bioRxiv Subject Collection: Neuroscience's Journal

Pause characteristics of sentence production in Parkinson's disease: insights from sentence complexity and length
Purpose Parkinson's disease (PD) affects forward flow of speech including fluency disruptions in 90% of individuals. One of the main parameters affecting flow and fluency of speech is pause behaviour. However, the precise language characteristics of pauses, including sentence complexity and length, and how they contribute to the fluency disruptions of PD are not fully understood. This study examined how sentence complexity and length affect pause behaviour in PD. Method Seventy-one participants, comprising individuals with PD (n = 32) and neurotypical controls (n = 39), read a speech passage aloud. The number and duration of pauses, categorised by location (between, within sentences), sentence complexity (simple, complex), and sentence length (short, long) were analysed. Cognitive ability, assessed using the Montreal Cognitive Assessment (MoCA), and motor speech deficits (i.e., dysarthria) severity, assessed using a speech perceptual ranking, were evaluated and correlated with pause characteristics. Results Individuals with PD produced significantly more pauses across all categories compared to controls. However, only between-sentence and long-sentence pauses were significantly longer in duration. Pause frequency and duration in both groups were higher in more complex and longer sentences. Significant negative correlations were found between MoCA scores and number of pauses. Significant positive correlations were observed between dysarthria severity and duration of pauses. Conclusion These findings suggest that increased cognitive-linguistic demands--indexed by sentence complexity and length--may underlie pausing behaviour and contribute to fluency disruptions in individuals with PD. The results extend previous research by highlighting the potential cognitive-linguistic basis of motor speech dysfunction in PD.

Revisiting Amplitude of Low-Frequency Fluctuations (ALFF) in Resting-state fMRI: Clarifications and Improvements
The amplitude of low-frequency fluctuations (ALFF) and its related measure, fractional ALFF (fALFF), are widely used resting-state fMRI techniques for quantifying spontaneous neural activity within specific frequency bands. However, inconsistencies in the definition and implementation of ALFF have led to confusion in the field. In this study, we provide a mathematical clarification of ALFF and fALFF by introducing two variants: the arithmetic mean-defined ALFF/fALFF (amALFF/amfALFF) and the quadratic mean-defined ALFF/fALFF (qmALFF/qmfALFF). We examine the relationships between mean BOLD intensity (MBI), amALFF, and qmALFF across both subjects and voxels using two independent datasets mapped onto different brain templates. Additionally, we investigate the impact of z-scoring the original BOLD signal on ALFF and fALFF metrics. Our key findings include: (1) MBI is positively correlated with both amALFF and qmALFF, highlighting the need for normalization to subject-level means; (2) normalized qmALFF and qmfALFF are highly correlated with normalized amALFF and amfALFF, respectively, at both the subject and voxel levels; (3) z-scoring the BOLD signal does not affect amfALFF or qmfALFF, but it substantially alters amALFF and qmALFF. Based on these findings, we present a comprehensive flowchart of the (f)ALFF algorithm implemented in the temporal domain. The full procedure is implemented in R, and the corresponding script is available at: https://github.com/lejianhuang/ALFF.

Adaptive behavior is guided by integrated representations of controlled and non-controlled information
Understanding how task knowledge is encoded neurally is crucial for uncovering the mechanisms underlying adaptive behavior. Here, we test the theory that all task information is integrated into a conjunctive task representation by investigating whether this representation simultaneously includes two types of associations that can guide behavior: stimulus-response (non-controlled) associations and stimulus-control (controlled) associations that inform how task focus should be adjusted to achieve goal-directed behavior. We extended the classic item-specific proportion congruency paradigm to dissociate the electroencephalographic (EEG) representations of controlled and non-controlled associations. Behavioral data replicated previous findings of association-driven adaptive behaviors. Decoding analyses of EEG data further showed that associations of controlled and non-controlled information were represented concurrently and differentially. Brain-behavioral analyses also showed that the strength of both associations was associated with faster responses. These findings support the idea that controlled and non-controlled associations are governed by an integrated task representation to guide adaptive behaviors simultaneously.

Optimal colors can predict luminosity thresholds in natural scenes
Luminosity thresholds define the luminance boundary at which a surface color shifts in appearance from being perceived as an illuminated surface to appearing self-luminous. Previous research suggests that the human visual system infers these thresholds based on internal references of physically realizable surface colors under a given illumination, referred to as the physical gamut. A surface is perceived as self-luminous when its luminance exceeds the upper limit of this empirically internalized gamut. However, the precise structure and boundaries of these gamuts remain uncertain. Optimal colors, which represent theoretical surface reflectances under specific illuminants, have been shown to provide an effective model for visualizing and computing the physical gamuts. In prior studies, optimal colors have successfully predicted luminosity thresholds; however, these findings were limited to highly simplified, abstract stimuli. Whether this framework generalizes to more naturalistic viewing conditions has remained an open question. In the present study, we demonstrate that the theory of an internal reference in the form of an empirically constructed physical gamut, visualized through optimal colors, remains valid under more natural conditions. Our results confirm that optimal colors can still accurately predict luminosity thresholds in such settings. Moreover, our findings suggest that the luminosity thresholds encompass both self-luminosity and naturalness concepts. Subsequently, this may imply that the notion of physical gamut could envelope both concepts as well and could be defined as "all physically possible colors in a scene for an object that does not emit light." These insights can have profound potential implications for both applied fields (i.e., XR or projection mapping) and fundamental science (e.g., understanding human visual processing mechanisms).

The Semantic Underpinnings of Speech Disorganization in Schizophrenia
Effective communication relies on shared semantic representations and their context-sensitive retrieval. Accordingly, difficulties in communicating coherently, as seen in some patients with schizophrenia, may reflect impairments at the level of semantic structure or the retrieval process operating on this structure. Disentangling these components is challenging. Here, we address this problem using a word association task and magnetoencephalography (MEG), allowing us to examine behavioral markers of semantic structure and retrieval as well as semantic representation in the brain. For the latter, we detail an approach that contrasts the brain's encoding of concepts as predicted by generic versus personalized semantic models. In so doing, we provide converging evidence for semantic structure idiosyncrasy in patients with schizophrenia who also exhibit speech disorganization. The findings advance our understanding of the semantic underpinnings of speech disorganization in schizophrenia, while also revealing the potential of using personalized semantic models to explain neural representation and behavior.

Dynamic, behavior-dependent interactions between dorsal striatal dopamine and glutamate release predict cognitive flexibility and punishment resistant cocaine use
Cognitive inflexibility covaries with substance use disorder (SUD) risk. To determine if there is a neural relationship between these phenomena, glutamate and dopamine release in the dorsomedial (DMS) and dorsolateral (DLS) striatum were measured as rats performed a discrimination and strategy switching test. Elevations in glutamate release, with reductions in dopamine, at trial initiation (DLS) and prior to choice (DMS and DLS) predicted fast strategy switching and punishment sensitive cocaine seeking. Elevations in DLS and DMS dopamine release at these respective timestamps predicted slow switching and punishment resistance. Orbitofrontal cortex and intralaminar thalamus were significant contributors to DLS and DMS glutamate release, but their relative contributions differed between rats that were fast or slow strategy switchers, and in how they affected behavior. As such, these data describe a neural signature of flexibility and associated circuitry that could be used to predict and treat SUDs in humans.

Distinct goal location beta frequency dynamics in hippocampus and prefrontal cortex across learning
Neural activity at goal locations contributes to learning by providing feedback on the success of preceding actions. This period engages neocortical and hippocampal networks, which serve distinct functions in processing reward and forming associations with experience that lead to reward. A neocortical network signature for reward feedback processing is beta oscillations (15-30Hz). Beta oscillations are thought to coordinate distributed neural processes across brain regions. However, it is unknown whether beta oscillations coordinate hippocampal-neocortical networks during the goal period, or how their dynamics relate to learning. Here, we show that beta oscillations occur in both hippocampal CA1 and the prefrontal cortex (PFC) when rats reach goal locations in spatial navigation tasks. Despite the presence of beta oscillations in both regions after goal entry, beta activity in each region differed in spectral and temporal properties. These differences suggest that the hippocampus and PFC are not strongly coupled at the beta frequency. We found the strengths of PFC and CA1 beta oscillations across learning were inversely related: PFC beta activity increasing in strength and CA1 beta activity decreasing in strength. Beta burst properties in PFC also had an inverse relationship to those of hippocampal sharp wave-ripples (SWRs), a prominent hippocampal process required for learning. We found a subset of PFC neurons modulated by both beta and hippocampal SWRs, which had distinct task-related firing patterns. Our results suggest that during outcome processing at goal locations, the neocortex and hippocampus are independently modulated by beta oscillations before becoming coordinated for memory-related processes during SWRs.

Spreading alpha-Synuclein Oligomers Trigger Astrocyte Reactivity and Astrocyte-glutamatergic Neuron system dysfunction in an Age-Dependent Manner
Parkinson's disease (PD) is characterized by the progressive accumulation and spatio-temporal spread of -synuclein (-syn) oligomers and a progressive loss of dopaminergic neurons. To investigate the transcriptional and cellular consequences of -syn oligomer spreading, we employed spatial transcriptomics and single-nucleus RNA sequencing (snRNA-seq) in a transgenic PD mouse model. We identified -syn spreading to the substantia nigra and defined a transcriptional "Spreading Signature" associated with -syn pathology. We found an age specific increase in reactive astrocytes, close interactions between reactive astrocytes and -syn, and transcriptional dysregulation of the astrocyte-glutamatergic neuron network. We further identified two subtypes of glutamatergic neurons that are vulnerable to astrocytic changes. Comparative analysis with human PD snRNA-seq data revealed conserved astrocytic dysfunctions, underscoring the translational relevance of our findings. Based on our results, we propose a revised model of -syn spreading and highlight 36 genes as potential therapeutic targets for mitigating astrocyte-neuron dysfunction in PD.

The Microglial TREM2 Receptor Programs Hippocampal Development in a Mouse Model of Childhood Deprivation
Childhood neglect and deprivation are the most common forms of adversity, yet their biological impact on cognitive development--and how enrichment mitigates these effects--remains unclear. Using limited bedding (LB) as a mouse model of deprivation, we previously showed that abnormal microglial-mediated synaptic pruning during the second and third postnatal weeks leads to impaired synaptic connectivity and hippocampal dysfunction, particularly in males. Here, we demonstrate that LB reduces expression of Triggering Receptor Expressed on Myeloid cells 2 (TREM2) in different mouse strains and that TREM2 deficiency contributes to, but does not fully explain, impaired microglial pruning. Overexpressing TREM2 restored microglial phagocytic function and rescued deficits in hippocampal connectivity and fear learning. Brief postnatal enrichment (P14-P17) also normalized synaptic pruning in a TREM2-dependent manner. Together, our findings identify TREM2 as a key molecular mediator of experience-dependent plasticity, revealing its central role in linking early-life deprivation and enrichment to cognitive outcomes later in life.

The Relationship between Social Reward Behavior and Mesolimbic Dopamine Release
Deficits in social behavior, such as reduced motivation and social avoidance, are key symptoms in several psychiatric disorders. Distinct modes of reward, such as drug and social, may rely on different dopamine release patterns in the mesolimbic pathway. We investigated the relationship between social reward behaviors and dopamine release elicited by phasic and tonic stimulation patterns in C57BL/6J mice. Social conditioned place preference was used to assess motivation for social interaction, and in vivo fixed potential amperometry was used to measure nucleus accumbens dopamine release before and after cocaine (10 mg/kg, ip). Additional measures included the frequency and duration of social interactions during conditioning sessions, with the first and last session representing novel and familiar social interactions respectively. No relationship was found between baseline (pre-cocaine) dopamine and social place preference in either sex. However, in males, social place preference negatively correlated with cocaine-induced phasic dopamine release, indicating that increased social motivation was associated with a reduced phasic dopaminergic response to cocaine. In contrast, greater novel interaction was associated with increased baseline dopamine elicited by tonic stimulations. These relationships were not observed in females. Overall, these findings suggest distinct, sex-dependent roles for phasic and tonic dopamine release in mediating social reward.

Distinct Flower signaling domains orchestrate cellular fitness via secreted vesicles in Aβ-induced neurodegeneration
Flower isoforms regulate cellular fitness and are implicated in neurodegeneration and tumor progression, yet their mechanisms remain unclear. Using Flower knockout mice, we identify the N- and C-terminal domains as distinct signaling modules: the C-terminus promotes survival of fitter (winner) cells via intrinsic signaling, while the N-terminus induces apoptosis of neighboring less-fit (loser) cells via extrinsic signaling. Notably, Flower signaling operates via secreted extracellular vesicles (EVs), with the win isoform exhibiting ~25-fold higher secretion than the lose, thereby facilitating intercellular fitness selection. In Alzheimer disease human brain tissue and APP transgenic mice, win isoforms are enriched in astrocytes near amyloid-{beta} plaques, suggesting localized fitness surveillance. In cortical neuron-glia cultures, overexpression of the win isoform or its C-terminal domain enhances astrocytic A{beta} clearance and protects neurons from A{beta} toxicity. These findings uncover a novel Flower signaling pathway controlling cell death, providing new insights into EV-mediated communication with therapeutic potential in neurodegeneration.

Calcium chelation promotes microtubule regrowth and axonal recovery after laser-induced axonal injury
Axons that are damaged locally due to stretch or crush injury often undergo widespread and irrecoverable damage. Transient elevation of cytosolic free calcium across extended regions of the axon, and calcium mediated disintegration of cytoskeletal elements is one of the main causes of this neurodegeneration. When axonal damage involves complete transection of axons, any recovery has to be driven by the formation of fresh motile tips (growth-cones). However, axons could also undergo partial damage where the axonal plasma membrane retains its continuity while cytoskeletal elements undergo extensive disintegration triggered by elevated free calcium. We invoke such a condition using a partial laser ablation technique, and we show that complete axonal recovery, mainly mediated by microtubule regrowth, is possible in such cases when extracellular calcium is depleted. This method allows us to explore the dynamics of cytoskeletal damage after injury and the modes of recovery of microtubules and actin filaments. Apart from understanding the mechanisms of cytoskeletal damage and recovery, our results can have significant impact on devising treatment strategies for crush injuries to nerves.

Increased numbers of CD4+ T-cells in hypocretin/orexin region of Narcolepsy Type 1
Narcolepsy type 1 (NT1) is proposed to be an autoimmune disorder targeting hypothalamic hypocretin (orexin, Hcrt) neurons. Hcrt reactive T-cells have been identified in blood and cerebrospinal fluid (CSF) of NT1 patients. However, it remains unknown whether T-cells infiltrate the brain. Since T-cells can be a lifelong retained in tissues as tissue resident memory T-cells after primary antigen exposure, we now systematically studied CD4+ and CD8+ T-cells in NT1 brains to determine their regional distribution and potential autoimmune involvement in NT1 neuropathology. In post-mortem brain tissue of NT1 patients (n=5) and matched controls (n=5) we immunohistochemically stained and quantified CD4+ and CD8+ T-cells in the Hcrt region, paraventricular nucleus (PVN) and median eminence (ME) as well as in the substantia nigra (SN) and locus coeruleus (LC). To phenotypic characterization of those CD4+ T-cells, we performed double staining with CD49a or C-X-C chemokine receptor type 6 (CXCR6). Furthermore, we stained for fibrinogen to estimate blood-brain barrier integrity, microglia markers and an astrocyte marker to evaluate acute immune reactivities in the Hcrt region of NT1. In NT1 there was a 11-folds increase in total CD4+ T-cells in the Hcrt region, but not in other hypothalamic regions, the PVN, ME, nor SN or LC. These CD4+ T-cells exhibited tissue residential memory features with double staining with CD49a or CXCR6. There was no changes in blood-brain barrier integrity, microglia and astrocyte between NT1 and controls. In addition, the total number of CD4 T-cells in the Hcrt region showed significant negative correlations with mean sleep latency in NT1 cases. Our findings suggest an enrichment of CD4+ T-cells specifically in the Hcrt region of NT1 indicating prior local antigen engagement. Moreover, greater CD4 T-cell presence in the Hcrt region of NT1 was associated with increased symptom severity, as reflected by shorter sleep latency. This evidence support the hypothesis that CD4 T-cells infiltrate the Hcrt region, may contribute to the autoimmune process which initiates NT1.

High frequency broadband activity detected noninvasively in infants distinguishes wake from sleep states
High frequency broadband activity (HFB; 70-150 Hz) indexes local brain activity. It is predominantly studied using invasive measures due to signal drop off from skull attenuation. We hypothesized that HFB is detectable in infants noninvasively through fontanelles and thin skull that have not fully developed. We analyzed scalp electroencephalography (EEG) data during wake and sleep states in 19 channels from 18 infants (1-4 months, both sexes). At the group level, linear mixed-effects models revealed greater HFB power in wake versus sleep states in midline and central channels near fontanelles, as well as in occipital channels over thin skull. These differences were detected with 90% reliability using as few as 25 seconds of data per state in as few as 10 subjects. On the individual level, linear mixed-effects models revealed the same wake > sleep effect with a mean reliability of 60% when using at least 50 seconds of data per state. These findings establish that noninvasive HFB detection in infants is not only possible at sites where the skull has not fully developed, but sufficiently robust to enable systematic investigation of early cognitive development.

Effects of Tilia tomentosa on sleep architecture and circadian rhythm
Tilia extract has been used for centuries as a sedative and hypnotic compound. The impact of Tilia on sleep architecture and circadian rhythm remained, however, elusive. Here, we addressed these open questions by analyzing the behavior and the EEG signals recorded in freely moving mice fed an enriched diet with Tilia extracts. We found that Tilia significantly increased the amount of sleep, mostly during the dark hours, which correspond to the most active phase in nocturnal animals, such as mice. Furthermore, in darkness, the length of wake episodes did not increase as in controls. Spectral analyses of the EEG showed that the features of the sleep and wake signals remained unaltered upon Tilia treatment. Interestingly, the power in the gamma band resulted significantly decrease while the theta band power resulted increased in treated mice. All together, our data demonstrated a clear effect of Tilia on sleep architecture, highlighting its dependency on the circadian rhythm.

Parallel and convergent pathways for multifeature visual processing in larval zebrafish sensorimotor decision-making
Animals continuously extract and evaluate diverse sensory information from the environment to guide behavior. Yet, how neural circuits integrate multiple, potentially conflicting, inputs during decision-making remains poorly understood. Here, we use larval zebrafish to address this question, leveraging their robust optomotor response to coherent random dot motion and phototaxis towards light. We demonstrate that animals employ an additive behavioral algorithm of three visual features: motion coherence, luminance level, and changes in luminance. Using brain-wide two-photon imaging, we identify the loci of these computations, with the anterior hindbrain emerging as a multifeature sensory integration hub. Through single-cell neurotransmitter and morphological analyses of functionally identified neurons, we characterize potential connections within and across computational nodes. These experiments reveal three parallel and converging pathways, matching our behavioral results. Our study provides a mechanistic brain-wide account of how a vertebrate brain integrates multiple features to drive sensorimotor decisions, bridging the algorithmic bases of behavior and its neural implementation.

The olfactory bulb reflects structural plasticity within a genetically stable olfactory network
The olfactory bulb (OB), the first central relay of the olfactory pathway, plays a critical role in odor perception and exhibits remarkable structural plasticity shaped by environmental influences. This raises a fundamental question about the extent to which the structure of the OB is genetically determined. While both OB volume and function have been implicated in a range of neurodegenerative and neuropsychiatric disorders, such as Parkinsons disease, schizophrenia, and depression, all of which involve known genetic risk factors, the heritability of the OB structure remains poorly understood. Here, we investigated the heritability of OB volume and the broader olfactory network architecture in a large sample of healthy young adults (n = 941; aged 22-35 years), including monozygotic and dizygotic twin pairs. Using a deep learning-based segmentation model, we first automatically segmented OB volumes and then employed a support vector machine framework to classify zygosity based on within-pair morphological similarity. The OB volume alone showed weak classification performance in distinguishing between monozygotic and dizygotic twins, suggesting limited heritability. However, integrating OB volume with morphometric features from olfactory-associated brain regions, including the hippocampus, parahippocampal gyrus, entorhinal cortex, and medial orbitofrontal cortices, substantially improved classification performance. This effect was specific to the olfactory network, underscoring the distributed nature of genetic influence in this system. These findings suggest that the OB, despite being a highly plastic and environmentally responsive structure, is embedded within a genetically coordinated neural network.

Two pore domain THIK2 channel is involved in acute and chronic pain signal regulation
Two-pore domain potassium channels (K2P) regulate neuronal excitability by acting as hyperpolarizing leak channels. Among them, THIK2 remain poorly characterized. Although no study has yet clearly linked them to excitability or pain, their selective expression in nociceptive neurons of the Dorsal Root Ganglia (DRG) suggests a role in nociception and pain regulation. This project investigates THIK2 channels in pain pathophysiology through molecular, electrophysiological, and behavioral approaches. We mapped THIK expression patterns and in THIK2 knock-out mice, we examined DRG neuron excitability and pain sensitivity. Results reveal thermal hypersensitivity under both naive and inflammatory conditions, indicating that THIK2 normally limits neuronal hyperexcitability. These findings position THIK2 as a potential therapeutic target in chronic inflammatory pain, with peripheral inhibition potentially offering analgesia without central opioid side effects.

Short-term sensory memory mediates adaptation, habituation, and a paradoxical neural-behavioral transformation in C. elegans
Repeated exposure to stimuli elicits decreasing sensory neural responses over time (adaptation). However, resulting behavioral responses can either weaken over time (habituation) or remain invariant, indicating that the neural-behavioral link is not fixed. To investigate neural adaptation and its flexible translation into behavioral decision making, we created a mathematical framework hypothesizing (1) that sensory networks optimize the speed and accuracy of encoding exogenous stimuli, and (2) that representations form along two time scales, one embedding immediate information and the other stimulus history. Using experimental recordings of the nematode C. elegans, we validated normative model predictions of this optimal encoding strategy, specifically how neural dynamics and adaptation levels vary with stimulus timing. A parametric Bayesian decoder architecture predicted conditions leading to behavioral habituation or invariance, but also paradoxical inversion, whereby appetitive stimuli elicit aversive responses. Experiments with food odors validated that inversion behavior occurred after several repetitions with a long stimulation time and low odor concentrations. Mechanistically, during sensory neural adaptation, weaker immediate stimulus representations can be compensated by secondary processes through memory effects, with biological origins that remain to be studied.

Protocol to isolate oligodendrocytes, microglia, endothelial cells, astrocytes, and neurons from a single mouse brain using magnetic-activated cell sorting
The isolation of specific cell types of the brain is essential to study cell-type-specific differences in complex neurological diseases such as Alzheimers disease. This protocol isolates oligodendrocytes, microglia, endothelial cells, astrocytes, and neurons from a single mouse brain. The process involves gentle tissue homogenization, debris removal, and sequential sorting of five distinct cell types. We validate cell purity and viability using flow cytometry and RT-qPCR. This protocol is well-suited for a range of downstream applications, including genomics, transcriptomics, and proteomics.

Development of a genetically encoded melanocortin sensor for high sensitivity intravital imaging
The central melanocortin system, composed of peptides derived from pro-opiomelanocortin (POMC) such as the melanocyte-stimulating hormones (-, {beta}-, {gamma}-MSH) and melanocortin 4 receptors (MC4R), along with the agouti-related protein (AgRP), plays a pivotal role in controlling energy balance. To elucidate the dynamic role of -MSH release in regulating appetite, specific, sensitive, and spatiotemporally resolved genetic sensors are required. The melanocortin 1 receptor (MC1R) scaffold was leveraged for its robust plasma membrane expression, high affinity for melanocortin peptides and low affinity for AgRP to design a -MSH selective sensor for in vivo use. This was achieved by integrating circularly permuted green fluorescent protein (cpGFP) into the receptor, which we named Fluorescence Amplified Receptor sensor for Melanocortin (FLAREMC). The FLAREMC sensor shows high potency and selectivity in heterologous and homologous expressing cells for -MSH and the synthetic melanocortin agonist MTII but not to the inverse agonist AgRP. The sensor exhibited impaired signaling, with reduced G protein activation, no {beta}-arrestin coupling, and failed to internalize upon agonist stimulation. In vitro, FLAREMC displayed high photostability and reversible photoactivation. These properties suggest that the FLAREMC is suitable for long-term activity recording in the brain without desensitizing or interfering with endogenous melanocortin receptor signaling. When expressed in the paraventricular nucleus (PVN) of the mouse hypothalamus, the primary site of anorexigenic -MSH signaling, FLAREMC demonstrated its effectiveness in detecting changes associated with melanocortin responses in vivo. FLAREMC enables the study of melanocortin system in cultured cells and intravitally. This first of its class highly sensitive melanocortin sensor will serve as a valuable tool to advance our understanding of the complex dynamics governing melanocortin-dependent appetite regulation and related processes in the brain.

Time-series models can forecast long periods of human temporalEEG responses to randomly alternating visual stimuli
Visual stimuli changing at constant temporal frequencies induces sharp peaks in the power spectrum of the electroencephalogram (EEG) over the visual cortex at the driving frequency and its harmonics, known as steady state visual evoked potentials (SSVEPs). Visual stimuli that alternate according to randomized temporal patterns can also result in predictable EEG patterns. While such EEG responses are robust and predictable, the underlying biophysical mechanisms that shape these responses are not fully understood. To better understand the relationship between the stimuli and associated EEG responses, and ultimately inform a biophysical model, we examine these relationships using EEG data from a controlled experiment. We model the EEG using several statistical time series models with components that loosely mimic biophysical mechanics: an autoregressive (AR) model, with an exogenous input (ARX), adding moving average terms (ARMAX), and a seasonality term (SARMAX). We fit these models using the Box-Jenkins methodology and assess EEG forecast performance for a relatively long period of several seconds out-of-sample. We find in-sample fits are good in all models despite the complexities of the visual pathway, and that all models can capture aspects of out-of-sample EEG, including the distribution of values (point-wise in time), the point-wise Pearson's correlation of EEG and model, and the frequency content. Surprisingly, we find little variation in the performance of all models, with the most sophisticated and detailed model (SARMAX) performing comparatively poorly in some instances. Taken together, our results suggest the simplest AR model is valuable because it is easy to understand and can perform just as well as more complicated models. Since these models are relatively simple and more transparent than contemporary predictive models with numerous parameters, our study may provide insights into the biological mechanisms of the temporal dynamics of human EEG response that could generalize to other visual stimuli.

Disrupted Transcriptional Networks by Mutant Atrophin-1 in a Cell Culture Model of Dentatorubral-Pallidoluysian Atrophy
Dentatorubral-Pallidoluysian Atrophy (DRPLA) is a dominant neurodegenerative disease caused by CAG triplet repeat expansion in ATN1, which encodes the transcriptional co-repressor Atrophin-1. DRPLA features motor, cognitive, and epileptic symptoms and shares pathogenic mechanisms with other polyglutamine (polyQ) disorders, including protein misfolding, impaired autophagy, and transcriptional dysregulation. To understand disease mechanisms, we performed RNA-seq on HEK293T cells stably expressing wild-type or polyQ-expanded ATN1. Cells expressing pathogenic ATN1 exhibited a distinct transcriptomic profile, including disruptions in synaptic organization, extracellular matrix remodeling, ion channel expression, and neurotransmission. Several genes tied to neurodevelopmental, neurodegenerative, and oncogenic pathways were fully activated or silenced. Dysregulated pathways also included inflammation, chromatin remodeling, stress responses, and redox imbalance. Heat shock protein expression changes suggested proteotoxic stress and impaired protein quality control, with some findings conserved in a previously reported Drosophila melanogaster model of DRPLA. These transcriptomic signatures expand our understanding of molecular events related to degeneration in DRPLA and may lead to the identification of therapeutic targets.

Manual lymph drainage massage of the head and neck improves cognition and reduces pathological biomarkers in the 5x-FAD mouse model of Alzheimers disease
Alzheimers disease (AD) affects 6.9 million people over the age of 65 in the US and is expected to double by 2060. While FDA approved immunotherapies slow cognitive decline in some individuals with AD, they do not improve cognition, are costly, and have significant side-effects. Therefore, new targets, approaches, and treatments for AD are a necessity. There are no FDA approved therapies for AD that target the brains lymphatic system. It is well established that the toxic protein, amyloid-beta (AB), accumulates in the AD brain. Recent studies have shown that AB is cleared via interstitial fluid and cerebrospinal fluid through a pathway involving the glymphatic system-meningeal lymphatic vessels-leading to deep and superficial cervical lymphatic vessels and nodes. Therefore, any blockage along this route can cause inefficient drainage and result in pathological buildup of AB, which can lead to AD. Here, we propose a new approach to treating AD by manual lymph drainage (MLD), which is a light skin massage traditionally used to reduce fluid accumulation in lymphedema. This therapy has also been demonstrated to be safe in individuals with AD, but its effects on cognition and biomarkers of AD has never been investigated. In this study we demonstrate that repeated MLD of the head and neck, including the superficial cervical lymphatic vessels (scLVs), improves cognitive function in AD as measured in both the Y-maze and nest-building tests. We also show that this coincides with a reduction in plasma levels of neurofilament light chain (NfL), a non-specific biomarker for neuronal cell death and axonal damage. MLD was also shown to reduce AB in the hippocampus of these mice. Combined, this data provides compelling proof-of-principle evidence for the potential of MLD as a standalone or adjunct therapy in the treatment of AD.

Effects of experimentally induced fatigue on motor learning: A scoping review
The literature on the effect of fatigue on motor learning is limited and marked by inconsistent findings. This scoping review aimed to explore the available knowledge on the effects of fatigue induced by physical and cognitive exertion on motor learning, and to compile and understand how it is studied. A comprehensive search strategy using relevant index terms and keywords was conducted across MEDLINE, EMBASE, SPORTDiscus, Web of Science, PsycINFO, CINAHL, ERIC, and Dissertations & Theses Global. Twenty-two studies met the inclusion criteria. The findings revealed considerable inconsistencies in how fatigue and motor learning were defined and measured. None of the studies examined the effect of fatigue induced by combined physical and cognitive exertion, and only 27% investigated fatigue induced by cognitive exertion. Acuity tasks were the most frequently used to assess motor learning, employed in 59% of the studies. Notably, all participants were between 19 and 31 years of age, and reporting of key demographic and physiological characteristics such as sex, gender, physical activity level, and body mass index was inconsistent or absent. This review highlights the need for comprehensive definitions of both fatigue and motor learning to improve consistency and reproducibility across studies. Given the limited research on the effects of fatigue induced by cognitive and combined physical and cognitive exertion, future studies should prioritize using these experimental manipulations. Also, future studies should diversify the motor learning tasks used in research to allow both direct and conceptual replication. Additionally, broader age ranges and comprehensive participant profiling should be prioritized.

Transcutaneous auricular vagus nerve stimulation during short-term motor practice drives cortical plasticity without behavioral improvement
Transcutaneous auricular vagus nerve stimulation (taVNS) is emerging as a promising non-invasive neuromodulation technique to augment neurorehabilitation, yet its mechanisms in humans remain poorly understood. Animal studies suggest that VNS delivered during motor skill practice drives task-specific plasticity in the primary motor cortex (M1), but direct evidence in humans has been lacking. Here, we provide the first demonstration that taVNS paired with motor skill practice selectively enhances cortical plasticity without boosting motor performance beyond practice alone during short-term training. Thirty-one healthy right-handed adults practiced a novel implicit motor task, rotating two balls with the non-dominant hand for 15 minutes. Participants were randomized to receive taVNS to the left tragus or sham stimulation during practice. Motor performance, M1 hand representation (TMS mapping), and spinal excitability (F-wave) were assessed pre- and post-practice, while pupil diameter was continuously monitored as an index of noradrenergic activity. Motor performance improved similarly in both groups, whereas cortical map expansion was significantly greater in the taVNS group than in the sham group. F-wave amplitude increased only in the sham group, suggesting that taVNS-driven plasticity was restricted to cortical circuits. Moreover, taVNS uniquely elicited pupil dilation during practice, consistent with noradrenergic system engagement. These findings reveal that taVNS can promote task-specific cortical reorganization in humans independent of immediate behavioral improvement. By linking taVNS-induced plasticity to noradrenergic modulation and dissociating cortical from spinal effects, this study provides novel mechanistic insight into how taVNS may lay the neural groundwork for enhanced motor recovery, with critical implications for neurorehabilitation.

Transcutaneous vagus nerve stimulation reduces total striatal GABA content and facilitates early-phase motor learning
Background: Transcutaneous vagus nerve stimulation (tVNS) has emerged as a promising non-invasive technique for modulating neuroplasticity. Previous studies have suggested that changes in regional brain GABA signaling contribute to these effects, but empirical neurophysiological evidence remains limited. Methods: We investigated the neurophysiological and behavioral effects of tVNS (200 microsecond pulses at 20 Hz, alternating 30 s ON, 1 s OFF cycles, 30 min total duration) in healthy adults using two experimental paradigms. In Experiment 1, GABA levels were measured in the left striatum (STR), dorsolateral prefrontal cortex (DLPFC), and sensorimotor cortex (SM) of 34 participants by magnetic resonance spectroscopy (MRS) before and after ipsilateral tVNS. In Experiment 2, 28 participants performed a right-hand force-control motor learning task before, during, and after tVNS. Results: Administration of tVNS significantly reduced GABA levels in the left STR compared to sham stimulation (p < 0.05), and also significantly improved motor task performance compared to the sham group at 10 minutes after stimulus onset (p < 0.05). Conclusion: Transcutaneous VNS may facilitate early-phase motor learning by reducing striatal GABA levels and consequently inducing corticobasal circuit disinhibition. These findings support tVNS as a potential noninvasive intervention to enhance motor learning for neurorehabilitation and motor disorder treatment.

Global and local origins of trial-to-trial spike count variability in visual cortex
Sensory neuron spiking responses vary across repeated presentations of the same stimuli, but whether this trial-to-trial variability represents noise versus unidentified signals remains unresolved. Some of the variability can be attributed to correlations between neural activity and arousal, locomotion, and other overt movements. We hypothesized that correlations with global activity factors, i.e., patterns of neural activity observable in other brain regions, may explain additional variability in spike count responses of visual cortical neurons. To test this, we used Neuropixels 2.0 probes to record neural activity in mouse primary visual cortex (V1) while subjects passively viewed images. We recorded videos of behavior alongside neural activity from other brain regions, either spiking activity of neural populations in anterior cingulate area (ACA) or widefield calcium signals from across the dorsal cortex. We then used a model based on reduced rank regression to partition the explainable variability of visual cortical responses by source. Some of the trial-to-trial variability in V1 spike counts was attributable to locally shared patterns of activity uncorrelated with either behavior or global activity patterns. Locally shared activity patterns explained trial-to-trial variability that was in excess of Poisson spike generation. Of the parts of variability attributable to non-local sources, global cortical activity predicted significantly more V1 spike count variability than behavioral factors. Additionally, behavioral factors explained little variability uniquely and comprised a geometric subspace of the globally predictable V1 activity. Finally, optogenetically perturbing ACA directly impacted V1 activity, and ACA activity patterns predicted V1 spike count variability even on trials without overt behaviors. Our data indicate that globally shared factors from other cortical areas contribute substantially to shared spike count variability in V1, with only a minority of shared variability confined to local V1 circuits.

Function aligns with geometry in locally connected neuronal networks
The geometry of the brain imposes fundamental constraints on its activity and function. However, the mechanisms linking its shape to neuronal dynamics remain elusive. Here, we investigate how geometric eigenmodes relate to functional connectivity gradients within three-dimensional structures using numerical simulations and calcium imaging experiments in larval zebrafish. We show that functional connectivity gradients arising from network activity closely match the geometric eigenmodes of the network's spatial embedding when neurons are locally connected. By systematically varying network parameters such as the connectivity radius and the prevalence of long-range connections introduced via edge swaps, we reveal a robust geometry-function correspondence that progressively deteriorates as local connectivity is disrupted. Additionally, we demonstrate that spatial filtering can artificially impose geometric structure on functional gradients, even at modest levels. To support our computational results, we conduct volumetric calcium imaging experiments at cellular resolution in the optic tectum of zebrafish larvae. We uncover cellular functional gradients that closely align with geometric eigenmodes up to a certain eigenmode wavelength that reflects the spatial extent of neuronal arborizations measured in single-neuron reconstructions, as predicted by our simulations. Our findings highlight the importance of short-range anatomical connectivity in shaping the geometric structure of brain activity.

Mental Imagery abilities affect visual working memory performance: evidence from aphantasic participants
Visual imagery refers to the mental generation of visual representations of stimuli, while visual working memory involves retaining visual information for a short period without external input. Due to the conceptual overlap between these two constructs, successful performance on visual working memory tasks may rely on the use of visual imagery to rehearse items during the retention interval. Consequently, individuals with aphantasia, who lack voluntary visual imagery, may experience difficulties with such tasks. However, prior research has suggested that some individuals with aphantasia might employ non-visual strategies to compensate for this deficit. In two experiments, we examined visual working memory performance in aphantasic and control participants across a range of stimulus types. In Experiment 1, participants completed a change localization task using color squares and complex fractals; in Experiment 2, stimuli included real words, phonologically valid pseudowords, and phonologically invalid pseudowords. Across both experiments, aphantasic participants demonstrated significantly impaired visual working memory compared to controls. Notably, their performance was equally impaired for stimuli that were easily verbalizable (i.e., colors and words) and those that were not (i.e., fractals and pseudowords). Furthermore, individual differences in visual imagery ability, as measured by the Vividness of Visual Imagery Questionnaire (VVIQ), significantly predicted working memory performance across all stimulus types. These findings provide direct evidence for the critical role of visual imagery in supporting visual working memory.

Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention
The brain is a complex dynamical system whose activity reflects changes in internal states, such as attention. While prior work has shown that large-scale brain activity reflects attention, the mechanism governing this association in a time-varying and task-dependent manner remains unknown. Here, we tested a hypothesis that the geometry of neural dynamics on the attractor landscape, or the movement along the "hills and valleys", reflects changes in attentional states over time and variations across controlled and naturalistic contexts. We fit a parametric dynamical systems model to fMRI data collected during rest, task performance, and naturalistic movie-watching. The model decomposes neural dynamics into components that are intrinsic versus extrinsically driven by stimuli. Model parameters were biologically meaningful, reflecting both cognitive states and individual differences. Model simulations revealed a set of attractors that mirrored functional brain networks, spanning the canonical gradient from sensorimotor to default mode network regions. The speed and direction of neural trajectories toward these attractors systematically varied across attentional states in a context-dependent manner. When participants were paying attention to effortful tasks, neural dynamics converged directly toward a task-relevant attractor, suggesting that it occupied a steeper region of the attractor landscape. In contrast, when participants were engaged in sitcom episodes, neural dynamics were in a flattened region of the landscape, directed away from the attractors. These findings demonstrate that while the positions of the attractors are largely determined by the cortical organization, the geometry of neural dynamics on the attractor landscape changes systematically across attentional states and situational contexts.

Heartbeat-evoked responses in M/EEG: A systematic review of methods with suggestions for analysis and reporting
Heartbeat-evoked responses (HER), as measured by electroencephalography (EEG) or magnetoencephalography (MEG), have become widely used as a marker of cardiac interoception in the study of brain-body interactions. However, HER studies report largely variable findings, at least partially due to methodological variability. To achieve consensus on HER processing and improve the reproducibility of findings, the field urgently requires a structured summary of the methods employed so far. To this end, we conducted a systematic review of 132 HER studies using non-invasive M/EEG recordings in humans. Our results reveal substantial heterogeneity across most steps of HER analysis, ranging from data acquisition and preprocessing to HER estimation and statistical approaches. The large diversity in the processing choices is accompanied by considerable proportions of unreported methodological information across reviewed studies, reaching up to 80% for key processing steps. In addition, less than 33% of studies had enough statistical power to reliably detect meta-level HER effects, while their reported spatiotemporal locations varied substantially. We provide a comprehensive reporting and quality control checklist to aid in the development of more standardized procedures, highlighting critical steps for robust HER investigations. Additionally, we share the full extracted dataset, including an interactive version, to support other researchers in answering additional specific questions they may have. We hope that these resources will improve the robustness, reproducibility, and transparency of research in the growing HER field.

A quantitative framework for predicting odor intensity across molecule and mixtures
In vision and hearing, standardized units such as lumens (for brightness) and decibels (for loudness) allow consistent quantification of stimulus intensity, enabling precise control of sensory experiences. Olfaction, by contrast, currently lacks a robust quantitative framework linking physical stimulus properties directly to perceived odor intensity, complicating efforts to accurately characterize and manipulate aromas. To bridge this gap, we used a precisely controlled odor delivery system combined with deep learning models to predict the intensity of both single molecules and mixtures from physical properties. These models allowed us to develop an automated, quantitative method that accurately identifies which volatile components meaningfully contribute to aroma perception, overcoming the limitations of traditional heuristic approaches such as odor activity values and demonstrating practical utility in complex naturalistic odors.

Rapid cognitive testing predicts real-world driving risk in commercial and medically at-risk drivers
Road safety is a major public and occupational health issue. Safe driving requires numerous cognitive and sensorimotor skills, and past literature suggests that cognitive testing can predict safe or unsafe driving in both healthy and medically at-risk drivers. However, such testing is often time-consuming and inaccessible. In this study, we designed a modified version of the Trail Making Test (TMT) which can be completed on a smartphone in approximately 5 minutes. We recruited 4405 commercially-licensed drivers and 314 medically at-risk drivers to complete the TMT, plus an on-road test of their driving abilities. We then trained and tested a logistic regression model using 50-50 splits on each dataset. The results of the model showed that the longer it took drivers in both groups to complete the TMT, the more likely they were to fail the on-road driving test. Accuracy for the commercial group was 83.8%, with a positive predictive value (PPV) of 34.4% and a negative predictive value (NPV) of 85.3%. Accuracy for the medically at-risk group was 63.1%, with a PPV of 55.8% and an NPV of 65.8%. Overall accuracy was 82.5%, with a PPV of 43.0% and an NPV of 84.3%. Log-transformed reaction time to targets was significantly associated with on-road failure in both driving groups. The results of this study suggest that a rapid and accessible version of the TMT can predict unsafe driving with comparable accuracy to more time-consuming and administratively burdensome means of testing.

When Firing Rate Falls Short: Spike Synchrony to Efficiently Disentangle Stimulus Saliency and Familiarity
The function of temporal coding in the brain remains controversial, with debate centering on the following question: does spike timing information, such as their temporal synchrony, play a meaningful role in neural computation which cannot be attributed to firing rate? We propose the solution to this dilemma: spike synchrony provides crucial information about stimulus familiarity under conditions when the firing rate alone is insufficient - namely, when the input stimulus is varied in saliency. Using simulations of recurrent spiking networks, we show that synchrony is particularly effective in distinguishing familiar stimuli of low saliency from novel stimuli of high saliency - an important distinction for both biological perception and artificial agents navigating dynamic environments. We evaluate familiarity detection across two frameworks: a biologically inspired model of the primary visual cortex (V1) and an abstract associative memory model. We show that in both cases, synchrony is more sensitive to recurrent connectivity, that encodes prior experiences, compared to input firing rate. This highlights the relevance of synchrony for familiarity encoding in a scenario of realistic input variability.

Online supervised learning of temporal patterns in biological neural networks under feedback control
In vitro biological neural networks (BNNs) provide a well-defined model system to constructively investigate how living cells interact with their environment to shape high-dimensional dynamics that could be used to generate a coherent temporal output, such as those required for motor control. Here, we developed a real-time closed-loop BNN system capable of generating periodic and chaotic temporal signals by integrating cultured cortical neurons with microfluidic devices and high-density microelectrode arrays. We show that training a simple linear decoder with fixed feedback weights enables the system to learn and autonomously generate diverse temporal patterns. When feedback was switched on, irregular activity in BNNs is transformed into low-dimensional, structured dynamics, producing coherent trajectories characterized by stable transitions between neural states. BNNs trained on different target frequencies--ranging from 4 to 30 s--could be trained to sustain oscillations at distinct frequencies, demonstrating their adaptability. Importantly, a top-down control of self-organized network formation with microfluidic devices is the key to suppress excessive synchronization and increase dynamical complexity in BNNs, facilitating the training and robust output generation. This work offers a biologically inspired platform for understanding the physical basis of cortical computation and for advancing energy-efficient neuromorphic computation.

Significance StatementReservoir computing is a machine learning paradigm that exploits the transient dynamics of high-dimensional nonlinear systems. Although it was originally inspired by the mammalian brain and widely explored in physical systems, its implementations in biological neural networks (BNNs) have been limited due to their excessive connectivity and global synchrony in vitro. Here, we use microfluidic devices to construct modular, nonrandomly connected BNNs and integrate them with microelectrode arrays in a closed-loop reservoir computing environment. We show that the system can be trained to autonomously output various temporal signals, with the modular connectivity that is essential for learning. In vitro BNNs provide unique alternatives for physical reservoirs with dynamic adaptability.

The Hsp40 co-chaperone DNAJC7 modifies polyglutamine but not polyglycine aggregation
Polyglutamine (polyQ) diseases, including Huntingtons disease and several spinocerebellar ataxias, are caused by abnormally expanded CAG nucleotide repeats, which encode aggregation-prone polyQ tracts. Substantial prior evidence supports a pathogenic role for polyQ protein misfolding and aggregation, with molecular chaperones showing promise in suppressing disease phenotypes in cellular and animal models. In this study, we developed a FRET-based reporter system that models polyQ aggregation in human cells and used it to perform a high-throughput CRISPR interference screen targeting all known molecular chaperones. This screen identified as a strong suppressor of polyQ aggregation the Hsp40 co-chaperone DNAJC7, which has previously been shown to modify aggregation of other disease proteins (tau and TDP-43) and has mutations causative for amyotrophic lateral sclerosis. We validated this phenotype and further established a physical interaction between DNAJC7 and polyQ-expanded protein. In contrast, DNAJC7 did not modify aggregation of polyglycine (polyG) in a FRET-based model of neuronal intranuclear inclusion disease. In addition to establishing new inducible, scalable cellular models for polyQ and polyG aggregation, this work expands the role of DNAJC7 in regulating folding of disease-associated proteins.

Females with epilepsy show abnormal changes to perimenstrual sensory induced long-term potentiation
IntroductionCatamenial epilepsy refers to a cyclical exacerbation of seizure frequency and/or severity linked to stages of the menstrual cycle. To investigate the contribution of underlying excitatory and inhibitory mechanisms of this exacerbation we induced and measured long-term potentiation (LTP). Oestradiol modulates primarily NMDA receptor driven excitation and would enhance visual LTP. Allopregnanolone modulates GABA receptor mediated inhibition and would suppress visual LTP.

MethodA control cohort (n = 25) and a cohort with epilepsy (n = 20) were recruited. Participants took part in a visual LTP task while EEG was recorded. Three study visits were timed to capture the mid-follicular (days +5 to +8), mid-luteal (days -9 to -5) phases, as well as the hormone withdrawal in the perimenstrual phase (days -3 to +2). Blood samples confirmed session timing. Generative computational modelling of thalamocortical changes in the EEG was paired with standard evoked potential analysis.

Result17 (age 26 {+/-} 9.40) participants with epilepsy, and a control cohort of 25 (age 30 {+/-} 7.06) without epilepsy completed the study. The P2 visually evoked potential (a positive-going component of the visual event related potential waveform) was significantly more enhanced by LTP induction in the mid-follicular phase than the mid-luteal or perimenstrual phases in the control cohort (F(2, 144) =11.54, p = 0.004 corrected). In contrast, in the epilepsy cohort the P2 was significantly more potentiated in the perimenstrual phase than the mid-follicular or mid-luteal phases (F(2,108) = 12.21, p = 0.005 corrected). The effect of cycle on visual LTP was robust, with only 3/17 participants with epilepsy showing highest P2 modulation in the mid-follicular phase (which is the pattern the healthy cohort showed).

The computational modelling showed that in epilepsy, the perimenstrual phase was associated with a decrease in superficial pyramidal grain control as well as decreased LTP driven modulation of feed-forward superficial connections to layer 5 and layer 6 to thalamus.

DiscussionOur finding of enhanced visual LTP in the luteal phase and perimenstrual phase of females with epilepsy indicates a breakthrough of glutamatergic excitation. Given the relationship between oestrogen and n-methyl-D-aspartate (NMDA) receptor dependent LTP this is plausibly NMDA receptor driven. Several thalamocortical model parameter changes observed warrant further investigation in studies stratifying participants by catamenial epilepsy type. Overall, while research into allopregnanolone and its inhibitory effects dominate perimenstrual catamenial epilepsy research, this study justifies consideration of the role of seizure exacerbating oestradiol.

B Cell Tolerance and BCR Signaling Dysregulation in NF155-Mediated Autoimmune Nodopathies
ObjectiveAutoimmune nodopathies (AINs) are a group of rare, acquired autoimmune neuropathies with distinct clinical features and the presence of circulating autoantibodies - often of the immunoglobulin G4 (IgG4) subclass - targeting proteins at the node of Ranvier. Defects in B cell tolerance checkpoints have been implicated in several autoimmune diseases. Prior work identified defective B cell tolerance--reflected by a high frequency of self-reactive naive B cells--in patients with MuSK-positive myasthenia gravis (MG), mediated by IgG4 autoantibodies. Here, we investigated whether tolerance defects exist in neurofascin-155-mediated AIN (NF155-AIN), similar to MuSK+ MG. Additionally, we analyzed B and T cell transcriptomics and interactions at the single-cell level to explore the underlying pathomechanism.

MethodsUsing a well-established assay, we assessed B cell tolerance fidelity by generating recombinant antibodies from new emigrant (NE) and mature naive (MN) B cell populations-- directly downstream of key tolerance checkpoints--from three NF155-AIN patients, and testing these antibodies for polyreactivity and autoreactivity, thereby determining the frequency of polyreactive and autoreactive B cells. The transcriptome of peripheral blood mononuclear cells (PBMC) was studied, with a special focus on naive B cells and CD4+ T cells at the single-cell level, along with characterization of cell-cell interactions.

ResultsNF155-AIN patients have an elevated frequency of polyreactive B cells in the NE (37.4% compared to 9.7% in healthy controls (HCs), p = 0.03) and MN (31.5% compared to 10.5% in HCs, p = 0.03) compartments with increased B cell clones expressing autoreactive antibodies, consistent with a breach in early tolerance checkpoints. We observed abnormal B cell receptor (BCR) signaling characterized by low CD79B, CSK, BLNK, and BTK expression, which may contribute to a breach in B cell tolerance. We also observed evidence of impaired follicular helper T cells (Tfh) and regulatory T cells (Treg), which may limit the normal development and suppression of autoreactive B cells. Moreover, comparative gene expression analysis of B cells and CD4+ T cells from three patients with chronic inflammatory demyelinating polyneuropathy (CIDP) --a related autoimmune neuropathy-- confirmed that these differences are largely specific to NF155-AIN, supporting a distinct pathophysiology in this subset.

ConclusionThese findings demonstrated a breach in early B cell tolerance checkpoints, defective BCR signaling, and disrupted T cell-B cell interactions in NF155-AIN, all of which may contribute to the development of pathogenic autoreactivity. These immunologic abnormalities appear distinct from those seen in CIDP, supporting NF155-AIN as a unique immunopathologic entity.

Generalized brain-state modeling with KenazLBM
The large-scale functional state of a human brain remains difficult to characterize, much less predict. Regardless, humans have engineered techniques to electrically neuromodulate the brain to treat a subset of neurologic and psychiatric diseases with moderate efficacy. Accurate characterization of a brains instantaneous functional state has stymied the development of more effective neuromodulation paradigms. Advanced computational methods are required to address this gap and enable large-scale neuroscience. Here we define the concept of generalized brain-state modeling across humans as Large Brain-State Modeling (LBM) and present KenazLBM as the worlds first example. KenazLBM can instantaneously characterize the functional state of a persons brain with raw iEEG data, and predict future brain-states. KenazLBM was trained on over 17.9 billion unique multichannel tokens from people undergoing intracranial electroencephalography (iEEG) recordings, and has learned to meld brain-states between people into a common interpretable topology. Most importantly, the model generalizes to unseen subject data with significant recording channel heterogeneity from the training set. We offer KenazLBM as a first generalized brain-state model to serve as a new paradigm of basic neuroscience inquiry and potential translation into neuromodulation therapeutics.

Comparison and Dynamic interaction between Auditory Cortex and Prefrontal Cortex of Behaving Monkeys during Novelty Detection
The ability to detect deviations from expected sensory input is critical for adaptive behavior. Here, we investigated the neural dynamics of novelty detection in the auditory cortex (AC) and prefrontal cortex (PFC) of macaques performing an auditory oddball task. Using high-density electrocorticography and Granger causality analyses (GCA), we identified distinct repetition-related signals in both regions: the AC showed both suppression and facilitation to repeated stimuli, while the PFC exhibited robust enhancement, particularly in low-frequency (2 Hz) oscillatory activity. These predictive responses were accompanied by bidirectional AC-PFC coupling in the delta band, reflecting a temporally coordinated predictive state. Deviant tones elicited early responses in the AC, followed by PFC activation, and were associated with increased feedforward and feedback connectivity across delta, theta, and gamma bands. Task engagement amplified both prediction and prediction-error signals, enhancing interareal interactions and behavioral selectivity. Our results reveal that the AC encodes early sensory regularities and violations, while the PFC supports integrative and state-dependent prediction, jointly forming a distributed cortical network for hierarchical predictive coding.

SynAnno: Interactive Guided Proofreading of Synaptic Annotations
Connectomics, a subfield of neuroscience, aims to map and analyze synapse-level wiring diagrams of the nervous system. While recent advances in deep learning have accelerated automated neuron and synapse segmentation, reconstructing accurate connectomes still demands extensive human proofreading to correct segmentation errors. We present SynAnno, an interactive tool designed to streamline and enhance the proofreading of synaptic annotations in large-scale connectomics datasets. SynAnno integrates into existing neuroscience workflows by enabling guided, neuron-centric proofreading. To address the challenges posed by the complex spatial branching of neurons, it introduces a structured workflow with an optimized traversal path and a 3D mini-map for tracking progress. In addition, SynAnno incorporates fine-tuned machine learning models to assist with error detection and correction, reducing the manual burden and increasing proofreading efficiency. We evaluate SynAnno through a user and case study involving seven neuroscience experts. Results show that SynAnno significantly accelerates synapse proofreading while reducing cognitive load and annotation errors through structured guidance and visualization support. The source code and interactive demo are available at: https://github.com/PytorchConnectomics/SynAnno.

Geometric Characterization of Pediatric Multiple Sclerosis Lesion Morphology: A Cross-Sectional and Temporal Analysis using Differential Geometry
Pediatric-onset multiple sclerosis (POMS) involves aggressive inflammation and large lesions; however, its detailed shapes and structures remain poorly understood. Traditional volumetric metrics often overlook the complex geometry of these lesions. The main objective of this study is to develop a differential geometry-based framework to quantify the shape and structure of lesions in pediatric multiple sclerosis(MS) patients using longitudinal 3D FLAIR MRI. The goal was to identify reproducible lesion morphotypes and to track their evolution over time. Our approach sensitively tracked shape changes over time and revealed consistent progression patterns. Our findings suggest that geometric biomarkers offer a powerful new lens for decoding MS heterogeneity and tracking disease activity in the pediatric population.

Specific Combinations of Physiological Tau Phosphorylation Regulate Tau-Microtubule Interactions in Developing Neurons
Tau phosphorylation is a defining feature of Alzheimers disease, yet it also plays an essential physiological role in stabilizing microtubules (MTs) during normal neuronal development. While individual phosphorylation sites have been well-studied in pathology, it remains largely unknown how combinatorial phosphorylation is regulated under physiological conditions. Here, we uncover distinct, site-specific phosphorylation patterns on tau in developing human neurons. With top-down mass spectrometry we find that functional, endogenous tau is highly modified, with up to 21 phosphates per molecule. We identify patterns of co-occurrence between phosphorylation sites that are in proximity in the linear protein sequence, such as epitopes S202/T205/T212/T217 and T231/S235/S262. Moreover, these phospho-epitopes define discrete pools of tau and regulate tau-MT interactions in coordination, providing a mechanism for fine-tuning the binding of tau to MTs. Intriguingly, we find that co-occurring phospho-epitopes are dynamically regulated in response to changes in MT integrity; chemical perturbation of neuronal MTs promotes rapid tau dephosphorylation by phosphatase PP2a at most sites to enhance tau-MT interactions and counteract destabilization. We then use the PS19 tauopathy mouse model to demonstrate that developmental and pathological tau phosphorylation patterns partially overlap, and that co-occurring phospho-epitopes exhibit similar associations with the insoluble fraction in aged mice. Our results reveal an isoform-dependence on the effects of site-specific tau phosphorylation on its behavior. Together, these findings define a combinatorial phosphorylation code that modulates taus physiological function in neurons and raises the possibility that MT destabilization precedes tau phosphorylation in disease. This work provides a mechanistic framework for distinguishing functional from pathological tau phosphorylation, with implications for the development of therapies that specifically target disease-associated tau proteoforms.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/669485v1_ufig1.gif" ALT="Figure 1">
View larger version (35K):
org.highwire.dtl.DTLVardef@25d0f5org.highwire.dtl.DTLVardef@b9a5b9org.highwire.dtl.DTLVardef@2f4f86org.highwire.dtl.DTLVardef@e2515_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO Schematic model of our findings: Tau phosphorylation is dynamically regulated in specific combinatorial patterns in response to shifting MT growth cycles.

C_FIG

Basal ganglia-independent thalamic bursts do not wake cortex during sleep
The thalamus is a key forebrain structure that gates peripheral, subcortical, and cortico-cortical communication1,2. Awake thalamic bursts provide the cortex with a "wake-up" signal2-4. Paradoxically, thalamic neurons discharge tonically during cellular depolarization and activated brain states (wakefulness, REM sleep) but burst during hyperpolarization and NREM sleep5-9. It has been proposed that NREM thalamic bursts do not awaken the cortex because of their periodic and synchronized nature2-4; however, this has never been tested in vivo during natural sleep. We simultaneously recorded polysomnographic signals, local field potentials, and spiking activity from multiple thalamic neurons in the ventral anterior and centromedian nuclei of two female non-human primates during naturally occurring vigilance states. These nuclei receive GABAergic output from the basal ganglia10,11, with discharge rate and GABA outflow decreasing during NREM sleep12. We found that despite the expected thalamic depolarization, bursting increased significantly. NREM bursts were neither periodic nor highly synchronized. However, EEG activity time-locked to burst onset during NREM sleep differed markedly from that observed during wakefulness and REM sleep. These results support a modulatory, rather than a driving, relationship between the basal ganglia and thalamus. NREM thalamic bursts do not awaken the cortex, probably due to unique state-dependent thalamocortical dynamics.