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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Towards a biologically annotated brain connectome

Nature reviews. Neuroscience, Journal Year: 2023, Volume and Issue: 24(12), P. 747 - 760

Published: Oct. 17, 2023

Language: Английский

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Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators

Neural Networks, Journal Year: 2023, Volume and Issue: 163, P. 178 - 194

Published: April 1, 2023

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped stochastic generative process, which itself provides the basis for inference and prediction local global dynamics affected by disorders. However, calculation likelihood function at whole-brain scale often intractable. Thus, likelihood-free algorithms are required efficiently estimate parameters pertaining hypothetical areas, ideally including uncertainty. In this study, we introduce simulation-based virtual epileptic patient model (SBI-VEP), enabling us amortize approximate posterior process from low-dimensional representation patterns. The state-of-the-art deep learning conditional density estimation used readily retrieve statistical relationships between observations through sequence invertible transformations. We show that SBI-VEP able distribution linked extent epileptogenic propagation zones sparse intracranial electroencephalography recordings. presented Bayesian methodology can deal non-linear latent parameter degeneracy, paving way fast reliable on disorders neuroimaging modalities.

Language: Английский

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Brain states as wave-like motifs

Trends in Cognitive Sciences, Journal Year: 2024, Volume and Issue: 28(6), P. 492 - 503

Published: April 5, 2024

Language: Английский

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

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 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.

Language: Английский

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Synchronization in spiking neural networks with short and long connections and time delays

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2025, Volume and Issue: 35(1)

Published: Jan. 1, 2025

Synchronization is fundamental for information processing in oscillatory brain networks and strongly affected by time delays via signal propagation along long fibers. Their effect, however, less evident spiking neural given the discrete nature of spikes. To bridge gap between these different modeling approaches, we study synchronization conditions, dynamics underlying synchronization, role delay a two-dimensional network model composed adaptive exponential integrate-and-fire neurons. Through parameter exploration neuronal properties, map behavior as function unidirectional long-range connection microscopic properties demonstrate that principal behaviors comprise standing or traveling waves activity depend on noise strength, E/I balance, voltage adaptation, which are modulated connection. Our results show interplay micro- (single neuron properties), meso- (connectivity composition network), macroscopic (long-range connectivity) parameters emergent spatiotemporal brain.

Language: Английский

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The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging

NeuroImage, Journal Year: 2023, Volume and Issue: 283, P. 120403 - 120403

Published: Oct. 20, 2023

The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization white matter tracts functional brain networks. Here, we built a network modeling framework to infer the causal link between structural connectivity architecture consequent in aging. By applying in-silico interhemispheric degradation connectivity, reproduced process dedifferentiation Thereby, found global modulation dynamics by increase age, which was steeper older adults poor performance. We validated our hypothesis via deep-learning Bayesian approach. Our results might be first mechanistic demonstration leading decline.

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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Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2024, Volume and Issue: 34(1)

Published: Jan. 1, 2024

We propose a machine-learning approach to construct reduced-order models (ROMs) predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent fMRI series. Our is three stage one. First, we exploit manifold learning and, particular, diffusion maps (DMs) discover set variables that parametrize latent space which emergent series evolve. Then, ROMs embedded via two techniques: Feedforward Neural Networks (FNNs) and Koopman operator. Finally, for predicting ambient space, solve pre-image problem, i.e., construction map from low-dimensional original (ambient) by coupling DMs with Geometric Harmonics (GH) when using FNNs modes per se. For our illustrations, have assessed performance proposed schemes benchmark series: (i) simplistic five-dimensional model stochastic discrete-time equations used just “transparent” illustration approach, thus knowing priori what one expects get, (ii) real dataset recordings during visuomotor task. show operator provides, any practical purposes, equivalent results FNN-GH bypassing need train non-linear use GH extrapolate predictions space; can instead low-frequency truncation function L2-integrable functions entire list coordinate problem.

Language: Английский

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Deep learning on medical image analysis

CAAI Transactions on Intelligence Technology, Journal Year: 2024, Volume and Issue: unknown

Published: June 24, 2024

Abstract Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features patterns from extensive datasets. The paper covers the structure of CNN its advances explores different types transfer learning strategies well classic pre‐trained models. also discusses how has been applied to areas within medical analysis. This comprehensive overview aims assist researchers, clinicians, policymakers by providing detailed insights, helping them make informed decisions about future research policy initiatives improve patient outcomes.

Language: Английский

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