Virtual brain twins: from basic neuroscience to clinical use

National Science Review, Journal Year: 2024, Volume and Issue: 11(5)

Published: Feb. 27, 2024

ABSTRACT Virtual brain twins are personalized, generative and adaptive models based on data from an individual’s for scientific clinical use. After a description of the key elements virtual twins, we present standard model personalized whole-brain network models. The personalization is accomplished using subject’s imaging by three means: (1) assemble cortical subcortical areas in subject-specific space; (2) directly map connectivity into models, which can be generalized to other parameters; (3) estimate relevant parameters through inversion, typically probabilistic machine learning. We use healthy ageing five diseases: epilepsy, Alzheimer’s disease, multiple sclerosis, Parkinson’s disease psychiatric disorders. Specifically, introduce spatial masks demonstrate their physiological pathophysiological hypotheses. Finally, pinpoint challenges future directions.

Language: Английский

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DySCo: A general framework for dynamic functional connectivity

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(3), P. e1012795 - e1012795

Published: March 7, 2025

A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across areas change over time. However, the main approaches have been developed and applied mostly empirically, lacking common theoretical framework clear view on interpretation results derived matrices. Moreover, community has not using most efficient algorithms compute process matrices efficiently, which prevented showing its full potential with datasets and/or real-time applications. In paper, we introduce Symmetric Matrix (DySCo), associated repository. DySCo presents commonly used measures language implements them computationally way. This allows study activity at different spatio-temporal scales, down voxel level. provides single to: (1) Use as tool capture interaction patterns data form easily translatable imaging modalities. (2) Provide comprehensive set quantify properties evolution time: amount connectivity, similarity between matrices, their informational complexity. By combining it possible perform analysis. (3) Leverage Temporal Covariance EVD algorithm (TCEVD) store eigenvectors values then also EVD. Developing eigenvector space orders magnitude faster more memory than naïve space, without loss information. The methodology here validated both synthetic dataset rest/N-back task experimental fMRI Human Connectome Project dataset. We show all proposed are sensitive changes configurations consistent time subjects. To illustrate computational efficiency toolbox, performed level, demanding but afforded by TCEVD.

Language: Английский

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Does the brain behave like a (complex) network? I. Dynamics

Physics of Life Reviews, Journal Year: 2023, Volume and Issue: 48, P. 47 - 98

Published: Dec. 12, 2023

Language: Английский

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Simulation-based Inference on Virtual Brain Models of Disorders

Machine Learning Science and Technology, Journal Year: 2024, Volume and Issue: 5(3), P. 035019 - 035019

Published: July 11, 2024

Abstract Connectome-based models, also known as virtual brain models (VBMs), have been well established in network neuroscience to investigate pathophysiological causes underlying a large range of diseases. The integration an individual’s imaging data VBMs has improved patient-specific predictivity, although Bayesian estimation spatially distributed parameters remains challenging even with state-of-the-art Monte Carlo sampling. imply latent nonlinear state space driven by noise and input, necessitating advanced probabilistic machine learning techniques for widely applicable estimation. Here we present simulation-based inference on (SBI-VBMs), demonstrate that training deep neural networks both spatio-temporal functional features allows accurate generative disorders. systematic use stimulation provides effective remedy the non-identifiability issue estimating degradation limited smaller subset connections. By prioritizing model structure over data, show hierarchical SBI-VBMs renders more effective, precise biologically plausible. This approach could broadly advance precision medicine enabling fast reliable prediction

Language: Английский

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EiDA: A lossless approach for dynamic functional connectivity; application to fMRI data of a model of ageing

Imaging Neuroscience, Journal Year: 2024, Volume and Issue: 2, P. 1 - 22

Published: March 1, 2024

Abstract Dynamic Functional Connectivity (dFC) is the study of dynamic patterns interaction that characterise brain function. Numerous numerical methods are available to compute and analyse dFC from high-dimensional data. In fMRI, a number them rely on computation instantaneous Phase Alignment (iPA) matrix (also known as Locking). Their limitations high computational cost concomitant need introduce approximations with ensuing information loss. Here, we analytical decomposition iPA. This has two advantages. Firstly, achieve an up 1000-fold reduction in computing time without Secondly, can formally alternative approaches analysis resulting time-varying connectivity patterns, Discrete Continuous EiDA (Eigenvector Analysis), related set metrics quantify total amount connectivity, drawn dynamical systems theory. We applied dataset 48 rats underwent functional magnetic resonance imaging (fMRI) at four stages during longitudinal ageing. Using EiDA, found provided robust markers ageing decreases metastability, increase informational complexity over life span. suggests reduces repertoire postulated support cognitive functions overt behaviours, slows down exploration this reduced repertoire, coherence its structure. summary, method extract lossless requires significantly less time, provides analytically principled for dynamics. These interpretable promising studies neurodevelopmental neurodegenerative disorders.

Language: Английский

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Advances in physiological and clinical relevance of hiPSC-derived brain models for precision medicine pipelines

Frontiers in Cellular Neuroscience, Journal Year: 2025, Volume and Issue: 18

Published: Jan. 6, 2025

Precision, or personalized, medicine aims to stratify patients based on variable pathogenic signatures optimize the effectiveness of disease prevention and treatment. This approach is favorable in context brain disorders, which are often heterogeneous their pathophysiological features, patterns progression treatment response, resulting limited therapeutic standard-of-care. Here we highlight transformative role that human induced pluripotent stem cell (hiPSC)-derived neural models poised play advancing precision for particularly emerging innovations improve relevance hiPSC physiology. hiPSCs derived from accessible patient somatic cells can produce various types tissues; current efforts increase complexity these models, incorporating region-specific tissues non-neural microenvironment, providing increasingly relevant insights into human-specific neurobiology. Continued advances tissue engineering combined with genomics, high-throughput screening imaging strengthen physiological thus ability uncover mechanisms, vulnerabilities, fluid-based biomarkers will have real impact neurological True understanding, however, necessitates integration hiPSC-neural biophysical data, including quantitative neuroimaging representations. We discuss recent cellular neuroscience provide direct connections through generative AI modeling. Our focus great potential synergy between pave way personalized becoming a viable option suffering neuropathologies, rare epileptic neurodegenerative disorders.

Language: Английский

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Who wants to live forever? Models shaping the future of aging research

BioTechniques, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 4

Published: Jan. 6, 2025

Language: Английский

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IUPHAR Review: Computational Psychiatry 2.0. A new tool for Supporting Combination therapy of Psychopharmacology with Neuromodulation in Schizophrenia

Pharmacological Research, Journal Year: 2025, Volume and Issue: unknown, P. 107718 - 107718

Published: March 1, 2025

Recent clinical trial successes in schizophrenia with non-dopaminergic agents have rejuvenated the field after a long period of unsuccesfull attempts. At same time, non-invasive neurostimulation has been increasingly applied other mental health disorders while few studies performed schizophrenia. The time arrived to consider combining psychotherapy neuromodulation. However, systematic approach optimize designs is needed. "Computational Psychiatry" defined as computational neuroscience modeling using biophysically and anatomically realistic representations key brain areas based on neuroimaging data biological knowledge. In this position paper, we will expand concept include drug exposure pharmacology combination This can be used impact active platform generates new silico biomarker, "information bandwidth", that might related outcomes assumption information processing capacity human represented by measure entropy quantifies level uncertainty associated processes. Previously shown readout model closed cortical-striatal-thalamocortical loop highly correlated changes positive symptoms antipsychotic treatment. paper present strategy how expanded Computational Psychiatry support optimization design neuromodulation psychopharmacology, well understanding mitigating placebo response.

Language: Английский

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Enhancing cognitive abilities through transcutaneous auricular vagus nerve stimulation: Findings from prefrontal functional connectivity analysis and virtual brain simulation

NeuroImage, Journal Year: 2025, Volume and Issue: unknown, P. 121179 - 121179

Published: March 1, 2025

Language: Английский

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Efficient Inference on a Network of Spiking Neurons using Deep Learning

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 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

Language: Английский

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