Learning how network structure shapes decision-making for bio-inspired computing

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Май 23, 2023

Abstract To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that used to build personalized brain models for 650 Human Connectome Project participants. We found participants with higher intelligence scores took more time solve difficult problems, and slower solvers had average functional connectivity. With simulations identified mechanistic link between connectivity, intelligence, processing speed synchrony trading accuracy in dependence of excitation-inhibition balance. Reduced led decision-making circuits quickly jump conclusions, while allowed integration evidence robust working memory. Strict tests were applied ensure reproducibility generality the obtained results. Here, identify links function enable learn connectome topology from noninvasive recordings map it inter-individual differences suggesting broad utility research clinical applications.

Язык: Английский

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

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

Опубликована: Янв. 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.

Язык: Английский

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Simulation-based Inference on Virtual Brain Models of Disorders

Machine Learning Science and Technology, Год журнала: 2024, Номер 5(3), С. 035019 - 035019

Опубликована: Июль 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

Язык: Английский

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Human connectome topology directs cortical traveling waves and shapes frequency gradients

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Апрель 26, 2024

Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While direction of traveling has been linked to brain function dysfunction, factors that determine this remain elusive. We hypothesized structural connectivity instrength - defined as gradually varying sum incoming connection strengths across cortex could shape both wave gradients. confirm presence connectome diverse cohorts parcellations. Using a cortical network model, we demonstrate how these direct Our model fits resting-state MEG functional best regime where instrength-directed emerge. further show subnetworks generate opposing directions observed alpha beta bands. findings suggest affect

Язык: Английский

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A Review of Brain–Computer Interface-Based Language Decoding: From Signal Interpretation to Intelligent Communication

Applied Sciences, Год журнала: 2025, Номер 15(1), С. 392 - 392

Опубликована: Янв. 3, 2025

Brain–computer interface (BCI) technologies for language decoding have emerged as a transformative bridge between neuroscience and artificial intelligence (AI), enabling direct neural–computational communication. The current literature provides detailed insights into individual components of BCI systems, from neural encoding mechanisms to paradigms clinical applications. However, comprehensive perspective that captures the parallel evolution cognitive understanding technological advancement in BCI-based remains notably absent. Here, we propose Interpretation–Communication–Interaction (ICI) architecture, novel three-stage an analytical lens examining development. Our analysis reveals field’s basic signal interpretation through dynamic communication intelligent interaction, marked by three key transitions: single-channel multimodal processing, traditional pattern recognition deep learning architectures, generic systems personalized platforms. This review establishes has achieved substantial improvements regard system accuracy, latency reduction, stability, user adaptability. proposed ICI architecture bridges gap computational methodologies, providing unified evolution. These offer valuable guidance future innovations their practical application assistive contexts.

Язык: Английский

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Revealing excitation-inhibition imbalance in Alzheimer’s disease using multiscale neural model inversion of resting-state functional MRI

Communications Medicine, Год журнала: 2025, Номер 5(1)

Опубликована: Янв. 15, 2025

Alzheimer's disease (AD) is a serious neurodegenerative disorder without clear understanding of pathophysiology. Recent experimental data have suggested neuronal excitation-inhibition (E-I) imbalance as an essential element AD pathology, but E-I has not been systematically mapped out for either local or large-scale circuits in AD, precluding precise targeting treatment. In this work, we apply Multiscale Neural Model Inversion (MNMI) framework to the resting-state functional MRI from Disease Neuroimaging Initiative (ADNI) identify brain regions with disrupted balance large network during progression. We observe that both intra-regional and inter-regional progressively cognitively normal individuals, mild cognitive impairment (MCI) AD. Also, find inhibitory connections are more significantly impaired than excitatory ones strengths most reduced MCI leading gradual decoupling neural populations. Moreover, reveal core comprised mainly limbic cingulate regions. These exhibit consistent alterations across thus may represent important biomarkers therapeutic targets. Lastly, multiple found be correlated test score. Our study constitutes attempt delineate progression, which facilitate development new treatment paradigms restore physiological The cells within brain, neurons, communicate using activity. Excitation-inhibition measure contribution communication. memory, thinking reasoning disrupted. people applied computational model imaging could potentially used treatments developed improve balance, possibly improving symptoms Li et al. multiscale modeling approach scale based on MRI. concentrates regions, long-range subjects impairment,

Язык: Английский

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Alpha Rhythm and Alzheimer’s Disease: Has Hans Berger’s Dream Come True?

Clinical Neurophysiology, Год журнала: 2025, Номер 172, С. 33 - 50

Опубликована: Фев. 14, 2025

Язык: Английский

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The Possibility Space Concept in Neuroscience: Possibilities, Constraints, and Explanations

European Journal of Neuroscience, Год журнала: 2025, Номер 61(5)

Опубликована: Март 1, 2025

ABSTRACT Although the brain is often characterized as a complex system, theoretical and philosophical frameworks struggle to capture this. For example, mainstream mechanistic accounts model neural systems fixed static in ways that fail their dynamic nature large set of possible behaviors. In this paper, we provide framework for capturing common type system neuroscience, which involves two main aspects: (i) constraints on (ii) system's possibility space available outcomes. Our analysis merges neuroscience examples with recent work philosophy science suggest concept essential types constraints, call hard soft constraints. focuses domain‐general notion present manifold representations, phase diagrams dynamical theory, paradigmatic cases, such Waddington's epigenetic landscape model. After building apply it three neuroscience: adaptability, resilience, phenomenology. We explore how supports toolkit helps advance scientific explanations, impossibility explanations. show fruitful connections between can support conceptual clarity, advances, identification similar across different domains neuroscience.

Язык: Английский

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

Health Data Science, Год журнала: 2024, Номер 4

Опубликована: Янв. 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.

Язык: Английский

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Human Brain Project Partnering Projects Meeting: Status Quo and Outlook

eNeuro, Год журнала: 2023, Номер 10(9), С. ENEURO.0091 - 23.2023

Опубликована: Сен. 1, 2023

As the European Flagship Human Brain Project (HBP) ends in September 2023, a meeting dedicated to Partnering Projects (PPs), collective of independent research groups that partnered with HBP, was held on 4–7, 2022. The purpose this allow these present their results, reflect collaboration HBP and discuss future interactions Research Infrastructure (RI) EBRAINS has emerged from HBP. In report, we share tour-de-force were have made furthering knowledge concerning various aspects We describe briefly major achievements terms systems-level understanding functional architecture brain its possible emulation artificial systems. then recapitulate open discussions representatives about evolution as sustainable for after also wider scientific community.

Язык: Английский

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