Synchronous high-amplitude co-fluctuations of functional brain networks during movie-watching DOI Creative Commons
Jacob Tanner, Joshua Faskowitz, Lisa Byrge

et al.

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: Aug. 3, 2022

Abstract Recent studies have shown that functional connectivity can be decomposed into its exact framewise contributions, revealing short-lived, infrequent, and high-amplitude time points referred to as ``events.'' Although events contribute disproportionately the time-averaged pattern, improve identifiability brain-behavior associations, been linked endogenous hormonal fluctuations autism, their origins remain unclear. Here, we address this question using two independently-acquired imaging datasets in which participants passively watched movies. We find synchronize across individuals based on level of synchronization, categorized three distinct classes: those at boundaries between movies, during do not all. boundary events, compared other categories, exhibit greater amplitude, co-fluctuation patterns, temporal propagation. show underlying is a specific mode involving activation control salience systems alongside deactivation visual systems. Finally, strong positive relationship similarity time-locked patterns propensity for frames involve synchronous events. Collectively, our results suggest spatiotemporal properties are non-random locked time-varying stimuli.

Language: Английский

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

Maria Grazia

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(30)

Published: July 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.

Language: Английский

Citations

58
Living on the edge: network neuroscience beyond nodes DOI Creative Commons
Richard F. Betzel, Joshua Faskowitz, Olaf Sporns

et al.

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 27(11), P. 1068 - 1084

Published: Sept. 15, 2023

Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This come at expense anatomical functional connections that link these to one another. A new perspective namely emphasizes 'edges' may prove fruitful in addressing outstanding questions network neuroscience. We highlight recently proposed 'edge-centric' method review its current applications, merits, limitations. also seek establish conceptual mathematical links between this previously approaches science neuroimaging literature. conclude by presenting several avenues for future work extend refine existing edge-centric analysis.

Language: Английский

Citations

22
Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains DOI Creative Commons

Elisabeth Ragone,

Jacob Tanner, Youngheun Jo

et al.

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

Published: Jan. 24, 2024

Abstract Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no applied this data collected from model organisms. Here, we analyze structural and functional imaging lightly anesthetized mice through lens. We find evidence of “bursty” events - brief periods high-amplitude connectivity. Further, show that on a per-frame basis best explain static FC can be divided into series hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas largely adhere the boundaries algorithmically detected brain systems. then investigate connectivity undergirding patterns. induce modular bipartitions inter-areal axonal projections. Finally, replicate these same findings dataset. In summary, report recapitulates organism many phenomena observed previously analyses data. However, unlike subjects, murine nervous system is amenable invasive experimental perturbations. Thus, sets stage for future investigation causal origins co-fluctuations. Moreover, cross-species consistency reported enhances likelihood translation.

Language: Английский

Citations

5
Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity DOI Creative Commons
Leonard Sasse, Daouia I. Larabi, Amir Omidvarnia

et al.

Communications Biology, Journal Year: 2023, Volume and Issue: 6(1)

Published: July 10, 2023

Abstract Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within duration a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed computation an edge time series (ETS) and their derivatives. Evidence suggests that is driven by few points high-amplitude co-fluctuation (HACF) ETS, which may also contribute disproportionately interindividual differences. However, it remains unclear what degree different actually brain-behaviour associations. Here, we systematically evaluate this question assessing predictive utility estimates at levels using machine learning (ML) approaches. We demonstrate lower intermediate provide overall highest subject specificity as well capacity individual-level phenotypes.

Language: Английский

Citations

11
Recent trends in multiple metrics and multimodal analysis for neural activity and pupillometry DOI Creative Commons
Sou Nobukawa, Aya Shirama, Tetsuya Takahashi

et al.

Frontiers in Neurology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 2, 2024

Recent studies focusing on neural activity captured by neuroimaging modalities have provided various metrics for elucidating the functional networks and dynamics of entire brain. Functional magnetic resonance imaging (fMRI) can depict spatiotemporal dynamic characteristics due to its excellent spatial resolution. However, temporal resolution is limited. Neuroimaging such as electroencephalography (EEG) magnetoencephalography (MEG), which higher resolutions, are utilized multi-temporal scale multi-frequency-band analyzes. With this advantage, numerous EEG/MEG-bases revealed frequency-band specific involving connectivity multiple temporal-scale time-series patterns activity. In addition analyzing data, examination behavioral data unveil additional aspects brain through unimodal multimodal analyzes performed using appropriate integration techniques. Among assessments, pupillometry provide comprehensive spatial-temporal-specific features perspective, we summarize recent progress in development obtained from fMRI, EEG, MEG, well with a special focus data. First, review typical activity, emphasizing connectivity, complexity, state transitions whole-brain Second, examine related pupillary diameters discuss possibility that combine Finally, future perspectives these metrics.

Language: Английский

Citations

1
Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder DOI Creative Commons
Lei Li,

Xiaoran Su,

Qingyu Zheng

et al.

Human Brain Mapping, Journal Year: 2022, Volume and Issue: 43(15), P. 4722 - 4732

Published: July 4, 2022

Abstract Resting‐state functional connectivity (rsFC) approaches provide informative estimates of the architecture brain, and recently‐proposed cofluctuation analysis temporally unwraps FC at every moment in time, providing refined information for quantifying brain dynamics. As a network disorder, autism spectrum disorder (ASD) was characterized by substantial alteration FC, but contribution moment‐to‐moment‐activity cofluctuations to overall dysfunctional pattern ASD remains poorly understood. Here, we used approach explore underlying dynamic properties ASD, using large multisite resting‐state magnetic resonance imaging (rs‐fMRI) dataset (ASD = 354, typically developing controls [TD] 446). Our results verified that networks estimated high‐amplitude frames were highly correlated with traditional rsFC. Moreover, these showed higher average amplitudes participants than those TD group. Principal component performed on activity patterns aggregated over all subjects. The first principal (PC1) corresponds default mode (DMN), PC1 coefficients greater Additionally, increased symptom severity associated coefficients, which may result excessive internally oriented cognition social deficits individuals ASD. finding highlights utility prevalent neurodevelopmental disorders verifies aberrant DMN rsFC underline symptomatology adolescents youths

Language: Английский

Citations

3
Synchronous high-amplitude co-fluctuations of functional brain networks during movie-watching DOI Creative Commons
Jacob Tanner, Joshua Faskowitz, Lisa Byrge

et al.

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: Aug. 3, 2022

Abstract Recent studies have shown that functional connectivity can be decomposed into its exact framewise contributions, revealing short-lived, infrequent, and high-amplitude time points referred to as ``events.'' Although events contribute disproportionately the time-averaged pattern, improve identifiability brain-behavior associations, been linked endogenous hormonal fluctuations autism, their origins remain unclear. Here, we address this question using two independently-acquired imaging datasets in which participants passively watched movies. We find synchronize across individuals based on level of synchronization, categorized three distinct classes: those at boundaries between movies, during do not all. boundary events, compared other categories, exhibit greater amplitude, co-fluctuation patterns, temporal propagation. show underlying is a specific mode involving activation control salience systems alongside deactivation visual systems. Finally, strong positive relationship similarity time-locked patterns propensity for frames involve synchronous events. Collectively, our results suggest spatiotemporal properties are non-random locked time-varying stimuli.

Language: Английский

Citations

2