Hierarchical organization of spontaneous co-fluctuations in densely-sampled individuals using fMRI

Betzel, Richard F.; Cutts, Sarah A.; Tanner, Jaco; Greenwell, Sarah; Varley, Thomas F.; Faskowitz, Joshua; Sporns, Olaf

doi:10.1101/2022.03.06.483045

Cited by 9 publications

(14 citation statements)

References 86 publications

(111 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we calculate edge time series for 70 individuals, estimated events on a per-subject basis, pool the event co-fluctuation patterns together, and, following [33, 34], apply a data-driven clustering algorithm to these matrices, yielding three large states. The first pattern is typified by the collective co-fluctuations of visual, somatomotor, and dorsal attention networks with one another.…”

Section: Resultsmentioning

confidence: 99%

“…In our previous work, we demonstrated frames corresponding to high-amplitude co-fluctuations, i.e. “events”, corresponded to co-fluctuation patterns that were repeated across scans, were highly identifiable of individuals, and could explain large percentages of variance in the static FC [30, 32, 33]. Here, we applied an event detection algorithm to whole-brain edge time series from all subjects, extracted the corresponding co-fluctuation patterns, and clustered them using modularity maximization.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Multi-policy models of interregional communication in the human connectome

Betzel

Faskowitz

Mišić

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Network models of communication, e.g. shortest paths, diffusion, navigation, have become useful tools for studying structure-function relationships in the brain. These models generate estimates of communication efficiency between all pairs of brain regions, which can then be linked to the correlation structure of recorded activity, i.e. functional connectivity (FC). At present, however, communication models have a number of limitations, including difficulty adjudicating between models and the absence of a generic framework for modeling multiple interacting communication policies at the regional level. Here, we present a framework that allows us to incorporate multiple region-specific policies and fit them to empirical estimates of FC. Briefly, we show that many communication policies, including shortest paths and greedy navigation, can be modeled as biased random walks, enabling these policies to be incorporated into the same multi-policy communication model alongside unbiased processes, e.g. diffusion. We show that these multi-policy models outperform existing communication measures while yielding neurobiologically interpretable regional preferences. Further, we show that these models explain the majority of variance in time-varying patterns of FC. Collectively, our framework represents an advance in network-based communication models and establishes a strong link between these patterns and FC. Our findings open up many new avenues for future inquiries and present a flexible framework for modeling anatomically-constrained communication.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Multi-policy models of interregional communication in the human connectome

Betzel

Faskowitz

Mišić

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous studies have defined global events – intermittent, brief, and high-amplitude co-fluctuation patterns – as peaks in the RMS time series whose amplitude is statistically greater than that of a null distribution [10, 11, 17, 28, 29]. However, global events may inadvertently be driven by the behavior of larger brain systems, whose collective amplitude contributes disproportionately to the whole-brain RMS signal.…”

Section: Resultsmentioning

confidence: 99%

“…This observation has two immediate implications. First, it suggests that the global events characterized in previous studies are likely driven by the co-fluctuations of large brain systems [11, 17, 28, 29]. Indeed, the event co-fluctuation patterns described in those studies are remarkably consistent in that they implicate large and distributed brain systems, including the default mode, control, and cingulo-opercular networks (or territories that are typically associated with those systems; see [34, 35] for discussions on naming and describing system-level architecture).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

System-level high-amplitude co-fluctuations

Betzel

Chumin

Esfahlani

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Zheng

et al. 2022

Human Brain Mapping

View full text Add to dashboard Cite

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

show abstract

Hierarchical organization of spontaneous co-fluctuations in densely-sampled individuals using fMRI

Cited by 9 publications

References 86 publications

Multi-policy models of interregional communication in the human connectome

Multi-policy models of interregional communication in the human connectome

System-level high-amplitude co-fluctuations

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Contact Info

Product

Resources

About