Network Analysis of Time Series: Novel Approaches to Network Neuroscience DOI Creative Commons
Thomas F. Varley, Olaf Sporns

Frontiers in Neuroscience, Год журнала: 2022, Номер 15

Опубликована: Фев. 11, 2022

In the last two decades, there has been an explosion of interest in modeling brain as a network, where nodes correspond variously to regions or neurons, and edges structural statistical dependencies between them. This kind network construction, which preserves spatial, structural, information while collapsing across time, become broadly known “network neuroscience.” this work, we provide alternative application science neural data: network-based analysis non-linear time series review applications these methods data. Instead preserving spatial does reverse: it collapses information, instead temporally extended dynamics, typically corresponding evolution through some phase/state-space. allows researchers infer a, possibly low-dimensional, “intrinsic manifold” from empirical We will discuss three constructing networks nonlinear series, how interpret them context recurrence networks, visibility ordinal partition networks. By capturing continuous, dynamics form discrete show techniques science, theory can extract meaningful distinct what is normally accessible standard neuroscience approaches.

Язык: Английский

Controversies and progress on standardization of large-scale brain network nomenclature DOI Creative Commons
Lucina Q. Uddin, Richard F. Betzel, Jessica R. Cohen

и другие.

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

Процитировано

68
Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior DOI Creative Commons
Thomas F. Varley, Olaf Sporns, Stefan Schaffelhofer

и другие.

Proceedings of the National Academy of Sciences, Год журнала: 2023, Номер 120(2)

Опубликована: Янв. 5, 2023

One of the essential functions biological neural networks is processing information. This includes everything from sensory information to perceive environment, up motor interact with environment. Due methodological limitations, it has been historically unclear how changes during different cognitive or behavioral states and what extent processed within between network neurons in brain areas. In this study, we leverage recent advances calculation dynamics explore neural-level frontoparietal areas AIP, F5, M1 a delayed grasping task performed by three macaque monkeys. While was high all task, interareal varied widely: During visuomotor transformation, AIP F5 formed reciprocally connected unit, while no present memory period. Movement execution globally across predominance feedback direction. Furthermore, fine-scale structure reconfigured at neuron level response conditions, despite differences overall amount present. These results suggest that dynamically form higher-order units according demand information-processing hierarchically organized level, coarse determining state finer reflecting conditions.

Язык: Английский

Процитировано

58
Partial entropy decomposition reveals higher-order information structures in human brain activity DOI Creative Commons
Thomas F. Varley, Maria Pope,

Maria Grazia

и другие.

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

Процитировано

58
Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex DOI Creative Commons
Thomas F. Varley, Maria Pope, Joshua Faskowitz

и другие.

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

Процитировано

55
When makes you unique: Temporality of the human brain fingerprint DOI Creative Commons
Dimitri Van De Ville, Younes Farouj, Maria Giulia Preti

и другие.

Science Advances, Год журнала: 2021, Номер 7(42)

Опубликована: Окт. 15, 2021

Patterns of human brain activity emerge from temporally limited fMRI observations, allowing identification individuals.

Язык: Английский

Процитировано

88
Edges in brain networks: Contributions to models of structure and function DOI Creative Commons
Joshua Faskowitz, Richard F. Betzel, Olaf Sporns

и другие.

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

Процитировано

76
A computational model of neurodegeneration in Alzheimer’s disease DOI Creative Commons
David T. Jones, Val J. Lowe, Jonathan Graff‐Radford

и другие.

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

Опубликована: Март 28, 2022

Abstract Disruption of mental functions in Alzheimer’s disease (AD) and related disorders is accompanied by selective degeneration brain regions. These regions comprise large-scale ensembles cells organized into systems for functioning, however the relationship between clinical symptoms dementia, patterns neurodegeneration, functional not clear. Here we present a model association dementia degenerative anatomy using F18-fluorodeoxyglucose PET dimensionality reduction techniques two cohorts patients with AD. This reflected simple information processing-based description macroscale which link to AD physiology, networks, abilities. We further apply normal aging seven diseases functions. propose global processing that links neuroanatomy, cognitive neuroscience neurology.

Язык: Английский

Процитировано

59
Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics DOI Creative Commons
Maria Pope, Makoto Fukushima, Richard F. Betzel

и другие.

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

Процитировано

58
Individualized event structure drives individual differences in whole-brain functional connectivity DOI Creative Commons
Richard F. Betzel, Sarah A. Cutts,

Sarah Greenwell

и другие.

NeuroImage, Год журнала: 2022, Номер 252, С. 118993 - 118993

Опубликована: Фев. 19, 2022

Resting-state functional connectivity is typically modeled as the correlation structure of whole-brain regional activity. It studied widely, both to gain insight into brain's intrinsic organization but also develop markers sensitive changes in an individual's cognitive, clinical, and developmental state. Despite this, origins drivers connectivity, especially at level densely sampled individuals, remain elusive. Here, we leverage novel methodology decompose its precise framewise contributions. Using two dense sampling datasets, investigate individualized focusing specifically on role brain network "events" - short-lived peaked patterns high-amplitude cofluctuations. a statistical test identify events empirical recordings. We show that cofluctuation expressed during are repeated across multiple scans same individual represent idiosyncratic variants template group level. Lastly, propose simple model based event cofluctuations, demonstrating group-averaged cofluctuations suboptimal for explaining participant-specific connectivity. Our work complements recent studies implicating brief instants primary static, extends those studies, individualized, positing dynamic basis

Язык: Английский

Процитировано

53
Subcortical-cortical dynamical states of the human brain and their breakdown in stroke DOI Creative Commons
Chiara Favaretto, Michele Allegra, Gustavo Deco

и другие.

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

Опубликована: Авг. 29, 2022

The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics the ultra-slow frequency range (<0.01-0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, identify Dynamic Functional States (DFSs), transient but recurrent clusters of and subcortical regions synchronizing frequencies. We observe shifts temporally coincident with clusters, flexibly either limbic (hippocampus/amygdala), or nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus cortex, provoke anomalies fraction times, dwell transitions DFSs, causing a bias toward abnormal network integration. Dynamical observed 2 weeks after stroke recover time contribute to explaining neurological impairment long-term outcome.

Язык: Английский

Процитировано

45