bioRxiv Subject Collection: Neuroscience's Journal

Spine-brain dynamics during human defensive threat-reactions
Synchrony between central and autonomic nervous system responses during threat rely on spine-brain interactions. Activation of the parasympathetic autonomic nervous system during acute threat has been linked to optimized action preparation and longer-term stress-resilience. However, it is unclear how this is orchestrated by spine-brain interactions during our widely used models of threat conditioning. Here we used a well-established threat conditioning procedure to test whether freezing-related bradycardia modulates the strength of motor- and sensory-relevant spinal-brain connectivity patterns. Conditioning elicited the expected autonomic and psychophysiological responses, with significant bradycardia and heightened differentiation between conditioned stimuli. Heart rate bradycardia modulated activity in the cingulate cortex and spinal cord, emphasizing their role in freezing. Notably, bradycardia-modulated spinal cord activity during threat was functionally coupled with motor cortex activity, suggesting a preparatory response for defensive actions. Establishing these effects for the first time in humans helps discovery of biomarkers for threat coping-relevant body-brain interactions, essential for clinical evaluations.

Off-target effects of PLX5622 revealed: mixing microglial function in anesthesia and addiction withdrawal
Microglia have gained increasing attention as important regulators in the brain. Acute microglial depletion using the colony-stimulating factor 1 receptor (CSF1R) inhibitor, PLX5622, has become a popular approach to uncover microglial function. PLX5622 treatment has been recently shown to significantly promote anesthetic emergence. In characterizing underlying mechanisms, we found that PLX5622 treatment did not change the global neuronal activity during anesthesia, but robustly enhanced anesthetic metabolism. Moreover, PLX5622 dramatically induced the expression of hepatic enzymes that extensively modify xenobiotic and endobiotic metabolism. Blocking increased enzymatic activity significantly reduced the arousal-promoting effects of PLX5622 in anesthesia, irrespective of microglial elimination. In addition, we demonstrate that enhanced drug metabolism also contributed to PLX5622-induced attenuation in nicotine-withdrawal anxiety. By revealing previously unrecognized effects of PLX5622, our findings raise caution in interpreting data generated from PLX5622 treatment and bring forward the need for designing more specific CSF1R inhibitors.

AutoMorphoTrack: An Automated Python Package for Organelle Morphology, Motility, and Colocalization Analysis in Live-Cell Imaging
Understanding the dynamics and interactions of intracellular organelles is critical for understanding the mechanisms of cellular homeostasis and disease. Here, we present AutoMorphoTrack, an open-source Python package for automated detection (optional thresholding), morphology classification, motility tracking, and colocalization analysis of mitochondria and lysosomes in live-cell imaging data. The package provides a pipeline capable of processing multichannel time-lapse image stacks, generating organelle-specific segmentations, organelle-specific count, tracking organelle motility, quantifying morphology parameters, and performing frame-by-frame colocalization analysis. We demonstrate the utility of AutoMorphoTrack using a representative dataset, illustrating each analysis step and the associated outputs. This package offers a flexible and scalable tool for organelle dynamics studies, facilitating reproducible and quantitative analysis for the broader cell biology community.

Longitudinal Investigation of Structural and Resting-State Effective Connectivity Alterations in a Non-Human Primate Model of Huntington's Disease
Huntington's disease (HD) is a genetic neurodegenerative disorder caused by expanded CAG repeats in the huntingtin gene which produce a mutant huntingtin (mHTT) protein that contributes to progressive striatal, cortical, and white-matter atrophy, resulting in motor dysfunction and cognitive decline. Recently, a non-human primate (NHP) model of HD was developed via stereotaxic delivery of an adeno-associated viral vector expressing 85 CAG repeats (85Q) into the striatum. This model recapitulates several neuropathological changes and symptoms observed in people with HD (PwHD) including chorea and mild cognitive impairment. A previous longitudinal, multimodal MRI investigation in this model revealed volumetric and resting-state functional connectivity (rs-FC) changes compared to controls, in key regions involved in HD, over the course of 30 months. We aimed to study longitudinal changes in structural connectivity (SC), obtained from diffusion MRI scans from the same animals, comparing the 85Q animals to the control (Buffer) group. Additionally, going beyond the correlative rs-FC analyses, we investigated changes in causal, inter-regional functional interactions by estimating effective connectivity (EC) from rs functional MRI scans, constrained to strong structural connections. We found that the SC between basal ganglia regions and the cortex was reduced in the 85Q primates compared to the Buffer group at 14-months post virus injection, aligning with the pathological process observed in PwHD at later stages of the disease. EC from the caudate and putamen to the motor cortex was significantly reduced in the 85Q animals as early as 3-months post-injection providing novel insights into early alterations in causal functional interactions.

Cortical tracking of connected speech is interactively modulated by top-down predictions and bottom-up signal quality
Prediction facilitates speech comprehension, but how predictions are combined with sensory input during perception remains unclear. Prior studies suggest that prediction error computations, which represent the difference between heard and expected speech sound, play a central role in speech perception. However, these studies often rely on non-ecological listening conditions, involving isolated words predicted by artificial cues. In two experiments, we presented male and female participants with semantically coherent sentences that ended in words of varying predictability. We also manipulated the signal quality of the final word by applying time-specific degradation. Using linear encoding models of EEG responses, we found that neural representations of speech features were jointly influenced by top-down predictions and bottom-up signal quality. Specifically, for unpredicted final words, increasing signal quality resulted in enhanced neural tracking of spectral and temporal modulations. In contrast, for strongly predicted final words, greater signal quality led to reduced tracking of speech modulations. Computational simulations revealed that this interaction is consistent with prediction error computations, but not with alternative models such as signal sharpening. These findings extend the evidence for prediction error computations to more naturalistic listening situations.

Proprioceptive integration in motor control
Muscle vibration alters both perceived limb position and velocity by increasing type Ia afferent firing rates, although these afferents mainly encode velocity. Predictive frameworks of sensori-motor control, such as Active Inference and Optimal Feedback Control, suggest that velocity signals should inform position estimates. Such a function would predict that errors in perceived limb position and velocity should be correlated, but this prediction remains empirically underexplored. We hypothesized that an online evaluation of the integral of sensed velocity influences the perceived arm position during active movements. Such a mechanism would explain how vibratory stimulation of velocity-sensitive Ia afferents leads to the classic pattern of biased movement endpoint accuracy. Using a virtual reality-based reaching task, we investigated how vibration-biased proprioceptive feedback influences voluntary movement control. Our results suggest that muscle vibration biases perceived movement velocity, with downstream effects on perceived limb position and reflexive corrections of movement speed. We found that (i) antagonist vibration during active movement caused participants to both overestimate their movement speed while also slowing down, (ii) movement speed and endpoint errors were correlated, with muscle vibration affecting both, and (iii) adjustments in movement speed to muscle vibration are sufficiently fast to be reflexive. Together, these findings support the hypothesis that proprioceptive velocity signals are integrated to augment inference of position, consistent with predictive frameworks of sensorimotor control.

No Risky Bets: The Brain Avoids All-In Predictions During Naturalistic Multitalker Listening
Speech comprehension requires dealing with variability and uncertainty, especially when we are not familiar with the talker. Listeners achieve robust speech comprehension by tracking acoustic variability across talkers and implementing predictions based on their prior knowledge and contextual information. However, how perceptual adaptation to speech and predictive processing influence each other is still largely unknown. In this EEG study, we examined how listeners process continuous speech when exposed to a single familiar talker (Single condition) versus multiple unfamiliar talkers (Multi condition), all with native Italian pronunciation. By applying multivariate Temporal Response Function (TRF), we aimed to determine whether increased talker variability in the Multi condition influences phonemic encoding and predictive processing, as indexed by neural responses to cohort entropy (phonological uncertainty among lexical candidates) and semantic surprisal (lexical prediction error). Results showed increased neural responses to phonemic categories and to cohort entropy in the Multi than in the Single condition. These effects suggest that acoustic variability across talkers increases uncertainty in perceiving individual phonemes, prompting the brain to avoid committing to specific lexical candidates during word processing. Importantly, semantic surprisal responses were comparable between conditions, indicating that the modulation primarily affects early stages of processing. This study provided a critical test for how listeners compensate for increased bottom-up uncertainty by implementing probabilistic predictions during language comprehension. Our findings also offer insights into the flexibility of the human speech processing system in dynamically adapting to variable social and acoustic environments.

Goal Uncertainty Attenuates Sensorimotor Adaptation
Implicit sensorimotor adaptation - the automatic correction of movement errors through feedback and practice - is driven by a perceptual prediction error, the mismatch between the perceived movement outcome and its intended goal. While perceptual uncertainty is known to attenuate adaptation, the impact of goal uncertainty on adaptation remains unknown. We employed a visuomotor adaptation task that isolates implicit adaptation (N = 180), manipulating goal uncertainty by varying how precisely the goal's midpoint could be identified. Display format was varied independently to control for the objective size of visual features, and targets were hidden at movement onset, ensuring identical visual input at the moment the error was experienced. We found that goal uncertainty significantly attenuated implicit adaptation, independent of low-level visual and kinematic features. Together, these results demonstrate that a precise internal representation of the goal is essential for supporting implicit sensorimotor adaptation.

Mapping the visual cortex with Zebra noise and wavelets
Studies of the early visual system often require characterizing the visual preferences of large populations of neurons. This task typically requires multiple stimuli such as sparse noise and drifting gratings, each of which probe only a limited set of visual features. Here we introduce a new dynamic stimulus with sharp-edged stripes called Zebra noise and a new analysis model based on wavelets, and show that in combination they are highly efficient for mapping multiple aspects of the visual preferences of thousands of neurons. We used two-photon calcium imaging to record the activity of neurons in the mouse visual cortex. Zebra noise elicited strong responses that were more repeatable than those evoked by traditional stimuli. The wavelet-based model captured the repeatable aspects of the resulting responses, providing measures of neuronal tuning for multiple stimulus features: position, orientation, size, spatial frequency, drift rate, and direction. The method proved efficient, requiring only 3 minutes of stimulation (repeated 3 times) to characterize the tuning of thousands of neurons across visual areas. In combination, the Zebra noise stimulus and the wavelet-based model provide a broadly applicable toolkit for the rapid characterization of visual representations, promising to accelerate future studies of visual function.

Correctness is its own reward: bootstrapping error signals in self-guided reinforcement learning
Reinforcement learning (RL) offers a compelling account of how agents learn complex behaviors by trial and error, yet RL is predicated on the existence of a reward function provided by the agent's environment. By contrast, many skills are learned without external guidance, posing a challenge to RL's ability to account for self-directed learning. For instance, juvenile male zebra finches first memorize and then train themselves to reproduce the song of an adult male tutor through extensive practice. This process is believed to be guided by an internally computed assessment of performance quality, though the mechanism and development of this signal remain unknown. Here, we propose that, contrary to prevailing assumptions, tutor song memorization and performance assessment are subserved by the same neural circuit, one trained to predictively cancel tutor song. To test this hypothesis, we built models of a local forebrain circuit that learns to use contextual input from premotor regions to cancel tutor song auditory input via plasticity at different synaptic loci. We found that, after learning, excitatory projection neurons in these circuits exhibited population error codes signaling mismatches between the tutor song memory and birds' own performance, and these signals best matched experimental data when networks were trained with anti-Hebbian plasticity in the recurrent pathway through inhibitory interneurons. We also found that model learning proceeds in two stages, with an initial phase of sharpening error sensitivity followed by a fine-tuning period in which error responses to the tutor song are minimized. Finally, we showed that the error signal produced by this model can train a simple RL agent to replicate the spectrograms of adult bird songs. Together, our results suggest that purely local learning via predictive cancellation suffices for bootstrapping error signals capable of guiding self-directed learning of natural behaviors.

Internalization of exogenous myelin by oligodendroglia promotes lineage progression
Oligodendrocytes, traditionally recognized for their role in central nervous system myelination, have emerged during the last decades as key participants maintaining brain homoeostasis in response to metabolic demands and stress. In addition, injury to myelin prompts a regenerative response that leads to the formation of new myelin sheaths. However, the signals regulating effective remyelination by oligodendrocytes are still not completely understood. Here, we report that oligodendrocytes can internalize exogenous myelin both in vitro and in vivo, which leads to an increase in their proliferation and differentiation when their functions are not compromised. RNA sequencing reveals that myelin debris alters oligodendrocyte transcriptional profile, suppressing immune-related pathways and de novo cholesterol and fatty acid biosynthesis, while inducing lipid droplet formation to store and process internalized myelin particles. As a result, progression of the oligodendroglial lineage is enhanced in primary cell cultures, as shown by increased viability, proliferation and differentiation. Oligodendrocytes also acquire a more differentiated phenotype, with larger cell areas, a more complex morphology and myelination of synthetic nanofibers. Stereotaxic injection of fluorescent myelin into mouse cortex shows internalization by microglia and, to a lesser extent, by oligodendroglia. Notably, in the zebrafish model, ventricular injections of myelin also increase the number of ventral oligodendrocytes in the spinal cord, further supporting that myelin can promote lineage progression. These findings challenge the classical view that myelin debris intrinsically inhibits oligodendrocyte proliferation, suggesting instead that oligodendrocytes can use myelin to support self-renewal and maturation, acting as a trophic factor in the absence of pathological cues.

When word order matters: human brains represent sentence meaning differently from large language models
Large language models based on the transformer architecture are now capable of producing human-like language. But do they encode and process linguistic meaning in a human-like way? Here, we address this question by analysing 7T fMRI data from 30 participants reading 108 sentences each. These sentences are carefully designed to disentangle sentence structure from word meaning, thereby testing whether transformers are able to represent aspects of sentence meaning above the word level. We found that while transformer models match brain representations better than models that completely ignore word order, all transformer models performed poorly overall. Further, transformers were significantly inferior to models explicitly designed to encode the structural relations between words. Our results provide insight into the nature of sentence representation in the brain, highlighting the critical role of sentence structure. They also cast doubt on the claim that transformers represent sentence meaning similarly to the human brain.

Exploring neural mechanisms underlying error-related impairments in active working memory suggests an adaptive shielding of contents during cognitive control
Goal-directed behavior relies on cognitive flexibility - the ability to rapidly adapt ongoing thoughts and behaviors while preserving task-relevant information. The performance monitoring system optimizes such behavior by detecting and evaluating errors, while the working memory (WM) system maintains relevant information and protects it from interference. We investigated how these two systems interact. In prior work (Wessel et al., 2022), we found that motor errors impaired active WM maintenance (Error-Related Impairment of Active working Memory; ERIAM). Here, we aimed to identify the source of ERIAM by tracking a neurophysiological marker of visual WM maintenance - the contralateral delay activity (CDA) - throughout the error-making process. Forty-two human participants maintained visual information in WM while performing a motoric task during the delay period. Consistent with prior results, a significant ERIAM effect occurred: motor errors impaired WM performance. Critically, CDA amplitudes did not differ between motor correct and error trials before the flanker task, ruling out a general performance deficit. The CDA was also unaffected immediately after flankers, ruling out a perceptual interference explanation. Significant CDA differences only emerged after motor errors, supporting a genuinely error-related origin of the ERIAM effect. Contrary to prediction, however, CDA was more disrupted after correct responses than errors, and greater disruptions predicted a smaller ERIAM effect. These findings suggest that participants might store WM in multiple states to reduce interference from errors and that the CDA dynamics reflect these adaptive shielding strategies. These findings provide new insights into the source of error-related interference in active WM.

Charting structural brain asymmetry across the human lifespan
Lateralization is a fundamental principle of structural brain organization. In vivo imaging of brain asymmetry is essential for deciphering lateralized brain functions and their disruption in neurodevelopmental and neurodegenerative disorders. Here, we present a normative framework for benchmarking brain asymmetry across the lifespan, developed from an aggregated sample of 128 primary neuroimaging studies, including 177,701 scans from 138,231 individuals, jointly spanning the age range from 20 post menstrual weeks to 102 years. This resource includes comprehensive, hemisphere-specific brain growth charts for multiple neuroimaging phenotypes: regional cortical grey matter volume, thickness, surface area, and subcortical volumes. Our findings reveal distinct spatial patterns of asymmetry, with early leftward asymmetry observed in association cortices and late rightward asymmetry in sensory regions. These trajectories support theories of the neuroplasticity of asymmetry and the role of both genetic and environmental factors in shaping brain lateralization. Additionally, we provide tools to generate asymmetry centile scores, which allow the quantification of individual deviations from typical asymmetry throughout the lifespan and can be applied to unseen data or clinical populations. We demonstrate the utility of these models by highlighting group-level differences in asymmetry in autism spectrum disorder, schizophrenia, and Alzheimer's disease, and exploring genetic correlations with hemispheric specialization. To facilitate further research, we have made this normative framework freely available as an interactive open-access resource (upon publication), offering an essential tool to advance both basic and clinical neuroscience.

Distinctive neurophysiological correlates of sound onset and offset perception in humans
Accurate detection of sound onsets and offsets is vital for speech perception. Sound-onset event-related potentials (ERPs) have been well-characterised in electroencephalography (EEG) studies, but the characteristics of sound-offset ERPs have often been obscured by temporal confounds in experimental design. Here, two EEG studies were conducted in human listeners performing an interval duration discrimination task with (i) noise intervals in silence or (ii) silent intervals (gaps) in noise. Stimuli used in the active task were also presented under passive listening conditions. This design enabled us to investigate whether features of the offset ERP could be masked by task demands, and whether the usual temporal precedence of the onset cue in a sound influences the relative magnitude and shape of onset and offset ERPs. The morphology of the offset ERP was distinct from that of the onset ERP in both noise and silent interval duration discrimination tasks, even though the roles of onset and offset cues as initial versus final markers of interval duration were reversed. This observation corroborates evidence from animal studies that there are fundamental differences in brain mechanisms of onset and offset perception. In all experimental conditions, the amplitude of the offset ERP was one-third to one-half that of the onset ERP. Differences between active and passive listening conditions were largely explained by enhancement of ERPs for whichever cue (onset or offset) marked the end of the intervals compared in the duration discrimination task. However, this context dependence emerged in earlier ERP waves for offset than onset responses.

Elucidating directed neural dynamics of scene construction across memory and imagination
Autobiographical memory (AM) and imagination both rely on the brain's ability to construct vivid, coherent mental representations of past or imagined experiences. A central cognitive process underlying these functions is scene construction - the mental generation of spatially organized, imagery-rich representations of environments. Using ultra-high field 7T fMRI combined with Dynamic Causal Modeling (DCM), we examined directed effective connectivity among key nodes of the default mode network: the ventromedial prefrontal cortex (vmPFC), hippocampus, and precuneus during object imagery, single-scene construction, and AM retrieval. Our results revealed distinct patterns of network dynamics depending on task demands. During AM retrieval, effective connectivity was characterized by vmPFC-driven top-down modulation primarily targeting the precuneus, supporting the temporal and self-relevant structuring of episodic memory. In contrast, extended scenario imagination engaged hippocampal bidirectional influences with both vmPFC and precuneus, reflecting the dynamic simulation and integration of unfolding events. Single Scene construction shifted network leadership to the precuneus, which exerted modulatory effects on both vmPFC and hippocampus, consistent with its pivotal role in spatial integration and the generation of coherent mental scenes. Object imagery showed minimal stable connectivity within this network, suggesting limited engagement of the hippocampal-default mode circuit during simpler visual representations. Together, our findings highlight a flexible, task-dependent reorganization of effective connectivity within the vmPFC-hippocampus-precuneus network during the construction of rich mental experiences. Scene construction emerges as a unifying mechanism linking memory and imagination, with the direction and strength of neural interactions adapting according to whether temporal or spatial demands predominate.

Hippocampal input-driven plasticity of prefrontal interneurons reveals a circuit basis for impaired spatial working memory.
Dynamic functional connectivity between the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC) is essential for spatial working memory (SWM). Interactions between vHPC projections and mPFC interneurons, and their plasticity, are uniquely positioned to influence SWM, yet the nature of these interactions remains unclear. Here, we combined in vivo optical stimulation of vHPC inputs to mPFC with calcium recordings of discrete mPFC interneuron populations in mice, revealing class-specific response profiles and plasticity. Repeated vHPC input stimulation persistently depressed activity in vasoactive intestinal peptide (VIP)-expressing interneurons and potentiated activity in somatostatin-expressing interneurons. Ex vivo whole-cell electrophysiology and computational modeling revealed that these divergent effects likely arise from a primary weakening of monosynaptic vHPC input onto VIP interneurons. Leveraging this plasticity to inform the circuit interactions that support SWM, we found that mice with prior vHPC input stimulation displayed elevated VIP interneuron activity during the delay epoch in early SWM task training, and this enhanced activity correlated with poorer training performance. Accordingly, mice modeling the schizophrenia-predisposing 22q11.2 deletion syndrome with known SWM learning deficits recapitulated this aberrant VIP interneuron activity profile and showed reduced vHPC targeting of mPFC VIP interneurons. Together, these findings reveal novel cell-type-specific plasticity in cognition-supporting circuits and illustrate how reweighting of inputs to VIP interneurons may contribute to working memory dysfunction.

Astrocytic Chromatin Remodeler ATRX Gates Hippocampal Memory Consolidation through Metabolic and Synaptic Regulation
Astrocytes are increasingly recognized as active regulators of synaptic transmission and memory, yet the epigenetic mechanisms underlying their contribution to cognitive processes remain poorly defined. Here, we investigated the role of the chromatin remodeler ATRX in astrocytes by generating mice with inducible, astrocyte-specific Atrx deletion (aiKO) using tamoxifen administration at postnatal days 10-12, resulting in ATRX loss in approximately half of hippocampal and cortical astrocytes. Transcriptomic profiling of hippocampal tissue at one and three months revealed a progressive increase in differentially expressed genes, with early enrichment for cytoskeletal and immune pathways and later dysregulation of energy metabolism, ion transport, and synaptic gene sets. Electrophysiological recordings from CA1 pyramidal neurons in aiKO slices demonstrated increased neuronal excitability, reduced membrane capacitance, and decreased frequency of spontaneous excitatory postsynaptic currents, indicating non-cell-autonomous neuronal dysfunction. Morphological analysis identified a transient reduction in dendritic branching at one month and a selective loss of thin dendritic spines by three months, without changes in total dendrite length or overall spine density. Behaviorally, aiKO mice displayed normal locomotion, anxiety, and short-term memory, but exhibited deficits in 24-hour novel object recognition and long-term spatial memory in the Morris water maze. These findings demonstrate that ATRX-mediated chromatin remodeling in astrocytes is essential for maintaining hippocampal transcriptional homeostasis, neuronal function, and long-term memory. Our results highlight a critical role for astrocytic epigenetic regulation in cognitive processes and suggest that astrocyte dysfunction may contribute to the pathogenesis of ATR-X syndrome and related intellectual disability disorders, underscoring the importance of targeting multiple cell types for therapeutic intervention.

Dopamine release effects on striatal blood oxygenation and whole brain plasticity underlying associative learning
Dopaminergic signaling in the nucleus accumbens (NAc) is central to reward-based learning, but its relationship to brain-wide hemodynamics remains unclear. Using concurrent fMRI and dopamine photometry in awake, behaving mice, we reveal that associative learning induces a gradual temporal shift in NAc blood oxygenation responses that mirrors dopamine release dynamics. This shift emerges with cue-reward learning and extends across a distributed network including prefrontal, insular, and hypothalamic regions. Further, dopamine transients tightly correspond with local BOLD signals, and variations in reward value modulate delayed BOLD responses in both the NAc and additional subcortical structures. Removing dopaminergic contributions abolishes this reward-related modulation, demonstrating that BOLD signals encode dopaminergic value prediction. These findings establish a mechanistic link between dopamine signaling and widespread neural plasticity during learning.

Prematurity Reprograms Cerebellar Development and Long-Term Behavior
Premature survivors face increased risk of motor and socio-cognitive impairments, implicating cerebellar dysfunction, though the underlying mechanisms remain unclear. The cerebellums rapid development in late gestation makes it especially vulnerable to preterm birth. This study uses a double-hit (DH) mouse model combining maternal immune activation (MIA) and neonatal hypoxia (Hx), to mimic the sequential insults associated with prematurity. Validation with human postnatal cerebellar tissue demonstrated that the DH model recapitulates key structural and molecular features of early-life cerebellar injury. We characterized the cerebellar response to Hx, identifying motor and social deficits associated with S-phase arrest of mature granule cells and impaired synaptic integration by Purkinje cells. Conversely, DH mice exhibited delayed granule cell progenitor maturation, G2-phase retention, and progressive neuronal mitochondrial dysfunction, leading to motor-cognitive impairments while preserving sociability through distinctive and adaptive social interaction kinematics. These findings reveal insult-specific mechanisms of cerebellar developmental disruption and guide potential neuroprotective strategies.

Differentiated SH-SY5Y Cells Exhibit Neuronal Features but Lack Synaptic Maturity
A vital question in neuroscience is if and how efficiently cellular models may be differentiated into functional neuronal cells in culture. Despite the frequent use of the human neuroblastoma cell line SH-SY5Y, differentiation protocols vary extensively, with the most common being differentiation via the addition of retinoic acid and brain-derived neurotrophic factor. However, due to the lack of a reliable evaluation method, their adequacy as synaptic models remains unclear. Here, we investigate whether SH-SY5Y cells constitute a functional model for synaptic studies by phenotypically and ultra structurally analyzing synaptogenesis in SH-SY5Y cells subjected to different differentiation protocols. Electron microscopy (EM) techniques, including conventional EM, cryo-EM, and cryo-electron tomography, were systematically applied to characterize synaptogenesis in SH-SY5Y cells. Further characterization was performed through immunostaining and functional assays, such as live exocytosis assays and whole-cell patch-clamp electrophysiology. Despite exhibiting some presynaptic-like features, differentiated SH-SY5Y cells do not form morphologically or functionally complete synapses under the conditions tested. Immunostaining results were consistent with previous findings, showing synaptic markers. However, functional investigations did not detect synaptic activity. High-throughput EM analyses revealed an absence of synaptic structures in these cells. Additionally, an alternative differentiation approach incorporating additional neurotrophic factors promoted the formation of pre-synaptic-like compartments containing synapse-like synaptic vesicles (SVLVs). Though these SVLVs exhibited pleomorphic size distributions, differing from typical synaptic vesicles, and lacked connectors. These findings emphasize the need for cautious interpretation of results derived from SH-SY5Y cells when studying molecular synaptic architecture or neurodegenerative diseases.

KCNQ2 Loss-of-Function variants disrupt neuronal maturation via early hyperexcitability followed by maladaptive network remodeling
Loss-of-function (LOF) variants in the potassium channel subunit KCNQ2 cause a spectrum of neonatal epilepsies from self-limiting familial neonatal epilepsy (SeLFNE) to severe developmental and epileptic encephalopathy (DEE). To dissect the developmental consequences of KCNQ2 LOF, we conducted a longitudinal and multimodal comparative analysis in a human neuronal model generated from patients with KCNQ2-DEE and KCNQ2-SeLFNE. KCNQ2-LOF induced a biphasic network dysfunction, with early Kv7-driven hyperexcitability rescued by acute Retigabine (RTG) treatment, followed by maladaptive remodeling in the opposite direction. Transcriptomic analysis mirrored this biphasic dynamic trajectory, revealing an initial upregulation followed by a subsequent downregulation of synaptic genes. Structural analysis showed a steeper decline in presynaptic density alongside a distal shift in the axon initial segment (AIS) throughout maturation, and impaired AIS plasticity at later stages. Overall, KCNQ2-LOF disrupts human neuronal maturation through dynamic, biphasic changes in function, gene expression and structure, offering insights into disease mechanisms and therapeutic options.

Hippocampal circuit-specific enhancement of GABA-inhibition caused by discrete gene regions in a Down syndrome model
Although Down syndrome (DS), trisomy 21, affects ~6 million people worldwide, the neural circuit mechanisms underlying the neurophenotypes of impaired learning, memory and language are unknown. A prominent candidate mechanism involves dysfunctional GABA-signalling and GABAA receptor ligands have been proposed as therapeutics to reverse the neurophenotypic effects of DS. By investigating GABA neurotransmission in brain regions important for cognition in mouse DS models, we reveal that excessive inhibition is not a ubiquitous feature of DS but instead is brain circuit-specific demonstrating increased phasic and tonic inhibition in the dentate gyrus with no comparative changes to inhibition in CA1 and medial prefrontal cortex. In the dentate, elevated extrasynaptic GABA signalling, and interneuron numbers, likely underpin spike firing defects. We show that increased GABA inhibition is caused by increased dosage of Olig1, Olig2 and Dyrk1a. Overall, DS mice are characterised by circuit-specific dysfunctional inhibition predicted to affect cognition via sparse coding in the hippocampus.

Structural brain correlates of poor reading comprehension
Poor comprehenders have typical word reading skill and intelligence but poorer than expected reading comprehension. While the prevalence of poor comprehenders is similar to that of individuals with dyslexia (poor decoders, individuals who have difficulty accurately and fluently converting written text into spoken language), less is known about the neurobiological substrates of poor comprehension. A number of studies have found small differences in grey matter volume between poor comprehenders and poor or typically reading peers. However, a detailed quantification of specific cortical morphometric features and white matter integrity remains unexplored. Behavioral assessments and neuroimaging data from approximately 2,100 children (1,200 with imaging data), aged 8-16 were analyzed to determine if there is a distinct neuroanatomy associated with poor reading comprehension. Specifically, we computed grey matter cortical and subcortical volume, cortical thickness, and surface area, as well as white matter measures including mean diffusivity, fractional anisotropy, neurite orientation, and neurite density for poor comprehenders and compared to that of poor decoders and typical readers in this large and diverse sample. Additionally, given wide variation in approaches used to classify poor comprehenders in the field, we applied and compared three widely used methods for classifying poor comprehenders. Results revealed small but widespread white matter differences, but no grey matter differences between poor comprehenders and other readers. Specifically, poor comprehenders showed decreased white matter integrity (increased mean diffusivity and decreased neurite density) in tracts previously associated with reading, including the Superior Longitudinal Fasciculus and Inferior Longitudinal Fasciculus, and in tracts that have been associated with cognitive performance such as the Uncinate Fasciculus and Frontal Aslant tract. These results suggest that diffuse structural connectivity differences may underlie reading comprehension weaknesses in the face of intact decoding skills. This is consistent with the behavioral profile of poor comprehenders who exhibit a broad pattern of subclinical impairments in language and integrative cognitive processes.

Shared genetic basis for brain structure, insulin resistance and inflammation in schizophrenia: a colocalization study
Background: Psychotic disorders such as schizophrenia (SZ) are associated with structural brain changes and increased cardiometabolic disease risk, potentially linked through shared inflammatory mechanisms. However, whether these associations reflect shared genetic architecture remains unclear. We investigated genetic overlap between SZ, brain structure, insulin resistance (IR), and interleukin (IL)-6 signalling to uncover shared biological mechanisms. Methods: We performed Bayesian multi-trait colocalization (HyPrColoc) analysis of SZ and validated markers of structural MRI (bilateral cortical thickness, grey matter volume), IR (TG:HDL-C ratio, fasting insulin), and IL-6 signalling (IL-6, IL-6R, IL-6ST), at 137 trait-associated SNPs (GWAS p < 5x10^-8). in silico characterisation of candidate SNPs included protein (p)QTL gene set enrichment and developmental brain expression profiling in publicly available datasets. Results: Primary analysis identified colocalization between SZ and at least one trait from brain structure, IR, or IL-6 signalling at 6 loci. After sensitivity analysis, the SLC39A8 missense variant rs13107325 remained colocalized across SZ, bilateral cortical thickness, TG:HDL-C, and IL-6ST (PPcoloc = 0.93-0.99). SZ also colocalized with IL-6R at MHC-region variant rs2853986 (PPcoloc = 0.66-0.83). Pathway analysis of rs13107325 pQTLs revealed enrichment in synaptic development, glycan processing, and glycolysis pathways. Gene set enrichment highlighted links with coronary artery disease, myocardial infarction, and arteriosclerosis. SLC39A8 expression was enriched in cerebral vascular cells and increased during foetal and early postnatal brain development. Conclusion: We identify shared genetic architecture between SZ, brain structure, IR, and inflammation at rs13107325. These findings underscore the importance of pleiotropic genetic effects in identifying new biological targets in psychosis.

The misplaced mouse Pax6 interneuron subclass: A cross-species transcriptomic reassignment
Subclass cell-types are strongly conserved across mammalian brains, making the systemic absence of a caudal ganglionic eminence (CGE) Pax6 interneuron subclass in mouse transcriptional atlases unexpected. Through cross-species transcriptomic analysis, we identify a Pax6 subclass homolog in mouse and uncover primate-specific divergence in the Sncg subclass. Our results highlight the pitfalls of relying on single marker genes and provide evolution-aware annotations and marker sets to support robust cross-mammal interneuron comparisons.

Precision functional mapping reveals less inter-individual variability in the child vs. adult human brain
Human brain organization shares a common underlying structure, though recent studies have shown that features of this organization also differ significantly across individual adults. Understanding the developmental pathways that lead to individually unique brains is important for advancing models of cognitive development and neurodevelopmental disorders. Here we use highly personalized precision neuroimaging methods to map brain networks within 12 individual children, ages 8-12 years. We demonstrate fMRI functional connectivity maps that substantially exceed the reliability of traditional techniques, allowing us to measure individual differences after overcoming biases from measurement noise. Children share core functional network topography, with greatest inter-individual variability in association regions, consistent with adult findings. However, children show less between-subject variability than adults, suggesting increasing individual differentiation in brain networks with development. This pediatric precision neuroimaging dataset is publicly available to support future brain development research and provides a high-fidelity foundation for studying individual variation in atypical development.

A reciprocal interplay between 5-HT2A and mGlu5 receptors underlies neuroplasticity
The serotonin (5-HT)2A receptor is the primary target of numerous psychoactive drugs including serotonergic psychedelics, and mediates psychedelics-induced neuroplasticity, but the signaling mechanisms involved remain poorly characterized. Using quantitative phosphoproteomics, we show that the administration of the hallucinogenic 5-HT2A receptor agonist 2,5-dimethoxy-4-iodoamphetamine (DOI) to mice promotes the phosphorylation of synaptic proteins belonging to a strongly interconnected protein network and comprising the metabotropic glutamate (mGlu)5 receptor and the scaffolding protein Shank3. Functional studies revealed that hallucinogenic and non-hallucinogenic 5-HT2A receptor agonists promote synaptic targeting of mGlu5 receptor and its association with Shank3. Furthermore, they gate neuroplasticity in cortical neurons through a mechanism requiring mGlu5 receptor, protein kinase C and Shank3. Conversely, neuroplasticity elicited by mGlu5 receptor activation depends on 5-HT2A receptor. Collectively, these findings demonstrate that neuroplasticity-promoting properties of psychedelics depend on a functional, reciprocal interplay between 5-HT2A and mGlu5 receptors involving the synaptic scaffolding protein Shank3.

Functional covariance modes reveal aligned fetal and neonatal brain functional connectomes.
Multiple lines of evidence suggest that that spatially distributed functional networks in the brain may first start emerging before birth. Reliably demonstrating this in utero using fMRI remains a very challenging problem due to a variety of MRI-adverse factors, including distant coil positioning and motion-induced magnetic field perturbations. Here, we introduce a novel approach to functional network analysis, called seed-based functional covariance modes (FCMs), which leverages inter-subject variability in connectivity between seed regions and the rest of the brain to infer whole-brain network configurations. We first applied the FCMs approach to neonatal data to benchmark it against group-level independent component factorisation - a standard in fMRI network analysis - and found a high degree of concordance between the results produced by the two methods. We then applied it to the fetal data, where the standard approach has consistently failed to reveal spatially distributed networks. For the first time, and despite fundamental differences in signal characteristics between fetal and neonatal data, we detected network-like patterns with high spatial correspondence to neonatal functional networks. In particular, the FCMs approach efficiently recovered interhemispheric connections, a landmark feature of neonatal functional networks. Systematic organisation of interhemispheric fetal networks was observed; they tended to cluster along the brain midline but also were present in lateral sensorimotor and temporal areas as well as cortical limbic territories in ventral orbitofrontal cortex and temporal pole. By aligning fetal and neonatal connectomes, this study represents a crucial step towards supporting the biological veracity of observations made using fetal fMRI. Meanwhile, the concordance between FCMs and independent component factorisation in neonates prompts a re-evaluation of how inter-individual variability contributes to network structure inference in methods that ostensibly emphasise shared correlation patterns across subjects.

Assembly-based computations through contextual dendritic gating of plasticity
Neuronal assemblies -- groups of strongly connected neurons -- are considered the basic building blocks of perception and memory in the brain by encoding representations of specific concepts. Despite recent evidence for the biological basis behind the existence and formation of such assemblies, computational models often fall short of showing how assemblies can be flexibly learned and combined to perform real-world computations. A prominent problem is 'catastrophic forgetting', where learning a new assembly can disrupt existing connectivity structure and lead to forgetting previously learned assemblies. We propose a biologically plausible computational model, where dendritic compartments (instead of neurons) are the loci for learning and inhibition gates learning in a dendrite-specific manner, to flexibly learn new stimuli without forgetting of old ones. By learning stable projections from one brain region into another and associations between different brain regions, we demonstrate how the proposed assembly framework implements the basic building blocks for diverse computations. In a visual-auditory association task, we demonstrate how the context-specific assembly computations can be used to correctly separate ambiguous stimuli based on their dendritic representations. Our models provide unique insights and predictions for how hierarchically connected brain areas use their biological components to implement flexible yet robust learning.

Dentate gyrus network regulation by somatostatin- and parvalbumin-expressing interneurons differentially impacts hippocampal spatial memory processing
GABAergic interneurons regulate circuit dynamics in hippocampal structures such as CA1 that appear to be essential for memory processing. The dentate gyrus (DG) is known to play a role in pattern recognition and spatial working memory. However, the role of the DG in different stages of long-term spatial memory is poorly understood. Moreover, the roles of the predominant interneuron subtypes within the DG - somatostatin-expressing (SST+) and parvalbumin-expressing (PV+) - in different stages of memory processing are unknown. We tested how chemogenetic manipulation of DG SST+ and PV+ interneurons in mice influences the encoding, consolidation, and retrieval of hippocampus-dependent object-location memory (OLM). We find that activation of DG SST+ interneurons impairs both OLM encoding and retrieval, dramatically suppresses DG granule cell cFos expression, and (in the case of encoding) suppresses downstream CA1 network activity. Among individual mice, the degree of DG granule cell suppression is proportional to the extent of SST+ interneuron activation, and predicts the extent of OLM deficits. In striking contrast, PV+ interneuron activation selectively disrupts encoding, but not retrieval, of OLM, and minimally impacts DG or downstream hippocampal network activity. These findings demonstrate that regulation of the DG network by SST+ and PV+ interneurons differentially contributes to the various stages of spatial memory processing, and suggest that distinct network mechanisms are engaged in the hippocampus during each processing stage.

Voluntary eating of saltier food by mice and acute stress each abrogate reductions in a neuroinflammatory marker across sexes
Both consuming excess salt (NaCl) and experiencing environmental stress can elevate neuroinflammation and enhance risk for non-communicable diseases. Most rodent studies investigating these topics use only males, and assess salt or stress separately. Here, we used adult female and male mice to investigate how the combination of access to food high in salt (4% NaCl, w/w) and experiencing an acute stressor interact to affect levels of a proxy measure for neuroinflammation (Iba1). We hypothesized eating salty food and experiencing stress would each individually augment neuroinflammation, and their combination would be additive. Further, we anticipated salty food consumption would increase active stress coping behaviors, and that all of these effects would be enhanced in female mice. Over 4 or 8 weeks, we further evaluated how mice responded to choice access to low (0.4%) and high salt food simultaneously. Our hypothesis that mice across sexes would eventually prefer high over low salt food was supported, while our expectations regarding neuroinflammation and stress did not consistently align with our findings. Instead, we found modest changes in passive coping behaviors driven by our choice condition, unanticipated reductions in sham stress neuroinflammation by high salt in brain region- and biological sex-specific patterns after 4 weeks, and distinct sex- and salt-selective increases in swim stress neuroinflammation after 8 weeks. Though some of our results were unexpected, they include multiple novel and translationally relevant outcomes. Mice willingly choose to eat saltier food over time, akin to people, and this could sex-specifically decrease (females) or augment (males) passive coping stress strategies while eliciting distinctive stress- and brain region-dependent neuroinflammatory patterns over time. Future studies implementing more complex behavior tests and stress manipulations will advance identification of the hidden ways through which salty food and stressful experiences interact to affect risk for non-communicable diseases.

Synaptic sign switching mediates online dopamine updates
In the mammalian brain, excitatory and inhibitory synapses are generally distinct and have fixed synaptic signs. Therefore, unlike in artificial neural networks, learning in biological networks is thought to be manifested by plasticity mechanisms that modify synaptic weights but not signs. Here, we demonstrate experience-dependent sign switching at synapses between glutamate and GABA co-releasing neurons of the entopedunculus (EP) and their targets in the lateral habenula (LHb). Pairing of reward or punishment with activation of EP co-releasing neurons makes EP-LHb synapses relatively more inhibitory or excitatory, respectively. Synaptic sign switching modulates downstream dopaminergic signaling, correlates with recent dopamine updates, and contributes to reinforcement learning. These data unveil a plasticity mechanism that alters both synaptic signs and weights to rapidly update dopamine release and drive learning.

Controlling spatio-temporal sequences of neural activity by local synaptic changes
The neural basis of behavior is believed to consist of sequential patterns of neural activity in the relevant brain regions. Behavioral flexibility also requires neural circuit mechanisms that support dynamic control of sequential activity. However, mechanisms to control and reconfigure sequential activity have received little attention. Here, we show that recurrently connected networks with heterogeneous connectivity and a smooth spatial in-degree landscape (which may arise due to asymmetric neuron morphologies) provide a robust mechanism to evoke and control sequential activity. By modulating the synaptic strength of only a few neurons in local neighborhoods, we uncovered high-impact locations which can start, stop, extend, gate, and redirect sequences. Interestingly, high-impact locations coincide with mid in-degree regions. We demonstrate that these motifs can flexibly reconfigure sequential activity, and hence, provide a framework for fast and flexible computations on behavioral time scales, while the individual parts of the pathways remain rigid and reliable.

A Data-Driven Closed-Loop Control Approach to Drive NeuralState Transitions for Mechanistic Insight
Repetitive negative thinking (RNT) is a transdiagnostic risk factor for mood disorders, consistently associated with altered biological substrates, including functional connectivity in key brain networks. As a stable cognitive feature linked to vulnerability across disorders, RNT presents a compelling target for intervention. However, leveraging RNT as a modifiable mechanism requires a deeper understanding of its causal neural dynamics and how targeted modulation can induce adaptive change. This study introduces a data-driven framework combining dynamical system reconstruction (DSR) with model predictive control (MPC) to infer optimal control policies for transitioning between resting and sad mood brain states, based on functional magnetic resonance imaging (fMRI) data. Using generative DSR models trained on fMRI data from participants with a remitted major depressive disorder (rMDD) and matched healthy controls from a resting state period and a sad mood induction task, we reconstruct nonlinear brain dynamics and derive region-specific control strategies for transitioning from resting to sad mood states. Our results demonstrate that small brain regions, such as the subgenual anterior cingulate cortex (sgACC), exhibit higher controllability, requiring less energy to drive state transitions. Notably, rMDD group shows reduced control energy demands and stronger neural connectivity, particularly relating to dorsolateral prefrontal cortex (DLPFC)-hippocampal pathways, suggesting heightened susceptibility to relapse into negative mood states. These findings highlight the potential of closed-loop control approaches to uncover mechanistic insights into RNT and inform targeted interventions for mood disorders in the future.

Whole-Brain Cell-Cell Interaction Axes Explaining Tissue Vulnerability Across the Neurodegenerative Spectrum
Cell-cell communication is essential for proper brain functioning and health. Here, we characterize whole-brain patterns of cellular interactions that spatially align with tissue damage across 13 neurodegenerative conditions, including early- and late-onset Alzheimer's disease (EOAD, LOAD), presenilin-1 mutations (PS1), frontotemporal lobar degeneration, Parkinson's disease (PD), dementia with Lewy bodies (DLB), and amyotrophic lateral sclerosis (ALS). By integrating spatial gene expression with structural MRI data, we created over 1,000 whole-brain maps of ligand-receptor interactions. Multivariate analysis revealed three principal axes of cell-cell communication underlying brain tissue vulnerability. The first axis involved neuron-astrocyte-microglia interactions, explaining atrophy patterns shared by frontotemporal lobar degeneration and Alzheimer's disease subtypes. Moreover, the first axis was enriched with genes involved in regulation or spreading of amyloid beta/tau and/or Alzheimer's disease risk. Two complementary axes involving neurons, endothelial cells, and astrocytes explained patterns specific to PS1 and PD. Notably, we confirmed that cell-cell interactions identified for LOAD were also associated with frontal cortex atrophy in 375 participants from an independent database. These findings reveal both distinctive and shared molecular signaling pathways underlying tissue vulnerability across neurodegenerative disorders, clarifying disease mechanisms and highlighting potential cellular targets for therapeutic interventions.

Distinct Roles of Central and Peripheral Vision in Rapid Scene Understanding
Central and peripheral vision loss, caused by conditions such as age-related macular degeneration and retinitis pigmentosa, disrupt visual processing in distinct ways, yet their impact on natural scene perception remains poorly understood. Here, we used a real-time, gaze-contingent simulation to examine how central vision loss and peripheral vision loss alter eye movements and scene understanding. Sighted participants (n = 32, 5 males) viewed 120 natural scenes under one- or three-saccade constraints and described each scene; description quality was quantified via semantic similarity to ground-truth responses. Peripheral vision loss observers produced significantly less informative descriptions than both central vision loss and control participants, particularly for social interaction scenes, suggesting that peripheral vision is critical for rapid extraction of scene semantics. In contrast, central vision loss primarily disrupted oculomotor behavior, including increased saccade amplitudes, delayed saccade initiation, and reduced inter-subject fixation consistency. Description quality was not predicted by fixation similarity to controls, but by fixations to annotated humans and critical objects, underscoring the role of semantically informative sampling. These results reveal a dissociation between perceptual and oculomotor consequences of vision loss and highlight the neural importance of peripheral input for natural scene understanding.

Molecular unbalances between striosome and matrix compartments characterize the pathogenesis of Huntington's disease model mouse
'The pathogenesis of Huntington's disease is still incompletely understood, despite the remarkable advances in identifying the molecular effects of the Htt mutation in this disease. When we focus on movement disorders, clinical studies offer us some hints about this issue. Human studies employing positron emission tomography have identified a reduction in phosphodiesterase 10A (PDE10A) as the earliest event in the brain of patients with Huntington's disease, which occurs about 25 years before symptom onset. A PDE10A mutation is also known to cause childhood-onset chorea. GNAL encodes the olfactory type G-protein subunit (Golf), strongly expressed in the striatum, and its mutation causes familial dystonia. PDE10A and Golf are both critical regulators of cyclic AMP and are abundant in striatal spiny projection neurons. These findings suggest that maintaining cyclic AMP levels in the striatum might be an essential target for the pathogenesis of movement disorders such as chorea and dystonia. Why and how these changes in the striatum cause movement disorder are still a mystery. Here we suggest that a key might be evaluating these messenger systems in light of the circuit-level compartmental organization of the striatum, in which there is particular vulnerability of the striosome compartment. We developed machine learning algorithms to define with high precision and reproducibility the borders of striosomes in the brains of q175 Huntington's disease model mice from 3-12 months of age. We demonstrate that multiple molecules including Golf, PDE10A, dopamine D1 and D2 receptors, adenosine 2A receptors, and mu-opioid receptors differentially change their expression patterns in striosomes across ages by comparison with their expression patterns in the matrix compartment. An early and pronounced differential vulnerability of striosomes has been demonstrated in studies of post-mortem Huntington's brains. Our findings here, mapping the molecular distributions across age in a widely studied mouse model of Huntington's disease, may help to pinpoint the pathogenic mechanisms of Huntington's disease by demonstrating the differential molecular changes in the striosome compartment.

It is about time: neural temporal scaling accounts for robust hunting behavior across temperatures
Animals are often required to maintain stable performance in critical behaviors despite environmental fluctuations. Temperature broadly affects neural activity, and even localized shifts in brain temperature can alter behavior. However, whether widespread changes across the brain, such as those experienced by ectotherms, disrupt survival-critical behaviors remains unclear. Here, we demonstrate that larval zebrafish exhibit robust hunting performance across a ten degrees Celsius ecological range. Although behavior accelerates with temperature, spatial parameters, such as bout distance and turn angle, remain stable. This invariance results from coordinated adjustments in tail beat frequency and movement duration. Brain-wide calcium imaging revealed that behavioral temporal scaling is mirrored at the level of single neurons. A simple rate model showed that temperature-dependent changes in neural time constants can account for compensatory tail dynamics, enabling stability without active regulation. These findings suggest that neural temporal scaling can preserve performance under diffuse temperature fluctuations, supporting robust behavior in natural environments.

A Parietal Memory Strength Signal Linked to Evidence Accumulation in Recognition Decisions
Recognising objects from memory requires an integration of sensory and mnemonic information. This process has been theorised to occur via a stochastic evidence accumulation process implemented within the parietal cortex. Here, we provide evidence for this type of mnemonic accumulator using a combination of electroencephalographic (EEG) recordings and generative computational modelling. We recorded EEG from participants making recognition judgements based on either studied or novel words. We first demonstrate that the widely studied parietal Late Positive event-related potential Component (LPC) reflects a dynamic decision-making variable occurring prior to the recognition judgments. By fitting Diffusion Decision Models to neural and behavioural data using specialised neural network tools, we then show that the LPC amplitudes are selectively associated with the rate of evidence accumulation, signifying memory strength. This association was stronger for previously studied words compared to novel words. Our findings therefore recast the LPC as a neural signature of mnemonic strength in evidence accumulation for recognition memory judgments, especially for memorised objects.

Molecular and structural remodeling of stress granules in slowly and rapidly progressive Alzheimer's disease
Stress granules (SGs) are dynamic ribonucleoprotein condensates that modulate RNA metabolism during cellular stress. Although SG dysfunction has been increasingly linked to neurodegenerative diseases, their structural and molecular remodeling in Alzheimer's disease (AD), particularly rapidly progressive AD (rpAD), remains poorly understood. Here, we present a comprehensive multi-omics characterization of SGs from postmortem frontal cortex tissues of control slowly progressive AD (spAD), and rpAD subjects. SGs were immunoprecipitated using Anti-TIAR antibodies and analyzed via transmission electron microscopy (TEM), LCMS/MS-based proteomics, and RNA sequencing. Key protein findings were validated in human cortical brain homogenates and a 3xTg mouse model of A{beta} and tau pathology. TEM revealed disease-specific SG morphologies: small spherical granules in controls; moderate clustering in spAD; and large, amorphous aggregates in rpAD. Proteomic profiling identified 1,667 high-confidence SG-associated proteins, including RNA-binding proteins and disease-linked proteins such as MAPT, APP, and SNCA. SGs in rpAD were significantly enriched for pathways involved in MAPK signaling, proteostasis, and neuroinflammation, while showing reduced abundance of key cytoskeletal and translational regulators, such as TUBA1B and EEF1A2. Transcriptome analysis revealed widespread depletion of long, GC-rich, protein coding RNAs in rpAD SGs. Notably dynamic dysregulation of TUBA1B was also observed in the 3xTg mouse model and human cortical tissues, highlighting cytoskeletal vulnerability during disease progression Together, these findings uncover profound structural and molecular remodeling of SGs in AD, with rpAD displaying a distinctive shift towards pathological SG composition and function. Together, these findings uncover profound structural and molecular remodeling of SGs in AD, with rpAD exhibiting a distinctive shift towards pathological SG composition and function. Our results highlight a link between SG alterations and aggressive AD subtypes, providing new mechanistic insights and suggesting new potential targets for therapeutic intervention

pBOSC: A method for source-level identification of neural oscillations in electromagnetic brain signals
Neural oscillations are recognized as a fundamental component of brain electromagnetic activity. They are implicated in a wide range of cognitive processes and proposed as a core mechanism for brain communication. Nonetheless, detecting genuine neural oscillations remains a methodological challenge, particularly due to the difficulty of distinguishing them from aperiodic background activity. To identify episodes of oscillatory activity directly at their sources, we developed pBOSC, which extends the BOSC (Better OSCillation detection) family of algorithms. Consistent with existing approaches, pBOSC detects oscillatory episodes that exceed both a defined power threshold and a minimum duration criterion. In pBOSC, however, the detection of oscillatory episodes also relies on identifying peaks (i.e., local maxima) in the power spectra as well as throughout the brain volume. Using a series of simulated signals, we tested the ability of pBOSC to detect and localize oscillations across multiple scenarios. Our results show that most oscillatory episodes were accurately detected at their sources, achieving around 95% accuracy under optimal conditions (i.e., high signal-to-noise ratio, lower frequency, and longer oscillation duration). In addition, we validated pBOSCs performance on real resting-state magnetoencephalography (MEG) data. By extracting the natural frequency of each brain voxel from the detected oscillatory episodes, we observed a topographic distribution consistent with previous work. In conclusion, pBOSC offers a novel approach for identifying oscillatory activity in electrophysiological signals. It extends previous algorithms by operating in source space and verifying the presence of genuine spectral peaks, thereby enabling new possibilities for exploring brain dynamics.