Reconstructing whole-brain structure and dynamics using imaging data and personalized modeling

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

Abstract Brain structure plays a pivotal role in shaping neural dynamics. Current models lack the anatomical and functional resolution needed to accurately capture both structural dynamical features of human brain. Here, we introduce FEDE (high FidElity Digital brain modEl) pipeline, generating anatomically accurate digital twins from imaging data. Using advanced techniques tissue segmentation finite-element analysis, reconstructs with high spatial resolution, while also replicating whole-brain activity. We demonstrated its application by creating first twin toddler autism spectrum disorder (ASD). Through parameter optimization, replicated time-frequency recorded Notably, predicted patient-specific aberrant values excitation inhibition ratio, coherently ASD pathophysiology. represents significant leap forward modeling, paving way for more effective applications experimental clinical settings.

Язык: Английский

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Role of hierarchical heterogeneity in shaping seizure onset dynamics: Insights from structurally-based whole-brain dynamical network models

Communications in Nonlinear Science and Numerical Simulation, Год журнала: 2023, Номер 130, С. 107721 - 107721

Опубликована: Ноя. 22, 2023

Язык: Английский

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Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 16, 2024

Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings FC and RSNs are still not well-understood. It remains debated whether resting state activity is best characterized as noise-driven fluctuations around a single stable state, or instead, nonlinear dynamical system with nontrivial attractors embedded RSNs. Here, we provide evidence for latter, by constructing whole-brain systems models from individual fMRI (rfMRI) recordings, using Mesoscale Individualized NeuroDynamic (MINDy) platform. The MINDy consist hundreds neural masses representing parcels, connected fully trainable, individualized weights. We found that our manifested diverse taxonomy attractor landscapes including multiple equilibria limit cycles. when projected anatomical space, these mapped onto limited set canonical RSNs, default mode network (DMN) frontoparietal control (FPN), which were reliable at level. Further, creating convex combinations models, bifurcations induced recapitulated full spectrum dynamics via fitting. These findings suggest traverses dynamics, generates several distinct but anatomically overlapping landscapes. Treating rfMRI unimodal stationary process (i.e., conventional FC) may miss critical properties structure within brain. Instead, be better captured through modeling analytic approaches. results new generative mechanisms intrinsic spatiotemporal organization networks.

Язык: Английский

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Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI

IEEE Transactions on Biomedical Engineering, Год журнала: 2024, Номер 71(8), С. 2391 - 2401

Опубликована: Фев. 27, 2024

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human exhibits a remarkable modular structure networks, with each module comprised of functionally interconnected regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such modularity prior. In this paper, we propose modularity-constrained dynamic representation framework interpretable analysis, consisting graph construction, via novel network (MGNN), prediction biomarker detection. designed MGNN constrained by three core neurocognitive modules ( i.e. , salience network, central executive default mode network), encouraging ROIs within same to share similar representations. To further enhance discriminative ability learned features, encourage preserve topology input graphs reconstruction constraint. Experimental results 534 subjects rs-fMRI scans from two datasets validate effectiveness proposed method. identified connectivities be regarded as potential biomarkers aid clinical diagnosis.

Язык: Английский

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Efficient Inference on a Network of Spiking Neurons using Deep Learning

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 26, 2024

Abstract The process of making inference on networks spiking neurons is crucial to decipher the underlying mechanisms neural computation. Mean-field theory simplifies interactions between produce macroscopic network behavior, facilitating study information processing and computation within brain. In this study, we perform a mean-field model gain insight into likely parameter values, uniqueness degeneracies, also explore how well statistical relationship parameters maintained by traversing across scales. We benchmark against state-of-the-art optimization Bayesian estimation algorithms identify their strengths weaknesses in our analysis. show that when confronted with dynamical noise or case missing data presence bistability, generating probability distributions using deep density estimators outperforms other algorithms, such as adaptive Monte Carlo sampling. However, class generative models may result an overestimation uncertainty correlation parameters. Nevertheless, issue can be improved incorporating time-delay embedding. Moreover, training Neural ODEs enables system dynamics from microscopic states. summary, work demonstrates enhanced accuracy efficiency learning harnessed solve inverse problems

Язык: Английский

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Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers

PLoS Computational Biology, Год журнала: 2024, Номер 20(7), С. e1011642 - e1011642

Опубликована: Июль 11, 2024

The Virtual Epileptic Patient (VEP) refers to a computer-based representation of patient with epilepsy that combines personalized anatomical data dynamical models abnormal brain activities. It is capable generating spatio-temporal seizure patterns resemble those recorded invasive methods such as stereoelectro EEG data, allowing for the evaluation clinical hypotheses before planning surgery. This study highlights effectiveness calibrating VEP using global optimization approach. approach utilizes SaCeSS, cooperative metaheuristic algorithm parallel computation, yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking on synthetic our proposal successfully solved set different configurations models, demonstrating better scalability and superior performance against other solvers. These results were further enhanced Bayesian framework hyperparameter tuning, significant gains in terms both accuracy cost. Additionally, we added scalable uncertainty quantification phase after model calibration, used it assess variability estimated parameters across problems. Overall, this has potential improve estimation pathological areas drug-resistant epilepsy, thereby inform decision-making process.

Язык: Английский

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Revealing excitation-inhibition imbalance in Alzheimer’s disease using multiscale neural model inversion of resting-state functional MRI

Communications Medicine, Год журнала: 2025, Номер 5(1)

Опубликована: Янв. 15, 2025

Alzheimer's disease (AD) is a serious neurodegenerative disorder without clear understanding of pathophysiology. Recent experimental data have suggested neuronal excitation-inhibition (E-I) imbalance as an essential element AD pathology, but E-I has not been systematically mapped out for either local or large-scale circuits in AD, precluding precise targeting treatment. In this work, we apply Multiscale Neural Model Inversion (MNMI) framework to the resting-state functional MRI from Disease Neuroimaging Initiative (ADNI) identify brain regions with disrupted balance large network during progression. We observe that both intra-regional and inter-regional progressively cognitively normal individuals, mild cognitive impairment (MCI) AD. Also, find inhibitory connections are more significantly impaired than excitatory ones strengths most reduced MCI leading gradual decoupling neural populations. Moreover, reveal core comprised mainly limbic cingulate regions. These exhibit consistent alterations across thus may represent important biomarkers therapeutic targets. Lastly, multiple found be correlated test score. Our study constitutes attempt delineate progression, which facilitate development new treatment paradigms restore physiological The cells within brain, neurons, communicate using activity. Excitation-inhibition measure contribution communication. memory, thinking reasoning disrupted. people applied computational model imaging could potentially used treatments developed improve balance, possibly improving symptoms Li et al. multiscale modeling approach scale based on MRI. concentrates regions, long-range subjects impairment,

Язык: Английский

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Exploring dynamical whole-brain models in high-dimensional parameter spaces

PLoS ONE, Год журнала: 2025, Номер 20(5), С. e0322983 - e0322983

Опубликована: Май 12, 2025

Personalized modeling of the resting-state brain activity implies usage dynamical whole-brain models with high-dimensional model parameter spaces. However, practical benefits and mathematical challenges originating from such approaches have not been thoroughly documented, leaving question value utility unanswered. Studying a coupled phase oscillators, we proceeded low-dimensional scenarios featuring 2–3 global parameters only to cases, where additionally equipped every region specific local parameter. To enable optimizations for fitting empirical data, applied two dedicated optimization algorithms (Bayesian Optimization, Covariance Matrix Adaptation Evolution Strategy). We thereby optimized up 103 simultaneously aim maximize correlation between simulated functional connectivity separately 272 subjects. The obtained demonstrated increased variability within subjects reduced reliability across repeated runs in Nevertheless, quality validation (goodness-of-fit, GoF) improved considerably remained very stable reliable together connectivity. Applying results phenotypical found significantly higher prediction accuracies sex classification when GoF or coupling values spaces were considered as features. Our elucidate can contribute an well its application frameworks inter-individual brain-behavior relationships.

Язык: Английский

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

Опубликована: Май 21, 2025

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

Язык: Английский

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

Опубликована: Май 21, 2025

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

Язык: Английский

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