On co-activation pattern analysis and non-stationarity of resting brain activity

Matsui, Teppei; Pham, Trung Quang; Jimura, Koji; Chikazoe, Junichi

doi:10.1016/j.neuroimage.2022.118904

Cited by 29 publications

(27 citation statements)

References 33 publications

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Section: Resultssupporting

confidence: 88%

“…TSPs calculated based on the null data were quite similar to those calculated based on the original data. Spatial correlation between STPs of real and null data was high (Supplementary Figure S5A), similar to prior studies (Laumann et al, 2017;Matsui et al, 2022a). However, internal cluster validity index (Calhoun et al, 2014) differed between null and real data (Supplementary Figure S5B), suggesting that the cluster structure of TSPs was not fully explained by lag cross correlation of fMRI signals and that these clusters may correspond to distinct states constituting non-stationary resting-brain activity (Calhoun et al, 2014).…”

Section: Examination Of Clusterssupporting

confidence: 82%

“…The clusters extracted above may be a statistical artifact due to sampling variability rather than reflecting distinct brain states (Laumann et al, 2017 : Liegeois et al, 2017 ; Matsui et al, 2022a ). Therefore, we tested whether similar clusters were found in null data, which was constructed to be stationary (Liegeois et al, 2017 ).…”

Section: Resultsmentioning

confidence: 99%

“…However, such temporal characteristics may also reflect sampling variability (Laumann et al, 2017 ), evoked attention, head motion (Power et al, 2017 ) and/or sleep (Tagliazucchi and Laufs, 2014 ; Stoffers et al, 2015 ; Hindriks et al, 2016 ; Laumann et al, 2017 ). Recent studies showed that not only the spatial patterns but also temporal dynamics of the putative brain states were reproducible in null data that were stationary by construction (Liegeois et al, 2017 ; Matsui et al, 2022a ). Although some temporal aspects of the clusters in the present study were not fully explained by null data ( Supplementary Figure S5B ), as in some previous studies (Allen et al, 2014 ; Matsui et al, 2019 ), the differences were small.…”

Section: Discussionmentioning

confidence: 99%

“…MRI signal timecourses were extracted from 264 ROIs distributed across the whole brain identified in prior studies (Power et al, 2011 ). We have selected this ROI atlas because it is one of the most frequently used ones to examine brain-wide functional networks in humans (Keerativittayayut et al, 2018 ; Matsui et al, 2022a ), thus allowing us to compare present results directly with other literatures.…”

Section: Methodsmentioning

confidence: 99%

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A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations

Noro

Matsui

et al. 2023

Front. Neuroinform.

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Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest.

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Section: Resultssupporting

confidence: 88%

Section: Examination Of Clusterssupporting

confidence: 82%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations

Noro

Matsui

et al. 2023

Front. Neuroinform.

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Thalamic contributions to psychosis susceptibility: Evidence from co‐activation patterns accounting for intra‐seed spatial variability (μCAPs)

Delavari,

Sandini,

Kojovic

et al. 2024

Human Brain Mapping

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The temporal variability of the thalamus in functional networks may provide valuable insights into the pathophysiology of schizophrenia. To address the complexity of the role of the thalamic nuclei in psychosis, we introduced micro‐co‐activation patterns (μCAPs) and employed this method on the human genetic model of schizophrenia 22q11.2 deletion syndrome (22q11.2DS). Participants underwent resting‐state functional MRI and a data‐driven iterative process resulting in the identification of six whole‐brain μCAPs with specific activity patterns within the thalamus. Unlike conventional methods, μCAPs extract dynamic spatial patterns that reveal partially overlapping and non‐mutually exclusive functional subparts. Thus, the μCAPs method detects finer foci of activity within the initial seed region, retaining valuable and clinically relevant temporal and spatial information. We found that a μCAP showing co‐activation of the mediodorsal thalamus with brain‐wide cortical regions was expressed significantly less frequently in patients with 22q11.2DS, and its occurrence negatively correlated with the severity of positive psychotic symptoms. Additionally, activity within the auditory–visual cortex and their respective geniculate nuclei was expressed in two different μCAPs. One of these auditory–visual μCAPs co‐activated with salience areas, while the other co‐activated with the default mode network (DMN). A significant shift of occurrence from the salience+visuo‐auditory‐thalamus to the DMN + visuo‐auditory‐thalamus μCAP was observed in patients with 22q11.2DS. Thus, our findings support existing research on the gatekeeping role of the thalamus for sensory information in the pathophysiology of psychosis and revisit the evidence of geniculate nuclei hyperconnectivity with the audio‐visual cortex in 22q11.2DS in the context of dynamic functional connectivity, seen here as the specific hyper‐occurrence of these circuits with the task‐negative brain networks.

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Frequency-specific functional difference between gyri and sulci in naturalistic paradigm fMRI

Wang,

Yang,

et al. 2024

Brain Struct Funct

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On co-activation pattern analysis and non-stationarity of resting brain activity

Cited by 29 publications

References 33 publications

A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations

A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations

Thalamic contributions to psychosis susceptibility: Evidence from co‐activation patterns accounting for intra‐seed spatial variability (μCAPs)

Frequency-specific functional difference between gyri and sulci in naturalistic paradigm fMRI

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