bioRxiv Subject Collection: Neuroscience's Journal

Forward masking in the Inferior Colliculus: Dynamics of Discharge-rate Recovery after Narrowband Noise Maskers
In forward masking the detection threshold for a target sound (probe) is elevated due to the presence of a preceding sound (masker). Although many factors are known to influence the probe response following a masker, the current work focused on the temporal separation (delay) between the masker and probe and the inter-trial interval (ITI). Human probe thresholds recover from forward masking within 150 to 300 ms, similar to neural threshold recovery in the IC within 300 ms after tone maskers. Our study focused on recovery of discharge rate of IC neurons in response to probe tones after narrowband gaussian noise (GN) forward maskers, with varying time delays. Additionally, we examined how prior masker trials influenced IC rates by varying ITI. Our findings showed that previous masker trials impacted probe-evoked discharge rates, with full recovery requiring ITIs over 1.5 s after 70 dB SPL narrowband GN maskers. Neural thresholds in the IC for probes preceded by noise maskers were in the range observed in psychoacoustical studies. Two proposed mechanisms for forward masking, persistence and efferent gain control, were tested using rate analyses or computational modeling. A physiological model with efferent feedback gain control had responses consistent with trends in the physiological recordings.

Maternal separation disrupts noradrenergic control of adult coping behaviors
Early life stress (ELS) in humans and preclinical rodent models profoundly impacts the brain and correlates with negative affective behaviors in adulthood. The locus coeruleus (LC), a stress-responsive brainstem nucleus that supplies most of the brain with norepinephrine (NE), is known to modulate negative affect. Here we used repeated maternal separation stress (MSS) to investigate the impact of ELS on the LC and stress-related behaviors in adulthood. Using ex vivo cell-attached electrophysiology, we recorded spontaneous LC firing across the lifespan from early development, pre-adolescence, adolescence, through adulthood. MSS significantly increased LC firing during early development and adulthood compared to No MSS mice. We next examined potential changes in the expression of genes linked to LC function. MSS decreased mRNA levels for both the alpha-2A adrenergic receptor and dopamine beta-hydroxylase, the enzyme necessary for NE synthesis. At the behavioral level, MSS increased locomotion in approach-avoidance exploratory assays and increased immobility in the forced swim test. Forced swim increased LC cFos expression, a marker for neuronal excitation, in both No MSS and MSS mice. However, MSS mice had significantly less cFos than No MSS controls. We then sought to reverse this MSS-induced increase in immobility by inhibiting the LC during the forced swim test. In No MSS mice, LC inhibition increased immobility time, however, LC inhibition did not affect MSS immobility. Together, this study demonstrates that MSS dysregulates LC-NE activity across the lifespan and disrupts the role of the LC in regulating coping strategies during stressful events.

Illusions of Alignment Between Large Language Models and Brains Emerge From Fragile Methods and Overlooked Confounds
Emerging research seeks to draw neuroscientific insights from the neural predictivity of large language models (LLMs). However, as results continue to be generated at a rapid pace, there is a growing need for large-scale assessments of their robustness. Here, we analyze a wide range of models, methodological approaches, and neural datasets. We find that some methodological approaches, particularly the use of shuffled train-test splits, have led to many impactful yet unreliable findings, and that the method by which activations are extracted from LLMs can bias results to favor particular model classes. Moreover, we find that confounding variables, particularly positional signals and word rate, perform competitively with trained LLMs and fully account for the neural predictivity of untrained LLMs. In summary, our results suggest that theoretically interesting connections between LLMs and brains on three neural datasets are driven largely by fragile methodologies and overlooked confounds.

Modulation of microRNA-502-3p significantly influences synaptic activity, dendritic spine density and mitochondrial morphology in the mice brain
Synapse dysfunction is the root cause of Alzheimers disease (AD). Uninterrupted and regulated synapse action is crucial to maintain healthy brain function. Our previous study discovered microRNA-502-3p (miR-502-3p), a synapse-specific miRNA, highly expressed at the AD synapses. Further, in vitro studies unveiled the biological relevance of miR-502-3p in modulating GABA receptor function, synaptic activity and mitochondrial morphology. Current study focuses to investigate the role of miR-502-3p in vivo using stereotaxic injection of miR-502-3p overexpression (OE) and suppression (sponge) lentivirus (LV) into the hippocampus of C57BL/6 wild-type (WT) mice. MiR-502-3p OE and sponge LV were characterized by transducing HT22 cells followed by QRT-PCR and miRNAScope analysis of miR-502-3p. MiR-502-3p OE LV showed a very high-fold upregulation and sponge LV showed significant reduction in miR-502-3p levels. MiR-502-3p OE and sponge LV were injected into three months old WT mice brain hippocampus. Overexpression and suppression effects of miR-502-3p were studied on synaptic proteins, synapse number, mitochondrial morphology and dendritic spine density at eight-weeks post-injection. Mice injected with miR-502-3p OE LV showed reduced levels of synaptic proteins, diminished synapse formation, defective mitochondrial morphology and reduced dendritic spine density relative to control LV treated mice. While mice treated with sponge LV showed elevated levels of synaptic proteins, augmented synapses, improved mitochondrial morphology and elongated dendrites and spine density. Our in vivo study unveiled translational abilities of miR-502-3p to restore synapse dysfunction in AD and other neurological disorders.

The effect of virtual reality modality level of immersion and locomotion on spatial learning and gaze measures
The widespread adoption of head-mounted display (HMD) virtual reality (VR) systems has emerged in various fields, including spatial learning research. This study investigated the effects of VR modality level of immersion, locomotion interface, and proprioception on spatial learning and physiological measures using eye-tracking (ET) in VR. We translated the classic T-maze task from Barnes et al. (1980) to humans for the first time, comparing three VR modalities: 3D HMD VR with physical walking, 3D HMD VR with controller-based movement, and 2D desktop VR. Results revealed that human participants employed a mixture of cue, place, and response strategies when navigating the virtual T-maze, mirroring rodent behavior. In both samples, no significant differences were found between the two HMD VR conditions in learning performance, nor consistent ones in strategy choices. However, 2D desktop navigation was associated with slower initial learning, though this discrepancy diminished in subsequent sessions. These results were supported by spatial presence, immersion, and naturalness reports. Gaze measures showed that participants who physically walked devoted more visual attention to environmental cues compared to controller users. Predictive models for identifying spatial learning strategies based on ET and behavioral measures demonstrated significant accuracy in some models, particularly in the VR walking condition and second session. Our findings enhance the understanding of spatial learning strategies and the effects of VR modality on cognition and gaze behavior. This work demonstrates the potential of integrated ET data and holds implications for early detection and personalized rehabilitation of neurodegenerative conditions related to spatial cognition.

Transcriptomic Analysis of CAD Cell Differentiation
CAD cells were derived from Cath.a cells, a mouse central nervous system catecholaminergic cell line. Serum-starved CAD cells undergo morphological changes and resemble isolated neurons when observed by microscopy. We carried out an RNAseq transcriptomic analysis to examine differentiated CAD cells for expression signatures related to neuronal functions, identifying ~1900 transcripts whose expression changed with differentiation. Pathview analysis identified ~80 KEGG pathway gene sets that were differentially expressed, including upregulation of at least 13 neuron-related pathways. This dataset can be explored more deeply, allowing further investigation into expression changes relevant to studying neuronal functions in this easy-to-culture model system.

Multiregional blood-brain barrier phenotyping identifies the prefrontal cortex as the most vulnerable region to ageing in mice
Age-associated vascular alterations make the brain more vulnerable to neuropathologies. Research in humans and rodents have demonstrated structural, molecular, and functional alterations of the aged brain vasculature that suggest blood-brain barrier (BBB) dysfunction. However, these studies focused on particular features of the BBB and specific brain regions. Thus, it remains unclear if and which BBB age-associated phenotypes are conserved across brain areas. Moreover, there is very limited information about how BBB dysfunction and cell-specific phenotypes relate to each other. In this manuscript, we use immunofluorescence, transmission electron microscopy (TEM), and permeability assays to assess how age-associated BBB molecular, structural, and functional phenotypes correlate between the BBB cell types at three brain regions (prefrontal cortex, hippocampus, and corpus callosum) during mouse early ageing. We discovered that at 18-20 months of age, the mouse prefrontal cortex BBB is the most affected region, with alterations in brain endothelial cell protein expression, BBB permeability, basement membrane thickness, and astrocyte endfoot size when compared to young mice. Here, we deliver a detailed multicellular characterisation of region-dependent BBB changes at early stages of ageing. Our data paves the way for future studies to investigate how region-specific BBB dysfunction may contribute to disease-associated regional vulnerability.

Application of machine learning to discriminate photoreceptor cell species in xenotransplanted chimeric retinas
Photoreceptor transplantation is being studied to improve visual function in retinal diseases causing blindness, such as age-related macular degeneration, hereditary eye diseases, and traumatic retinopathy, among others. Preclinical studies often involve the delivery of exogenous human photoreceptor cells into the retinas of animal models. In such experiments, a key readout is the differential frequency of donor cell somatic integration versus artificial labeling secondary to material transfer of cytosolic or nuclear labels from donor to recipient cells. For this analysis, the ability to recognize photoreceptor nuclei as being of donor (human) versus animal is key, but purely immunohistology discrimination can be challenging because of antigenic species overlap or intercellular antigen transfer. To address this challenge, we sought to develop and validate a computational technique to discriminate between photoreceptor cells of different animal species based on machine learning of nuclear morphology. Here, we aimed to evaluate the feasibility of using computer-assisted detection of separate nuclei and employing random forest classification to automate the species differentiation, among DAPI-stained photoreceptors after xeno-transplantation of human photoreceptors into the retinas of mice and pigs. Our models were trained on single-species samples and validated with mixed-species samples. We then transplanted human embryonic stem cell-derived retinal organoid cells into rodent and pig retinal degeneration models. The random forest model accurately determined cell identity post-xenotransplantation, validated by histological assessment using an anti-human nuclear antibody. Our results support the potential efficacy of employing machine learning image analysis and classification techniques that may promote experimental rigor, minimize observer bias, and enable high throughput semi-automated workflows for transplantation outcomes analysis. The methodological framework reported here may enable a more nuanced and precise analysis of the behavior of transplanted photoreceptors for the purposes of human retinal regeneration.

Deepening sleep using an EEG wearable featuring modeling-based closed-loop neurostimulation
Objective. Closed-loop neurostimulation (CLNS) during slow-wave sleep (SWS) has been shown to enhance slow-wave activity, predominantly using laboratory equipment. To further advance CLNS research and its potential applications, there is a need for user-friendly EEG wearable equipment featuring CLNS that can support long-term CLNS studies in both clinical and home settings. Approach. Here we evaluate whether modeling-based CLNS (M-CLNS) with acoustic stimulation of slow oscillations (SOs) can be effectively implemented using a self-applicable EEG headband with forehead electrodes. We assess the performance of M-CLNS stimulus targeting over the EEG headband together with short-term, stimulus-locked, and more enduring enhancement of SWS. We validate our results against simultaneous recordings obtained using gold-standard laboratory PSG equipment. Main results. Our findings demonstrate that the SO phase can be reliably assessed and accurately targeted using M-CLNS with the EEG headband. We show an immediate enhancement of SO dynamics in the short-term, as well as an enduring increase in power spectral density across SO and delta frequencies (0.75 - 5 Hz) across SWS. These results are in line with previous studies using M-CLNS with laboratory equipment. Significance. These findings demonstrate that M-CLNS with acoustic stimulation can successfully be applied using an EEG headband on the forehead to target SOs, leading to both immediate and enduring enhancements of SWS. In conclusion, M-CLNS in a self-applicable EEG headband may offer a promising tool for portable and non-invasive enhancement of SWS, with future potential for clinical and home-based applications.

Proteomic characterization of the Alzheimer's disease risk factor BIN1 interactome
The gene BIN1 is the second-largest genetic risk factor for late-onset Alzheimer's disease (LOAD). It is expressed in neurons and glia in the brain as cell-type specific and ubiquitous isoforms. BIN1 is an adaptor protein that regulates membrane dynamics in many cell types. Previously, we reported that BIN1 predominantly localizes to presynaptic terminals in neurons and regulates presynaptic vesicular release. However, the function of neuronal BIN1 in relation to LOAD is not yet fully understood. A significant gap in the field is the unbiased characterization of neuronal BIN1-interacting proteins and proximal neighbors. To address this gap and help define the functions of neuronal BIN1 in the brain, we employed TurboID-based proximity labeling to identify proteins biotinylated by the neuronal BIN1 isoform 1-TurboID fusion protein (BIN1iso1-TID) in cultured mouse neuroblastoma (N2a) cells in vitro and in adult mouse brain neurons in vivo. Label-free quantification-based proteomic analysis of the BIN1iso1-TID biotinylated proteins led to the discovery of 361 proteins in N2a cells and 897 proteins in mouse brain neurons, identified as BIN1iso1-associated (proximal) or interacting proteins. A total of 92 proteins were common in both datasets, indicating that these are high-confidence BIN1-interacting or proximity proteins. SynapticGO analysis of the mouse brain dataset revealed that BIN1iso1-TurboID labeled 159 synaptic proteins, with 60 corresponding to the synaptic vesicle cycle. Based on phosphorylation site analysis of the neuronal BIN1iso1-TID interactome and related kinase prediction, we selected AAK1, CDK16, SYNJ1, PP2BA, and RANG for validation through immunostaining and proximity ligation assays as members of the BIN1 interactome in the mouse brain. By identifying several previously unknown proximal and potential interacting proteins of BIN1, this study establishes a foundation for further investigations into the function of neuronal BIN1.

Stimulus dependencies---rather than next-word prediction---can explain pre-onset brain encoding during natural listening
The human brain is thought to constantly predict future words during language processing. Recently, a new approach to investigating linguistic predictions emerged which aims to capture predictive pre-activation directly by using neural network representations of words to predict brain activity prior to word onset. However, it is unclear what exactly is driving the predictability of pre-stimulus brain activity. Here we show, across two datasets, that both proposed hallmarks of neural pre-activation?i.e. (i) pre-onset brain response predictability and (ii) its modulation by word expectedness?is not only observed in brain responses, but also in representations of the stimulus material itself. We show that various structural and incidental dependencies existing in natural language can explain previously reported hallmarks of prediction without assuming any pre-activation in the neural data. This suggests that pre-onset prediction of brain activity might only reflect dependencies within the stimulus material rather than predictive computations, and questions the extent to which this new prediction-based method can be used to study prediction in the brain.

Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling
Functional MRI studies have identified functional connectivity (FC) patterns associated with behavioral traits using whole-brain or region-wise predictive models. However, whole-brain approaches often suffer from limited generalizability and interpretability due to the high-dimensionality of FC data. Conversely, region-wise models inherently isolate predictions, ineffective for characterizing contributions of the whole brain FC patterns in predicting a target trait. In this study, we propose an interpretable predictive model that learns fine-grained FC patterns predictive of behavioral traits, jointly at the regional and participant levels, to characterize the overall association of FC patterns with a target trait. Our model learns both a relevance score and a dedicated prediction model for each brain region, then integrates the regional predictions to generate a participant-level prediction, capturing the collective association of FC patterns with the trait. We validated our method using FC data from 6798 participants in the Adolescent Brain and Cognitive Development (ABCD) study for predicting cognition. Our interpretable predictive model identified the cingulo-parietal, retrosplenial-temporal, dorsal attention, salience, and cingulo-opercular networks as collectively predictive of cognitive traits. The interpretable model significantly improved prediction accuracy and facilitated the characterization of fine-grained differences in FC patterns across cognitive domains. Furthermore, the learned relevance scores enhanced region-wise predictions of longitudinal cognitive measures in the ABCD cohort and cognitive traits in an external Human Connectome Project Development (HCP-D) cohort. These findings suggest that our method effectively characterizes generalizable and fine-grained FC patterns linked to cognition in youth.

Dynamic vs. Static Facial Color Changes: Evidence for Terminal Color Dominance in Expression Recognition
Facial color is closely linked to the perception of emotion, with reddish tones often associated with anger. While previous studies have shown that static reddish facial tones enhance this perception, it remains unclear whether dynamic changes in facial color further amplify this effect. This study investigated how variations in facial color influence the perception of expression using a judgment task that involved morphed facial stimuli (fearful to angry). The participants evaluated facial expressions under two conditions: faces with dynamic color changes and faces with static colors. Experiment 1 compared red (CIELAB a*+) faces to original-colored faces, and Experiment 2 compared green (a*-) faces to original-colored faces. Neither experiment revealed significant differences between dynamic and static facial colors. However, faces with a final reddish color (higher a* value) were more likely to be perceived as angry. These findings suggest that the final facial color influences the perception of anger independent of whether the color change is dynamic or static. Our findings support the idea that the recognition of anger is modulated by the relationship between an angry expression and the color red and provide a new perspective on facial color changes in the interaction between facial expression and facial color.

Motivational Cognitive Maps for Self-Regulated Autonomous Navigation
The mammalian hippocampal formation plays a critical role in efficient and flexible navigation. Hippocampal place cells exhibit spatial tuning, characterized by increased firing rates when an animal occupies specific locations in its environment. However, the mechanisms underlying the encoding of spatial information by hippocampal place cells remain not fully understood. Evidence suggests that spatial preferences are shaped by multimodal sensory inputs. Yet, existing hippocampal models typically rely on a single sensory modality, overlooking the role of interoceptive information in the formation of cognitive maps. In this paper, we introduce the Motivational Hippocampal Autoencoder (MoHA), a biologically inspired model that integrates interoceptive (motivational) and exteroceptive (visual) information to generate motivationally modulated cognitive maps. MoHA captures key hippocampal firing properties across different motivational states and, when embedded in a reinforcement learning agent, generates adaptive internal representations that drive goal-directed foraging behavior. Grounded in the principle of biological autonomy, MoHA enables the agent to dynamically adjust its navigation strategies based on internal drives, ensuring that behavior remains flexible and context-dependent. Our results show the benefits of integrating motivational cognitive maps into artificial agents with a varying set of goals, laying the foundation for self-regulated multi-objective reinforcement learning.

Development of Potent, Selective cPLA2 Inhibitors for Targeting Neuroinflammation in Alzheimer's Disease and Other Neurodegenerative Disorders
Chronic neuroinflammation plays a key role in the progression of Alzheimer's disease (AD), and the cytosolic calcium-dependent phospholipase A2 (cPLA2) enzyme is a critical mediator of inflammatory lipid signaling pathways. Here we investigate the therapeutic potential of novel cPLA2 inhibitors in modulating neuroinflammation in AD. By leveraging the giga-scale V-SYNTHES 2.0 virtual screening in on-demand chemical space and conducting two rounds of optimization for potency and selectivity, we have identified BRI-50460, achieving an IC50 of 0.88 nM in cellular assays of cPLA2 activity. In vivo studies revealed favorable brain-to-plasma ratios, highlighting the ability of BRI-50460 to penetrate the central nervous system, potentially modulating neuroinflammatory pathways and restoring lipid homeostasis. In cultured astrocytes and neurons derived from human induced pluripotent stem cells, BRI-50460 mitigates the effects of amyloid beta 42 oligomers on cPLA2 activation, tau hyperphosphorylation, and synaptic and dendritic reduction. Our results suggest that small molecule inhibitors of the cPLA2 enzyme can modulate the downstream inflammatory lipid signaling pathways, offering a promising therapeutic strategy for AD and other neurodegenerative diseases.

Using a female-specific isoform of doublesex to explore male-specific hearing in mosquitoes
Animal reproduction relies on elaborate divisions of labour and multiple dimorphisms between the sexes. Primary dimorphisms affect core elements of reproduction, secondary dimorphisms affect more indirect traits, including complex behaviours. In disease-transmitting mosquitoes, males locate females acoustically prior to copulation (phonotaxis). No comparable acoustic behaviour is known for females. As a result, the males ears and hearing performance have evolved to become substantially more complex. Sex-specific hearing in mosquitoes is in part controlled by the doublesex (dsx) gene. Intriguingly, dsx forms a linker between primary and secondary dimorphisms: spermatogenesis and ear morphogenesis share considerable molecular overlap and both depend on dsx expression patterns. We have combined transcriptomics with functional-anatomical analyses to dissect dsx-dependent hearing in the malaria mosquito Anopheles gambiae. By cross-linking our auditory findings to the genetic bases of spermatogenesis we advance the molecular understanding of sex-specific hearing mechanisms in insects, highlighting the special roles of ciliary factors therein.

A genetic signature of resistance to activity-based anorexia in striatal projecting cortical neurons
Anorexia nervosa (AN) is a complex psychiatric disorder characterised by severe pathological weight loss and persistent cognitive inflexibility. Converging evidence from neuroimaging studies and genome-wide association study (GWAS) suggests the involvement of prefrontal cortex (PFC) and striatum dysfunction in the pathophysiology of AN. However, identifying the causal role of circuit-specific genes in the development of AN-like phenotype remains challenging and requires the combination of novel molecular tools and preclinical models. The activity-based anorexia (ABA) rat model recapitulates many aspects of the behavioural phenotypes of AN in humans and we have previously demonstrated that suppressing neural activity in the medial prefrontal cortex (mPFC)-nucleus accumbens shell (AcbSh) circuit prevented pathological weight loss in ABA rats. Here, we used a novel viral-based translating ribosome affinity purification (TRAP) technique to identify transcriptional changes within this neural pathway associated with susceptibility to ABA in female rats. We reveal 1424 differentially expressed genes between Susceptible and Resistant rats, highlighting important transcriptional changes associated with ABA within this pathway. The changes observed were independent of current calorie deficit and associated with metabolic, mitochondrial and neural functions. Further, we show that genes upregulated in Resistant rats were involved in mitochondrial function, while downregulated genes were associated with cytoskeletal, postsynaptic and axonal functions, supporting the hypothesis that hyperexcitability of cortico-striatal circuit function is a critical mediator of pathological weight loss in ABA. These findings provide novel insights into circuit-specific gene expression patterns that may contribute to susceptibility to developing AN in humans and highlight potential molecular targets for therapeutic intervention.

openretina: Collaborative Retina Modelling Across Datasets and Species
Studying the retina plays a crucial role in understanding how the visual world is translated into the brains language. As a stand-alone neural circuit with easily controllable input, the retina provides a unique opportunity to develop a complete and quantitatively precise model of a computational module in the brain. However, decades of data and models remain fragmente across labs and approaches. To address this, we have launched an open-source retina modelling platform on a shared GitHub repository, aiming to provide a unified data and modelling framework across species, recording techniques, stimulus conditions, and use cases. Our initial release consists of a Python package, openretina, a modelling framework based on PyTorch, which we designed for optimal accessibility and extensibility. The package includes different variations on a basic Core + Readout model architecture, easily adaptable dataloaders, integration with modern deep learning libraries, and methods for performing in-silico experiments and analyses on the models. We illustrate the versatility of the package by providing dataloaders and pre-trained models for data from several laboratories and studies across species. With this starter pack in place, openretina can be used within minutes. Through step-by-step examples, we here provide retina researchers of diverse backgrounds a hands-on introduction to modelling, including using models as tools for visualising retinal computations, generating and testing hypotheses, and guiding experimental design.

Predicting effects of E-I balance on the input-output properties of neurons
In sensory systems, stimuli are represented through the diverse firing responses and receptive fields of neuronal populations. These features emerge from the interaction between excitatory and inhibitory neuron populations within the network. Changes in sensory inputs alter this balance, leading to shifts in firing patterns and the input-output properties of individual neurons and the network. While these phenomena have been studied extensively with experiments and theory, the general operating principles for combining E and I inputs are still unclear. Here, mathematical rules for combining E and I inputs probabilistically are derived that describe how neurons in a feedforward inhibitory circuit respond to stimuli. The model provides insights into the conditions that lead to divisive gain modulation and the generation of various temporal firing patterns, shedding light on the cellular and network mechanisms involved.

Hyperactivity of the Amygdala Mediates Depressive-Like Phenotypes and Decreased Serotonin Release
Clinical and preclinical studies have consistently demonstrated a correlation between hyperactivity of the amygdala and the onset of depression. However, the underlying mechanisms influencing serotonin levels a critical neurotransmitter implicated in depression and a primary target for selective serotonin reuptake inhibitors (SSRIs) remain inadequately understood. In this study, we employed a restrained inescapable shock (RIS) model to investigate these mechanisms in mice. The RIS paradigm elicited depressive-like phenotypes, increased c-Fos expression in the amygdala, diminished serotonin levels, and elevated corticosterone concentrations. Notably, chemogenetic inhibition of the amygdala mitigated depressive symptoms, reduced neuronal activity in this region, and restored serotonin levels. Anatomical analyses revealed a significant connectivity between the central amygdala (CeA) and the dorsal raphe nucleus (DRN). Fiber photometry recordings indicated that serotonergic neuronal activity in the DRN decreased in response to aversive stimuli, accompanied by amygdala activation following RIS, with no notable alterations in DRN GABAergic activity. These findings suggest that chronic stress may exacerbate amygdala hyperactivity, which subsequently inhibits serotonin release in the brain, potentially intensifying depressive states. Therefore, targeting amygdala hyperactivity may represent a novel therapeutic strategy for the management of stress-related depressive and anxiety disorders.

Cerebellum involvement in visuo-vestibular interaction for the perception of gravitational direction: a repetitive transcranial magnetic stimulation study
Accurate perception of the direction of gravity relies on the integration of multisensory information, particularly from the visual and vestibular systems, within the brain. Although a recent study of patients with cerebellar degeneration suggested a cerebellar role in visuovestibular interaction in the perception of gravitational direction, direct evidence remains limited. To address this gap, we conducted two experiments with 42 healthy participants to evaluate the impact of repetitive 1Hz transcranial magnetic stimulation (rTMS) over the posterior cerebellum on visual dependency, quantified by the subjective visual vertical bias induced by rotating optokinetic stimulation (OKS). Electric field simulations in high-resolution head models were used to ensure focal stimulation of the cerebellum at the group level. The results demonstrated that repetitive transcranial magnetic stimulation (rTMS) applied to the cerebellar vermis significantly attenuated the OKS induced shift in visual vertical (SVV) bias. This effect was not observed when stimulation was applied to the early visual cortex (V1 and V2) or the cerebellar hemisphere. Also, the vermis rTMS had no effect on the judgement precision in the absence of visual motion cues, suggesting that the rTMS may reduce visual weight in visuovestibular processing by increasing visual motion noise rather than affecting vestibular function. These findings suggest a direct involvement of the cerebellar vermis in the visuovestibular interaction underlying the perception of gravitational direction, providing new insights into cerebellar contributions in human spatial orientation.

Macroscopic cerebral energy efficiency corresponds to neuron reorganization in awake and anesthetized mice
Non-invasive imaging of brain function and energy supply is crucial for diagnosing and treating brain disorders. Conventional imaging struggles to capture altered relationships between energy supply and utilization caused by brain diseases. A novel method, which can be translated to human patients, is to calculate relative power (rPWR) and relative cost (rCST) to assess cerebral energy efficiency. However, whether rPWR/rCST can track individual changes and neural activity remains unproven. Our study compared these non-invasive measures with invasive two-photon microscopy in awake and anesthetized mice. We found that rPWR/rCST distributions were similar between awake mice and humans, but changed in anesthetized mice, indicating a shift in the brain's economic balance. Furthermore, changes in rPWR/rCST were linked to the reorganization of microscopic neural networks, observed with two-photon microscopy. Our work highlights the potential of rPWR/rCST for medical applications, and that neural network reorganization is linked to the brain's economic balance.

Brainstem circuit for sickness-induced sleep
Increased sleep induced by immune activation plays a crucial role in facilitating recovery from illness. However, the neural mechanisms underlying sickness-induced sleep remain poorly understood. Here, we identify a brainstem circuit originating in the nucleus of the solitary tract (NST) that mediates sickness-induced sleep. Using activity-dependent genetic labeling, we tagged NST neurons activated by lipopolysaccharide (LPS) injection and showed that their chemogenetic activation strongly promotes non-rapid eye movement (NREM) sleep. These NST neurons project extensively to the parabrachial nucleus (PB), where LPS-activated neurons also promote NREM sleep. Fiber photometry imaging of several wake-promoting neuromodulators using their biosensors showed that evoked norepinephrine (NE) release from locus coeruleus (LC) neurons is markedly reduced by either LPS injection or direct activation of NST or PB sickness neurons. These results suggest that sickness-induced sleep is mediated in part by a brainstem circuit that regulates neuromodulator signaling.

Predicting cognition using estimated structural and functional connectivity networks and artificial intelligence in multiple sclerosis
Background: Our prior work demonstrated that estimated structural and functional connectomes (eSC and eFC) generated using multiple sclerosis (MS) lesion masks and artificial intelligence (AI) models can predict disability as effectively as SC and FC derived from diffusion and functional MRI in MS. The goal of this study was to assess the ability of eSC and eFC in predicting baseline and 4-year follow-up cognition in MS patients. Methods: One hundred seventy-one MS patients (age: 42.67 {+/-} 10.41, 74% females) were included. The Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT), and Brief Visuospatial Memory Test (BVMT) were used to assess cognition. The Network Modification tool was performed to estimate SC, which was then used as an input to Krakencoder, an encoder-decoder model, to estimate FC. Ridge regression was performed to predict cognition using regional eSC and eFC, along with demographics and clinical information as well as conventional MRI metrics. Baseline cognition was added to the models that were used to predict the follow-up cognition. Spearman's correlation (r) was used to assess the prediction accuracy. Results: The highest accuracy was obtained when predicting follow-up SDMT using regional eSC or eFC (median r=0.58 for eSC and r=0.56 for eFC). Decreased eSC and eFC in the cerebellum and increased eFC in the default mode network were associated with lower follow-up SDMT scores. Baseline SDMT, clinical subtype, and age were the most important non-connectome metrics in predicting follow-up SDMT. Conclusions: Our findings demonstrate that eSC and eFC derived from clinically acquired MRI and AI models can effectively predict cognition. The use of lesion-based estimates of connectome disruptions may potentially improve cognition-related individualized treatment planning.

Homeostatic Forces Shaping the Daily Pattern of Sleep Propensity.
The discrepancy between high sleep need and the ability to initiate sleep underlies insomnia, the most prevalent sleep disorder, whose nature remains obscure. Sleep need increases monotonically with prolonged wakefulness, as reflected in the rising intensity of sleep after varying wake intervals. In contrast, sleep propensity - the ability to transition from wake to sleep - follows a bimodal pattern, peaking in the mid-afternoon, dipping in the evening, and rising again near bedtime. Previously, we demonstrated that sleep structure dynamics can be effectively modeled using probability waves. Here, we extend this wave model of homeostatic regulation of sleep to the period of wakefulness and show that its extrapolation predicts the bimodal pattern of wake-to-sleep transitions. This pattern arises from the interplay of two key factors in state transitions: wake-state instability and interaction strength between states. While wake-state instability increases monotonically, interaction strength follows a bimodal pattern. Their combined effect produces a bimodal probability of state transitions, aligning closely with experimental data. The mid-afternoon peak corresponds to maximal interaction at the homeostatic equilibrium of sleep and wake states, whereas the evening dip reflects minimal interaction, counteracting high wake-state instability. An exponential rise in both factors by the end of the day facilitates sleep onset at bedtime. Our experimental findings on sleep deprivation support the model predictions. Understanding the relationship between sleep need and ability to initiate sleep may offer valuable insights for optimizing daytime performance and sleep quality in both health and disease.

Region-specific variations in the cerebrovasculature underlie disease progression in Parkinson's disease
Parkinson's disease is a progressive neurodegenerative disorder characterised by motor dysfunction, dopaminergic neuronal loss in the substantia nigra and abnormal accumulation of -synuclein Lewy bodies. Research suggests that the cerebrovascular system plays a role in fluid dynamics, waste clearance, and removal of abnormal proteins. Imaging studies show that this waste clearance system, known as the glymphatic system, is disrupted in Parkinson's disease, highlighting its involvement in the disease. This immunohistochemical human brain tissue study quantified changes in the cerebrovascular system (perivascular space, string vessels, pericytes, aquaporin-4 and astrocytes) in Parkinson's disease (n=18) cases with variable disease durations (median=14, range= 19) compared to age and post-mortem matched (P >0.05) control cases (n=7). Analysis was carried out in brain regions variably affected by cell loss (substantia nigra) and protein deposition (substantia nigra and medial temporal cortex). The occipital cortex was included, as this region is not affected by cell loss or protein deposition. Group differences were analysed and the relationship with protein deposition (Lewy body stage, amyloid score, neurofibrillary tangle score) was assessed. Although total astrocyte density did not change (P >0.05), Parkinson's disease cases exhibited reduced aquaporin-4 in astrocytic endfeet and enlargement of the arteriolar and venular perivascular space. Significant changes in the capillary network were also observed with increased string vessel formation (P <0.001) and pericyte loss (P <0.001), changes likely to impact blood flow and its regulation. The formation of string vessels significantly correlated with disease duration (P <0.05), especially in the occipital cortex. The occipital cortex demonstrated the greatest decreases in pericytes (P <0.001) and aquaporin-4 mislocalisation (P <0.05), while changes in pericyte density were also significant in the substantia nigra. In contrast, these changes were not significant in the medial temporal cortex despite protein deposition in this region. Although no Lewy pathology was detected in the occipital cortex, there was a positive relationship between Lewy body stage and perivascular space size (Rho =0.6, P <0.05). These findings reveal progressive, region-specific alterations in the cellular components of the glymphatic system and vascular integrity in Parkinson's disease. Notably, the correlation between string vessel formation and disease duration, even in a region unaffected by protein deposition, suggests that vascular changes may play an important role in disease progression. These results emphasize the need for further investigation into the interplay between regional vascular changes and Parkinson's disease progression, which may offer novel insights for therapeutic strategies.

Diffusion MRI microstructure markers of changes in the human brain across the lifespan
Understanding how the brain develops, matures, ages, and declines is one of the fundamental questions facing neuroscience. Recent advances in diffusion MRI microstructure analysis have allowed for detailed descriptions of neuronal change in humans. However, it is essential that findings from these studies are appropriately contextualized to general age-related changes in the brain. This study uses 3-tissue constrained spherical deconvolution (3T-CSD) to examine the relationship between brain diffusion microstructure and chronological age. 3T-CSD is able to quantify signal fraction measurements at the voxel-wise level from three different tissue microenvironments found in the brain: extracellular free water, intracellular isotropic, and intracellular anisotropic. This study applies 3T-CSD analysis to the Nathanial Kline Institute's Rockland cohort, a large-scale community sample of brain MRI data across the lifespan. Microstructural measurements were taken in a number of structures throughout the white matter, subcortical gray matter, and lobar cortical regions while additionally evaluating lateral differences in microstructural measurements. The general trajectory of signal fraction measurements was a positive relationship with age and extracellular signal fraction, a negative relationship between age and intracellular isotropic signal fraction, and an inverted U-shaped trajectory for the intracellular anisotropic signal fraction. In individual sub-areas these trends tended to still be present, with some notable exceptions. However there were large differences in 3T-CSD microstructure measurements between individual structures, including significant lateral differences between hemispheres for each of the subcortical gray matter structures and for each of the cortical regions. These results demonstrate that 3T-CSD is able to describe age-related change across the brain and lifespan. By using a healthy population cohort this study can be used as a point of comparison for 3T-CSD analysis of microstructure changes in the presence of pathology. Finally, the detailed analysis of lateralized ROI results can inform diffusion microstructure studies examining cortical and subcortical regions.

Inhibition of NF-κB signaling pathway in astrocytes facilitates amyloid-β clearance by kallikrein-related peptidase 7
Alzheimer disease (AD) is characterized by the deposition of amyloid-{beta} peptide (A{beta}). Decreased A{beta} clearance is observed in sporadic AD patients, suggesting that enhancing A{beta} clearance is a potential therapeutic approach for AD. We identified kallikrein-related peptidase 7 (KLK7) as an astrocyte-derived A{beta}-degrading protease, and its mRNA expression is reduced in AD brains. Memantine, an N-methyl-D-aspartate (NMDA) receptor antagonist, upregulates KLK7 expression in astrocytes; however, the regulatory mechanism remains unclear. Here, we show that the NMDA receptor signaling negatively regulates KLK7 mRNA expression via nuclear factor-{kappa}B (NF-{kappa}B). Inhibition of NF-{kappa}B signaling pathway in astrocytes increases KLK7 expression and promotes A{beta} degradation. Moreover, the mRNA expression level of the NF-{kappa}B family is elevated in AD brains and shows a negative correlation with KLK7 mRNA expression. Finally, the injection of an NF-{kappa}B inhibitor significantly upregulates Klk7 expression and reduces A{beta} levels in vivo. These findings suggest that the NMDA receptor-NF-{kappa}B signaling axis in astrocytes negatively regulates KLK7 expression and modulates KLK7-mediated A{beta} clearance.

Expectation management in humans and LLMs
Mirative markers, such as "surprisingly", explicitly encode a violation of expectations. Such markers are used for expectation management during communication. Sensitivity to mirative markers relies on two abilities: i) updating expectations upon recognizing a mirative marker, and ii) identifying expectation violations warranting the use of a mirative marker. In this study we compared sensitivity to mirative markers in humans and large language models (LLMs). In part 1, we used a sentence-completion task, where humans and LLMs were presented with sentence fragments and asked to continue them. Results show that for both humans and LLMs, the presence of a mirative marker significantly increased response entropy and decreased top response probability, in line with theoretical proposals for mirative markers. In part 2, we created a novel task of mirative polarity selection where humans and LLMs are presented with a sentence pair and asked to select whether it was connected by a mirative marker ("surprisingly") or an anti-mirative marker ("unsurprisingly"). Results show that LLMs perform at an impressive human level. We conclude that both humans and LLMs use mirative markers as cues for calibrating their subsequent expectations during sentence processing.

High throughput identification of genetic regulators of microglial inflammatory processes in Alzheimer's disease
Genome-wide association studies (GWAS) have identified over a hundred genetic risk factors for Alzheimer disease (AD), many of which are predominantly expressed in microglia. However, the pathogenic role for most of them remains unclear. To systematically investigate how AD GWAS variants influence human microglial inflammatory responses, we conducted CRISPR inhibition (CRISPRi) screens targeting 119 AD GWAS hits in hiPSC-derived microglia (iMGLs) and used the production of reactive oxygen species (ROS) in response to the viral mimic poly(I:C) as a functional readout. Top hits whose knockdown either increased or decreased ROS levels in response to poly(I:C) were further analyzed using Perturb-seq to integrate CRISPRi with single-cell RNA sequencing (scRNA-seq). This analysis identified 9 unique microglial clusters, including a poly(I:C)-driven inflammatory cluster 2. Emerging evidence supports a pathogenic role of viral infections in AD and cross comparison of our scRNA-seq data with iMGLs xenotransplanted into an AD mouse model shows significant overlap between our clusters and AD-relevant microglial clusters. Knockdown of MS4A6A and EED, which resulted in elevated ROS production in the presence of poly(I:C), increased the proportion of cluster 2 cells and induced functionally related changes in gene expression. In addition, KD of MS4A6 led to a reduction in the proportion of iMGLs in the DAM (disease associated microglia) cluster under all conditions, suggesting that this gene may modulate the DAM response. In contrast, KD of INPP5D or RAPEP1 which lead to low levels of ROS in the presence of poly(I:C), did not significantly affect the proportion of cells in cluster 2 but rather shaped the inflammatory response. This included the upregulation of an HLA-associated inflammatory cluster (cluster 6) by INPP5D knockdown under all conditions, independent of poly(I:C) stimulation. Importantly, KD of INPP5D or RAPEP1 had many shared differentially expressed genes (DEGs) under both vehicle and poly(I:C) treated conditions. Overall, our findings demonstrate that despite the diverse biological functions of AD GWAS variants, they converge functionally to regulate human microglial states and shape inflammatory responses relevant to AD pathology.

A functional anatomical shift from the lateral frontal pole to dorsolateral prefrontal cortex in emotion action control underpins elevated levels of anxiety: partial replication and generalization of Bramson et al., 2023
Flexible control over emotional behavior represents a promising target for novel interventions for mental disorders. Accumulating evidence has indicated a key role of the lateral frontal pole (FPl) and its connections with other cortical and subcortical systems in emotional action regulation. A recent study from Bramson et al., (2023) employed a multi-modal neuroimaging approach to demonstrate a functional-anatomical shift from FPI to dorsolateral prefrontal cortex (DLPFC) in a sample of anxious individuals during emotional action control. While these findings might represent a venue for interventions in anxiety disorders, conventional neuroimaging strategies are often limited with respect to generalizability and reproducibility. Against this background we capitalized previous large-scale fMRI data in n = 250 participants using an affective linguistic Go/NoGo paradigm to examine the robustness of the reported associations with trait social anxiety across samples, cultures and paradigms. Additionally, context-dependent functional connectivity patterns were explored to examine action control in different emotional contexts. In line with previous study, we found no difference between high- and non-anxious group on the behavioral congruency-effect. The neural results showed that non-social anxious group engaged the left FPl while the high-social anxious group specifically recruited the DLPFC, however in the absence of significant between-group differences. Importantly, the level of trait social anxiety was significantly positively related with DLPFC activity and negatively with left FPl activation across groups. Furthermore, context-dependent functional connectivity analyses revealed a negative context-specific neural shift from the sgACC-FPl to sgACC-DLPFC specifically in the high anxiety group. Together, the present study employed a different task paradigm, population and analytic methods, partially replicated the findings described by Bramson et al., (2023) and additionally determined context-specific changes in the communication with the sgACC in high anxiety. The findings provide further evidence for target-based interventions of persistent emotional control deficits in anxiety disorders.

Towards an informed choice of diffusion MRI image contrasts for cerebellar segmentation
The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural magnetic resonance imaging data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA), leveraging cerebellar region labels from the atlas-based SUIT cerebellar segmentation pipeline. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b=1000 s/mm2 shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI.

Local and distributed information coding in the ventral stream
Neuroscience is awash with studies showing how virtually every cognitive or neural function is distributed across many regions. Yet, it is left unclear whether findings of widespread information processing imply large-scale integrative computations or instead should be seen as showing multitudes of localized modules. To investigate this distinction, we used fMRI data (N=60) from participants viewing objects across four tasks, and we examined the relationships between regions in terms of information coding. We demonstrate that coding in the occipital lobe is overwhelmingly localized and small-scale population codes are the foundation of early perceptual representation. In contrast, different portions of the inferior temporal lobe coordinate as a distributed unit to produce multi-regional population codes for semantic information. No other brain area, neither the parietal nor prefrontal cortices, shows the preference for distributed coding seen in the inferior temporal lobe. The inferior temporal lobe additionally displays uniquely low levels of redundancy in its information coding across its constituent regions, and we demonstrate how this supports lobe-wide semantic processing. Taken together, these results outline a framework of how the ventral stream transitions from local to distributed neuronal coding as information progresses from perceptual to semantic representations.

Lasting effect of psilocybin on sociability can be blocked by DNA methyltransferase inhibition
The recent renaissance in research on psychedelics such as psilocybin has highlighted their therapeutic potential including their lasting influences on brain function. Here we report that a single systemic administration of the serotonergic psychedelic psilocybin can durably promote social behaviour in the Cntnap2-knockout mouse model of autism. This effect can be blocked by pharmacological inhibition of DNA methyltransferase I, indicating an epigenetic mechanism underlying the long-lasting effect of psilocybin.

Metabolic Atlas of Early Human Cortex Identifies Regulators of Cell Fate Transitions
Characterization of cell type emergence during human cortical development, which enables unique human cognition, has focused primarily on anatomical and transcriptional characterizations. Metabolic processes in the human brain that allow for rapid expansion, but contribute to vulnerability to neurodevelopmental disorders, remain largely unexplored. We performed a variety of metabolic assays in primary tissue and stem cell derived cortical organoids and observed dynamic changes in core metabolic functions, including an unexpected increase in glycolysis during late neurogenesis. By depleting glucose levels in cortical organoids, we increased outer radial glia, astrocytes, and inhibitory neurons. We found the pentose phosphate pathway (PPP) was impacted in these experiments and leveraged pharmacological and genetic manipulations to recapitulate these radial glia cell fate changes. These data identify a new role for the PPP in modulating radial glia cell fate specification and generate a resource for future exploration of additional metabolic pathways in human cortical development.

Paradoxical ventral tegmental area GABA signaling drives enhanced morphine reward after adolescent nicotine
Background: An important yet poorly understood risk factor for opioid use disorder is adolescent nicotine use. We investigated the neural mechanisms underlying this understudied interaction. Methods: Male and female adolescent mice received two-weeks of nicotine water (Adol Nic) or plain water (Adol Water). In adulthood, mice underwent three morphine tests: conditioned place preference (CPP), locomotor sensitization, and two-bottle choice. Ex vivo ventral tegmental area (VTA) brain slices were assessed via patch clamp for GABA and dopamine (DA) neuron morphine responses. Finally, VTA GABA neurons were chemogenetically inhibited during morphine CPP. Results: In adulthood, Adol Nic mice had greater morphine CPP, more morphine locomotor sensitization, and more choice-based oral morphine consumption vs. Adol Water mice. In contrast, adult mice given nicotine vs. water had similar morphine CPP. Patch clamp analysis of VTA neurons from adult Adol Water mice showed canonical cell-type responses to bath-applied morphine: fewer action potentials in GABA neurons and more in DA neurons. Paradoxically, VTA GABA and DA neurons from adult Adol Nic mice did not show these morphine responses. In support of a causal relationship between GABA neuron firing and reward behavior, chemogenetic inhibition of VTA GABA neurons in Adol Water mice during pairing increased morphine CPP. In contrast, inhibition of VTA GABA neurons in Adol Nic mice brought morphine CPP down to control levels. Conclusions: These data reveal an electrophysiological mechanism by which adolescent nicotine intake promotes morphine reward later in life, showing that adolescent nicotine exposure alters reward circuitry well into adulthood.

Inhibitory effects of dopamine agonists on pain-responsive neurons in the central nucleus of the amygdala
The central nucleus of the amygdala (CeA) is a heterogenous region of primarily GABAergic neurons that contributes to numerous behaviors, including fear learning, feeding, reward, and pain. Dopaminergic inputs to the CeA have been shown to regulate many of these behaviors, but how dopamine exerts these effects at the cellular level has not been well characterized. We used the Targeted Recombination in Active Populations (TRAP) mouse line to fluorescently label pain-responsive CeA neurons, and then targeted these cells for patch-clamp recordings in acute slices to test the effects of dopamine agonists. The D1 agonist SKF-38393 and D2 agonist quinpirole both had inhibitory effects, reducing the input resistance and evoked firing and increasing rheobase of labeled CeA neurons. Both agents also inhibited the NMDA component of excitatory postsynaptic currents (EPSCs) evoked by basolateral amygdala (BLA) stimulation, but did not affect the AMPA component. D1 activation, but not D2, also appeared to have a presynaptic effect, increasing the frequency of spontaneous EPSCs. These results provide new insights into how dopamine regulates activity within pain-responsive CeA networks.

Psychedelics Align Brain Activity with Context
Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their effects are context-sensitive. To understand how this reorganisation depends on the context, we collected and comprehensively analysed the largest psychedelic neuroimaging dataset to date. Sixty-two adults were scanned with fMRI and EEG during rest and naturalistic stimuli (meditation, music, and visual), before and after ingesting 19 mg of psilocybin. Half of the participants ranked the experience among the five most meaningful of their lives. Under psilocybin, fMRI and EEG signals recorded during eyes-closed conditions became similar to those recorded during an eyes-open condition. This change manifested as an increase in global functional connectivity in associative regions and a decrease in sensory areas. For the first time, we used machine learning to directly link the subjective effects of psychedelics to neural activity patterns characterised by low-dimensional embeddings. We show that psilocybin reorganised these low-dimensional trajectories into cohesive patterns of brain activity that were structured by context and quality of subjective experience, with stronger self- and boundary-related effects - which were linked to day-after mindset changes - leading to more structured and distinct neural representations. This reorganisation induces a state of 'embeddedness' - a coherent integration of brain networks that normally segregate internal and external processing - dissolving perceptual boundaries in a way that aligns neural dynamics with context. Beyond its transient expression, embeddedness serves as a construct for understanding the subjective and therapeutic effects of psychedelics. These findings provide a new account of the large-scale neurocognitive effects of psychedelics and demonstrate the utility of using machine learning methods in assessing state- and context-dependent neural dynamics and their association with psychological outcomes.

Limitations of Dynamic Causal Modelling for Multistable Cortical Circuits
Dynamic causal modelling (DCM) has been used extensively for inferring effective connectivity from neuroimaging data. However, it is unclear whether the DCM approach can reveal neural circuits with multistable dynamic states with global dynamical structures. In this work, we used excitatory-inhibitory cortical columnar neural mass models endowed with multistable dynamical states as ground truths to evaluate whether DCM can accurately identify their models' architecture and connectivity strengths. Specifically, we simulated three recurrently connected neural mass models with different types of multistability, and generated local field potential data for DCM. The first model has bistable fixed points and exhibiting binary decision-making behaviour, and the second model has co-existence of two stable oscillatory frequencies (period doubling). The third model exhibits deterministic chaos, with a continuum of confined states. For each of the three models, DCM's Bayesian model selection was able to correctly identify the correct model architecture among various options in model space. However, DCM's Bayesian model averaging of the winning model was unable to accurately elucidate all the models' extrinsic connectivity strengths, leading to the behaviours of the reconstructed models to differ substantially from their corresponding ground-truth models. Further, we found DCM's estimation was highly sensitive to the sampling frequency used during its training. Overall, this work reveals the limitations of DCM in evaluating complex, multistable neural dynamical states and hence caution its use under these conditions.

The extremely low mechanical force generated by nano-pulling induces global changes in the microtubule network, nuclear morphology, and chromatin transcription in neurons
Mechanical force plays a pivotal role in every aspect of axon development. In this paper, we explore the use of nano-pulling, a technology that enables the intracellular generation of extremely low mechanical forces. We demonstrate that force-mediated axon growth also exerts global effects that extend to the nuclear level. Our mechanistic studies support a model in which exogenous forces induce stabilization of microtubules, and a significant remodeling of perinuclear microtubules, which preferentially align perpendicularly to the nuclear envelope. We observed an increase in the lateral tension of the nucleus, leading to substantial remodelling of nuclear morphology, characterized by an increase in nuclear grooves and higher sphericity index (indicating less flattened nuclei). Notably, these changes in nuclear shape are linked to chromatin remodelling, resulting in global transcriptional activation.

ARIH1 deficiency impairs spatial learning and memory via GIRK2 upregulation in hippocampal CaMKII-expressing neurons in mice
Alterations of Ariadne RBR E3 Ubiquitin Protein Ligase 1 (ARIH1), a human homologue of Drosophila Ari, have been associated with a number of human diseases. Given the importance of ubiquitin-proteasome system in learning and memory, whether ARIH1 involves in the process has not been explored. Here we report that ARIH1-deficent mice exhibited a defect in learning and memory evidenced in Morris water maze and in novel object recognition tests without changes in basal motor activity, anxiety, and depressive behaviors. We found that ARIH1 deficiency resulted in an upregulation of G protein-gated inwardly rectifying potassium channel 2 (GIRK2) in dorsal hippocampus that was attributed to the impaired ubiquitination and degradation. Locally injection of ARIH1-expressing lentivirus to restore the ARIH1 expression of dorsal hippocampus in ARIH1+/- mice restored the impaired learning and memory. Moreover, selective knockdown ARIH1 in dorsal hippocampal calcium-calmodulin-dependent protein kinase II (CaMKII)-expressing neurons, but not for parvalbumin+ (PV) or somatostatin+ (SST) neurons, in naive mice was sufficient to mimic the damage in learning and memory of ARIH1+/- mice. Lastly, we demonstrated that systemically or locally inhibition of GIRK activity was able to improve ARIH1 deficiency-induced decline of learning and memory in ARIH1+/- mice. The present study discovered the clear role of ARIH1 in mediating learning and memory, defect of ARIH1 resulted in upregulation of GIRK2 in hippocampal CaMKII-expressing neurons via modulating the ubiquitination and degradation GIRK2.

Ultrasound Directly Activates Sparse Neurons and Modulates Visual Circuits in Deafened Mice
Focused ultrasound neuromodulation (FUN) is widely regarded as a next-generation technology for neural modulation, with applications spanning rodents, non-human primates, and humans. While mechanistic studies are advancing, a persistent confound-auditory interference-casts doubt on whether ultrasound exerts direct mechanical effects or merely indirect auditory responses. To resolve this, we engineered a circular ultrasound transducer compatible with two-photon calcium imaging and examined its effects on the primary visual cortex (V1) of surgically deafened mice, eliminating auditory contributions. Our results reveal that ultrasound directly activates a sparse subset of ultrasound-sensitive neurons (USSN) (response rate >30%) in V1, comprising only a small fraction of the total population and exhibiting a spatially sparse distribution. The proportion of USSN scale with ultrasound pressure, confirming a direct neuromodulatory effect independent of audition. Intriguingly, despite this sparse activation, ultrasound significantly modulates V1 circuitry: it alters the dynamics of light-sensitive neurons (LSN), with subsets showing excitation, inhibition, or no change in response to visual stimuli. These findings provide the first rigorous in-vivo evidence that FUN induces direct mechanical effects on neural activity, disentangling them from auditory confounds. By demonstrating both the specificity and broader circuit-level impact of ultrasound in deafened mice, this study reframes our understanding of FUN's mechanisms and strengthens its potential as a precise neuromodulatory tool.

Transcranial focused ultrasound stimulation of the anterior temporal lobe enhances semantic memory by modulating brain morphology, neurochemistry and neural dynamics
Understanding neural functioning and plasticity of the brain is a fundamental goal of neuroscience. The ventromedial anterior temporal lobe (ATL) has been suggested as the centre-point of a core transmodal hub for semantic memory, playing a crucial role in the representation of coherent conceptual knowledge. However, non-invasive direct modulation of the ventromedial ATL has remained challenging. Transcranial ultrasound stimulation (TUS) is an emerging neuromodulatory technique that delivers acoustic energy with high spatial precision, making it uniquely suited for targeting deep brain structures non-invasively. In this study, we investigated whether theta-burst TUS (tbTUS) to the ventromedial ATL could enhance semantic memory performance in the adult brain. Using a multimodal neuroimaging approach, magnetic resonance spectroscopy (MRS), functional MRI (fMRI), and voxel-based morphometry (VBM), we assessed tbTUS-induced changes in neurochemical concentrations, functional network connectivity, structural plasticity, and semantic memory performance. Compared to control stimulation (ventricle), tbTUS at the ventromedial ATL significantly improved semantic task performance in healthy individuals. MRS analysis revealed that tbTUS decreased GABA and increased Glx levels, reflecting shifts in excitation-inhibition balance. Additionally, tbTUS increased neurometabolites in the ATL, including NAA, creatine and choline, suggesting enhanced neuronal function and metabolism. fMRI analysis demonstrated that tbTUS reduced task-induced regional activity in the ATL and functionally connected semantic regions, while also enhancing intrinsic and effective connectivity across the semantic network. Structural analysis revealed increased grey matter volume in the ATL following tbTUS compared to control stimulation. These findings provide the first convergent evidence that tbTUS can modulate neurochemistry, functional dynamics, and brain morphology to enhance semantic memory function. Our results highlight TUS as a powerful neuromodulatory tool with potential applications in cognitive enhancement and neurorehabilitation, offering a promising intervention for dementia and neurodegenerative disorders.

An intrinsic neuronal manifold underlies brain-wide hierarchical organization of behavior in C. elegans
Large-scale neuronal recordings have revealed complex behavior-related activity patterns. Surprisingly, these patterns are broadly distributed across brain regions, including sensory areas. The origins and functions of such activity patterns are incompletely understood. Using whole-brain imaging in freely behaving C. elegans, we find rich and distributed brain activity. Within these dynamics, a low dimensional manifold encodes the major action sequence of C. elegans. This manifold originates largely from intrinsic dynamics, with minor contributions from movement-induced sensations. Moreover, multiple neurons encode faster time-scale motor patterns that are gated by the manifold, meaning that they selectively encode behavior during specific brain states. We therefore propose that one function of brain-wide dynamics is to enable hierarchical organization of behavior: information about major actions is broadcast via a distributed manifold, which enables the hierarchical nesting of granular motor patterns within those major actions.

Synaptic Gpr85 influences cerebellar granule cells electrical properties and light-induced behavior.
GPR85/SREB2 is an exceptionally well-conserved orphan 7TM receptor with unclear biological attributes. In this study, we used several zebrafish genetic models to investigate Gpr85 properties and functions throughout development and adulthood. We describe that, like in mammals, gpr85 is expressed by various neuronal populations throughout zebrafish development and adulthood in the central nervous system, retina and intestine. The overexpression of a fluorochrome-tagged version of the receptor in gpr85-expressing neurons provides the first in vivo evidence that Gpr85 is enriched at the synaptic level in the brain and retina. Transcriptomic analysis of Gpr85 knock-out zebrafish cerebellar granule cells revealed changes in gene expression related to enhanced neuronal activity. Further investigations using ex vivo cerebellar slices demonstrate that the electrophysiological activity of Gpr85 deficient granule cells are increased. We also observed that Gpr85 deficient larvae exhibit enhanced light-triggered startle responses in dark/light paradigm behavioral experiments. Altogether, our results provide evidence that, in zebrafish, Gpr85 is enriched at the synaptic level in vivo and influences granule cell electrophysiological properties as well as light-triggered motor responses.

The intrinsic cortical geometry of reading
Reading involves dual streams of sensory propagation that lie within a broader sensory-to-transmodal hierarchy. This study explores whether individual differences in this functional organization can be explained by cortical geometry. Through a modified connectome predictive modeling approach, we identified cortical distances (CD) measured along the cortical surface that predict reading performance. We found that interindividual variation in CD robustly predicts reading performance comparably to functional connectivity (FC). Moreover, combining CD and FC outperforms single-modality models, indicating that these measures offer complementary perspectives of cortical organization. Notably, the geodesic distances that drive our predictions align with the spatial layout of the dual-route hypothesis of reading. We note that within these streams, reading performance is predicted by shorter distances between the visual cortex and higher-order transmodal regions. We thus show how the geometry underlying reading supports an emerging hypothesis that individual differences in cortical geometry shape how we think and act.

Direct cerebellar control over motor production in a species with extreme cerebellar enlargement
The cerebellum is thought to fine-tune movement without being required for its production. However, this textbook view derives mainly from studies of mammalian species with highly developed cerebral cortices. Here we examined cerebellar function in the elephant-nose fish, a member of a family of African weakly electric fish (Mormyridae) in which the cerebellum is massively enlarged. The elephant-nose fish is named for a flexible facial appendage that is used to probe surfaces and extract prey from substrate. Results from microstimulation, electrophysiological recordings, and lesions support a direct role for the C1 region of the mormyrid cerebellum in controlling movement of this appendage. These findings suggest that the cerebellum is capable of performing functions typically ascribed to the cerebral cortex, emphasizing the importance of evolutionary history on the functional specialization of brain regions.

Frequency-Specific Resting-State MEG Network Characteristics of Tinnitus Patients Revealed by Graph Learning
Tinnitus, the perception of sound without an external source, affects a significant portion of the population, yet its impact on brain communication diagram known as the functional connectome, remains limited. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index and coherence rely on pairwise comparisons and are therefore limited in providing a holistic encoding of FC. Here, we employ an alternative approach to estimate the entire connectivity structure by analyzing all time-courses simultaneously. This approach is robust even for short-duration recordings, facilitating faster functional connectome identification and real-time applications. Using resting-state MEG recordings from controls and individuals with tinnitus, we demonstrated that the learned connectomes outperform correlation-based connectomes in fingerprinting, that is, identifying an individual from test-retest acquisitions. Group-level analysis revealed distinct altered FC in tinnitus across multiple frequency bands, affecting the default mode, auditory, visual, and salience networks, suggesting a reorganization of these large-scale networks beyond auditory areas. Our study reveals that tinnitus presents highly individualized and heterogeneous whole-brain connectome profiles, highlighting the need to focus on individual variability rather than group-level differences to gain a more nuanced understanding of tinnitus. Personalized FC could enable patient-specific tinnitus models, optimizing treatment strategies for individualized care.

The relationship between cortisol, grid-like representations and path integration
Acute stress triggers the release of cortisol, which broadly affects cognitive processes. Path integration, a specific navigational process, relies heavily on grid cells in the entorhinal cortex (EC). The EC contains glucocorticoid receptors and is therefore likely to be influenced by cortisol, though little is known about this relationship. Given the role of the EC in neurological diseases such as Alzheimers Disease, investigating the effects of cortisol on this brain region may offer insights into how stress affects these diseases. In this study, we examined the effects of cortisol on human path integration in thirty-nine healthy participants across two sessions. On each day, they received either 20mg cortisol or a placebo and performed a virtual homing task during functional magnetic resonance imaging (fMRI). Cortisol markedly impaired path integration performance, independent of path distance or the presence of spatial cues. Additionally, cortisol altered navigational strategies, leading participants to navigate further away from landmarks, which was associated with worse performance. FMRI results showed that cortisol increased the activation of right caudate nucleus in the presence of landmarks. Using a representational similarity analysis, we observed grid-like representations in the right posterior-medial EC specifically on day one under placebo, but these were diminished by cortisol. Grid-like representations facilitated performance over short distances but hindered it over longer ones, suggesting that grid cells support PI specifically in case of short trajectories. Overall, the study indicates that cortisol-induced disruption in grid cell function in the EC may underly stress effects on path integration.