A parsimonious description of global functional brain organization in three spatiotemporal patterns

Nature Neuroscience, Год журнала: 2022, Номер 25(8), С. 1093 - 1103

Опубликована: Июль 28, 2022

Язык: Английский

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Controversies and progress on standardization of large-scale brain network nomenclature

Network Neuroscience, Год журнала: 2023, Номер 7(3), С. 864 - 905

Опубликована: Янв. 1, 2023

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level macroscale organization brain, beginning to confront challenges associated with developing a taxonomy its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy NETworks (WHATNET) was formed 2020 as an Organization Human Brain Mapping (OHBM)-endorsed best practices committee provide recommendations on points consensus, identify open questions, and highlight areas ongoing debate service moving field toward standardized reporting network neuroscience results. conducted survey catalog current large-scale brain nomenclature. A few well-known names (e.g., default mode network) dominated responses survey, number illuminating disagreement emerged. We summarize results initial considerations from workgroup. This perspective piece includes selective review this enterprise, including (1) scale, resolution, hierarchies; (2) interindividual variability networks; (3) dynamics nonstationarity (4) consideration affiliations subcortical structures; (5) multimodal information. close minimal guidelines cognitive communities adopt.

Язык: Английский

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Partial entropy decomposition reveals higher-order information structures in human brain activity

Proceedings of the National Academy of Sciences, Год журнала: 2023, Номер 120(30)

Опубликована: Июль 19, 2023

The standard approach to modeling the human brain as a complex system is with network, where basic unit of interaction pairwise link between two regions. While powerful, this limited by inability assess higher-order interactions involving three or more elements directly. In work, we explore method for capturing dependencies in multivariate data: partial entropy decomposition (PED). Our decomposes joint whole into set nonnegative atoms that describe redundant, unique, and synergistic compose system's structure. PED gives insight mathematics functional connectivity its limitation. When applied resting-state fMRI data, find robust evidence synergies are largely invisible analyses. can also be localized time, allowing frame-by-frame analysis how distributions redundancies change over course recording. We different ensembles regions transiently from being redundancy-dominated synergy-dominated temporal pattern structured time. These results provide strong there exists large space unexplored structures data have been missed focus on bivariate network models. This structure dynamic time likely will illuminate interesting links behavior. Beyond brain-specific application, provides very general understanding variety systems.

Язык: Английский

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Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex

Communications Biology, Год журнала: 2023, Номер 6(1)

Опубликована: Апрель 24, 2023

One of the most well-established tools for modeling brain is functional connectivity network, which constructed from pairs interacting regions. While powerful, network model limited by restriction that only pairwise dependencies are considered and potentially higher-order structures missed. Here, we explore how multivariate information theory reveals in human brain. We begin with a mathematical analysis O-information, showing analytically numerically it related to previously established theoretic measures complexity. then apply O-information data, synergistic subsystems widespread Highly typically sit between canonical networks, may serve an integrative role. use simulated annealing find maximally subsystems, finding such systems comprise ≈10 regions, recruited multiple systems. Though ubiquitous, highly invisible when considering connectivity, suggesting form kind shadow structure has been unrecognized network-based analyses. assert interactions represent under-explored space that, accessible theory, offer novel scientific insights.

Язык: Английский

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Edges in brain networks: Contributions to models of structure and function

Network Neuroscience, Год журнала: 2021, Номер unknown, С. 1 - 28

Опубликована: Авг. 13, 2021

Abstract Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights highlight distinct groupings specialized functional contributions nodes. Importantly, these are determined expressed by web their interrelationships, formed edges. Here, we underscore important made for understanding Different types different relationships, including connectivity similarity among Adopting a specific definition can fundamentally alter how analyze interpret network. Furthermore, associate into collectives higher order arrangements, time series, form edge communities provide topology complementary to traditional node-centric perspective. Focusing on edges, or dynamic information they provide, discloses previously underappreciated aspects structural

Язык: Английский

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Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics

Proceedings of the National Academy of Sciences, Год журнала: 2021, Номер 118(46)

Опубликована: Ноя. 8, 2021

The topology of structural brain networks shapes dynamics, including the correlation structure activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that connectivity fluctuates over time, exhibiting patterns vary in spatial arrangement correlations among segregated systems. Recently, an exact decomposition into frame-wise contributions has revealed fine-scale dynamics are punctuated by brief and intermittent episodes (events) high-amplitude cofluctuations involving large sets regions. Their origin is currently unclear. Here, we demonstrate similar readily appear silico using computational simulations whole-brain dynamics. As empirical data, simulated events contribute disproportionately to long-time connectivity, involve recurrence patterned cofluctuations, can be clustered distinct families. Importantly, comparison event-related underlying reveals modular organization present coupling matrix cofluctuations. Our work suggests brief, partly shaped connectivity.

Язык: Английский

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A mathematical perspective on edge-centric brain functional connectivity

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Май 16, 2022

Edge time series are increasingly used in brain imaging to study the node functional connectivity (nFC) dynamics at finest temporal resolution while avoiding sliding windows. Here, we lay mathematical foundations for edge-centric analysis of neuroimaging series, explaining why a few high-amplitude cofluctuations drive nFC across datasets. Our exposition also constitutes critique existing studies, showing that their main findings can be derived from under static null hypothesis disregards correlations. Testing analytic predictions on MRI data Human Connectome Project confirms explain most variation edge FC matrix, communities, large cofluctuations, and corresponding spatial patterns. We encourage use dynamic measures future research, which exploit structure cannot replicated by models.

Язык: Английский

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High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle

Network Neuroscience, Год журнала: 2023, Номер 7(3), С. 1181 - 1205

Опубликована: Янв. 1, 2023

Abstract Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and connectivity. However, how hormonal impact fast changes network organization over short timescales remains unknown. Here, we leverage a recently proposed framework for modeling co-fluctuations activity of pairs regions at framewise timescale. In previous showed time points corresponding to high-amplitude disproportionately contributed time-averaged functional connectivity pattern these co-fluctuation patterns could be clustered into low-dimensional set recurring “states.” assessed relationship states quotidian variation concentrations. Specifically, were interested whether frequency with which occurred was related concentration. We addressed this question using dense-sampling dataset (N = 1 brain). dataset, single individual sampled course two states: natural menstrual cycle while subject underwent selective progesterone suppression via oral contraceptives. During each cycle, 30 daily resting-state fMRI scans blood draws. Our analysis imaging data revealed repeating states. found state scan sessions significantly correlated follicle-stimulating luteinizing also constructed representative networks session only “event frames”—those when an event determined occurred. weights specific subsets connections robustly concentration not hormones, but estradiol.

Язык: Английский

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The emergence of multiscale connectomics-based approaches in stroke recovery

Trends in Neurosciences, Год журнала: 2024, Номер 47(4), С. 303 - 318

Опубликована: Фев. 23, 2024

Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments readout technologies progress network modeling have revolutionized current understanding the effects on at macroscale, reorganization smaller scale remains incompletely understood. In this review, we use conceptual framework graph theory to define from nano- macroscales. Highlighting stroke-related connectivity studies multiple scales, argue that multiscale connectomics-based approaches may provide new routes better evaluate structural functional remapping after during recovery.

Язык: Английский

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On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances

Frontiers in Systems Neuroscience, Год журнала: 2022, Номер 15

Опубликована: Янв. 21, 2022

In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees kinds may be adaptive (or maladaptive) in different contexts, specifically focusing measures related either more disjoint or cohesive dynamics. While disjointed useful assessing neural entropy, potentially serve as proxy self-organized criticality fundamental property enabling behavior complex systems. Particular attention is given recent studies which have been used investigate neurological cognitive maturation, well breakdown conscious processing under levels anesthesia. further these findings might contextualized within Free Energy Principle with respect fundamentals brain organization biological functioning generally, potential methodological advances paradigm. Finally, relevance computational psychiatry, propose research program obtaining better understanding ways that networks relate forms psychological flexibility, single most important factor ensuring human flourishing.

Язык: Английский

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