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

---

The Digital Twin Brain: A Bridge between Biological and Artificial Intelligence

Intelligent Computing, Journal Year: 2023, Volume and Issue: 2

Published: Jan. 1, 2023

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities to understand complexity of brain its emulation using computational systems. Cutting-edge advancements research revealed intricate relationship between structure function, success neural networks has highlighted importance network architecture. It is now time bring these together better how emerges from multiscale repositories brain. this article, we propose Digital Twin Brain (DTB)—a transformative platform that bridges gap biological intelligence. comprises three core elements: structure, which fundamental twinning process, bottom-layer models generating functions, wide spectrum applications. Crucially, atlases provide a vital constraint preserves brain’s organization within DTB. Furthermore, highlight open questions invite joint efforts interdisciplinary fields emphasize far-reaching implications The DTB can offer insights into emergence neurological disorders, holds tremendous promise advancing our understanding both intelligence, ultimately propel development general facilitate precision mental healthcare.

Language: Английский

---

A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models

Neural Computation, Journal Year: 2024, Volume and Issue: 36(7), P. 1433 - 1448

Published: May 22, 2024

Mean-field models are a class of used in computational neuroscience to study the behavior large populations neurons. These based on idea representing activity number neurons as average mean-field variables. This abstraction allows large-scale neural dynamics computationally efficient and mathematically tractable manner. One these methods, semianalytical approach, has previously been applied different types single-neuron models, but never quadratic form. In this work, we adapted method integrate-and-fire neuron with adaptation conductance-based synaptic interactions. We validated model by comparing it spiking network model. should be useful interacting synapses.

Language: Английский

---

Breaking Balance: Synaptic Interneuron Properties Shift the E-I Balance in FCD I Epilepsy

Epiliepsy currents/Epilepsy currents, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

[Box: see text]

Language: Английский

---

A neural mass model with neuromodulation

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

Published: June 28, 2024

Abstract The study of brain activity and its function requires the development computational models alongside experimental investigations to explore different effects multiple mechanisms at play in central nervous system. Chemical neuromodulators such as dopamine roles regulating dynamics neuronal populations. In this work, we propose a modular framework capture neural mass level. Using framework, formulate specific model for affecting D1-type receptors. We detail dynamical repertoire associated with concentration evolution. Finally, give one example use basal-ganglia network healthy pathological conditions.

Language: Английский

---

Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models

Journal of Computational Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Language: Английский

---

Linking fast and slow: The case for generative models

Network Neuroscience, Journal Year: 2023, Volume and Issue: 8(1), P. 24 - 43

Published: Nov. 1, 2023

Abstract A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic recordings. The implicit opportunity for understanding the self-organised brain calls new analysis methods that link temporal scales: from order of milliseconds which dynamics evolve, minutes, days, even years experimental observations unfold. This review article demonstrates how hierarchical generative models Bayesian inference help characterise activity across different scales. Crucially, these go beyond describing statistical associations among about underlying mechanisms. We offer an overview fundamental concepts state-space modeling suggest a taxonomy methods. Additionally, we introduce key mathematical principles underscore separation scales, slaving principle, are being used test hypotheses with multiscale data. hope this will serve useful primer computational neuroscientists on state art current directions travel complex systems modelling literature.

Language: Английский

---

A mean-field to capture asynchronous irregular dynamics of conductance-based networks of adaptive quadratic integrate-and-fire neuron models

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: June 24, 2023

Abstract Mean-field models are a class of used in computational neuroscience to study the behaviour large populations neurons. These based on idea representing activity number neurons as average “mean field” variables. This abstraction allows large-scale neural dynamics computationally efficient and mathematically tractable manner. One these methods, semi-analytical approach, has previously been applied different types single-neuron models, but never quadratic form. In this work, we adapted method integrate-and-fire neuron with adaptation conductance-based synaptic interactions. We validated mean-field model by comparing it spiking network model. should be useful interacting synapses.

Language: Английский

---

Analysis of the optimal target node to reduce seizure-like discharge in networks

Chinese Physics B, Journal Year: 2024, Volume and Issue: 33(5), P. 058703 - 058703

Published: March 13, 2024

Network approaches have been widely accepted to guide surgical strategy and predict outcome for epilepsy treatment. This study starts with a single oscillator explore brain activity, using phenomenological model capable of describing healthy epileptic states. The ictal number seizures decreases or remains unchanged increasing the speed excitability in each seizure, there is an tendency duration respect speed. underlying reason that strong conducive reduce transition behaviors between two attractor basins. Moreover, selection optimal removal node estimated by indicator proposed this study. Results show when less than threshold, removing driving more possible significantly, while exceeds could be one. Furthermore, such potential target stimulating it obviously effective suppressing seizure-like activity compared other nodes, propensity can reduced 60% increased stimulus strength. Our results provide new therapeutic ideas surgery neuromodulation.

Language: Английский

---

Dynamical modeling and analysis of epileptic discharges transition caused by glutamate release with metabolism processes regulation from astrocyte

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2024, Volume and Issue: 34(12)

Published: Dec. 1, 2024

Glutamate (Glu) is a crucial excitatory neurotransmitter in the central nervous system that transmits brain information by activating receptors on neuronal membranes. Physiological studies have demonstrated abnormal Glu metabolism astrocytes closely related to pathogenesis of epilepsy. The astrocyte processes mainly involve uptake through EAAT2, Glu–glutamine (Gln) conversion, and release. However, relationship between these epileptic discharges remains unclear. In this paper, we propose novel neuron-astrocyte model integrating dynamical modeling processes, which include consisting uptake, Glu–Gln diffusion, resulting release as well Glu-mediated bidirectional communication neuron astrocyte. Furthermore, influences multiple dynamics transition are verified numerical experiments analyses from various nonlinear perspectives, such time series, phase plane trajectories, interspike intervals, bifurcation diagrams. Our results suggest downregulation expression EAAT2 slowdown conversion rate, excessively elevated equilibrium concentration can cause an increase released astrocytes, aggravation seizures. Meanwhile, discharge states bursting mixed-mode spiking tonic firing induced combination processes. This study provides theoretical foundation analysis methodology for further exploring evolution physiopathological mechanisms

Language: Английский

---