Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

Translational Psychiatry, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 23, 2024

Язык: Английский

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Digital Twin Brain Predicts rTMS Effects on Brain State Dynamics in Chronic Tinnitus

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

Опубликована: Апрель 30, 2025

Abstract Predicting repetitive transcranial magnetic stimulation (rTMS) effects on whole-brain dynamics in clinical populations is crucial for developing personalized therapies and advancing precision medicine brain disorders. This study provides the first proof-of-concept demonstrating that Digital Twin Brain (DTB) can forecast rTMS state individuals with disorders (chronic tinnitus). First, we identified two aberrant states predominantly overlapped somatomotor default mode networks, respectively. Subsequently, developed DTB patients derived regional responses each region, revealing distinct roles of parieto-occipital frontal regions. Mechanistically, examined biological plausibility using tinnitus-specific risk genes investigated multi-scale neurobiological relevance. Clinically, found predict an independent, longitudinal dataset (all r > 0.78). Particularly, predictive capacity exhibits a state-specific nature. Overall, this work proposes novel DTB-based framework predicting empirical evidence supporting its utility. approach may be generalizable to other neuromodulation techniques, promoting broader advancements health.

Язык: Английский

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Stage-dependent Neural Mechanisms in Human Methamphetamine Abstinence: Insights from the Digital Twin Brain Model

Biological Psychiatry, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

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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The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder

Опубликована: Март 14, 2024

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through mechanistic modeling of this disorder. Using the Kolmogorov Theory consciousness (KT), we develop foundational model where algorithmic agents interact with world to maximize an Objective Function evaluating affective \textit{valence}. Depression, defined in context by state persistently low valence, may arise from various factors---including inaccurate models (cognitive biases), dysfunctional (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, map agent brain circuits functional networks, framing etiological routes linking depression biotypes. Finally, explore how stimulation, psychotherapy, plasticity-enhancing compounds such as psychedelics can synergistically repair neural optimize therapies using personalized computational models.

Язык: Английский

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Gaming expertise induces meso-scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling

NeuroImage, Год журнала: 2024, Номер 293, С. 120633 - 120633

Опубликована: Май 3, 2024

Video games are a valuable tool for studying the effects of training and neural plasticity on brain. However, underlying mechanisms related to plasticity-associated brain structural changes their impact dynamics unknown. Here, we used semi-empirical whole-brain model study linked video game expertise. We hypothesized that expertise is associated with plasticity-mediated in connectivity manifest at meso‑scale level, resulting more segregated functional network topology. To test this hypothesis, combined data StarCraft II players (VGPs, n = 31) non-players (NVGPs, 31), generic fMRI from Human Connectome Project computational models, generate simulated recordings. Graph theory analysis was performed during both resting-state conditions external stimulation. VGPs' characterized by integration, increased local frontal, parietal, occipital regions. The same analyses level showed no differences between VGPs NVGPs. Regions strength known be involved cognitive processes crucial task performance such as attention, reasoning, inference. In-silico stimulation suggested FC NVGPs emerge noisy contexts, specifically when increased. This indicates connectomes may facilitate filtering noise stimuli. These alterations drive observed individuals gaming Overall, our work sheds light triggered experiences.

Язык: Английский

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Competitive interactions shape brain dynamics and computation across species

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

Опубликована: Окт. 22, 2024

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling examine dynamical relevance of interactions mammalian connectome. Across human, macaque, mouse show that models most faithfully reproduce activity, consistently combines modular with diffuse, long-range interactions. The model outperforms cooperative-only model, excellent fit both spatial properties living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive effective connectivity produce greater levels synergistic information local-global hierarchy, lead superior capacity when used neuromorphic computing. Altogether, this work provides a mechanistic link between architecture, properties, computation brain.

Язык: Английский

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Neural geometrodynamics, complexity, and plasticity: a psychedelics perspective

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Авг. 17, 2023

Abstract We explore the intersection of neural dynamics and effects psychedelics in light distinct timescales a framework integrating concepts from dynamics, complexity, plasticity. call this geometrodynamics for its parallels with general relativity’s description interplay spacetime matter. The geometry trajectories within dynamical landscape “fast time” are shaped by structure differential equation connectivity parameters, which themselves evolve over “slow driven state-dependent state-independent plasticity mechanisms. Finally, adjustment processes (metaplasticity) takes place an “ultraslow” time scale. Psychedelics flatten landscape, leading to heightened entropy complexity as observed neuroimaging modeling studies linking increases disruption functional integration. highlight relationship between criticality, fast synaptic Pathological, rigid, or “canalized” result ultrastable confined repertoire, allowing slower plastic changes consolidate them further. However, under influence psychedelics, destabilizing emergence complex leads more fluid adaptable state process that is amplified plasticity-enhancing psychedelics. This shift manifests acute systemic increase disorder possibly longer-lasting affecting both short-term long-term processes. Our offers holistic perspective these substances their potential impacts on function.

Язык: Английский

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Alzheimer’s Disease: Insights from Large-Scale Brain Dynamics Models

Brain Sciences, Год журнала: 2023, Номер 13(8), С. 1133 - 1133

Опубликована: Июль 28, 2023

Alzheimer's disease (AD) is a degenerative brain disease, and the condition difficult to assess. In past, numerous dynamics models have made remarkable contributions neuroscience from microcosmic macroscopic scale. Recently, large-scale been developed based on dual-driven multimodal neuroimaging data neurodynamics theory. These bridge gap between anatomical structure functional played an important role in assisting understanding of mechanism. Large-scale widely used explain how macroscale biomarkers emerge potential neuronal population level disturbances associated with AD. this review, we describe emerging approach studying AD that utilizes biophysically model. particular, focus application model discuss directions for future development analysis models. This will facilitate virtual field diagnosis treatment add new opportunities advancing clinical neuroscience.

Язык: Английский

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