Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

Hindriks, Rikkert; Adhikari, Mohit H.; Murayama, Yusuke; Ganzetti, Marco; Mantini, Dante; Logothetis, Nikos K.; Deco, Gustavo

doi:10.1016/j.neuroimage.2015.11.055

Cited by 572 publications

(622 citation statements)

References 45 publications

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“…Another essential point pertaining to any sliding window analysis is the extent to which observed FC fluctuations are truly the reflection of dynamic changes in brain connectivity, rather than artefactual correlates of the employed methodology (Hindriks et al, 2016). In the resting‐state literature, several approaches have been suggested for this purpose, from the computation of FC across separate subjects (Keilholz, Magnuson, Pan, Willis, & Thompson, 2013) to the generation of appropriate null data with disrupted dynamics (Betzel, Fukushima, He, Zuo, & Sporns, 2016; Leonardi et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation

Bolton

Jochaut

Giraud

et al. 2018

Human Brain Mapping

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To refine our understanding of autism spectrum disorders (ASD), studies of the brain in dynamic, multimodal and ecological experimental settings are required. One way to achieve this is to compare the neural responses of ASD and typically developing (TD) individuals when viewing a naturalistic movie, but the temporal complexity of the stimulus hampers this task, and the presence of intrinsic functional connectivity (FC) may overshadow movie‐driven fluctuations. Here, we detected inter‐subject functional correlation (ISFC) transients to disentangle movie‐induced functional changes from underlying resting‐state activity while probing FC dynamically. When considering the number of significant ISFC excursions triggered by the movie across the brain, connections between remote functional modules were more heterogeneously engaged in the ASD population. Dynamically tracking the temporal profiles of those ISFC changes and tying them to specific movie subparts, this idiosyncrasy in ASD responses was then shown to involve functional integration and segregation mechanisms such as response inhibition, background suppression, or multisensory integration, while low‐level visual processing was spared. Through the application of a new framework for the study of dynamic experimental paradigms, our results reveal a temporally localized idiosyncrasy in ASD responses, specific to short‐lived episodes of long‐range functional interplays.

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Section: Discussionmentioning

confidence: 99%

Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation

Bolton

Jochaut

Giraud

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Indeed, recent studies using resting-state fMRI in humans reported that the temporal fluctuation of FC cannot be distinguished from that in a model assuming stationary FC and statistical sampling error [23,24]. Applying the same analysis to the mouse data, we found that, in both neuronal calcium and hemodynamic signals, the temporal dynamics of FC were not fully explained by stationary FC [16].…”

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confidence: 83%

Mouse optical imaging for understanding resting-state functional connectivity in human fMRI

Matsui

Murakami

Ohki

2018

Communicative & Integrative Biology

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Resting-state functional connectivity (FC), which measures the temporal correlation of spontaneous hemodynamic activity between distant brain areas, is a widely accepted method in functional magnetic resonance imaging (fMRI) to assess the connectome of healthy and diseased human brains. A common assumption underlying FC is that it reflects the temporal structure of large-scale neuronal activity that is converted into large-scale hemodynamic activity. However, direct observation of such relationship has been difficult. In this commentary, we describe our recent progress regarding this topic. Recently, transgenic mice that express a genetically encoded calcium indicator (GCaMP) in neocortical neurons are enabling the optical recording of neuronal activity in large-scale with high spatiotemporal resolution. Using these mice, we devised a method to simultaneously monitor neuronal and hemodynamic activity and addressed some key issues related to the neuronal basis of FC. We propose that many important questions about human resting-state fMRI can be answered using GCaMP expressing transgenic mice as a model system.

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“…Dynamic functional connectivity among the 80 nodes was computed over a 44.64 s tapered (rectangle convolved with a Gaussian) sliding window incremented 0.72 s over 14.4-minute node timeseries 81 . In the absence of information about the timescale of dynamic fluctuations in connectivity, the probability of detecting such fluctuations in resting fMRI data is optimized for a sliding window approximately 50 s in duration 56 . Because the data included more features than observations, the pairwise connectivity matrix during each time window was computed as a regularized precision matrix 32,[82][83][84] .…”

Section: Methodsmentioning

confidence: 99%

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Ćirić

Nomi

Uddin

et al. 2016

Preprint

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Investigations of the human brain's connectomic architecture have produced two alternative models: one describes the brain's spatial structure in terms of static localized networks, and the other describes the brain's temporal structure in terms of dynamic whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using resting fMRI data, we investigated the assumptions undergirding current models of the human connectome. Consistent with state-based models, our results suggest that static localized networks are superordinate approximations of underlying dynamic states. Furthermore, each of these localized, dynamic connectivity states is associated with global changes in the whole-brain functional connectome. By nesting localized dynamic connectivity states within their whole-brain contexts, we demonstrate the relative temporal independence of brain networks. Our assay for functional autonomy of coordinated neural systems is broadly applicable, and our findings provide evidence of structure in temporal state dynamics that complements the well-described static spatial organization of the brain.A major endeavor in neuroscience is to characterize the spatiotemporal organization of the brain into functional systems 1 . By identifying patterns of synchronous brain activity, functional magnetic resonance neuroimaging (fMRI) techniques have partitioned the human brain into large-scale networks 2,3 . These functional networks are stable across individuals and populations [4][5][6] , are roughly consistent across task-evoked and resting data 7,8 , and are present across mammalian species 9, 10 . A hierarchically modularized set of canonical networks is now widely accepted as an organizational principle of the brain 11,12 . Indeed, an expanding literature relates networks to specific psychological functions and individual differences [13][14][15][16] , with the potential for improved clinical diagnosis or treatment outcome metrics [17][18][19] . However, a growing body of work has called into question how accurately this canonical network model represents underlying neural architecture. In particular, many methods used to delineate networks rely on two implicit assumptions. First is the 'spatial assumption' that each brain region participates in exactly one network. Casting doubt on this are models suggesting that brain regions can engage with several different networks [20][21][22][23][24][25][26] , dynamic causal models showing that connectivity between brain regions changes as a function of the experimental context 27 , and graph theoretic models intimating the existence of neural hubs that recruit multiple networks [28][29][30][31] . Second is the 'temporal assumption' that the connectivity within each network remains relatively stable over time. This, too, has been called into question, with recent work 32 suggesting that the brain is dynamically multistable. That is, the brain may occupy any of a number of connectivity states over time, each with a distinct ne...

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Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

Cited by 572 publications

References 45 publications

Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation

Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation

Mouse optical imaging for understanding resting-state functional connectivity in human fMRI

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

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