The diversity and multiplexity of edge communities within and between brain systems

Jo, Youngheun; Esfahlani, Farnaz Zamani; Faskowitz, Joshua; Chumin, Evgeny J.; Sporns, Olaf; Betzel, Richard F.

doi:10.1016/j.celrep.2021.110032

Cited by 39 publications

(42 citation statements)

References 117 publications

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“…Even if the current study is not focused on improving predictability of stroke recovery, we provide novel fMRI connectivity methods that could be used in the future for that purpose. Given that the eFC matrix can be employed to boost the identifiability of fMRI data ( Jo et al, 2021a ), future work could explore if similar improvements to predictive models could be obtained using eFC and related edge-centric data structures.…”

Section: Discussionmentioning

confidence: 99%

Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

Idesis

Faskowitz

Betzel

et al. 2022

NeuroImage: Clinical

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Section: Discussionmentioning

confidence: 99%

Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

Idesis

Faskowitz

Betzel

et al. 2022

NeuroImage: Clinical

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“…In the past decade, several research groups have investigated the overlapping nature of brain communities (62)(63)(64)(65). Among other findings, this work has revealed that select brain regions connect both within their community, and also across communities.…”

Section: Spatial Scale Resolution and Hierarchiesmentioning

confidence: 99%

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

Uddin¹,

Betzel²,

Cohen³

et al. 2022

Preprint

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Progress in scientific disciplines is often accompanied by the standardization of terminology and nomenclature. Network neuroscience, as applied at the level of macro-scale organization of the brain, has emerged over the past decade from interdisciplinary collaborations. The field is only beginning to confront the challenges associated with developing and refining a taxonomy of its fundamental explanatory constructs. Despite initial attempts, there is currently a lack of consensus around basic questions such as “What constitutes a brain network”?, “Are there universal and reproducible brain networks that can be observed across individuals”? and “What naming and reporting conventions could be adopted to facilitate cross-laboratory communication?” The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed committee on best practices in large-scale brain network nomenclature. The objective is to provide concrete reporting recommendations similar to those produced by the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) and the magneto- and electroencephalography best practices committee. A working group was formed of cognitive and network neuroscientists, engineers, and philosophers who are actively engaged in research examining functional and structural brain networks using a range of neuroimaging modalities. The goal of WHATNET is to provide recommendations on points of consensus, identify open questions, and highlight areas of debate in the scientific community. The committee conducted a Qualtrics survey that was circulated by the OHBM executive office and advertised on Twitter to catalog current practices in large-scale brain network nomenclature. As expected, a few well-known network names (eg. default network) dominated responses to the survey. However, a number of interesting and illuminating points of disagreement emerged as well. The goal of this initiative is to move the field towards providing clear criteria and developing tools to aid in standardization of reporting network neuroscience results. Here we summarize survey results, discuss considerations, and provide initial recommendations from the workgroup. In doing so, we discuss multiple challenges to this enterprise, including: 1) network scale, resolution, and hierarchies; 2) inter-individual variability of networks; 3) consideration of network affiliations of subcortical structures; 4) consideration of multi-modal information, and 5) dynamics and non-stationarity of networks. We close with a set of minimal reporting guidelines that we urge the cognitive and network neuroscience communities to adopt while awaiting more concrete recommendations that we anticipate will be forthcoming from this group.

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“…Inspired by applications in network science [61,62], several recent studies have proposed "edge-centric" approaches for defining and characterizing the brain's system-level architecture [30,[63][64][65]. Unlike traditional methods for defining brain network systems, the "edgecentric" approach assigns cluster labels at the level of edges so that, from the perspective of each node, it maintains an affiliation to a plurality of clusters.…”

Section: Movies Decouple Sensorimotor Systems From One Anothermentioning

confidence: 99%

System-level high-amplitude co-fluctuations

Betzel

Chumin

Esfahlani

et al. 2022

Preprint

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Edge time series decompose interregional correlations (functional connectivity; FC) into their time-varying contributions. Previous studies have revealed that brief, high-amplitude, and globally-defined "events" contribute disproportionately to the time-averaged FC pattern. This whole-brain view prioritizes systems that occupy vast neocortical territory, possibly obscuring extremely high-amplitude co-fluctuations that are localized to smaller brain systems. Here, we investigate local events detected at the system level, assessing their independent contributions to global events and characterizing their repertoire during resting-state and movie-watching scans. We find that, as expected, global events are more likely to occur when large brain systems exhibit events. Next, we study the co-fluctuation patterns that coincide with system events--i.e. events detected locally based on the behavior of individual brain systems. We find that although each system exhibits a distinct co-fluctuation pattern that is dissimilar from those associated with global events, the patterns can nonetheless be grouped into two broad categories, corresponding to events that coincide with sensorimotor and attention systems and, separately, association systems. We then investigate system-level events during movie-watching, discovering that the timing of events in sensorimotor and attention systems decouple, yielding reductions in co-fluctuation amplitude. Next, we show that by associating each edge with its most similar system-averaged edge time series, we recover overlapping community structure, obviating the need for applying clustering algorithms to high-dimensional edge time series. Finally, we focus on cortical responses to system-level events in subcortical areas and the cerebellum. We show that these structures coincide with spatially distributed cortical co-fluctuations, centered on prefrontal and somatosensory systems. Collectively, the findings presented here help clarify the relative contributions of large and small systems to global events, as well as their independent behavior.

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The diversity and multiplexity of edge communities within and between brain systems

Cited by 39 publications

References 117 publications

Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

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

System-level high-amplitude co-fluctuations

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