Lag structure in resting-state fMRI

Mitra, Anish; Snyder, Abraham Z.; Hacker, Carl D.; Raichle, Marcus E.

doi:10.1152/jn.00804.2013

Cited by 168 publications

(400 citation statements)

References 96 publications

(109 reference statements)

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“…For instance, SPW-Rs, slow waves, and many other neural processes are enriched in (awake) post-task learning periods as well as sleep [68,73,74]. In fact, we have indeed found that propagation patterns in rs-fMRI are sensitive to physiological changes in state, including before versus after motor learning tasks [26], eyes open versus closed [26] and most dramatically, wake versus slow wave sleep [45]. These findings suggest that the propagation structure of rs-fMRI can be used to reveal fundamental neural 'events' or processes in spontaneous activity; however, future multi-modal studies are necessary to investigate whether events such as SPW-Rs underlie distinct BOLD signal propagation patterns.…”

Section: Biological Mechanismmentioning

confidence: 57%

“…Critically, the mean value in each of the off-diagonal blocks in the TD matrix is very nearly zero [26,45]. If one RSN were systematically earlier than another RSN, the expected offdiagonal block mean would be non-zero.…”

Section: Temporal Lags Analysismentioning

confidence: 95%

“…Instead, the correlation measure (e.g. functional connectivity) integrates over time to provide a static spatial view of brain organization [18,20,26]. However, ample evidence of structured propagated intrinsic activity has been reported in the mouse using voltage sensitive dyes as well as genetically encoded calcium imaging [27][28][29][30][31][32][33].…”

Section: Importance Of Intrinsic Activitymentioning

confidence: 99%

“…For example, vector autoregressive (VAR) methods, including Granger causality [34,35] and dynamic causal modelling [36,37], can be used to infer signal directionality. Although these methods are highly effective for testing causal models in small numbers of time series, VAR-based analyses have computational limits which prevent extending these approaches to account for the tens of thousands of voxel time series necessary to describe propagation in the whole brain [26]. Thus, we have opted to study BOLD signal propagation by analysing temporal lags across the whole brain.…”

Section: Dynamics In the Resting State Blood Oxygen Level-dependent Smentioning

confidence: 99%

“…The set of all such delays forms a time delay (TD) matrix [26]. Figure 1c illustrates a TD matrix derived from rs-fMRI data collected in 100 awake, healthy young adults.…”

Section: Temporal Lags Analysismentioning

confidence: 99%

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How networks communicate: propagation patterns in spontaneous brain activity

Mitra

Raichle

2016

Phil. Trans. R. Soc. B

113

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One contribution of 15 to a Theo Murphy meeting issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. Initially regarded as 'noise', spontaneous (intrinsic) activity accounts for a large portion of the brain's metabolic cost. Moreover, it is now widely known that infra-slow (less than 0.1 Hz) spontaneous activity, measured using resting state functional magnetic resonance imaging of the blood oxygen level-dependent (BOLD) signal, is correlated within functionally defined resting state networks (RSNs). However, despite these advances, the temporal organization of spontaneous BOLD fluctuations has remained elusive. By studying temporal lags in the resting state BOLD signal, we have recently shown that spontaneous BOLD fluctuations consist of remarkably reproducible patterns of whole brain propagation. Embedded in these propagation patterns are unidirectional 'motifs' which, in turn, give rise to RSNs. Additionally, propagation patterns are markedly altered as a function of state, whether physiological or pathological. Understanding such propagation patterns will likely yield deeper insights into the role of spontaneous activity in brain function in health and disease.This article is part of the themed issue 'Interpreting blood oxygen level-dependent: a dialogue between cognitive and cellular neuroscience'. Importance of intrinsic activityAs observed by Hans Berger, in reporting on the first measurements of the human electroencephalogram, spontaneous (intrinsic) neural fluctuations are a dominant feature of the brain's electrical activity [1]. In the context of studying task-evoked neural responses, spontaneous activity was long considered merely to be 'noise'. Thus, most early studies of neural activity employed computational strategies to suppress spontaneous activity. More recently, it has been appreciated that investigating spontaneous activity is essential for understanding brain function [2][3][4]. At the systems level, this paradigm shift was prompted by two major findings. First, although the human brain represents only 2% of total body mass, its intrinsic activity consumes 20% of the body's energy, most of which is used to support ongoing neuronal signalling ([5-9], but see also [10]). Task-related increases in neuronal metabolism are generally small (less than 5%) when compared with this large intrinsic energy consumption (for a recent review, see [9]). Thus, to understand how the brain operates, we must take into account the component that consumes most of the brain's energy: spontaneous activity.The second set of findings has been derived from resting state functional magnetic resonance imaging (rs-fMRI) of the blood oxygen level-dependent (BOLD) signal [11]. Biswal et al. used human rs-fMRI to discover that spontaneous infraslow (less than 0.1 Hz) fluctuations of the BOLD signal are highly correlated within the somatomotor system [12]. This basic result has since been extended to multiple functional networks spanning the entire brain ([13-17]; figure 1a,b). Spatial correlat...

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Section: Biological Mechanismmentioning

confidence: 57%

Section: Temporal Lags Analysismentioning

confidence: 95%

Section: Importance Of Intrinsic Activitymentioning

confidence: 99%

Section: Dynamics In the Resting State Blood Oxygen Level-dependent Smentioning

confidence: 99%

“…The set of all such delays forms a time delay (TD) matrix [26]. Figure 1c illustrates a TD matrix derived from rs-fMRI data collected in 100 awake, healthy young adults.…”

Section: Temporal Lags Analysismentioning

confidence: 99%

See 3 more Smart Citations

How networks communicate: propagation patterns in spontaneous brain activity

Mitra

Raichle

2016

Phil. Trans. R. Soc. B

113

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Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder

et al. 2019

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This study investigated whole‐brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting‐state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P‐value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default‐mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large‐scale functional brain networks may contribute to the ASD phenotype. Autism Res 2020, 13: 244–258. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. Lay Summary Autism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Using an ultra‐fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. We found that ASD individuals had altered information flow patterns across brain regions. Atypical patterns were concentrated in salience, executive, visual, and default‐mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.

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Atypical Inter‐Network Deactivation Associated With the Posterior Default‐Mode Network in Autism Spectrum Disorder

et al. 2020

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Previous studies have suggested that atypical deactivation of functional brain networks contributes to the complex cognitive and behavioral profile associated with autism spectrum disorder (ASD). However, these studies have not considered the temporal dynamics of deactivation mechanisms between the networks. In this study, we examined (a) mutual deactivation and (b) mutual activation‐deactivation (i.e., anticorrelated) time‐lag patterns between resting‐state networks (RSNs) in young adults with ASD (n = 20) and controls (n = 20) by applying the recently defined dynamic lag analysis (DLA) method, which measures time‐lag variations peak‐by‐peak between the networks. In order to achieve temporally accurate lag patterns, the brain imaging data was acquired with a fast functional magnetic resonance imaging (fMRI) sequence (TR = 100 ms). Group‐level independent component analysis was used to identify 16 RSNs for the DLA. We found altered mutual deactivation timings in ASD in (a) three of the deactivated and (b) two of the transiently anticorrelated (activated‐deactivated) RSN pairs, which survived the strict threshold for significance of surrogate data. Of the significant RSN pairs, 80% included the posterior default‐mode network (DMN). We propose that temporally altered deactivation mechanisms, including timings and directionality, between the posterior DMN and RSNs mediating processing of socially relevant information may contribute to the ASD phenotype. Lay Summary To understand autistic traits on a neural level, we examined temporal fluctuations in information flow between brain regions in young adults with autism spectrum disorder (ASD) and controls. We used a fast neuroimaging procedure to investigate deactivation mechanisms between brain regions. We found that timings and directionality of communication between certain brain regions were temporally altered in ASD, suggesting atypical deactivation mechanisms associated with the posterior default‐mode network.

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Lag structure in resting-state fMRI

Cited by 168 publications

References 96 publications

How networks communicate: propagation patterns in spontaneous brain activity

How networks communicate: propagation patterns in spontaneous brain activity

Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder

Atypical Inter‐Network Deactivation Associated With the Posterior Default‐Mode Network in Autism Spectrum Disorder

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