Biology subjects

Woodman, M.

Publications and source records attributed to Woodman, M..

9 recordsLinked to original sources

bioRxiv2026

Structural integrity moderates the cognitive bearing of dopamine clearance in aging

Age-related cognitive decline in humans is well established, yet how neurobiological variations influence brain dynamics, and subsequently behavioral performance, remains insufficiently explored. Microscale accounts of cognitive aging revolve around attenuations in dopamine (DA) receptors and transporters (DAT). In contrast, macroscale accounts associate individual declines in cognitive performance with functional brain networks' reorganization and white-matter tracts' deterioration. We endeavor to bridge this gap with an amortized dopaminergic aging model (ADAM) which assembles aging brain twins by coupling dopaminergic modulation with structural connectomes to constrain emergent whole-brain dynamics. ADAM thereby augments established DAT decline with personalized estimates, through a reusable and generalizable estimator capable of disentangling neuromodulatory from structural contributions to age-related decline within each individual. To that end, we embed structural connectomes into a low-dimensional space exhibiting high reconstruction fidelity and -pseudonymized- individual identifiability. The spectrum of structural embeddings is subsequently updated with fMRI observations to estimate DA clearance timescales in 3,650 adults aged 36 to 95 years across five independent aging cohorts. Our analysis reveals an age-related elevation of DA clearance timescales. Critically, low performers exhibit elevated clearance in adults with marked structural degradation, and only within sliding age windows centred between 56 and 76 years. This supports a contribution of dopamine to aging, and reveals a structure- and age-conditional association between its clearance and cognitive performance. Our findings suggest that structural connectivity gates the macroscale reach of dopaminergic modulation, thus rendering the gain-control framework of neuromodulation mechanistically explicit across individuals and cohorts. ADAM therefore provides a biologically plausible framework that anchors interindividual variability in cognitive aging to perturbations in the dopaminergic system by learning cohort-scale mappings which can be shared across sites, establishing a transferable testbed for precision neuromodulation and future in-silico pharmacological trials.

neuroscience↗

bioRxiv2026

Cortico-subcortical multi-head self-attention as a substrate for cognitive performance

The neocortex is central to mammalian cognition, yet a computational framework that is both biologically constrained and capable of performing complex cognitive tasks remains missing. Here we show that cortico-thalamic circuits are well suited to implement multi-head self- and cross-attention, the mechanism underlying the cognitive abilities of transformer networks. We propose that layer 2/3 pyramidal cells maintain a recurrent key-value memory, while layer 5 pyramidal cells decode the memory retrieved by an incoming query. The computation of keys, values and queries maps onto core and matrix thalamo-cortical projections, distributed across the micro- and macro-columns of a cortical area. One cortical area forms an attention head, and cortex a multi-head self-attention network. The same thalamo-cortical microcircuit also calculates sensory prediction errors guiding gradient-based synaptic plasticity. A reward-prediction error gates via basal ganglia the cortical output and the re-activation of hippocampal memories. The trained network aligns with human intracranial recordings during speech perception. Overall, the suggested cortico-subcortical attention circuit may represent a substrate for the cognitive capacity of mammals.

neuroscience↗

bioRxiv2026

BRIDGE: A Computational Workflow from Single Neurons to Network of Mean-Field Models

Mean-field models are extensively used in large-scale brain simulations because they provide a wieldy description of population dynamics while preserving key features of neural activity. Despite their widespread adoption, no common and reproducible methodology currently exists to systematically derive and validate mean-field models starting from biologically grounded single neuron dynamics. As a result, implementations are often ad hoc, difficult to reproduce and rarely reusable. Here we introduce BRIDGE, a modular, open-source Python pipeline that enables the bottom-up reconstruction, analysis, validation, and simulation of mean-field models from single neurons. The framework integrates single neurons modelling, network simulations, extraction of population statistics, parameters analysis, quantitative comparisons between spiking neural networks and corresponding mean-field representations, and simulation of network of mean-fields. Its flexible architecture allows users to incorporate different neuron models and to generate region-specific or state-dependent mean-field formulations. BRIDGE provides a reproducible foundation for developing biologically informed mean-field models suitable for large-scale and whole-brain simulations, supporting the transition from generic homogeneous population models toward region-specific ones. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/742067v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1124404org.highwire.dtl.DTLVardef@2f8b2aorg.highwire.dtl.DTLVardef@1598f37org.highwire.dtl.DTLVardef@c9814b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Cross-Species Efficacy of Combinatorial Gene Therapy for Osteoarthritis and Correction of Neuro-Inflammatory Pain Mechanisms

Osteoarthritis is a leading cause of chronic pain and disability, which lacks disease-modifying treatment. Given the complex multi-tissue and multifactorial drivers behind disease progression, effective treatments will require simultaneously targeting several mechanisms underlying joint degeneration and pain. Here, we developed and evaluated a combinatorial gene therapy, consisting of a high-capacity adenoviral vector carrying two therapeutic genes to target distinct pathological mechanisms: inflammation (IL-1Ra) and chondrocyte health (PRG4). Intra-articular delivery of this treatment improved functional, structural, and pain outcomes in murine and equine osteoarthritis models. In addition, treatment normalized inflammatory environments in joint tissues, as well as in the dorsal root ganglia (DRG) known to harbor joint-innervating sensory neurons. Moreover, gene therapy reversed OA-induced molecular signatures of neural hyperexcitability, suggesting amelioration of peripheral sensitization. Collectively, these findings support combinatorial gene therapy as a promising treatment for osteoarthritis, while identifying neuroinflammatory signatures for correction of disease progression and pain. One Sentence SummaryA single intra-articular injection of a combinatorial gene therapy slows OA progression and reduces pain in small and large animal models.

systems biology↗

bioRxiv2025

A deformable attractor manifold organizes human resting-state brain dynamics

Intrinsic brain activity is often described as wandering within a continuous multivariate space, yet the organizing principles that constrain these dynamics remain unclear. Here, we show that spontaneous human brain activity during rest is structured by a deformable attractor manifold. Using large-scale fMRI datasets and a latent dynamical model, we find that cortical activity occupies two reproducible regimes: a low-coherence state with a unimodal latent distribution and a high-coherence state that exhibits bimodality, consistent with transient bistability across association networks. A compact two-parameter energy landscape explains these dynamics, revealing that transitions arise not from switching between discrete states, but from continuous deformation of the manifold that reshapes attractor geometry. Excursions into the bistable regime occur as rapid "jumps", whereas returns follow slow drifts along the manifold, reflecting network-specific timescales. Individuals with greater expression of the bistable regime show higher cognitive fluidity, and manifold parameters differentiate mild cognitive impairment from matched controls. These findings identify an organizing geometric and dynamical principle of resting activity, linking large-scale cortical coordination, cognitive variability, and vulnerability to pathology.

neuroscience↗

bioRxiv2025

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational models of brain dynamics (placed at each network node) with individual brain imaging data (to coordinate and connect the nodes), advancing our understanding of the complex dynamics of the brain and its neurobiological underpinnings. However, there remains a critical need for automated model inversion tools to estimate control (bifurcation) parameters at large scales associated with neuroimaging modalities, given their varying spatio-temporal resolutions. This study aims to address this gap by introducing a flexible and integrative toolkit for efficient Bayesian inference on virtual brain models, called Virtual Brain Inference (VBI). This open-source toolkit provides fast simulations, taxonomy of feature extraction, efficient data storage and loading, and probabilistic machine learning algorithms, enabling biophysically interpretable inference from non-invasive and invasive recordings. Through in-silico testing, we demonstrate the accuracy and reliability of inference for commonly used whole-brain network models and their associated neuroimaging data. VBI shows potential to improve hypothesis evaluation in network neuroscience through uncertainty quantification, and contribute to advances in precision medicine by enhancing the predictive power of virtual brain models.

neuroscience↗

bioRxiv2024

Dynamic Causal Modeling in Probabilistic Programming Languages

Understanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic Causal Modelling (DCM) presents a statistical framework that embraces causal relationships among brain regions and their responses to experimental manipulations, such as stimulation. In this study, we perform Bayesian inference on a neurobiologically plausible generative model that simulates event-related potentials observed in magne-to/encephalography data. This translates into probabilistic inference of latent and observed states of a system driven by input stimuli, described by a set of nonlinear ordinary differential equations (ODEs) and potentially correlated parameters. We provide a guideline for reliable inference in the presence of multimodality, which arises from parameter degeneracy, ultimately enhancing the predictive accuracy of neural dynamics. Solutions include optimizing the hyperparameters, leveraging initialization with prior information, and employing weighted stacking based on predictive accuracy. Moreover, we implement the inference and conduct comprehensive model comparison in several probabilistic programming languages to streamline the process and benchmark their efficiency. Our investigation shows that model inversion in DCM extends beyond variational approximation frameworks, demonstrating the effectiveness of gradient-based Markov Chain Monte Carlo methods. We illustrate the accuracy and efficiency of posterior estimation using a self-tuning variant of Hamiltonian Monte Carlo and the automatic Laplace approximation, effectively addressing parameter degeneracy challenges. This technical endeavor holds the potential to advance the inversion of state-space ODE models, and contribute to neuro-science research and applications in neuroimaging through automatic DCM.

neuroscience↗

bioRxiv2024

Principles and Operation of Virtual Brain Twins

Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanism-based trials remain underutilized in neuroscience due to the brains complexity. A Virtual Brain Twin (VBT) is a personalized digital replica of an individuals brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This manuscript outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, epilepsy, and multiple sclerosis. Finally, we discuss potential extensions to other neurological disorders, such as Parkinsons disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brain-machine integration. "The brain is conceived as a self-organizing system operating close to instabilities where its activities are governed by collective variables, the order parameters, that enslave the individual parts, i.e., the neurons." Professor Hermann Haken (1927-2024).

neuroscience↗

bioRxiv2017

The Virtual Mouse Brain: A Computational Neuroinformatics Platform To Study Whole Mouse Brain Dynamics

Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brains structure-function relationship, necessitating the close integration of diverse neuroinformatics fields.\n\nHere we extend the open-source simulation software The Virtual Brain to whole mouse brain network modeling based on individual diffusion Magnetic Resonance Imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease.\n\nThe Virtual Mouse Brain enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions.

neuroscience↗