bioRxiv Subject Collection: Neuroscience's Journal

A FMRF-amide peptide that regulates cell non-autonomous protein homeostasis in C. elegans.
The coordination of protein homeostasis from the brain to periphery is essential for the health and survival of all animals. In C. elegans, glia serve a central role in coordinating organismal protein homeostasis and longevity via the unfolded protein response of the endoplasmic reticulum (UPRER). However, the full extent of the cell non-autonomous response and the identity of the signaling molecules required remained unknown. Here, we show that glial UPRER activation induces robust transcriptomic changes in specific tissue types across the animal, particularly in pathways related to neuropeptide signaling. We performed neuropeptidomics and loss and gain-of-function genetic screens and identified a single neuropeptide, FLP-17, that is sufficient but not necessary to induce cell non-autonomous activation of the UPRER. FLP-17 is sufficient to protect against chronic ER stress and age-dependent protein aggregation. We determined that FLP-17 acts through the receptor, EGL-6, to activate cell non-autonomous UPRER. This work reveals a complex peptidergic signaling network initiated by glial activation of the UPRER to regulate organismal protein homeostasis.

Co-Activation Patterns in Neonates using High-Density Diffuse Optical Tomography: Insights into Early Dynamic Functional Connectivity
Dynamic functional connectivity (FC) in neonates is a growing area of interest due to the developmental significance of early functional networks. There are several emerging techniques to measure dynamic FC, adding new perspectives to well-studied static FC networks. Recent dynamic FC studies suggest that adult resting state networks are driven by key moments of dynamic activity rather than sustained correlations. Co-activation pattern (CAP) analysis leverages this theory, clustering high-activity frames to identify recurring configurations of significant activity. High-density diffuse optical tomography (HD-DOT) is an infant-friendly modality that measures hemodynamic changes in the cortex. HD-DOT has been used to investigate static FC in term-aged infants. The CAP approach is a promising avenue to examine dynamic FC in neonates, but it is yet untested in neonatal HD-DOT. This study validates the CAP approach for neonatal HD-DOT and presents novel decompositions of neonate FC networks. HD-DOT data were acquired from a cohort of sleeping term newborns at the Rosie Hospital, Cambridge UK (n = 44, postmenstrual age = 40+3 (range: 38+2 - 42+6) weeks). The top 15% of seed-selected frames were clustered using the K-means algorithm for three regions of interest (ROIs: frontal, central, and parietal) to identify significant seed-associated patterns of co-activation or co-deactivation. These patterns (CAPs) were characterized using four metrics: consistency, fractional occupancy, dwell time, and transition likelihood. Distinct CAPs were identified for frontal, central, and parietal regions of interest. These CAPs had high consistency scores, validating the efficacy of the CAP methodology for newborn HD-DOT. The CAP decomposition revealed significant patterns obscured by static analysis. Several neonate CAPs illustrate frontoparietal activity, potentially reflecting early default mode network activity, which is immature and modular for the first year after birth. This work demonstrates the utility of CAP analysis with newborn HD-DOT and provides new insight into brain dynamics of early resting state networks.

Ontogeny of the spinal cord dorsal horn
The dorsal horn of the mammalian spinal cord is an exquisite example of form serving function. It is comprised of diverse neuronal populations stacked into laminae, each of which receives different circuit connections and plays specialized roles in behavior. An outstanding question is how this organization emerges during development from an apparently homogeneous pool of neural progenitors. Here, we found that dorsal neurons are diversified by time, with families of related cell types born as temporal cohorts, and by a spatial-molecular gradient that specifies the full array of individual cell types. Excitatory dorsal neurons then settle in a chronotopic arrangement that transforms their progressive birthdates into anatomical order. This establishes the dorsal horn laminae, as these neurons are also required for spatial organization of inhibitory neurons and sensory axons. This work reveals essential ontogenetic principles that shape dorsal progenitors into the diverse cell types and architecture that subserve sensorimotor behavior.

Network geometry shapes multi-task representational transformations across human cortex
The human brain's ability to perform a wide variety of possible tasks raises fundamental questions about how distributed neural circuits transform and integrate diverse task-related information. Using fMRI data from participants performing 16 diverse tasks and at rest, we investigated how intrinsic connectivity patterns shape task representations across the human cortex. We found that cross-region connectivity dimensionality strongly predicts representational transformations between brain regions: low-dimensional connectivity corresponds to representational compression, while high-dimensional connectivity corresponds to expansion. Modeling activity flow over these connections, we determined that task-evoked activity flows over intrinsic connections generate diverse representational geometries along the cortical hierarchy. Critically, in nearly all brain regions, connectivity-based transformations produced activity patterns that more closely matched their targets than their source representations, demonstrating that connectivity patterns actively transform neural representations. Additionally, we found that regions with lower dimensional connectivity show stronger cross-task similarities, indicative of shared latent task features that generalize neural processing across tasks. In contrast, we found that regions with higher-dimensional connectivity exhibit strong conjunctive coding of task variables, enabling representations for task-specific scenarios. These findings demonstrate how the network geometry of the brain's intrinsic connectivity architecture systematically shapes the transformation of task representations across the cortex to support performance of diverse tasks.

HIPPIE: A Multimodal Deep Learning Model for Electrophysiological Classification of Neurons
Extracellular electrophysiological recordings present unique computational challenges for neuronal classification due to noise, technical variability, and batch effects across experimental systems. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In Extracellular recordings), a deep learning framework that combines self-supervised pretraining on unlabeled datasets with supervised fine-tuning to classify neurons from extracellular recordings. Using conditional convolutional joint autoencoders, HIPPIE learns robust, technology-adjusted representations of waveforms and spiking dynamics. This model can be applied to electrophysiological classification and clustering across diverse biological cultures and technologies. We validated HIPPIE on both in vivo mouse recordings and in vitro brain slices, where it demonstrated superior performance over other unsupervised methods in cell-type discrimination and aligned closely with anatomically defined classes. Its latent space organizes neurons along electrophysiological gradients, while enabling batch and individual corrected alignment of recordings across experiments. HIPPIE establishes a general framework for systematically decoding neuronal diversity in native and engineered systems.

Muscarinic type 1 receptor activated effectors in principal neurons of the rat basolateral amygdala
The basolateral amygdala (BLA) plays a crucial role in context-specific learning and memory by integrating valence-specific stimuli with internal physiological states. Cholinergic signaling systems modulate neural excitability to influence information processing in the BLA. Muscarinic acetylcholine receptors (mAChRs) are of particular interest because aberrant mAChR signaling in BLA circuits is associated with neuropsychiatric disorders, cognitive impairment, substance use, and age-related cognitive decline. This study evaluates mAChR activation in BLA principal neurons (PNs) in juvenile rat brain slices using whole-cell patch-clamp recordings. We found that carbachol (CCh, a selective mAChR agonist) excites BLA PNs voltage clamped near the resting potential (as indicated by a downward shift in holding current), but produces a biphasic change in membrane resistance, indicating an involvement of multiple effectors. More specifically, we observed that CCh excites BLA PNs by inhibiting the afterhyperpolarization (AHP), by reducing a steady state inhibitory current, and by promoting an afterdepolarization (ADP). We further identify and characterize a CCh-induced and calcium-activated non-selective cation current (ICAN) that underlies the ADP in voltage clamp. Overall, our findings provide new insights into specific effectors modulated by activation of pirenzepine sensitive mAChRs expressed by BLA PNs. We also reveal new details about the time- and voltage-dependence of current carried by the CCh/M1R-activated ICAN like current in BLA PNs, and highlight its ability to promote a suprathreshold ADP capable of generating sustained firing after a brief excitatory stimulus. Improved understanding of these effectors will provide potentially valuable new insights on the wide range of mechanisms through which cholinergic system dysfunction can lead to impaired executive function.

Neural network-based encoding in free-viewing fMRI with precision models
Representations learned by convolutional neural networks (CNNs) exhibit a remarkable resemblance to information processing patterns observed in the primate visual system on large neuroimaging datasets collected under diverse, naturalistic visual stimulation but with instruction for participants to maintain central fixation. However, this condition diverges significantly from ecologically valid visual behaviour, suppresses activity in visually active regions, and imposes substantial cognitive load on the viewing task. We present a modification of the encoding model framework, adapting it for use with naturalistic vision datasets acquired under fully natural viewing conditions -- without fixation -- by incorporating eye-tracking data and receptive field maps. Our precision encoding models were trained on the StudyForrest dataset, which features fully naturalistic movie viewing. By combining voxel-specific population receptive field estimates with eye-tracking data for each frame, we generate subject- and voxel-specific feature time series. These time series are constructed by sampling only the locally and temporally relevant elements of the CNN feature map for each voxel. Our results demonstrate that precision encoding models outperform conventional encoding models. This framework provides a strong foundation and justification for future large-scale data collection under fixation-free, fully naturalistic viewing conditions.

Differential Glutamatergic Inputs to Semilunar Granule Cells and Granule Cells Underscore Dentate Gyrus Projection Neuron Diversity
Semilunar Granule Cells (SGCs) are sparse dentate gyrus projection neurons whose role in the dentate circuit, including pathway specific inputs, remains unknown. We report that SGCs receive more frequent spontaneous excitatory synaptic inputs than granule cells (GCs). Dual GC-SGC recordings identified that SGCs receive stronger medial entorhinal cortex and associational synaptic drive but lack short-term facilitation of lateral entorhinal cortex inputs observed in GCs. SGCs dendritic spine density in proximal and middle dendrites was greater than in GCs. However, the strength of commissural inputs and dendritic input integration, examined in passive morphometric simulations, were not different between cell types. Activity dependent labeling identified an overrepresentation of SGCs among neuronal ensembles in both mice trained in a spatial memory task and task naive controls. The divergence of modality specific inputs to SGCs and GCs can enable parallel processing of information streams and expand the computational capacity of the dentate gyrus.

SpikeMAP: An unsupervised spike sorting pipeline for cortical excitatory and inhibitory neurons in high-density multielectrode arrays with ground-truth validation
Large-scale extracellular recording techniques represent a major advance in interrogating the structure and dynamics of neuronal circuits. However, methods that can resolve cell-type identity in a principled way, while simultaneously scaling to thousands of neurons, are currently lacking. Here, we introduce spikeMAP, a pipeline for the analysis of large-scale recordings of in vitro cortical activity that not only allows for the detection of spikes produced by single neurons (spike sorting), but also allows for the reliable distinction between genetically determined cell types by utilizing viral and optogenetic strategies as ground-truth validation. This approach tightly integrates the data analysis pipeline to an optogenetic, viral, and pharmacological protocol allowing for the dynamical probing of distinct cell-types while simultaneously recording from large populations. The novelty of spikeMAP is to combine a stream of well-established analysis techniques in an end-to-end fashion, creating a unified framework as follows. First, individual spike waveforms are fitted by spline interpolation to estimate their half-amplitude and peak-to-peak durations. These values are then entered in a principal component analysis with k-means clustering to identify uncorrelated signals from single channels on the array. Optimal separability of clusters is assessed by linear discriminant analysis. Finally, each channels source location is identified using spatiotemporal characteristics of spike waveforms across the array. We show that spikeMAP can resolve cell type identity in high-density arrays by analyzing activity monitored from mouse prefrontal cortex in vitro slices with an array of 4,096 closely-spaced channels. Using an optotagging functional strategy, we show an effective distinction of regular-spiking excitatory neurons from fast-spiking inhibitory interneurons using measures of action potential waveform, Fano factor, and spatially-dependent cross-correlations. In sum, the approach introduces a toolbox, validated by an experimental pipeline, that allows for a comprehensive characterization of neuronal activity obtained from different cell-types in high-density multielectrode recordings. This provides a scalable approach to investigate the interplay between distinct cell types in microcircuits of the brain.

Temporal misalignment in scene perception: Divergent representations of locomotive action affordances in human brain responses and DNNs
The human visual system processes scenes with remarkable speed, enabling the extraction of essential information to navigate our surroundings in a single glance. To elucidate how the brain transforms visual inputs into neural representations of navigationally relevant information, we collected electroencephalography (EEG) responses to diverse indoor and outdoor scenes along with behavioral annotations of locomotive action affordances (e.g., walking, cycling), object annotations, and low-level image features to model distinct types of scene information. Using representational similarity analysis, we examined the neural representation of locomotive action affordances over time, their co-localization within scene-selective cortex, and their computational alignment with deep neural networks (DNNs). Our results show that locomotive action affordance representations emerge within 200 ms of visual processing, showing unique contributions to EEG responses at temporally distinct time-points from objects and low-level properties. Spatiotemporal fusion with functional magnetic resonance imaging (fMRI) recordings in scene-selective brain regions reveals that both the parahippocampal (PPA) and occipital place region (OPA), but not the medial place region (MPA), contribute to locomotive action affordance representations, with a distinct temporal hierarchy between them. While DNNs exhibit good predictivity of early EEG responses, they primarily capture low-level features and show limited alignment with affordance processing. These findings reveal a temporally distinct neural representation of action affordances and highlight a limitation of current DNNs in modeling affordance perception.

Radial astroglia cooperate with microglia to clear neuronal cell bodies during zebrafish optic tectum development
The clearance of dead cells by phagocytes is an essential component of neural development in many organisms. Microglia are the main phagocytes in the central nervous system (CNS), but the extent of participation by other glial cells remains unclear, especially under homeostatic conditions. During zebrafish optic tectum (OT) development, we observed radial astroglia forming dynamic, spherical projections from their basal processes. These projections, which we call scyllate heads, coincide with a wave of neuronal cell death in the OT. We show that scyllate heads surround the majority of dying neurons soon after phosphatidylserine exposure. However, unlike traditional phagosomes, scyllate heads persist for many hours and are rarely acidified or internalized. Instead, microglia invade scyllate heads and remove their contents for terminal degradation. Our study reveals an active role for radial astroglia in homeostatic cell clearance and cooperation between microglia and radial astroglia during zebrafish OT development.

The Adolescent Functional Connectome is Dynamically Controlled by a Sparse Core of Cognitive and Topological Hubs
Fundamental mechanisms that control the brain's ability to dynamically respond to cognitive demands are poorly understood, especially during periods of accelerated neural and cognitive maturation, such as adolescence. Using a sparsity-promoting feedback control framework we investigated the controllability of the adolescence functional connectome. Critical feedback costs associated with a region's control action on itself and the rest of the brain were estimated using resting-state fMRI data from an early longitudinal sample in the Adolescent Brain Cognitive Development (ABCD) study (n = 1394; median (IQR) age = 10.1 (1.1) years at baseline and 12.1 (1.1) years at follow-up). A highly reproducible, core set of predominantly highly connected regions retained their control action over the connectome under high feedback costs. They included posterior visual areas, retrosplenial cortex, cuneus and precuneus, superior parietal lobule, temporal ventral cortex and dorsolateral and lateral prefrontal cortices, i.e., both developed and developing brain regions. These regions were central to the topological organization of the connectome, consistently engaged during spontaneous coordination of resting-state networks, and overlapped with cognitive and topological brain hubs that play ubiquitous roles in cognitive function and the organization of the connectome. Also, most received (integrated) and distributed approximately equal amounts of neural information. These regions' control action was developmentally stable, i.e., critical feedback costs did not change significantly during puberty, suggesting that, despite ongoing maturation and topological changes in the adolescent brain, fundamental mechanisms of system controllability may be well developed to facilitate information processing and response to cognitive demands.

Schwann cells modified to secrete MANF is a potential cellular therapy for peripheral nerve regeneration
Despite several decades of research, an effective therapy for peripheral nerve regeneration is still lacking. The lack of knowledge of molecular candidates that equally promote axon regeneration and glial cell dynamics essential for regeneration poses challenges in developing effective therapies. Improper optimization of potential therapies leading to failures in ensuring their local availability in nerves also poses additional challenges. Here, we showed that the neurotrophic factor, the mesencephalic astrocyte-derived neurotrophic factor (MANF), equally promotes axon regeneration and glial cell dynamics favorable for nerve regeneration. We showed that while endogenous expression of MANF is primarily restricted to non-peptidergic sensory neurons in adult rats, exogenous MANF promotes the growth of all subtypes of adult rat sensory neurons. We also demonstrated that exogenous MANF promotes the proliferation and migration of adult rat primary Schwann Cells (SCs). Further, we found that local and repeated administration of exogenous MANF to injured mouse nerve promote axon regeneration. Finally, we devised a therapeutic approach by programming nerve resident SCs to locally and continuously deliver MANF to injured rat nerves and showed that this approach improved nerve regeneration indices. Overall, this work developed a therapeutic approach by harnessing the power of SCs as a local delivery system of MANF for improving nerve regeneration.

Retrieval practice prevents stress-induced inference impairment by restoring rapid memory reactivation
A hallmark of human memory is its ability to form novel inferences by linking discrete but related events. Our study examined whether acute stress impairs memory inference in humans and assessed the potential of retrieval practice to buffer this effect. Participants were trained on pairs of images, AB and BC, to establish interconnected triads (ABC) with a shared bridge element B. Twenty-four hours later, we induced acute stress in half of the participants and then tested for their capacity to infer the indirect AC associations. Behavioral results indicated that acute stress impaired memory inference, yet targeted retrieval practice of the AB and BC pairs after encoding could prevent the stress-induced impairment in A-C inference. Using multivariate decoding analysis of human electroencephalogram (EEG) recordings, we provided neural evidence that bridge element B is rapidly reactivated during the inferential process, a neural signature that is predictive for subsequent successful inference. Importantly, we showed that stress disrupts this rapid neural reactivation of the brdige element, but retrieval practice can buffer the stress effect and even enhanced the strength of reactivation signals beyond the non-stress condition. Collectively, our findings pinpoint rapid memory reactivation of bridge information as an essential neural mechanism underpinning memory-based inference, which, although susceptible to stress, can be enhanced through retrieval practice. These insights suggest that building robust memory traces could enable subsequent memory inferences to be resilient to stress, highlighting that retrieval practice could sustain normal flexible cognitions under stress.

Are online corrections really a distinct class of movement?
Humans have a remarkable capacity to adjust reaching movements rapidly and accurately when visual targets jump to a new location. The short latency of such online corrections has led to the hypothesis that they constitute a distinct class of movement and arise from an automatic pilot that is selectively engaged only during ongoing movements. However, concrete evidence for this hypothesis is scarce. Here, we test this idea by measuring muscle recruitment, force, and kinematics in a jumping target reaching task. In separate blocks of trials, participants were instructed to respond to target jumps by (1) following the jumped target, (2) stopping the on-going movement, or (3) ignoring the jumped target. This allowed us to establish the automaticity and timing of responses to target jumps and to compare such measures to the original reaching movement initiated from rest. We find that the earliest phase of muscle recruitment elicited by the jumped target corresponds to a subcortical reflex, beginning at ~80ms and ending by ~120ms, preceding the onset of voluntary recruitment at ~130ms. This reflex inexorably drives a reaching adjustment towards the new target in all three blocks; it is only somewhat reduced in the stop and ignore blocks. Critically, this earliest phase of muscle recruitment was also present at the exact same latency (80ms) for the original reaching movement initiated from rest. Thus, rather than supporting the model of online corrections as distinct class of movement that is mediated by an automatic pilot, our results suggest that all reaches, whether adjusted in mid-flight or initiated from rest, arise from a common nested control system featuring subcortical and cortical components whose influence can be strategically preset by task demands.

Temporally and Functionally Distinct Contributions to Value Based Choice Along the Anterior-Posterior Dorsomedial Striatal Axis
While the dorsoventral and mediolateral organization of striatum has resolved clear functional distinctions, far less is known about how the anterior-posterior striatal axis contributes to behavioral control. We explore this within the dorsomedial striatum (DMS), a key region for value-based choice, by comparing population neuronal activity and function within anterior (A-DMS) and posterior (P-DMS) subregions while mice operantly seek reward. Neural recordings show that P-DMS encoded action values and strategy information prior to choice selection while A-DMS activity represented recently selected choices and their anticipated values via a dynamic population reorganization immediately following action selection. Optogenetic perturbations were consistent with these temporally distinct coding properties as unilateral manipulation of the P-DMS prior to choice biased choice contralaterally in a value-dependent manner and unilateral inhibition of the A-DMS following choice impaired future value-based action selection. Using anterograde tracing, we found that the A-DMS and P-DMS projected to a common region within the ventromedial substantia nigra pars reticulata (vmSNr), which contained value-related signals combining aspects of upstream DMS processing. Together, our results support a model for temporally distributed influence on value-based choice across the anterior-posterior axis of the DMS.

Vagal afferent activation induces a NREM-sleep-like state with brain-body cooling, but locus coeruleus activation
Parasympathetic nervous system activity, vital for restorative sleep, is influenced by vagal sensory afferents, but how these shape brain-body correlates of sleep remains open. We investigated this by combining vagal sensory neuron (VSN) stimulation with EEG/EMG, heart rate, brain-body temperature, and noradrenergic locus coeruleus (LC) neuron population activity measures. Opto-/chemogenetic VSN manipulations in Vglut2-Cre mice were validated via in vitro synaptic physiology, cFos expression, and heart rate measures. Chemogenetic VSN stimulation during the resting phase induced a non-rapid-eye-movement sleep (NREMS)-like state with enriched, but homeostatically regulated, low-frequency (0.75-4 Hz) EEG activity, yet suppressed REMS. Simultaneously, brain-body temperatures dropped while LC activity increased. Recovery was gradual towards normal NREMS-REMS-cycles but accelerated by external warming, identifying cooling as key factor. We conclude that VSN activation can promote brain-body correlates as a continuum of normal NREM sleep while engaging forebrain neuromodulation, offering insights into mechanisms of sleep disruptions linked to autonomic dysregulation.

Unleashing the potential of OPM-MEG to study event-related fields against low-frequency artifacts: the case of sentence processing
OPM-MEG (optically pumped magnetometers-magnetoencephalography) offers un-precedented opportunity in its proximity to the brain and in mobility, outperforming other human MEG systems. However, movements induce low-frequency artifacts (up to ~ 3 Hz) in this system, calling for pipelines that could efficiently reduce these low-frequency noises. Although a high-pass filter of e.g., 4 Hz may minimize these artifacts, it may also eliminate many event-related field (ERF) components, such as the N400 response in sentence processing studies. Moreover, as an emerging technology with expensive sensors, many labs are starting with an experimental setup with fewer sensors (e.g., ~ 10), making it challenging to reject principal components based on visually inspecting the topographic field maps. In the current paper, we show that a combination of moderate high-pass filtering (1 Hz) and evoked-biased denoising source separation (evoked-biased DSS) can effectively reveal typical N400 deflections and effects (between nouns and verbs), with a nine-sensor OPM-MEG setup. Our current pipeline paves the way to studying ERFs with OPM-MEG in more budget-friendly setups with a small number of sensors.

Correlative light and electron microscopy reveals the fine circuit structure underlying evidence accumulation in larval zebrafish
Accumulating information is a critical component of most circuit computations in the brain across species, yet its precise implementation at the synaptic level remains poorly understood. Dissecting such neural circuits in vertebrates requires precise knowledge of functional neural properties and the ability to directly correlate neural dynamics with the underlying wiring diagram in the same animal. Here we combine functional calcium imaging with ultrastructural circuit reconstruction, using a visual motion accumulation paradigm in larval zebrafish. Using connectomic analyses of functionally identified cells and computational modeling, we show that bilateral inhibition, disinhibition, and recurrent connectivity are prominent motifs for sensory accumulation within the anterior hindbrain. We also demonstrate that similar insights about the structure-function relationship within this circuit can be obtained through complementary methods involving cell-specific morphological labeling via photo-conversion of functionally identified neuronal response types. We used our unique ground truth datasets to train and test a novel classifier algorithm, allowing us to assign functional labels to neurons from morphological libraries where functional information is lacking. The resulting feature-rich library of neuronal identities and connectomes enabled us to constrain a biophysically realistic network model of the anterior hindbrain that can reproduce observed neuronal dynamics and make testable predictions for future experiments. Our work exemplifies the power of hypothesis-driven electron microscopy paired with functional recordings to gain mechanistic insights into signal processing and provides a framework for dissecting neural computations across vertebrates.

Proteomic Subtyping of Alzheimer's Disease CSF links Blood-Brain Barrier Dysfunction to Reduced levels of Tau and Synaptic Biomarkers
Alzheimer's disease (AD) is characterized by significant clinical and molecular heterogeneity, influenced by genetic and demographic factors. Using an unbiased, network-driven approach, we analyzed the cerebrospinal fluid (CSF) proteome from 431 individuals (483 samples), including 111 African American participants, to identify core protein modules associated with AD, race, sex, and age. Our analysis revealed ten co-expression modules linked to distinct biological pathways and cell types, many of which correlated with established AD biomarkers such as {beta}-amyloid, tau, and phosphorylated tau. To further resolve disease heterogeneity, we applied a proteomic subtyping approach, identifying six distinct CSF subtypes spanning the clinical and pathological spectrum. These subtypes were validated across independent cohorts, with many aligning with previously defined AD subtypes, including those linked to neuronal hyperplasticity, immune activation, and blood-brain barrier (BBB) integrity. Notably, the BBB subtype, enriched with African Americans and men, was characterized by low CSF tau, high CSF/serum albumin ratios, and reduced synaptic protein levels. This subtype also exhibited increased levels of proteolytic enzymes, including thrombin and matrix metalloproteases, that cleave tau. Plasma dilution into the neuronal hyperplastic AD subtype CSF led to reduced tau and synaptic protein module levels, indicating that plasma protease activity contributes to tau and synaptic protein depletion independent of underlying brain pathology. These findings highlight the impact of BBB integrity on CSF tau levels, particularly in men and African Americans, and underscore the need for diversity-informed AD biomarker strategies to improve diagnostics and therapeutic targeting across populations.

Three types of remapping with linear decoders: a population-geometric perspective
Hippocampal remapping, in which place cells form distinct activity maps across different environments, is a robustly-observed phenomenon with many theories and interpretations. Some theories view remapping as the mechanism behind reduced interference between competing spatial memories, whereas others associate it with changes in an underlying latent state representation. However, it remains unclear how these interpretations of remapping relate to one another, and what types of activity changes they are compatible with. To shed some light on these questions, here we propose a neural coding and population geometry perspective to unify and elucidate the mechanisms behind remapping. Assuming that hippocampal population activity can be understood through a linearly-decodable latent space, we show that there are three possible mechanisms to induce activity changes that resemble remapping. Remapping can be due to (i) a true change in the mapping between neural and latent space, (ii) modulation of activity due to non-spatial mixed selectivity of place cells, or (iii) neural variability outside of the latent space that reflects a redundant code. We simulate and visualize examples of these remapping types in a network model, and relate the resultant remapping behavior to various models and experimental findings in the literature. Overall, our work serves as a unifying framework with which to visualize, understand, and compare the wide array of theories and experimental observations about remapping, and may serve as a testbed for understanding neural response variability under various experimental conditions.

Nanoscale Lattice Organization of Molecular Condensates Drives Compositional Degeneracy in Synaptic Plasticity
Synaptic plasticity is essential for neuronal communication, involving coordinated structural, molecular and functional changes that are shaped by the nanoscale alterations of the active zone and postsynaptic density. Emerging evidence suggests that synapses function as complex information processing machines, where unique molecular assemblies shape transmission properties. Central to this is the organization of voltage-gated calcium channels (VGCCs) and Bassoon within active zones. Utilizing advanced techniques like liquid-liquid phase separation, super-resolution microscopy, and data-driven models of synaptic transmission, we reveal how nanoscale "compositional degeneracy" in Bassoon and VGCCs enables synapses to achieve functional adaptability through multiple molecular configurations. By modulation of local uncertainty and implementing probabilistic inference, synapses fine-tune transmission efficiency by regulating dynamic entropy and free energy. These principles are especially evident during homeostatic scaling, where synaptic scaling mechanisms differ with neuronal maturity. This study highlights how distinct thermodynamic states in VGCC and Bassoon organization optimize information transfer at different plasticity stages. Our findings propose a refined framework for understanding synaptic transmission as an adaptable, entropy-modulated process, balancing resilience and efficiency.

C9orf72 Repeat Expansion Induces Metabolic Dysfunction in Human iPSC- Derived Microglia and Modulates Glial-Neuronal Crosstalk
The C9orf72 hexanucleotide repeat expansion mutation is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, but its cell type-specific effects on energy metabolism and immune pathways remain poorly understood. Using induced pluripotent stem cell (iPSC)-derived motor neurons, astrocytes and microglia from C9orf72 patients and their isogenic controls, we investigated metabolic changes at the single-cell level under basal and inflammatory conditions. Our results showed that microglia are particularly susceptible to metabolic disturbances. While C9orf72 motor neurons exhibited impaired mitochondrial respiration and reduced ATP production, C9orf72 microglia presented pronounced increases in glycolytic activity and oxidative stress, accompanied by the upregulation of the expression of key metabolic enzymes. These metabolic changes in microglia were exacerbated by inflammatory stimuli. To investigate how these changes affect the broader cellular environment, we developed a human iPSC-derived triculture system comprising motor neurons, astrocytes and microglia. This model revealed increased metabolic activity in all cell types and highlighted that microglia-driven metabolic reprogramming in astrocytes contributes to the vulnerability of motor neurons under inflammatory conditions. Our findings highlight the central role of microglia in driving metabolic dysregulation and intercellular crosstalk in ALS pathogenesis and suggest that targeting metabolic pathways in immune cells may provide new therapeutic avenues.

Relative strength variability measures for brain structural connectomes and their relationship with cognitive functioning
In this work, we propose a new class of graph measures for weighted connectivity information in the human brain based on node relative strengths: relative strength variability (RSV), measuring susceptibility to targeted attacks, and hierarchical RSV (hRSV), a first weighted statistical complexity measure for networks. Using six different network weights for structural connectomes from the UK Biobank, we conduct comprehensive analyses to explore relationships between the RSV and hRSV, and (i) other known network measures, (ii) general cognitive function ('g'). Both measures exhibit low correlations with other graph measures across all connectivity weightings indicating that they capture new information of the brain connectome. We found higher g was associated with lower RSV and lower hRSV. That is, higher g was associated with higher resistance to targeted attack and lower statistical complexity. Moreover, the proposed measures had consistently stronger associations with g than other widely used graph measures including clustering coefficient and global efficiency and were incrementally significant for predicting g above other measures for five of the six network weights. Overall, we present a new class of weighted network measures based on variations of relative node strengths which significantly improved prediction of general cognition from traditional weighted structural connectomes.

A hippocampal population code for rapid generalization
Generalizing from experience and applying prior knowledge to new situations is essential for intelligent behavior. Traditional models attribute this generalization to gradual statistical learning in the neocortex. However, such a slow process cannot account for animals' rapid generalization from limited experience. Here, we demonstrate that the hippocampus supports rapid generalization in mice by generating disentangled memory representations, where different aspects of experience are encoded independently. This code enabled the transfer of prior knowledge to solve new tasks. We identify specific circuit mechanisms underlying this rapid generalization. We show that the seemingly random changes in individual neuronal activity over time and across environments result from structured circuit-level processes, governed by the dynamics of local inhibition and cross-regional cell assemblies, respectively. Our findings provide computational and mechanistic insights into how the geometric structure and underlying circuit organization of hippocampal population dynamics facilitate both memory discrimination and generalization, enabling efficient and flexible learning.