Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

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

doi:10.1038/s41593-020-00719-y

Cited by 243 publications

(377 citation statements)

References 123 publications

Supporting

Mentioning

355

Contrasting

Order By: Relevance

“…Recently, we presented a method for decomposing FC into its framewise (instantaneous) contributions [19, 23]. Our work, in agreement with other recent studies [3, 4, 6, 7], demonstrated that all frames do not contribute equally to FC – rather only a small number of high-amplitude frames – “events” – when averaged together, are necessary for explaining a high proportion of variance in FC.…”

Section: Discussionsupporting

confidence: 86%

“…Here, we address these questions directly. Our approach leverages a recently-proposed method for decomposing FC into its framewise contributions, detecting events, and assessing the impact of events on timeaveraged FC [19,[23][24][25][26]. We apply this framework to two independently acquired datasets: the Midnight Scan Club [11,12] and the MyConnectome project [10,27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Individualized event structure drives individual differences in whole-brain functional connectivity

Betzel

Cutts

Greenwell

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Individualized event structure drives individual differences in whole-brain functional connectivity

Betzel

Cutts

Greenwell

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, Faskowitz et al [51] have noted that the traditional metric of functional connectivity should be complemented by more complex edge weights and introduced an edge-centric measure. Here we have proposed Ricci-Forman-curvature as an alternative concept.…”

Section: Discussionmentioning

confidence: 99%

Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

Lohmann

Lacosse

Ethofer³

et al. 2021

Preprint

View full text Add to dashboard Cite

A number of recent studies have investigated machine learning techniques for predicting individual behaviour from fMRI. Even though encouraging results have been obtained, excessive scan times – especially in resting state fMRI – are a limiting factor.Here we present a new machine learning algorithm for predicting individual behaviour of healthy human subjects using both resting state (rsfMRI) as well as task-based fMRI (tfMRI). It achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight.As a proof of concept, we focus on predicting fluid, crystallized and general intelligence scores. In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50 percent in tfMRI, and of around 59 percent (R2 ≈ 0.29) when results from two tasks are combined. We compare these results against a benchmark of existing methods that produced correlations below 50 percent in both rsfMRI and tfMRI. We conclude that with novel machine learning techniques applied to tfMRI it is possible to obtain significantly better prediction accuracies at a fraction of the scan time.

show abstract

“…Recently, we suggested a new approach to functional connectivity, by focusing on the dynamics and networks formed by “ edge time series ” (Esfahlani et al, 2020 ; Faskowitz, Esfahlani, Jo, Sporns, & Betzel, 2020 ; Jo, Faskowitz, Esfahlani, Sporns, & Betzel, 2020 ). The approach unwraps time-averaged FC into time series of co-fluctuating signals on network edges resolved at the timescale of single frames in MRI acquisition, thus allowing inspection of network dynamics at fine timescales.…”

Section: Introductionmentioning

confidence: 99%

Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series

Sporns

Faskowitz

Teixeira

et al. 2021

Network Neuroscience

Self Cite

102

View full text Add to dashboard Cite

Functional connectivity (FC) describes the statistical dependence between neuronal populations or brain regions in resting-state fMRI studies and is commonly estimated as the Pearson correlation of time courses. Clustering or community detection reveals densely coupled sets of regions constituting resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs but appear to fluctuate on shorter time scales. Here, we propose a new approach to reveal temporal fluctuations in neuronal time series. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair or edge, and allows tracking of fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging runs, capture individual differences, and disclose fine-scale temporal expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

show abstract

Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Cited by 243 publications

References 123 publications

Individualized event structure drives individual differences in whole-brain functional connectivity

Individualized event structure drives individual differences in whole-brain functional connectivity

Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series

Contact Info

Product

Resources

About