BOLD cofluctuation ‘events’ are predicted from static functional connectivity DOI Creative Commons
Zach Ladwig, Benjamin A. Seitzman,

Ally Dworetsky

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Jan. 27, 2022

ABSTRACT Recent work identified single time points (“events”) of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low points. This suggested that events might be a discrete, temporally sparse signal drives connectivity (FC) over the timeseries. However, different, not yet explored possibility is differences between are driven by sampling variability on constant, static, noisy signal. Using combination real and simulated data, we examined relationship structure asked if this was unique, or it could arise from alone. First, show discrete – there gradually increasing cofluctuation; ∼50% samples very strong structure. Second, using simulations predicted static FC. Finally, randomly selected can capture about as well events, largely because their temporal spacing. Together, these results suggest that, while exhibit particularly representations FC, little evidence unique timepoints drive FC Instead, parsimonious explanation for data but noisy, HIGHLIGHTS Past BOLD “events” fMRI connectivity, Here, were both stationary null model to test In >50% modularity similarity time- averaged Stationary models with similar behavior Events may transient driver rather an expected outcome it.

Language: Английский

Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series DOI Creative Commons
Olaf Sporns, Joshua Faskowitz, Andreia Sofia Teixeira

et al.

Network Neuroscience, Journal Year: 2021, Volume and Issue: 5(2), P. 405 - 433

Published: Jan. 1, 2021

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

Language: Английский

Citations

84
Edges in brain networks: Contributions to models of structure and function DOI Creative Commons
Joshua Faskowitz, Richard F. Betzel, Olaf Sporns

et al.

Network Neuroscience, Journal Year: 2021, Volume and Issue: unknown, P. 1 - 28

Published: Aug. 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

Language: Английский

Citations

75
Time-resolved structure-function coupling in brain networks DOI Creative Commons
Zhen-Qi Liu, Bertha Vázquez-Rodríguez, R. Nathan Spreng

et al.

Communications Biology, Journal Year: 2022, Volume and Issue: 5(1)

Published: June 2, 2022

The relationship between structural and functional connectivity in the brain is a key question systems neuroscience. Modern accounts assume single global structure-function that persists over time. Here we study coupling from dynamic perspective, show it regionally heterogeneous. We use temporal unwrapping procedure to identify moment-to-moment co-fluctuations neural activity, reconstruct time-resolved patterns. find patterns of are region-specific. observe stable unimodal transmodal cortex, intermediate regions, particularly insular cortex (salience network) frontal eye fields (dorsal attention network). Finally, variability region's related distribution its connection lengths. Collectively, our findings provide way relationships perspective.

Language: Английский

Citations

65
Individualized event structure drives individual differences in whole-brain functional connectivity DOI Creative Commons
Richard F. Betzel, Sarah A. Cutts,

Sarah Greenwell

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 252, P. 118993 - 118993

Published: Feb. 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

Language: Английский

Citations

52
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
BOLD cofluctuation ‘events’ are predicted from static functional connectivity DOI Creative Commons
Zach Ladwig, Benjamin A. Seitzman,

Ally Dworetsky

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 260, P. 119476 - 119476

Published: July 14, 2022

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low points. This suggested that events might be a discrete, temporally sparse signal drives connectivity (FC) over the timeseries. However, different, not yet explored possibility is differences between are driven by sampling variability on constant, static, noisy signal. Using combination real and simulated data, we examined relationship structure asked if this was unique, or it could arise from alone. First, show discrete - there gradually increasing cofluctuation; ∼50% samples very strong structure. Second, using simulations predicted static FC. Finally, randomly selected can capture about as well events, largely because their temporal spacing. Together, these results suggest that, while exhibit particularly representations FC, little evidence unique timepoints drive FC Instead, parsimonious explanation for data but noisy,

Language: Английский

Citations

27
Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder DOI Creative Commons
Farnaz Zamani Esfahlani, Lisa Byrge, Jacob Tanner

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 263, P. 119591 - 119591

Published: Aug. 27, 2022

The interaction between brain regions changes over time, which can be characterized using time-varying functional connectivity (tvFC). common approach to estimate tvFC uses sliding windows and offers limited temporal resolution. An alternative method is use the recently proposed edge-centric approach, enables tracking of moment-to-moment in co-fluctuation patterns pairs regions. Here, we first examined dynamic features edge time series compared them those window (sw-tvFC). Then, used compare subjects with autism spectrum disorder (ASD) healthy controls (CN). Our results indicate that relative sw-tvFC, captured rapid bursty network-level fluctuations synchronize across during movie-watching. from second part study suggested magnitude peak amplitude collective co-fluctuations (estimated as root sum square (RSS) series) similar CN ASD. However, trough-to-trough duration RSS signal greater ASD, CN. Furthermore, an edge-wise comparison high-amplitude showed within-network edges exhibited findings suggest by provide details about disruption dynamics could potentially developing new biomarkers mental disorders.

Language: Английский

Citations

27
Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery DOI Creative Commons
Sebastián Idesis, Joshua Faskowitz, Richard F. Betzel

et al.

NeuroImage Clinical, Journal Year: 2022, Volume and Issue: 35, P. 103055 - 103055

Published: Jan. 1, 2022

Most neuroimaging studies of post-stroke recovery rely on analyses derived from standard node-centric functional connectivity to map the distributed effects in stroke patients. Here, given importance nonlocal and diffuse damage, we use an edge-centric approach order provide alternative description this disorder. These techniques allow for rendering metrics such as normalized entropy, which describes diversity edge communities at each node. Moreover, enables identification high amplitude co-fluctuations fMRI time series. We found that entropy is associated with lesion severity continually increases across patients' recovery. Furthermore, not only relate but are also level The current study first application a clinical population longitudinal dataset demonstrates how different perspective data analysis can further characterize topographic modulations brain dynamics.

Language: Английский

Citations

26
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
A survey of brain functional network extraction methods using fMRI data DOI
Yuhui Du,

Songke Fang,

Xingyu He

et al.

Trends in Neurosciences, Journal Year: 2024, Volume and Issue: 47(8), P. 608 - 621

Published: June 20, 2024

Language: Английский

Citations

5