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

Betzel, Richard F.; Cutts, Sarah A.; Greenwell, Sarah; Faskowitz, Joshua; Sporns, Olaf

doi:10.1016/j.neuroimage.2022.118993

Cited by 54 publications

(108 citation statements)

References 92 publications

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“…We found evidence of three large clusters of cofluctuation patterns that persisted over multiple hierarchical levels, gradually refining their organization. Notably, the centroids of these clusters were consistent with those reported in our previous work [7], and were aligned with other recent findings. For instance, our clusters delineate task-positive and -negative systems [52,53], recapitulate spatial modes of variation in resting-state data time series [54], and closely resemble components of socalled "functional gradients" [55], which are frequently interpreted in terms of cognitive hierarchies [56].…”

Section: Co-fluctuation Patterns Are Hierarchically Organized In Timesupporting

confidence: 92%

“…Briefly, this procedure entails z-scoring parcel time series, generating edge time series for every pair of parcels, and calculating the root mean square (RMS) of co-activity at each time point. We elected to focus on local maxima in this RMS time series – “peaks” – rather than all frames, as our previous studies using this same dataset demonstrated that “troughs” in the RMS signal correspond to highly variable co-fluctuation patterns [7]. After motion censoring, we detected a total of 3124 peaks.…”

Section: Resultsmentioning

confidence: 99%

“…To address this question, we assigned each cofluctuation pattern a score indicating its “hierarchical depth.” That is, the number of hierarchical levels in which that pattern was present and included in a community. Then we followed the procedure outlined in [7] where, separately for each hierarchical level, we calculated the average co-fluctuation pattern across all patterns assigned to that level, and computed the correlation of that matrix with static FC (based on their upper triangle elements). We also repeated this procedure using a cumulative approach (starting with patterns at the highest level, gradually incorporating patterns from lower levels until all patterns were included) and a reverse cumulative approach (starting with the coarsest scale, gradually peeling away coarser and coarser partitions until only the most exclusive patterns were included).…”

Section: Resultsmentioning

confidence: 99%

“…Following our initial work, we showed that highamplitude co-fluctuations could be partitioned into at least two distinct clusters or "states." These states were shared at the group level, but refined individually, leading to the personalization of subjects' FC patterns [6,7]. This two-state description, however, was a direct consequence of the method used to detect states and precluded the possibility that co-fluctuation patterns were 1 We discovered later that this method had been reported at least once before, but had been applied in a narrow context [47].…”

Section: Co-fluctuation Patterns Are Hierarchically Organized In Timementioning

confidence: 99%

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

Betzel

Cutts

Tanner

et al. 2022

Preprint

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Edge time series decompose FC into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames, including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations. Here, we address those questions directly, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multi-scale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge and provide some practical guidance for future studies.

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Section: Co-fluctuation Patterns Are Hierarchically Organized In Timesupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Co-fluctuation Patterns Are Hierarchically Organized In Timementioning

confidence: 99%

See 2 more Smart Citations

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

Betzel

Cutts

Tanner

et al. 2022

Preprint

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“…FC is traditionally defined as the correlation between two brain region's activity over time. FC may be driven by two distinct features of brain activity: by the individualized spatial patterns of large-amplitude activations (Zamani Esfahlani et al, 2020), and by the amount of time spent in recurring patterns of activity (Betzel et al, 2021;Baker et al, 2014). In this paper we aim to identify group-level patterns of brain activity after stroke that relate to recovery, and assume that recurring activity patterns are shared across individuals but are expressed in different proportions.…”

Section: Introductionmentioning

confidence: 99%

Increased prevalence of a frontoparietal brain state is associated with better motor recovery after stroke affecting dominant-hand corticospinal tract

Olafson

Russello²,

Jamison

et al. 2022

Preprint

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Strokes cause lesions that damage brain tissue, disrupt normal brain activity patterns, and can lead to impairments in cognition and motor function. Although modulation of cortical activity is central to stimulation-based rehabilitative therapies, aberrant and adaptive patterns of brain activity after stroke have not yet been fully characterized. Here, we apply a brain dynamics analysis approach to study longitudinal brain activity patterns in individuals with ischemic pontine stroke. We first found 4 commonly occurring brain states largely characterized by high amplitude activations in the visual, frontoparietal, default mode, and motor networks. Stroke subjects spent less time in the frontoparietal state compared to controls, and, consistently, the default mode state had fewer transitions to the frontoparietal state in stroke subjects relative to controls. For individuals with dominant-hand CST damage, increased time spent in the frontoparietal state from 1 week to 3-6 months post-stroke was associated with better motor recovery over the same time period, an association which was independent of baseline impairment. Furthermore, the amount of time spent in brain states was linked theoretically and empirically to functional connectivity. This work suggests that when the dominant-hand CST is compromised in stroke, resting state configurations may shift to include increased activation of the frontoparietal network, which may facilitate compensatory neural pathways that support recovery of motor function when traditional motor circuits of the dominant-hemisphere CST are compromised. Determining subject- and stroke lesion-specific patterns of brain activity highly associated with recovery may help improve the design of clinical rehabilitation strategies, including non-invasive brain stimulation.

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Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment

Wang,

Du,

Wang

et al. 2023

Brain-X

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Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node‐centric network model, which only calculates correlations between brain regions. Considering the interaction of low‐order correlations between pairs of brain regions, we use an edge‐centric network model to study high‐order functional network correlations. Here, we compute edge time series (eTS) to obtain overlapping communities and study the relationship between subnetworks and communities in space. Then, based on the overlapping communities, we calculate the normalized entropy to measure the diversity of each node. Next, we compute the high‐amplitude co‐fluctuation of the eTS to explore the pattern of brain activity with temporal precision. Our results show that the normal control and EMCI patients differ in brain regions, subnetworks, and the whole brain. In particular, entropy values show a gradual decrease, and brain network co‐fluctuation increases with disease progression. Our study is the first to investigate the pathogenesis of EMCI from the perspective of spatiotemporal flexibility and cognitive diversity based on high‐order edge connectivity, further characterizing brain dynamics and providing new insights into the search for biomarkers of EMCI.

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

Cited by 54 publications

References 92 publications

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

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

Increased prevalence of a frontoparietal brain state is associated with better motor recovery after stroke affecting dominant-hand corticospinal tract

Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment

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