Recent Progress in Brain Network Models for Medical Applications: A Review

Health Data Science, Journal Year: 2024, Volume and Issue: 4

Published: Jan. 1, 2024

Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, network models (BNMs) enable quantitatively characterize aberrant dynamics underlying multiple neurological and psychiatric disorders. We delved into advancements BNM-based medical applications, discussed prevalent challenges within this field, provided possible solutions future directions. Highlights: This paper reviewed theoretical foundations current applications computational BNMs. Composed models, BNM framework allows investigate large-scale behind diseases by linking simulated signals empirical neurophysiological data, has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, predicting disease outcome. Despite that several limitations existed, one promising trend research field is precisely guide clinical neuromodulation treatment based on individual simulation. Conclusion: carries potential help understand mechanism how neuropathology affects dynamics, further contributing decision-making diagnosis treatment. Several constraints must be addressed surmounted pave way for its utilization clinic.

Language: Английский

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Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data

Network Neuroscience, Journal Year: 2024, Volume and Issue: 8(3), P. 965 - 988

Published: Jan. 1, 2024

Abstract This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil directionality and asymmetries of brain hierarchy. Effective connectivity (EC), state matrix dynamical causal modeling (DCM), is a commonly used metric studying properties within linear state-space system description. Here, we focused how are incorporated framework DCM resulting an asymmetric EC matrix. Our approach involves decomposing matrix, revealing steady-state differential cross-covariance that responsible information flow introducing time-irreversibility. Specifically, system’s dynamics, influenced by off-diagonal part covariance, exhibit curl component breaks detailed balance diverges dynamics from equilibrium. empirical findings indicate matrix’s outgoing strengths correlate with described cross while incoming primarily driven emphasizing conditional independence over directionality.

Language: Английский

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Resting-State Functional Connectivity Associated with Non-Judgmental Awareness Predicted Multiple Measures of Negative Affect

Mindfulness, Journal Year: 2024, Volume and Issue: 15(8), P. 1913 - 1927

Published: July 23, 2024

Language: Английский

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Competitive interactions shape brain dynamics and computation across species

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

Published: Oct. 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.

Language: Английский

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Simulated brain networks reflecting progression of Parkinson’s disease

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

Published: Jan. 12, 2024

Abstract Neurodegenerative progression of Parkinson’s disease affects brain structure and function and, concomitantly, alters topological properties networks. The network alteration accompanied with motor impairment duration the is not yet clearly demonstrated in progression. In this study, we aim at resolving problem a modeling approach based on large-scale networks from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover showing unexplored relationships clinical variables. We observe that simulated exhibit significant differences between healthy controls ( n =51) patients =60) strongly correlate severity patients. Moreover, results outperform empirical these measures. Consequently, study demonstrates utilizing provides an enhanced view alterations potential biomarkers for indices.

Language: Английский

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