SEARCH · Search bioRxiv

Results for “Neuroscience”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29Linked to original sources

bioRxiv2026

A novel event improves memory retrieval and divergent thinking in a naturalistic school environment

Stored memories are useless unless they are available for retrieval. Thus, investigating different ways to modulate retrieval is crucial. Novelty has been extensively studied as a modulator of memory. In this study, we investigated whether exposure to a novel event, an innovative neuroscience lesson, can enhance memory retrieval and divergent thinking in high school students. Across three experiments, we assessed the timing and mechanisms underlying these effects. In experiment 1, we found that memory retrieval was enhanced when the novel lesson occurred immediately before a memory test, but not when it was presented one hour earlier. In experiment 2, we found that the same immediate novelty exposure improved divergent thinking performance. Finally, in experiment 3, we explored potential shared mechanisms using a competition protocol and revealed that novelty improved divergent thinking regardless of its timing relative to memory retrieval. However, memory retrieval benefited only when tested immediately before the divergent thinking task. These results suggest that novelty boosts both memory retrieval and divergent thinking, but through partially distinct mechanisms. Our findings demonstrate that a simple, real-world classroom intervention can effectively enhance key cognitive functions in students. Significance StatementStored memories are only valuable if they can be retrieved, and memory retrieval plays a key role in creative thinking. Here, we tested whether a simple, novel event, a neuroscience lesson, could enhance memory retrieval and creative thinking in a real-world classroom setting. We found that novelty improved both memory retrieval and divergent thinking, an aspect of creative thinking, when presented immediately before the task. Finally, we revealed a non-reciprocal competition effect between memory retrieval and divergent thinking. These findings highlight a practical, low-cost intervention to boost key cognitive functions in students, demonstrating that brief, well-timed novel experiences can support both learning and creative thinking in educational environments.

neuroscience↗

bioRxiv2026

Decodanda: a Python toolbox for best-practice decoding and geometric analysis of neural representations

Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for studying the computationally relevant properties of representational geometry, revealing not only whether a variable is encoded, but also how it is encoded and which computations the collective activity of neural populations may support. However, decoding analyses present several technical challenges and common pitfalls that can lead to misleading conclusions if not handled carefully. Here, we introduce Decodanda, a Python toolbox for decoding and geometric analysis of neural population activity. Decodanda provides functions for decoding arbitrary variables and for quantifying geometric features of neural representations, including shattering dimensionality and cross-condition generalization performance (CCGP). Importantly, the package automates several essential best-practice safeguards, including trial-based cross-validation to avoid training-testing leakage from temporally correlated neural traces (a particularly important issue for calcium imaging data), null models for statistical significance, pseudo-population pooling, and cross-variable balancing to determine which of a set of correlated variables is genuinely encoded in the activity. Decodanda is agnostic to the specific classifier used for decoding, and it is designed to be both user-friendly and highly customizable, allowing researchers to assemble flexible analysis pipelines from modular building blocks. Here, we provide an overview of the design principles of Decodanda and illustrate its use cases in neuroscience research. Documentation, example notebooks, and source code are available at github.com/lposani/decodanda.

neuroscience↗

bioRxiv2026

Visualizing and sonifying neurodata (ViSoND) for enhanced observation

Neuroscience needs observation. Observation lets us evaluate data quality, judge whether models are biologically realistic, and generate new hypotheses. However, high-dimensional behavioral and neural data are too complex to be easily displayed and eye-tested. Computational methods can reduce the dimensionality of data and reveal statistically robust dynamical structure but often yield results that are difficult to relate back to the underlying biology. In addition, the choice of what parameters to quantify may not capture unexpectedly relevant aspects of the data. To supplement quantification with enhanced qualitative observation, we developed Visualization and Sonification of NeuroData (ViSoND), an open-source approach for displaying multiple data streams using video and sonification. Sonification is nothing new to neuroscience. Scientists have sonified their physiological preparations since Lord Adrians earliest recordings. We extend this tradition by mapping multiple physiological datastreams to musical notes using MIDI. Synchronizing MIDI to video provides an opportunity to watch an animals movement while listening to physiological signals such as action potentials. Here we provide two demonstrations of this approach. First, we used ViSoND to interpret behavioral structure revealed by a computational model trained on the breathing rhythms of freely behaving mice. Second, ViSoND revealed patterns of neural activity in mouse visual cortex corresponding to eye blinks, events that were previously filtered out of analysis. These use cases show that ViSoND can supplement quantitative rigor with observational interpretability. Additionally, ViSoND provides an accessible way to display data which may broaden the audience for communication of neuroscientific findings.

neuroscience↗

bioRxiv2026

Manifold geometry underlies a unified code for category and category-independent features

A central question in neuroscience and machine learning is how a single neural representation can support linear access to multiple kinds of information about the same stimulus. While this ability is widespread in biological and artificial systems, the representational principles that make such joint coding possible remain poorly understood. We address this question for an important class of representations: those jointly encoding discrete stimulus categories and continuous features that vary independently of category. Using the framework of category manifolds (the sets of neural representations elicited by stimuli from the same category) we extend existing theories of manifold classification to the equally essential task of regressing category-independent features, determining which aspects of manifold geometry govern regression performance. This provides a unified framework for understanding how classification- and regression-relevant geometry can be jointly optimized to implement an effective joint code. Applying this framework to convolutional neural networks (CNNs), we find that regression-relevant geometry can be optimized through subtle changes that largely preserve classification-relevant geometry. This explains why common representational-similarity measures previously failed to distinguish joint codes from codes optimized exclusively for classification. Motivated by prior work in visual neuroscience suggesting that macaque inferotemporal cortex may jointly encode object category and category-independent features such as object position and size, we use our framework to identify principled geometric signatures that distinguish joint codes from classification-only codes in CNNs and can be tested in future neural recordings. Finally, we characterize how these signatures are affected by common experimental constraints: limited stimulus categories and neural-population subsampling.

neuroscience↗

bioRxiv2026

A hierarchy of spatial predictions across human visual cortex during natural vision

Prediction is foundational to theories of perceptual processing and learning, in both neuroscience and AI. However, it remains unclear whether prediction occurs routinely during naturalistic perception, and at what level of abstraction the brain predicts. Here, we address both questions by analysing 7T fMRI recordings of humans viewing 73,000 natural images. We use deep generative models to quantify spatial predictability at multiple levels of abstraction, and relate these to retinotopically precise responses across V1-V4, while rigorously controlling for local image features. This reveals that, even during natural scene viewing, responses throughout visual cortex are modulated by spatial predictability, with more predictable inputs evoking weaker responses. In central vision, we observe a hierarchy of predictions that parallels the feature-encoding gradient: V1 is most sensitive to low-level unpredictability, with later areas progressively sensitive to higher-level unpredictability - diverging from recent proposals, in both neuroscience and AI, that prediction operates primarily at higher levels of abstraction. At higher eccentricities, prediction effects are amplified but even V1 is tuned to high-level predictability, consistent with these prior accounts. Together, these results suggest that the visual system implements distinct prediction regimes across the visual field, thereby reconciling conflicting accounts of what visual cortex predicts.

neuroscience↗

bioRxiv2026

Seeing clearly with CLARI-O: a window into cellular architecture, interactions, and morphology of organoid models.

Cortical organoids (COs) represent a powerful in vitro model system that recapitulates key aspects of human brain development, enabling the study of neurodevelopmental processes, cellular diversity, and disease mechanisms in a physiologically relevant 3D environment. However, traditional histological analysis of COs relies on tissue sectioning, which limits the ability to capture the full spatial complexity of organoid architecture. In this study, we establish a framework for applying CLARI-O, an improved tissue-clearing technique, for intact COs and organoid-based systems, enabling comprehensive 3D visualization and analysis of 3D organizational features. Using CLARI-O in combination with high-resolution imaging, we demonstrate the utility of tissue clearing for studying glial populations, including oligodendrocytes and microglia, considered to be underrepresented in COs, and their interactions with neurons. Additionally, we apply this method to forebrain assembloids (FAs) to visualize cellular heterogeneity and the interface between ventral and dorsal regions. Finally, we use CLARI-O to study mouse brains containing xenotransplanted COs (MB-COs) to evaluate human cell integration, migration, vascularization, and structural connectivity. This is the first study to demonstrate how tissue clearing can be used after functional assays such as calcium imaging to correlate neural activity with post hoc structural analysis in MB-COs. Together, this work establishes CLARI-O as a powerful tool for advancing 3D structural and functional interrogation of human CO-derived systems, enhancing their value for disease modeling, drug screening, and translational neuroscience. MotivationCortical organoids have become an increasingly powerful tool in neuroscience. Their complexity has expanded substantially, now incorporating exogenous lineages, fusing organoids with distinct regional identities (assembloids), and enabling xenotransplantation into in-vivo environments. These advancements require more sophisticated technological approaches that are capable of capturing the intricate three-dimensional cyotarchitecture and organization of intact organoid systems both in vitro and after xenotransplantation in vivo. Tissue-clearing methodologies offer a unique opportunity to visualize these structural and cellular features with exceptional depth and resolution. Graphical abstract HighlightsO_LIWe optimized clearing protocols to develop an organoid specific clearing method (CLARI-O) that enables high-resolution visualization of diverse neuronal and glial populations without tissue sectioning, preserving long-range connections and cellular processes. C_LIO_LIForebrain assembloids used to study neuronal and oligodendrocyte migration can be effectively processed using CLARI-O, allowing detailed visualization of fusion interface. C_LIO_LIWe established a robust framework for CLARI-O-based clearing of mouse brain tissue containing xenotransplanted human cortical organoids, enabling comprehensive 3D analysis of graft development, integration, and vascularization in vivo. C_LI

neuroscience↗

bioRxiv2026

Adoption of MMPose, a general purpose pose estimation library, for animal tracking

1Markerless pose estimation has emerged as a powerful technique for animal behavior quantification, capable of high resolution tracking of body parts. Many neuroscience labs rely on tools like DeepLabCut and SLEAP, which provide accessible interfaces but restrict users to a narrow set of models and configurations. In this work, we adopt MMPose an open source, general-purpose computer vision library to build a workflow for training and evaluating multiple state-of-the-art models on animal video datasets. We benchmark these models in two scenarios: (1) a complex maze assay with occlusions and varied backgrounds, and (2) a simpler open field arena with a high-contrast background. Our results show that a bottomup model (DEKR) delivers the highest accuracy in the complex task, whereas lighter-weight models (e.g., SLEAP) offer superior speed highlighting a clear trade-off between accuracy and throughput. We also evaluate a recently published foundation model (TopViewMouse-5K) trained on a large top-view mouse dataset to test its generalization. It performs poorly on our tasks at zero-shot, and even when we combine its data with our training set, we observe no consistent benefit. These findings emphasize the importance of context-specific model selection and the need for more diverse training data to create generalizable pose models. By leveraging a general-purpose vision library, researchers can flexibly choose models that best suit their experimental needs. This work illustrates how adopting advanced computer vision frameworks can accelerate behavioral neuroscience and genetics research, paving the way for more scalable, reproducible, and sensitive analysis of animal behavior.

neuroscience↗

bioRxiv2026

Sustaining Control and Agency Under Threat: Computational Pathways to Persistence and Escape

Significance StatementAdaptive behavior depends on knowing when to persist and when to let go; even when letting go appears as avoidance. While classical accounts of avoidance emphasize reward-effort trade-offs, we show that these decisions are critically guided by meta-control and inferences about outcome controllability and agency. Using a novel paradigm, we dissociate drivers of avoidance and demonstrate that threat does not uniformly promote disengagement. When outcome control is preserved, threat instead increases persistence, particularly following experiences that build agency in failure-safe contexts. We formalize these dynamics in the Meta-Arbitration of Control and Agency Q-learning (MACA-Q) model, which captures how experience-dependent beliefs about agency guide learning and choice across contexts. Our results show that similar avoidance behaviors can arise from distinct computational pathways. This shifts the focus from global avoidance biases to the dynamic regulation of agency as a core principle of adaptive behavior, with implications for neuroscience, psychiatry, and adaptive artificial intelligence. Adaptive behavior requires deciding when to persist and when to disengage under uncertainty and partial outcome control. Avoidance has often been studied as a response to threat or cost, yet existing paradigms cannot disentangle whether disengagement reflects threat sensitivity, expected failure, or reduced perceived control. We introduce a persistence-escape paradigm that independently manipulates incentive structures, effort demands, and outcome controllability. In a large online sample (N = 457), we show that avoidance is context-dependent rather than a stable, global trait. When outcome control was preserved under threat, the typical avoidance response reversed, promoting persistence rather than withdrawal. At the individual level, high-performing individuals were not uniformly more persistent, but more selective, disengaging when control was low. Moreover, higher anxiety symptoms were linked to cost-dominant evaluation and reduced use of accumulated competence. Conversely, higher depressive symptoms were linked to diminished sensitivity to effort and higher expected failure. To explain these behavioral patterns, we developed the Meta-Arbitration of Control and Agency Q-learning (MACA-Q) model, which embeds value learning and affective evaluation within a meta-control architecture. Critically, we formalize agency as a dynamically inferred learning gate, distinct from self-efficacy, that determines whether outcomes are treated as informative based on controllability and feedback reliability. The model explains context-specific avoidance and reveals that similar behaviors can arise from distinct computational pathways. It further shows how experience in failure-safe contexts guides subsequent behavior in adverse contexts. Our findings show that avoidance is guided by the dynamic regulation of engagement based on inferred controllability and competence. By combining a novel paradigm with a computational model, we provide a formal account of agency and a unifying framework in which meta-control regulates adaptive and maladaptive engagement across contexts, with implications for neuroscience, psychiatry, and adaptive artificial intelligence.

neuroscience↗

bioRxiv2026

An orthogonal near-infrared optical switch for wireless neuromodulation in freely behaving mice

Modulating neuronal activity with light is a powerful tool for neuroscience research. However, currently available technologies often require invasive fibers for delivery of visible light, causing tissue damage and limiting behavioral studies. Although near-infrared (NIR) neuronal modulation improves tissue penetration depth, sustained NIR illumination during neuromodulation raises significant concerns about photothermal effects. Here, we introduce a dual-wavelength near-infrared switch (Dual-NIR Switch) that uses two transcranial NIR inputs to initiate and terminate neuronal activation, enabling tether-free neuromodulation in freely moving mice. Dual-NIR Switch employs orthogonal dichromatic upconversion nanoparticles that emit blue and green light under 980 nm and 808 nm excitation, respectively, to activate and inactivate the step-function opsin SOUL. Therefore, transient 980 nm NIR illumination initiates neuronal excitation, which will remain excited without further stimulation but will be rapidly terminated on demand by a subsequent 808 nm illumination. Upon transcranial 980 nm and 808 nm NIR illuminations in freely moving mice, we achieve on-demand control of behavioral paradigms across tunable timescales, ranging from seconds to minutes and even extending to sub-hour durations. By eliminating the need for sustained NIR irradiation, Dual-NIR Switch offers an on-demand, duration-tunable neuromodulation tool for both basic neuroscience and potential therapeutic applications in treating brain diseases.

neuroscience↗

bioRxiv2026

Deep FLASH-seq profiling of purified canine sensory neurons uncovers species-specific signatures relevant to pain and itch

Naturally occurring pain and itch disorders in the domestic dog represent an important and underexploited opportunity for translational sensory neuroscience. These conditions largely mirror human disease, highlighting the need for detailed comparative understanding of canine somatosensory neurobiology. Here, we present a single-cell transcriptomic characterisation of the canine dorsal root ganglion (DRG), providing molecular insights into sensory neuron diversity in a species of direct veterinary and biomedical relevance. We develop a novel mechanical dissociation and fluorescence-activated cell sorting strategy enabling purification of intact whole neurons from adult canine DRG, followed by deep, full-length RNA sequencing using FLASH-seq. This approach yields high-quality transcriptional profiles with molecular depth analogous to deep neuronal profiling in human DRG, enabling resolution of neuronal identities and subtype-specific gene programs. Using these data, we identify canine sensory neuron clusters conforming to conserved principles of DRG molecular organization observed across species, including peptidergic and non-peptidergic nociceptors, low-threshold mechanoreceptors, proprioceptors, and thermosensory populations. Cross-species comparisons with human and mouse DRG datasets reveal broad conservation of pain- and itch-relevant pathways and therapeutic targets, alongside biologically meaningful divergence. We further identify species-specific differences in subtype-restricted expression of the pharmacologically relevant receptors IL31RA and SSTR2, which we validate using in situ hybridization and contextualize with human spatial transcriptomic data. Finally, we provide evidence that domestication-associated genes are non-randomly enriched in specific sensory neuron populations, suggesting that evolutionary history may have shaped somatosensory function. These data represent a resource for comparative sensory neuroscience and inform translational interpretation of pain and itch therapeutics across species.

neuroscience↗

bioRxiv2026

All Models are Wrong, Some are Annotated: Automating Metadata in Biomedical Repositories

ObjectiveHigh-quality metadata is essential for scientific discovery, yet sparse annotations in rapidly growing repositories leave many biologically relevant details uncaptured. We evaluated whether large language models (LLMs) can accurately infer ion channel and receptor subtype metadata from source code in a neuroscience repository. Materials and MethodsWe extracted 5,133 model files from ModelDB. A subset of 1,100 was manually annotated; 253 were held out for testing, and the remainder split into training (80%) and validation (20%) sets. LLM-based approaches (GPT-5.2 and GPT-mini) were evaluated under zero-shot and heuristic-augmented prompting. Performance was assessed at type and subtype levels using accuracy, precision, recall, and F1 score. A feature-engineered XGBoost model using text- and simulation-derived features served as a baseline. ResultsLLMs outperformed the XGBoost baseline. At the type level, GPT-mini with heuristic augmentation achieved the highest performance (accuracy 96.0%, F1 0.962). At the subtype level, both GPT-5.2+heuristics and GPT-mini+heuristics achieved identical accuracy (88.1%), with GPT-5.2+heuristics achieving the highest F1(0.878). Model outputs were consistent across runs and errors confined to related mechanistic families. Discussion and ConclusionLLMs demonstrate strong potential for metadata annotation directly from source code, outperforming feature-engineering approaches with minimal tuning. However, performance varied across subtypes, and errors often reflected ambiguity or bias toward more common labels. These findings suggest LLMs may serve as practical tools for scalable metadata generation in biomedical repositories, although careful evaluation and domain-specific validation remain important. While demonstrated in computational neuroscience, this approach may generalize to repository-agnostic metadata annotation in other scientific code repositories.

neuroscience↗

bioRxiv2026

A motif for domain-specific analysis applets that are easy to learn, reuse, test, and to compose into pipelines: application to vision science

Scientific progress depends on the analysis of primary data, yet the small, domain-specific programs that perform most scientific analyses are typically poorly documented, narrowly tested, and difficult to reuse outside the lab that created them. General-purpose pipeline tools address the problem of running steps in order but do not enforce documentation, testing, or standardized outputs. We describe a motif for building domain-specific analysis applets, which we call calculators, that constrains developer choices in order to produce code that is readable, tested, and reusable almost as a byproduct of following the template. Calculators operate on a typed, searchable database of documents, eliminating the need to explicitly wire inputs and outputs together; instead, each calculator searches the database for documents it can operate on and adds its results as new typed documents. Calculators must provide documentation in a standard location, self-tests that can be run and inspected interactively, adjustable input parameters, a single well-defined output document type, and a default plotting method. Sets of calculators compose naturally into pipelines whose outputs satisfy FAIR principles at every stage. We demonstrate the motif by implementing calculators for common analyses in vision science, including orientation and direction selectivity, contrast tuning, spatial and temporal frequency tuning, speed tuning, and Hartley reverse correlation. These calculators have been used in published work and are in active use across collaborating laboratories. We discuss the design principles of the motif, its advantages and limitations, and its applicability to domain-specific computation across neuroscience and beyond. Significance StatementScientists often must write small programs to analyze their own data. These programs are usually poorly documented, lightly tested, and hard for other labs to reuse. Mistakes in this kind of code have even caused well-known papers to be retracted. We describe a simple pattern for writing these programs, which we call a calculator. The pattern requires the programmer to include clear documentation, built-in tests, adjustable settings, and a standard form of output. Calculators work by searching a shared database for data they know how to handle, so many calculators can be chained together into a pipeline without extra setup. We show how this works by building calculators for common visual neuroscience analyses that other labs are already using.

neuroscience↗

bioRxiv2026

Causal Discovery of Synchronous Neural Oscillations based on Jacobian-informed VAR-LiNGAM

The primary objective of system neuroscience is to understand the functional mapping and its causation in the dynamics of the brain network. Some experimental and methodological studies suggest that functional modularity and its hierarchical information processing in the brain network are crucial to understanding the functional role of task-specific or state-specific information flow in the brain. However, because most of the established techniques for detecting effective network structures in the neuroscience research field are strongly based on the "Granger causality" perspective, existing causal discovery methods specified for brain network analysis cannot identify the causal hierarchy in the modular network in the brain due to spurious correlation issues and indistinguishability of causal direction under the Gaussianity of observational noise in a linear system. To address the issues, we developed a causal discovery method for synchronous neural dynamics, called the Jacobian-informed linear non-Gaussian acyclic model, "j-VAR-LiNGAM", by incorporating the information of the Jacobian matrix determined from a phase-coupled oscillator model estimated from observed neural data into the VAR-LiNGAM algorithms. The method was validated by showing that it could extract causal ordering in both synthetic data and empirical neural observed data. Moreover, by analyzing the observed neural oscillatory signals obtained from mice and humans, we confirmed that our method identified causally hierarchical structures in the brain, which aligned with the neurophysiological interpretations. These findings suggested that our proposed method can reveal the neural basis of hierarchical information processing in the brain network.

neuroscience↗

bioRxiv2026

Granger Sensori-Behavioral Taxonomy of Neuronal Ensemble Activity from Two-Photon Calcium Imaging Data

Understanding how neuronal populations interact to encode and transform sensory information is a fundamental challenge in computational neuroscience. Most existing studies, however, study neural encoding, behavioral readout, and functional connectivity as disjoint problems. Two-photon calcium imaging enables simultaneous recording of large neuronal ensembles in vivo, driven by diverse stimuli and eliciting distinct behaviors. However, extracting directional functional connectivity metrics as well as encoding and readout properties of neurons from such data remains difficult due to indirect and noisy observations of spiking activity, slow temporal dynamics, and the latent interplay between external stimuli and endogenous neural processes. Here, we introduce a unified conceptual and operational modeling and inference framework for directly extracting functional Granger causal (GC) effects between neurons, from external stimuli to neurons, and from neurons to behavior, from two-photon imaging data, in the sense of Granger. Inspired by the intersection information framework, we also identify neurons that encode features of sensory stimuli that inform behavioral readout. The resulting GC networks together with the taxonomy of functional sensori-behavioral relevance, which we call G-taxonomy, provides a powerful statistical analysis framework, enabled by the integration of several techniques including state-space modeling and inference, variational inference, and point processes. We applied the proposed framework to simulated and experimentally-recorded two-photon imaging from the mouse auditory cortex (A1) during both passive listening and active tone discrimination. Our simulation studies reveal significant improvement of our proposed methodology over existing techniques. Analysis of experimental data from the mouse A1 identifies distinct groups of cells with diverse sensori-behavioral relevance, as well as changes in functional connectivity associated with correct vs. incorrect behavior. In summary, this work provides a principled and data-driven methodology for uncovering directional interactions among the neurons, sensory stimuli, and behavior, all within the same statistical framework, offering new insights into how distributed cortical populations transform sensory inputs into behaviorally relevant representations. Author SummaryThe brain processes sensory inputs through the coordinated activity of large networks of neurons and produces readouts that elicit behavior. Understanding how information flows and is processed through these networks is a central goal of neuroscience. In this study, we present a new computational framework that identifies directional interactions among neurons in an ensemble as well as from sensory stimuli to neurons and from neurons to behavior. Utilizing the Granger formalism to identify directional effects, as opposed to common correlational measures, our framework extracts said effects directly from two-photon calcium imaging data. We tested our proposed method on both simulated data and recordings from the auditory cortex of mice during passive listening and active tone discrimination tasks. Our method revealed diverse groups of neurons in the auditory cortex with distinct functional roles and relevance to sensori-behavioral integration. Our framework provides a new way to study the flow of information in the brain and can be broadly applied to uncover neural computations across sensory and cognitive systems.

neuroscience↗

bioRxiv2026

HEALTHY AGING AS INFORMATION DIVERGENCE IN THE MULTIPLEX BRAIN

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding the co-evolution of the human brains structural scaffold and functional traffic across the adult lifespan remains a fundamental challenge in neuroscience. While age-related degradation in grey matter and functional activation is well-documented, the joint trajectory of the structural (SC) and functional (FC) connectomes is often overlooked due to the lack of an integrative framework. Here, we model the brain as a multiplex network to quantify the information-theoretic interdependencies between these two layers in a cross-sectional cohort of 589 healthy individuals (ages 18-88) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Using Jensen-Shannon Divergence and relative entropy metrics, we identify a fundamental organizing principle of healthy aging characterized by: a progressive information divergence where functional dynamics increasingly "untether" from their underlying structural constraints. Our results reveal that this decoupling follows a robust linear trajectory, yet is highly spatially hetero-geneous. Meso-scale community analysis using the Multiplex Map Equation identifies subcortical hubs--specifically the putamen, pallidum, caudate, and thalamus--as the primary epicenters of age-related SC-FC divergence. This topological shift toward functional independence in subcortical "switchboards" provides a mechanistic connectomic signature for the well-documented decline in fluid intelligence and motor adaptation that accompanies aging. In striking contrast, the limbic core (hippocampus and entorhinal cortex) exhibits remarkable stability across the lifespan, suggesting a biological imperative to preserve high-fidelity memory circuits amidst global communicative rewiring. By framing healthy aging as a systematic subcortical untethering alongside limbic resilience, our work provides a powerful new multiplex baseline to distinguish normative cognitive decline from the early topological signals of neurodegenerative disease. Significance StatementHealthy aging involves a fundamental reorganization of the brains information architecture. By modeling the brain as a multiplex network and leveraging information-theoretic measures, we demonstrate that adult lifespan development is characterized by a progressive "untethering" of functional dynamics from their underlying structural constraints. This decoupling is driven by a striking geometric paradox within subcortical hubs: while physical structural scaffolds undergo severe network constriction and isolation, their functional boundaries expand and blur in a state of neural de-differentiation. This architectural mismatch is mediated by a synchronized prefrontal-to-limbic pathway shift that operates collinearly with chronological age and mirrors the selective decline of fluid intelligence and motor adaptation that accompanies aging. Our work establishes a rigorous multiplex framework that moves beyond descriptive accounts of neural tissue loss to provide a normative baseline for distinguishing healthy brain aging from neurodegeneration.

neuroscience↗

bioRxiv2026

Emotion Representation and Neural Synchrony: Decoding Valence and Arousal with Wearable EEG

Emotions are dynamic experiences that unfold over time, yet most affective neuroscience studies have relied on static stimuli and laboratory-based EEG systems. This study examined whether emotional valence and arousal can be reliably decoded using a consumer-grade wearable EEG device in naturalistic contexts. Forty-three participants viewed video clips designed to elicit four core affect categories including high-arousal positive, low-arousal positive, high-arousal negative, and low-arousal negative, while EEG signals were continuously recorded. Multivariate analyses, including classification, multidimensional scaling (MDS), and intersubject correlation (ISC), were employed to assess affective representation and neural synchrony. Behavioral data demonstrated robust classification of both valence and arousal, whereas EEG data yielded consistent above-chance classification of valence but less stable decoding of arousal, particularly in within-participant analyses. MDS revealed that both behavioral and EEG responses were primarily organized along the valence dimension, with weaker separation along arousal. ISC analyses further indicated frequency- and region-specific neural synchrony, with stronger alignment in left and temporal electrodes, though overall ISC values were modest, likely reflecting the brief duration of stimuli. Taken together, these findings suggest that valence is more stably represented in both subjective and neural domains, whereas arousal may require time-resolved or longer-duration approaches for reliable decoding. This work demonstrates the feasibility and limitations of employing wearable EEG for theory-driven affective neuroscience, underscoring its potential for scalable and ecologically valid emotion research beyond laboratory settings.

neuroscience↗

bioRxiv2026

Generalisable signatures of anaesthesia in the large-scale functional organisation of the marmoset brain

How the activity and connectivity of the brain support consciousness remains a central question in neuroscience. Recent progress driven by the use of functional MRI has seen growing recognition that large-scale distributed functional organisation of the human and non-human primate brain are systematically and consistently reshaped by anaesthetic-induced unconsciousness, across anaesthetics and across human and macaque. Here, we generalise these results to a different primate species that is gaining traction as model organism in neuroscience, the marmoset (Callithrix jacchus). We also generalise results to an additional anaesthetic, isoflurane, which we compare with propofol and sevoflurane. We report that under anaesthesia with propofol, sevoflurane, or isoflurane, distributed brain activity from functional MRI is increasingly constrained by the underlying structural connectivity across scales. Anaesthesia also induces a collapse of the principal gradient and intrinsic functional geometry of the marmoset brain, coinciding with a breakdown of hierarchical integration. Altogether, the present results indicate generalisable signatures of anaesthesia in the large-scale organisation of the primate brain.

neuroscience↗

bioRxiv2026

A Structural Principle for Macroscopic Neural Dynamics Correlations

A central question in neuroscience is how the brains structural connectivity gives rise to its emergent, correlated dynamics. These large-scale dynamical correlations underlie functional networks that support cognitive functions. Here, we identify coupling correlation--the similarity between the input connectivity profiles of brain regions--as a key structural determinant of macroscopic neural dynamical correlation. Using dynamical mean-field theory (DMFT) and numerical simulations of random neural network models, we demonstrate that coupling correlation quantitatively governs dynamical correlation. The functional form of this structure-function mapping is dictated by the eigenvalue spectrum of the coupling correlation matrix: networks with bulk eigenspectra exhibit an exact linear relationship, whereas biologically plausible long-tailed spectra yield an approximately linear mapping except when the magnitude of coupling correlation approaches unity. Particularly, a long-tailed spectrum is necessary to reproduce the appropriate magnitude and size-invariance of coupling correlations observed in empirical data, thereby sustaining non-vanishing dynamical correlations that may support brain function in large systems. The theoretical prediction of approximate linearity is consistently validated using empirical datasets that include both structural coupling and neural dynamics in humans, mice, and Drosophila. Together, these results provide a mechanistic and quantitative framework linking macroscopic brain network structure to emergent neural dynamics--an essential step toward a theory of structure-function relationship in the brain. Significance StatementHow the brains wiring gives rise to its coordinated activity is a fundamental unsolved problem in neuroscience. Prior work has identified correlations between structural and functional connectivity, but these relationships lacked a mechanistic, first-principles explanation. Here, we derive an analytical framework using Dynamical Mean-Field Theory and random neural network models to show that a single structural statistic--coupling correlation, the similarity between the input connectivity profiles of brain regions--linearly and causally determines the magnitude of correlated neural dynamics. We further show that a long-tailed eigenvalue spectrum in biological structural connectivity is necessary to sustain the strong, size-invariant functional correlations observed across species. Validated in humans, mice, and Drosophila using multiple imaging and connectome modalities, this principle may provide a quantitative bridge between structural connectomics and emergent brain dynamics, with implications extending to a broad class of complex networked systems.

neuroscience↗