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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 469 records · Page 26Linked to original sources

bioRxiv2025

Individual differences shape conceptual representation in the brain

Each person experiences the world through a unique conceptual lens, shaped by personal experiences, natural variations, or disease. These individual differences have remained largely inaccessible to cognitive neuroscience and clinical neurology, limiting the development of precision medicine approaches to cognitive disorders. To overcome this limitation, here we develop a new statistical framework to measure and interpret individual differences in functional brain representations. We apply this framework to characterize how different individuals represent the same concepts. Twenty-four participants listened to narrative stories while their brain activity was measured with functional MRI (fMRI). Encoding models were used to recover how hundreds of concepts were represented in each persons brain. Despite listening to identical stories, participants showed systematic individual differences in conceptual representations. These differences reveal person-specific biases in how concepts are represented in the brain. Individual variability was highest in regions that represent social information. Because these regions are thought to integrate sensory information with personal beliefs and experiences, the observed individual differences may reflect cognitive traits unique to each person. Our work reveals that individual differences are a systematic, measurable principle of conceptual representations in the human brain. By enabling researchers to measure and interpret differences in person-specific functional brain representations, our work establishes a new paradigm for precision neuroscience. This paradigm provides a rigorous foundation for developing fMRI applications in precision medicine to diagnose and monitor cognitive disorders.

neuroscience↗

bioRxiv2025

Visual Regularities Underlie Hierarchical Object Representations in the Human Brain and Self-supervised DNN

NeuroAI develops the interplay of neuroscience and artificial intelligence, especially on visual processing. Human visual system organizes objects based on a representational hierarchy. However, it remains unclear whether this hierarchy arises from visual or semantic information. One hypothesis posits that the visual system is structured around statistical regularities of visual information. Here, we test this hypothesis using the THINGS datasets and pure-visual deep neural networks (DNN). We constructed a low-dimensional object space based on multiple abstract object properties, reflecting statistical patterns of visual regularities. By applying voxelwise encoding models, we identified clusters in the higher visual cortex based on their property tuning, and they were found to support specific object categories. These clusters serve as the middle level to reveal a property-cluster-object hierarchical organization. Subsequently, we investigated whether this hierarchical structure could be captured by a self-supervised DNN. Through activity similarity analysis, we mapped the brain clusters onto the DNN and independently found that the DNNs clusters exhibited distinct property tuning and influenced the classification accuracy of corresponding object categories, mirroring the effects observed in the human brain. Our results demonstrate similar hierarchical structures in the human brain and self-supervised DNN, suggesting that the visual regularities shape neural architecture of visual system. This study highlights the great potential of neural computational model in neuroscience study. Index Terms Visual Processing, Abstract Property, Hierarchical Representation, Self-supervised Visual DNN

neuroscience↗

bioRxiv2025

A common cross-species atlas of cortical gray matter

Translational neuroscience requires consistent anatomical frameworks to compare brain organization across species despite differences in size and specialization. Existing atlases are species-specific, limiting cross-species analyses. Here we developed a hierarchical common atlas delineating homologous cortical and subcortical gray matter regions across mouse, rat, marmoset, rhesus macaque, and human, built upon population-averaged minimal deformation templates and uniform tissue segmentation. The atlas was validated through cross-atlas Dice similarity against established human parcellations, cross-scale containment against species-specific atlases, cross-species geometric consistency of regional positioning, and comparison of independent mouse and marmoset invasive tracer connectivity, with a per-region homology confidence index quantifying the strength of each regional assignment. Cross-species tracer comparison revealed a structured gradient of correspondence, with sensorimotor connections showing strong conservation and association connections showing progressive divergence. This freely available atlas provides a unified coordinate system for comparative neuroscience, enabling quantitative evaluation of where cross-species correspondence holds and where species-specific divergence emerges.

neuroscience↗

bioRxiv2025

Dissecting the strain and sex specific connectome signatures of unanesthetized C57BL/6J and DBA/2J mice using magnetic resonance imaging

Mouse models are an essential tool for understanding behavior and disease states in neuroscience research. While genetic and sex-specific effects have been reported in many neurodegenerative and psychiatric illnesses, these factors may also alter baseline neuroanatomical features of mice. This raises the question of whether the observed changes are related to the disease being studied (i.e., pathological differences) or if there are baseline strain or sex differences that may potentially predispose animals to different responses. Over the past decade, tremendous effort has been made in mapping neural architecture at various scales; however, the complex relationships including identifying genetic and sex-specific differences in brain structure and function remain understudied. To bridge this gap, we used C57BL/6J and DBA/2J mice, two of the most widely used inbred mouse strains in neuroscience research, to investigate strain and sex-specific features of the brain connectome in awake animals using magnetic resonance imaging (MRI). By combining resting-state fMRI and diffusion MRI, we found that the motor, sensory, limbic, and salience networks exhibit significant differences in both functional and structural domains between C57BL/6J and DBA/2J mice. Further, functional and structural properties of the brain were significantly correlated in both strains. Our results underscore the importance of considering these baseline differences when interpreting the brain-behavior interactions in mouse models of human disorders.

neuroscience↗

bioRxiv2025

Persistent chromatin loops shape gene expression plasticity upon stimulation and restimulation of human neurons

Persistent molecular correlates of long-term memory storage remain an open question. Here, we stimulate and re-stimulate human neurons and use multi-modal single-nucleus technologies to query DNA methylation, higher-order chromatin folding, and gene expression. We find enduring traces of activity-gained and activity-lost chromatin loops. Genes anchoring persistent activity-gained loops exhibit activity-upregulated expression, whereas persistent activity-lost loops anchor activity-downregulated genes that remain repressed five days post-stimulation. CTCF-bound looped enhancers and promoters are refractory to activity-dynamic DNA methylation. Looped enhancers bound by CTCF can exhibit memory of activity-induced histone modifications and persistent expression of activity-upregulated genes. Upon second stimulation, activity-upregulated genes are robustly re-induced when unlooped but remain nonresponsive at persistent loops akin to habituation. Activity-independent gene expression can be downregulated when unlooped but protected from homeostatic downscaling when anchored in persistent loops. Our data reveal long-term genome folding persistence linked to plasticity of activity-dependent gene expression during recall in human neurons. Structured AbstractO_ST_ABSIntroductionC_ST_ABSA long-standing question in neuroscience is how memories of previous experiences are stored in the mammalian brain to facilitate recall over the lifetime of an individual. Classic models of memory posit that both synapse-specific events and cell-wide transcriptional programs are required for encoding, consolidation, and long-term storage of memory (1, 2). Learning involves synapse strengthening, and synapse weakening has been linked to memory loss (3, 4). Nascent transcription and protein synthesis also occur in response to neural activation in vitro and in vivo (5-7). A leading hypothesis, the synaptic tagging and capture model, asserts that specific synapses are biochemically marked during encoding and newly made cell-wide RNA/proteins act specifically on tagged synapses to maintain long-term memory (8). Multiple historic studies have pursued the identification of proteins linked to potentiated synapses (9-13), but few explore the possibility of persistent, activity-dependent patterns of DNA, chromatin, higher-order chromatin folding, or RNA in human models of long-term memory. Over the last decade, the molecular technique of Chromatin-Conformation-Capture has been employed to discover that the mammalian genome folds into thousands loops of (5, 14-28). Loops bring distal non-coding cis regulatory elements into contact with their target genes to influence gene expression (29). They form via the processive motion of the cohesin ring along chromatin until it stalls at the architectural protein CTCF, thus extruding out the intervening DNA (22, 30-32). Loops can markedly reconfigure during lineage commitment in development and in response to genetic perturbations, and their formation is critical for spatiotemporal regulation of expression (22, 30, 33-41). RationaleMultiple recent works suggest that loops connecting activity-dependent enhancers to distal genes and can be induced during neural stimulation in vitro and behavior paradigms in vivo (5, 27, 42, 43). Genetic elimination of the architectural proteins CTCF and cohesin prior to learning substantially impairs memory encoding across multiple behavior tasks in vivo (42, 44, 45). Cohesin-mediated loops are necessary for the establishment of new gene expression programs in post-mitotic neurons, including the upregulation of genes encoding axon guidance, dendritic spine morphology, and synaptic plasticity during neuron maturation in vivo and activity-dependent gene expression during neural stimulation in vitro (27). Fear conditioning recruits epigenetically plastic neurons to form the memory engram (46) and induces chromatin accessibility changes at enhancers that might persist at least five days after fear conditioning (43). Together, these data provide the rationale for our hypothesis that key structural features of higher-order chromatin folding could be persistent long after the exposure and removal of a pharmacological, environmental, or behavioral stimulus that causes neurons to fire action potentials. ResultsHere, we apply cutting-edge single-nucleus multi-modal technologies toward the goal of ascertaining the extent to which there are enduring traces of higher-order chromatin folding, DNA methylation, histone modifications at activity-dependent enhancers, and/or mRNA levels in single human neurons upon exposure to a repetitive stimulation and re-stimulation paradigm. We find that pharmacological stimulation of human induced pluripotent stem cell (iPSC)-derived neurons induces chromatin loop plasticity, including persistent activity-gained (PG) and persistent activity-lost (PL) loops that are dependent on CREB phosphorylation. We also query activity-dependent patterns of DNA methylation genome-wide and demonstrate that the majority of looped enhancers and promoters are negligibly differentially methylated in human neurons in response to pharmacological stimulation. CTCF binding to looped enhancers and promoters further protects such regulatory elements from activity-stimulated changes in DNA methylation. What are the potential functional effects of persistent chromatin loops? We find that activity-lost persistent loops anchor activity-downregulated genes that remain repressed five days post-stimulation. By contrast, activity-gained persistent loops are enriched for activity-induced gene expression at 2-5 hours post-stimulation, but mRNA levels resume baseline levels by five days after removal of the stimulus. A subset of promoter-enhancer loops bound by CTCF can exhibit enduring traces of the histone modification H3K27ac. Promoters persistently looped to CTCF-bound persistent activity-induced enhancers can exhibit memory of activity-upregulated gene expression for at least five days after stimulation. Finally, we re-stimulated our human neurons five days after the original stimulation event and assessed gene expression with single-nucleus RNA-seq. Upon second stimulation, we unexpectedly find that activity-upregulated genes can be robustly re-induced when unlooped but remain nonresponsive at persistent loops akin to habituation. We also unexpectedly find that activity-independent, invariant gene expression can be downregulated at second stimulation when unlooped but protected from homeostatic downscaling when anchored in persistent loops. ConclusionTaken together, our data reveal enduring traces of higher-order chromatin loops in human neurons after activity-simulation and their link to gene expression plasticity and habituation during re-stimulation and re-stimulation of human neurons. Our work sheds light on the role for chromatin and gene expression in an important unsolved paradox in neuroscience: How is information encoded in neural circuits on the timescale of years despite the rapid turnover of synaptic proteins/RNAs in hours to weeks? Persistent chromatin loops shape plasticity in cell-wide RNA levels, thus laying the foundation for future studies linking genes identified here to their subcellular localization and possible functional role at the synapse.

neuroscience↗

bioRxiv2025

A reference brain for the clonal raider ant

Ants exhibit remarkable collective and social behaviors, such as alloparental care1, chemical communication2, homing3, and cooperative group hygiene4. The clonal raider ant Ooceraea biroi is especially well-suited for investigating the neuronal and genetic underpinnings of these behaviors5. Unlike most ant species, O. biroi lacks a queen caste. Instead, colonies consist entirely of regular workers and slightly larger intercaste workers6. All workers reproduce in synchrony via parthenogenesis, giving rise to age-matched cohorts of clonally identical offspring7,8. This unique life history enables precise experimental control over age, genotype, and colony composition. These features have also facilitated the introduction of genetically encoded calcium indicators into O. biroi, enabling in vivo two-photon imaging to investigate the neural basis of social behaviors9. Despite its promise as a neuroscience model, the structure of the clonal raider ant brain has not been systematically characterized, and a representative reference brain does not exist. To address this gap, we imaged the brains of 40 age-matched, genetically identical individuals with confocal microscopy and, using 3D groupwise registration, generated the first reference brain for the species. We introduce a registration pipeline to align brains to this reference, facilitating the comparison of anatomical features across labeling experiments with high spatial precision. Unexpectedly, despite homogeneity in genotype, age, and external morphology, we discovered extensive interindividual variability across our collection of brain samples. This raises the possibility that behavioral division of labor in O. biroi is linked to individual differences in brain structure. This work provides a powerful resource for the emerging clonal raider ant neuroscience community and reveals novel features of the species neurobiology that may influence social behaviors and colony function.

neuroscience↗

bioRxiv2025

Data-Driven Reduced Modeling of Recurrent Neural Networks

Artificial Recurrent Neural Networks (RNNs) are widely used in neuroscience to model the collective activity of neurons during behavioral tasks. The high dimensionality of their parameter and activity spaces, however, often make it challenging to infer and interpret the fundamental features of their dynamics. In this study, we employ recent nonlinear dynamical system techniques to uncover the core dynamics of several RNNs used in contemporary neuroscience. Specifically, using a data-driven approach, we identify Spectral Submanifolds (SSMs), i.e., low-dimensional attracting invariant manifolds tangent to the eigenspaces of fixed points. The internal dynamics of SSMs serve as nonlinear models that reduce the dimensionality of the full RNNs by orders of magnitude. Through low-dimensional, SSM-reduced models, we give mathematically precise definitions of line and ring attractors, which are intuitive concepts commonly used to explain decision-making and working memory. The new level of understanding of RNNs obtained from SSM reduction enables the interpretation of mathematically well-defined and robust structures in neuronal dynamics, leading to novel predictions about the neural computations underlying behavior.

neuroscience↗

bioRxiv2025

Decoding with multivariate pattern analysis is superior for optically pumped magnetometer-based magnetoencephalography compared to superconducting quantum interference device-based systems

BackgroundMultivariate pattern analysis (MVPA) has become an increasingly important method for decoding distributed brain activity from neural electrophysiological recordings by leveraging both temporal and spatial features. These multivariate approaches have proven important for both cognitive neuroscience and brain-computer interfaces. MVPA might benefit from magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs), as these sensors can be placed closer to the scalp, providing higher spatial resolution compared to conventional MEG systems that rely on superconducting quantum interference devices (SQUIDs). As OPM-based MEG systems become available at more institutions, it is essential to experimentally compare their performance with traditional SQUID-based systems using MVPA. MethodsWe adapted a visual object-word paradigm from a previous study, originally implemented on a TRIUX MEGIN SQUID system, to the FieldLine HEDscan OPM system. Participants were recruited and we recorded their ingoing brain activity while did the same task in both systems. Visual stimuli of different objects were presented alternately in two modalities: pictures and the corresponding written words. For each modality, MVPA was used to classify the objects from OPM and SQUID magnetometers data respectively. To further investigate the advantages of OPM, we evaluated the effect classification accuracy of two spatial factors by controlling the number of sensors included and the spatial frequency content of the sensor data. ResultsWe found higher time-resolved decoding accuracy for the OPM compared to the SQUID data. Moreover, OPMs show higher classification performance compared to SQUIDs when controlling for the same number of sensors; consistently, the OPM system required fewer sensors to reach the performance limit of the SQUID system. Our analysis considering the spatial frequency content of the signal revealed that decoding accuracy plateaued for the SQUID system at lower spatial frequencies while the performance of the OPM system continued to improve when higher-order spatial components were included. ConclusionOur OPM-MEG system outperformed the SQUID-MEG system on MVPA on decoding of visual processing. This advantage of OPM is driven by its higher spatial resolution, resulting from the sensors being positioned closer to the head and thus able to capture higher spatial frequency components of the brain signal. OPM may facilitate cognitive neuroscience research as well as brain-computer interfaces by providing higher sensitivity when employing paradigms using multi-variate data analysis.

neuroscience↗

bioRxiv2025

A simple, open-source restraint system for magnetic resonance imaging in awake rats

Magnetic resonance imaging (MRI) is a critical tool for translational neuroscience, offering cross-species insights into brain structure and function; however, its application in preclinical research is constrained by routine anesthesia use or sedation, which alters neural activity and limits comparisons to awake human imaging. Awake rodent functional MRI (fMRI) provides a powerful platform for investigating brain function under physiologically relevant conditions, but implementation is limited by technical challenges, particularly head motion and stress during scanning. Most restraint systems employ initial anesthesia, compromising translatability of findings, and highlighting the need for improved designs. We developed a novel restraint system optimized for awake rat fMRI. The system consists of modular 3D-printed components and can be assembled in under five minutes. It is accompanied by a protocol that includes head-post implantation followed by an 11-day habituation period post-surgical recovery. The system eliminates the need for isoflurane anesthesia, ear bars, and bite bars, reducing stress and improving animal comfort. It supports integration with behavioral paradigms such as pupil tracking and licking responses. High-resolution T2-weighted anatomical images and functional scans obtained using the system showed excellent spatial clarity and minimal motion artifacts. Quality control metrics, including head motion parameters and temporal signal-to-noise ratio, confirmed the systems stability and suitability for awake imaging. Functional connectivity analysis revealed robust positive correlations between functionally relevant regions. This system offers a scalable, reproducible, and animal-friendly solution for awake rat fMRI. While the current design limits direct cranial access for multimodal recordings, it enables high-quality, behaviorally enriched imaging without anesthesia. Significance Statement: Most rodent fMRI studies, including awake studies, rely on anesthesia, which profoundly alters brain activity and limits the interpretation of the data. This study presents a novel restraint system that enables high-quality fMRI in fully awake rats, eliminating the need for anesthesia, ear bars, and bite bars. By reducing stress and motion, this simple restraint system allows for investigation of neural activity and connectivity without confounds from sedation or anesthesia. Its open-source, modular design supports behavioral tasks and broad accessibility, making it a valuable tool for neuroscience research seeking to bridge the gap between preclinical imaging and real-world brain function.

neuroscience↗

bioRxiv2025

Comparative gene editing reduces dopamine receptor levels across rodent species

Translational challenges in neuroscience originate from species-specific differences that limit the generalizability of experimental findings. Comparative approaches can help distinguish conserved from species-specific mechanisms, but their application has been limited by the lack of molecular tools beyond traditional model organisms, complicating direct comparisons of conserved and divergent mechanisms of neural function. This gap is particularly evident for the dopaminergic system, a key regulator of motivated behaviors across species and the principal pharmacological target for current psychotherapies. Building on our recent development of comparative gene editing, we here present an adeno-associated virus-mediated CRISPR/Cas9 strategy to reduce in vivo dopamine receptors D1 and D2 levels across the rodent phylogeny. Using this approach, we achieved specific reduction of receptor levels in three rodent species (house mouse, prairie vole, and Syrian hamster), which we demonstrate with radioactive ligand binding assays. This toolkit expands the reach of comparative gene editing approaches, enabling functional investigation of the dopaminergic system across rodent species. Thereby, it supports comparative neuroscience by facilitating the identification of conserved versus species-specific neural mechanisms with enhanced translational potential.

neuroscience↗

bioRxiv2025

Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks

Neuroscience-inspired neural networks bridge biology and technology, offering powerful tools to model brain function while enabling adaptive, efficient control in robotics. In this work, we present a neuroscience-inspired synaptic learning rule based on the synchronization of synaptic inputs to single excitatory neurons within a feedforward spiking neural network. The model consists of three excitatory layers and two feedback inhibitory layers, with initially low connection probabilities and weak synaptic weights assigned to the excitatory neurons. Under an unsupervised learning paradigm, stimulus patterns were presented to the network, allowing synaptic weights and connectivity to evolve dynamically across training trials. We investigated how these dynamics depended on feedback inhibition intensity and identified conditions under which the network achieved stable activity. Furthermore, we evaluated the models pattern separation efficacy and its relationship to network dynamics. The results highlight the critical role of feedback inhibition in both stabilizing the network and enhancing pattern separation. In particular, results show balanced synchronization between excitatory and inhibitory populations maximizes separation efficacy. Beyond providing a novel computational framework for understanding information processing in neural systems, this model also offers insights into cognitive disorders associated with impaired inhibition and pattern separation, such as autism and schizophrenia. Finally, we embedded the trained network within a simulated agent navigating a two-dimensional environment, where it was tasked with identifying a trained stimulus as an obstacle and avoiding it. The model offers a framework for advancing cognitive robotics by enabling novel approaches that mimic natural intelligence and support the learning of complex environmental patterns.

neuroscience↗

bioRxiv2025

Perceiving Instability: How Expectations Bias Sensorimotor Processing in Balance Control

Maintaining balance requires rapid integration of sensory input with top-down sensorimotor predictions. Predictive coding frameworks propose that mismatches between expected and actual sensory input ("prediction errors") drive perceptual inference. Although these frameworks have been highly influential in sensory neuroscience, it remains unclear whether they operate similarly in fast-acting sensorimotor systems such as human balance. Using electroencephalography (EEG) and discrete postural perturbations via support surface translations, we independently manipulated expectations and sensory input. Participants were primed to expect small or large perturbations, with occasional violations in which the delivered perturbation differed from expectation. We found that subjective perception of instability was shaped by expectation alone, regardless of perturbation magnitude. Similarly, pre-perturbation beta-band suppression and post-perturbation gamma activity tracked expected perturbation magnitude, with the latter strongly associated with expectation-induced perceptual biases (r = 0.69, pBON = 0.001). In contrast, both the balance N1 (a well-established stimulus-evoked cortical potential linked to the postural response) and objective behavioural markers of postural instability were determined primarily by perturbation magnitude. Crucially, neither perception nor early neural or behavioural markers appeared to encode prediction errors arising from expectation violations. Together, these findings identify boundary conditions on predictive coding in sensorimotor control, showing that during fast, reactive behaviour, perceptual inference may be shaped more strongly by expectations than by mismatch signals, even as early neural and motor responses remain driven primarily by sensory input. Significance StatementPerception is often explained through predictive coding, a framework in which the brain compares incoming sensory input with internal expectations and updates representations based on mismatches, or prediction errors. However, it remains unclear whether these perceptual inference processes operate similarly in fast, ecologically relevant sensorimotor systems, such as postural control. Our results show that top-down expectations shape perceptual experience and early cortical responses to postural perturbations, even when sensory input contradicts those expectations. Notably, these early neural markers showed limited sensitivity to prediction errors in this rapid, reactive context. Together, these findings identify conditions under which perceptual inference may rely more strongly on expectations than on sensory mismatch, refining predictive coding accounts of fast, reactive behaviour. ClassificationBiological Science (Neuroscience)

neuroscience↗

bioRxiv2025

Data-driven identification of functional networks in artificial and biological neural networks

Understanding how the brain represents information is a central challenge in neuroscience and a practical bottleneck for brain-computer interfaces. Existing analytical tools cannot identify neural representations directly from neural activity data. We introduce MultiPEC, a data-driven method that discovers neural representations by quantifying how sets of signals jointly reduce context prediction error. Applied to artificial neural networks, MultiPEC uncovered class-specific subnetworks whose targeted ablation disproportionately impaired performance, demonstrating their causal role in feature recognition. Applied to EEG recordings from 24 participants, MultiPEC revealed functional signatures of auditory and visual processing. Classification analyses showed that MultiPEC captured fine-grained stimulus submodalities (levels of stimulus meaningfulness) more effectively than broad stimulus domains, highlighting the context-sensitive nature of neural representations. Together, these results establish MultiPEC as a scalable approach for identifying data driven (natural) representations in both biological and artificial systems, with potential applications in adaptive neurotechnology, clinical diagnostics and cognitive neuroscience. HighlightsO_LIMultiPEC identifies neural representations by extracting information clusters from neural activity data. C_LIO_LIEEG analyses reveal individualized, condition-specific functional networks across auditory and visual modalities. C_LIO_LIIn convolutional neural networks, MultiPEC uncovers class-specific networks critical for model performance. C_LIO_LIProvides a data-driven, hypothesis-free framework for mapping neural representations in biological and artificial systems. C_LI

neuroscience↗

bioRxiv2025

Fast and Easy Whole-Brain Network Model Parameter Estimation with Automatic Differentiation

Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and extensible framework for gradient-based optimization of brain network models build on JAX. Leveraging automatic differentiation, we demonstrate direct optimization of thousands of parameters, from global coupling (N=2) to regional dynamics (N=168) to full structural connectivity matrices (N=14,028), across functional MRI and magnetoencephalography data. Forward simulations achieve 10x CPU speedup over reference implementations and scale efficiently to GPU and multi-device configurations. We establish best practices through documented workflows that combine coarse parameter space exploration with gradient refinement, often yielding superior solutions faster than gradient methods alone. By bridging mechanistic neuroscience with modern machine learning infrastructure, TVB-Optim enables large-scale personalized brain models, bringing computational neuroscience closer to clinical translation.

neuroscience↗

bioRxiv2025

Deep White-Matter Pathways Mediate the Link Between Docosahexaenoic Acid (DHA) Status and Cognitive Performance in Adolescence

Docosahexaenoic acid (DHA) is a polyunsaturated fatty acid enriched in neuronal membranes and myelin and associated with cognitive performance. However, nutritional interventions show inconsistent cognitive effects, partly due to limited knowledge of the neural pathways linking DHA status to human cognition during sensitive periods of white-matter maturation, such as adolescence. We addressed this gap by studying 99 adolescents drawn from both extremes of performance on a national scholastic examination. Participants completed assessments of scholastic achievement (SA) and intellectual ability (IA), provided erythrocyte DHA samples, and underwent multimodal MRI, including diffusion, T1-weighted, and T2-weighted imaging. Independent component analysis and Bayesian multivariate LASSO models identified brain components jointly associated with DHA and cognition. Across four MRI modalities, a single deep white-matter component consistently emerged as the strongest shared pathway linking DHA with cognition. Tract-resolved analyses highlighted predominant contributions from the fornix and thalamus-temporal fasciculus, with additional subcortical and cortical involvement. In joint models, these components predicted SA and IA after accounting for DHA and other fatty acids, consistent with an indirect, mediation-like pathway. These findings move beyond DHA-behavior correlations by identifying specific neuroanatomical pathways through which a modifiable dietary factor relates to adolescent learning and intellectual performance, offering mechanistic insight relevant to neuroscience, nutrition, and education. Significance StatementAdolescence is a sensitive period for the maturation of white-matter pathways that support learning and reasoning. Docosahexaenoic acid (DHA), an essential dietary fatty acid enriched in neuronal membranes and myelin, has been linked to cognitive performance, yet the neural mechanisms underlying this association remain unclear. Using multimodal MRI and Bayesian multivariate modeling in adolescents with high or low scholastic performance, identify a specific deep white-matter pathway--centered on the fornix and the thalamus-temporal fasciculus--as the principal route connecting DHA status with scholastic achievement and intellectual ability. Additional subcortical and cortical contributions reveal a coordinated system-level architecture. These findings move beyond correlations by providing mechanistic insight into how a modifiable nutritional factor relates to cognitive development, with implications for neuroscience, public health, and education.

neuroscience↗

bioRxiv2017

The Brain Dynamics Toolbox for Matlab

The Brain Dynamics Toolbox provides a graphical tool for simulating user-defined dynamical systems in O_SCPLOWMATLABC_SCPLOW. It supports three classes of differential equations commonly used in computational neuroscience: Ordinary Differential Equations, Delay Differential Equations and Stochastic Differential Equations. The design of the graphical interface fosters intuitive exploration of the dynamics, yet there is no barrier to scripting large-scale simulations and parameter explorations. System variables and parameters may range in size from simple scalars to large vectors or matrices. The toolbox is intended for dynamical models in computational neuroscience but it can be applied to continuous dynamical systems from any domain.

neuroscience↗

bioRxiv2017

Brain Modularity Mediates the Relation of Cognitive Performance to Task Complexity

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model (Chen & Deem, 2015) suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole brain organization from network neuroscience to cognitive processing.

neuroscience↗

bioRxiv2017

Myopic control of neural dynamics

Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment \"input\" to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This \"myopic\" controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this controls utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic controls utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders.

neuroscience↗