Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena

Cell Reports, Journal Year: 2023, Volume and Issue: 42(4), P. 112254 - 112254

Published: March 24, 2023

Much of systems neuroscience posits the functional importance brain activity patterns that lack natural scales sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for nature this scale-free activity. Here, we reconcile these across species modalities. First, link estimates excitation-inhibition (E-I) balance with time-resolved correlation distributed Second, develop an unbiased method sampling time series constrained by correlation. Third, use to show E-I account diverse phenomena without need attribute additional function phenomena. Collectively, our results simplify existing provide stringent tests on future theories seek transcend explanations.

Language: Английский

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Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: April 6, 2024

Abstract Neuroimaging studies have allowed for non-invasive mapping of brain networks in tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging often neglect signals, structures, functions within their presence. Therefore, both functional diffusion as well relationship with global patterns connectivity reorganization, poorly understood. Here, we explore the activity structure white matter fibers considering contribution whole a surgical context. First, find intertwined alterations frequency domain local spatially distributed resting-state potentially arising tumor. Second, propose fiber tracking pipeline capable anatomical information while still reconstructing bundles tumoral peritumoral tissue. Finally, machine learning healthy information, predict structural rearrangement after surgery given preoperative network. The generative model also disentangles complex reorganization different types Overall, show importance carefully designing including MR signals damaged tissues, they exhibit relate to non-trivial (dis-)connections or activity.

Language: Английский

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Benchmarking methods for mapping functional connectivity in the brain

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

Published: May 8, 2024

The networked architecture of the brain promotes synchrony among neuronal populations and emergence coherent dynamics. These communication patterns can be comprehensively mapped using noninvasive functional imaging, resulting in connectivity (FC) networks. Despite its popularity, FC is a statistical construct operational definition arbitrary. While most studies use zero-lag Pearson's correlations by default, there exist hundreds pairwise interaction statistics broader scientific literature that used to estimate FC. How organization matrix varies with choice statistic fundamental methodological question affects all this rapidly growing field. Here we benchmark topological geometric organization, neurobiological associations, cognitive-behavioral relevance matrices computed large library 239 statistics. We investigate how canonical features networks vary statistic, including (1) hub mapping, (2) weight-distance trade-offs, (3) structure-function coupling, (4) correspondence other neurophysiological networks, (5) individual fingerprinting, (6) brain-behavior prediction. find substantial quantitative qualitative variation across methods. Throughout, observe measures such as covariance (full correlation), precision (partial correlation) distance display multiple desirable properties, close structural connectivity, capacity differentiate individuals predict differences behavior. Using information flow decomposition, methods may arise from differential sensitivity underlying mechanisms inter-regional communication, some more sensitive redundant synergistic flow. In summary, our report highlights importance tailoring specific mechanism research question, providing blueprint for future optimize their method.

Language: Английский

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Revealing non-trivial information structures in aneural biological tissues via functional connectivity

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(4), P. e1012149 - e1012149

Published: April 14, 2025

A central challenge in the progression of a variety open questions biology, such as morphogenesis, wound healing, and development, is learning from empirical data how information integrated to support tissue-level function behavior. Information-theoretic approaches provide quantitative framework for extracting patterns data, but so far have been predominantly applied neuronal systems at tissue-level. Here, we demonstrate time series Ca2 + dynamics can be used identify structure other biological tissues. To this end, expressed calcium reporter GCaMP6s an organoid system explanted amphibian epidermis derived African clawed frog Xenopus laevis , imaged activity pre- post- puncture injury, six replicate organoids. We constructed functional connectivity networks by computing mutual between cells using medical imaging techniques track intracellular . analyzed network properties including degree distribution, spatial embedding, modular structure. find exhibit potential evidence more than null models, with our models displaying high hubs mesoscale community clustering. Utilizing networks, model suggests tissue retains non-random features after displays long range correlations structure, non-trivial clustering that not necessarily spatially dependent. In context reconstruction method results suggest increased integration possible cellular coordination response some type generative anatomy. While study epidermal cells, computational approach analyses highlight methods developed analyze tissues generalized any fluorescent signal type. discuss expanded improve non-neuronal processing highlighting bridge neuroscience basal modes processing.

Language: Английский

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Adaptive rewiring: a general principle for neural network development

Frontiers in Network Physiology, Journal Year: 2024, Volume and Issue: 4

Published: Oct. 29, 2024

The nervous system, especially the human brain, is characterized by its highly complex network topology. neurodevelopment of some features has been described in terms dynamic optimization rules. We discuss principle adaptive rewiring, i.e., reorganization a according to intensity internal signal communication as measured synchronization or diffusion, and recent generalization for applications directed networks. These have extended rewiring from oversimplified networks more neurally plausible ones. Adaptive captures all key brain topology: it transforms initially random regular into with modular small-world structure rich-club core. This effect specific sense that can be tailored computational needs, robust does not depend on critical regime, flexible parametric variation generates range variant configurations. Extreme associated at macroscopic level disorders such schizophrenia, autism, dyslexia, suggest relationship between dyslexia creativity. cooperates growth interacts constructively spatial organization principles formation topographically distinct modules structures ganglia chains. At mesoscopic level, enables development functional architectures, convergent-divergent units, sheds light early divergence convergence in, example, visual system. Finally, we future prospects rewiring.

Language: Английский

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A simulated annealing algorithm for randomizing weighted networks

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

Published: Feb. 28, 2024

Scientific discovery in connectomics relies on the use of network null models. To systematically evaluate prominence brain features, empirical measures are compared against statistics computed randomized networks. Modern imaging and tracing technologies provide an increasingly rich repertoire biologically meaningful edge weights. Despite prevalence weighted graph analysis connectomics, randomization models that only preserve binary node degree remain most widely used. Here, to adapt inference, we propose a simulated annealing procedure for generating strength sequence-preserving This model outperforms other commonly used rewiring algorithms preserving (strength). We show these results generalize directed networks as well wide range real-world networks, making them generically applicable neuroscience scientific disciplines. Furthermore, introduce morphospace representation tool assessment ensemble variability feature preservation. Finally, how choice can yield fundamentally different inferences about established organizational features such rich-club phenomenon lay out best practices inference. Collectively, this work provides simple but powerful inferential method meet challenges analyzing richly detailed next-generation datasets.

Language: Английский

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Revealing non-trivial information structures in aneural biological tissues via functional connectivity

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

Published: May 14, 2024

Abstract A central challenge in the progression of a variety open questions biology, such as morphogenesis, wound healing, and development, is learning from empirical data how information integrated to support tissue-level function behavior. Information-theoretic approaches provide quantitative framework for extracting patterns data, but so far have been predominantly applied neuronal systems at tissue-level. Here, we demonstrate time series Ca 2+ dynamics can be used identify structure other biological tissues. To this end, expressed calcium reporter GCaMP6s an organoid system explanted amphibian epidermis derived African clawed frog Xenopus laevis , imaged activity pre- post- puncture injury, six replicate organoids. We constructed functional connectivity networks by computing mutual between cells using medical imaging techniques track intracellular . analyzed network properties including degree distribution, spatial embedding, modular structure. find exhibit more than null models, with high hubs mesoscale community clustering. Utilizing networks, show tissue retains non-random features after displays long range correlations structure, non-trivial clustering that not necessarily spatially dependent. Our results suggest increased integration possible cellular coordination response some type generative anatomy. While study epidermal cells, our computational approach analyses highlight methods developed analyze tissues generalized any fluorescent signal type. therefore provides bridge neuroscience basal modes processing. Author summary understanding several diverse processes Significant progress has occurred via use observable live reporters throughout neural However, these same seen limited non-neural multicellular organisms despite similarities communication. Here utilize designed modify them work on type, demonstrating also contain potentially meaningful structures gleaned theoretic approaches. In case developing amphibians, informational over greater temporal scales those found tissue. This hints exploration into within types could deeper processing living beyond nervous system.

Language: Английский

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Distinctions between brain structure, complexity, and function. Comment on “Does the brain behave like a (complex) network? I. Dynamics” by Papo and Buldú

Physics of Life Reviews, Journal Year: 2024, Volume and Issue: 50, P. 30 - 31

Published: May 20, 2024

Language: Английский

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Unbiased and efficient sampling of timeseries reveals redundancy of brain network and gradient structure

Published: Jan. 16, 2023

Many studies in human neuroscience seek to understand the structure of brain networks and gradients. Few studies, however, have tested redundancy between these outwardly distinct features. Here, we developed methods directly enable such tests. We built on insights from linear algebra develop for unbiased efficient sampling timeseries with network or gradient constraints. used show considerable functional MRI data. On one hand, found that constraints largely accounted three major other seven networks. Our results suggest gradients may denote discrete continuous representations same aspects Integrated explanations can reduce by avoiding attribution independent existence function

Language: Английский

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Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: March 24, 2023

Abstract Much of systems neuroscience posits the functional importance brain activity patterns that lack natural scales sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for nature this scale-free activity. Here, we reconcile these across species modalities. First, link estimates excitation-inhibition (E-I) balance with time-resolved correlation distributed Second, develop an unbiased method sampling timeseries constrained by correlation. Third, use to show E-I account diverse phenomena without need attribute additional function phenomena. Collectively, our results simplify existing activity, provide stringent tests on future theories seek transcend explanations.

Language: Английский

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