neural correlates of episodic remembering in cognitively unimpaired older adults
Amyloid-{beta} (A{beta}) and tau pathology begin accumulating decades before clinical symptoms and are influenced by APOE {varepsilon}4, a key genetic risk factor for Alzheimers disease (AD). Although the presence of A{beta}, tau, and APOE {varepsilon}4 are thought to impact brain function, their effects on the neural correlates of episodic memory retrieval in preclinical AD remains unknown. We investigated this question in 159 cognitively unimpaired older adults (mean age, 68.9{+/-}5.8 years; 57% female) in the Stanford Aging and Memory Study. Participants completed an associative memory task concurrent with functional MRI. A{beta} was measured using CSF A{beta}42/A{beta}40 or Florbetaben-PET imaging and tau was measured using CSF pTau181. Hippocampal univariate activity and cortical reinstatement - that is, reinstatement of patterns of neocortical activity that were present during memory encoding - were measured during successful memory retrieval. Analyses revealed that APOE {varepsilon}4 was independently associated with greater A{beta} and tau burden, and that associations of AD biomarkers with brain function and memory were moderated by APOE {varepsilon}4. Among APOE {varepsilon}4 non-carriers, A{beta} burden was linked to a pattern of hippocampal hyperactivity. Among APOE {varepsilon}4 carriers, CSF pTau181 was linked to weaker cortical reinstatement during memory retrieval and lower memory performance. Thus, abnormal AD biomarkers and genetic risk synergistically impact neural and behavioral expressions of memory in preclinical AD. These findings highlight the critical role of APOE {varepsilon}4 in moderating effects of AD pathology on brain function and identify candidate mechanisms that may contribute to increased risk of memory impairment in preclinical AD.

Significance StatementHippocampus-dependent cortical reinstatement is a critical mechanism supporting episodic remembering that contributes to individual differences in memory performance in older adults. However, the contribution of early Alzheimers disease (AD) pathology to variability in this mechanism is unknown. We demonstrate that associations of AD biomarkers with hippocampal activity and cortical reinstatement are moderated by APOE {varepsilon}4 in cognitively unimpaired older adults. Amyloid-{beta}-related hyperactivity was observed in the hippocampus among APOE {varepsilon}4 non-carriers, while CSF pTau181 was linked to weaker cortical reinstatement during memory retrieval and lower memory performance among APOE {varepsilon}4 carriers. Our findings highlight synergistic effects of APOE and AD pathology on brain function and identify candidate mechanisms that may underlie increased risk of memory impairment in preclinical AD.

Does stimulus order affect central tendency and serial dependence in vestibular path integration?
The reproduction of a perceived stimulus, such as a distance or a duration, is often influenced by two biases. Central tendency indicates that reproductions are biased toward the mean of the stimulus distribution. Serial dependence reflects that the reproduction of the current stimulus is influenced by the previous stimulus. Although these biases are well-documented, their origins remain to be determined. Studies on duration reproduction suggest that autocorrelation within a stimulus sequence may play a role. In this study, we explored whether the level of autocorrelation in a stimulus sequence affects central tendency and serial dependence in vestibular path integration. Participants (n = 24) performed a vestibular distance reproduction task in total darkness by actively replicating a passively moved stimulus distance with a linear motion platform. We compared two conditions: a high-autocorrelation condition, where stimulus distances followed a random walk, and a no-autocorrelation condition, where the same distances were presented in a randomly shuffled order. We quantified both biases using two approaches: separate simple linear regressions and a joint multiple linear regression model that accounts for the autocorrelation in the stimulus sequence. Simple linear regressions revealed that central tendency was weaker and serial dependence reversed in the high-autocorrelation condition compared to the no-autocorrelation condition. However, these differences were no longer observed in the multiple linear regression analysis, indicating that these biases were independent of the specific stimulus sequence protocol. We conclude that these perceptual biases in vestibular path integration persist regardless of stimulus autocorrelation, suggesting that they reflect robust strategies of the brain to process vestibular information in self-motion perception.

Author summaryHow are we able to successfully navigate our surroundings? An essential part of navigation is distance estimation based on self-motion signals. We previously found that distance reproductions based on vestibular self-motion signals were affected by stimulus history. Reproductions showed a central tendency toward the mean of the stimulus distribution and an attractive serial dependence toward the immediately preceding stimulus distance. The stimulus distances were presented in a low-autocorrelation, randomized order. Here we ask whether reproductions show the same central tendency and serial dependence when consecutive stimulus distances are similar (i.e., in a high-autocorrelation, random-walk order). Participants performed a distance reproduction task in the dark: a linear motion platform first passively moved the participant over a stimulus distance, after which they actively reproduced this distance by steering the platform back to the estimated start position. We found that the reproductions showed similar central tendency and attractive serial dependence in both a no- and high-autocorrelation condition, but only if the analysis accounted for the covariation of the two effects in the high-autocorrelation condition. In conclusion, our findings indicate that central tendency and serial dependence of vestibular distance reproductions are not a result of the stimulus sequence protocol, but have neurocognitive origins.

Simulating Scalp EEG from Ultrahigh-Density ECoG Data Illustrates Cortex to Scalp Projection Patterns
Ultrahigh-density electrocorticography (ECoG) provides unprecedented spatial resolution for recording cortical electrical activity. This study uses simulated scalp projections from an ECoG recording to challenge the assumption that channel-level electroencephalography (EEG) reflects only local field potentials near the recording electrode. Using a 1024-electrode ECoG array placed on the primary motor cortex during finger movements, we applied Adaptive Mixture Independent Component Analysis (AMICA) to decompose activity into maximally independent grid activity components and projected these to 207 simulated EEG scalp electrode channels using a high-definition MR image-based electrical forward-problem head model. Our findings demonstrate how cortical surface-recorded potentials propagate to scalp electrodes both far from and near to the generating location. This work has significant implications for interpreting both EEG and ECoG data in clinical and research applications.

Clinical RelevanceThis study provides insights for interpreting scalp EEG data, demonstrating that scalp channel activity represents a complex mixture of distributed cortical source activities rather than primarily activity generated nearest to the scalp electrodes. These findings may hopefully spur improvement in EEG-based diagnostics for neurological disorders.

Amphetamine and Nicotine Reduce Sucrose Self-Administration Independent of Sex
Amphetamine and nicotine are two widely used and abused drugs that are taken for legitimate pharmaceutical purposes but are also highly abused through illicit recreational use. Both of these drugs have been widely shown to decrease food intake in both humans and pre-clinical models, and although amphetamine and nicotine clearly affect food intake under normal baseline ( homeostatic) conditions, there has been limited examination of the ability of these drugs to affect reward-related ( hedonic) aspects of feeding. Furthermore, there are sex differences in the behavioral responses to both drugs, but it is unclear if these sex differences also translate to their effects on feeding. This study examined whether nicotine and amphetamine regulate sucrose intake in a food self-administration paradigm in a sex-dependent manner across both fixed and progressive schedules of reinforcement. Amphetamine reduced operant responding for sucrose pellets and decreased acute intake of sucrose during ad libitum free-feeding access in a dose-dependent manner, whereas nicotine reduced sucrose self-administration and free intake only at higher doses that also impaired locomotor activity in open field tests. The effects of both amphetamine and nicotine did not differ by sex for either drug. Overall, these results suggest that the mechanisms mediating the addictive qualities of these drugs and their appetite suppressing effects may be distinct and therefore could be a potential target for future obesity therapeutics.

Multi-omics comparative analyses of synucleinopathy models reveal distinct targets and relevance for drug development
BackgroundThe discovery and development of therapeutics for Parkinsons disease (PD) requires preclinical models and an understanding of the disease mechanisms reflected in each model is crucial to success.

ObjectiveTo illuminate disease mechanisms and translational value of two commonly utilized rat models of synucleinopathy - AAV-delivered human mutant hA53T alpha synuclein (-Syn) and -Syn preformed fibril (PFF) injection - using a top-down, unbiased, large-scale approach.

MethodsTandem mass tag mass spectrometry (TMT-MS), RNA sequencing, and bioinformatic analyses were used to assess proteins, genes, and pathways disrupted in rat striatum and substantia nigra. Comparative analyses were performed with PD drug candidate targets and an existing human PD and dementia with Lewy body (DLB) proteomics dataset.

ResultsUnbiased proteomics identified 388 proteins significantly altered by hA53T--Syn and 1550 by PFF--Syn compared to sham controls. Pathway and correlation analyses of these revealed common and distinct pathophysiological processes altered in each model: dopaminergic signaling/metabolism, mitochondria and energy metabolism, and motor processes were disrupted in AAV-hA53T--Syn, while immune response, intracellular/secretory vesicles, synaptic vesicles, and autophagy were more impacted by PFF--Syn. Synapses, neural growth and remodeling, and protein localization were prominently represented in both models. Analyses revealed potential biomarkers of disease processes and proteins and pathways also altered in patients, elucidating drug targets/ disease mechanisms the models best reflect.

ConclusionsAlignment of unbiased multi-omics analyses of AAV-hA53T and PFF--Syn models of synucleinopathy with PD and DLB patient data and PD drug development pipeline candidates identifies optimal models for testing novel therapeutics based on biological mechanisms.

SLAy-ing oversplitting errors in high-density electrophysiology spike sorting
The growing channel count of silicon probes has substantially increased the number of neurons recorded in electrophysiology (ephys) experiments, rendering traditional manual spike sorting impractical. Instead, modern ephys recordings are processed with automated methods that use waveform template matching to isolate putative single neurons. While scalable, automated methods are subject to assumptions that often fail to account for biophysical changes in action potential waveforms, leading to systematic errors. Consequently, manual curation of these errors, which is both time-consuming and lacks reproducibility, remains necessary. To improve efficiency and reproducibility in the spike-sorting pipeline, we introduce here the Spike-sorting Lapse Amelioration System (SLAy), an algorithm that automatically merges oversplit spike clusters. SLAy employs two novel metrics: (1) a waveform similarity metric that uses a neural network to obtain spatially informed, time-shift invariant low-dimensional waveform representations, and (2) a cross-correlogram significance metric based on the earth-movers distance between the observed and null cross-correlograms. We demonstrate that SLAy achieves ~ 85% agreement with human curators across a diverse set of animal models, brain regions, and probe geometries. To illustrate the impact of spike sorting errors on downstream analyses, we develop a new burst-detection algorithm and show that SLAy fixes spike sorting errors that preclude the accurate detection of bursts in neural data. SLAy leverages GPU parallelization and multithreading for computational efficiency, and is compatible with Phy and NeuroData Without Borders, making it a practical and flexible solution for large-scale ephys data analysis.