Cross-module switching diversity of brain network nodes in resting and cognitive states

Yi, Chao; Fan, Yongchen; Wu, Ying

doi:10.1007/s11571-022-09894-z

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…Thus, we propose that lower regional signal variability may be related to consistency in connectivity patterns in hubs and highly clustered regions, for better information processing. A recent fMRI study may support this argument by demonstrating that in both resting and task states, nodes with lower CC change their memberships across different modules frequently (Yi, Fan, & Wu, 2022). Therefore, less regional signal variability for a highly clustered region (node with greater CC) may indicate that the membership of the region in a certain network is consistent.…”

Section: Discussionmentioning

confidence: 97%

Exploring the Interplay between BOLD Signal Variability, Complexity, and Static and Dynamic Functional Brain Network Features during Movie Viewing

Ghaderi

Wang

Protzner

2023

Preprint

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As the brain is dynamic and complex, knowledge of brain signal variability and complexity is crucial in our understanding of brain function. Recent resting-fMRI studies revealed links between BOLD signal variability or complexity with static/dynamics features of functional brain networks (FBN). However, no study has examined the relationships between these brain metrics. The association between brain signal variability and complexity is still understudied. Here we investigated the association between movie naturalistic-fMRI BOLD signal variability/complexity and static/dynamic FBN features using graph theory analysis. We found that variability positively correlated with fine-scale complexity but negatively correlated with coarse-scale complexity. Hence, variability and coarse-scale complexity correlated with static FC oppositely. Specifically, regions with high centrality and clustering coefficient were related to less variable but more complex signal. Similar relationship persisted for dynamic FBN, but the associations with certain aspects of regional centrality dynamics became insignificant. Our findings demonstrate that the relationship between BOLD signal variability, static/dynamic FBN with BOLD signal complexity depends on the temporal scale of signal complexity. Additionally, altered correlation between variability and complexity with dynamic FBN features may indicate the complex, time-varying feature of FBN and reflect how BOLD signal variability and complexity co-evolve with dynamic FBN over time.

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Section: Discussionmentioning

confidence: 97%

Exploring the Interplay between BOLD Signal Variability, Complexity, and Static and Dynamic Functional Brain Network Features during Movie Viewing

Ghaderi

Wang

Protzner

2023

Preprint

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The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

Kristanto,

Burkhardt,

Thiel

et al. 2024

Neuroscience & Biobehavioral Reviews

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The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

Kristanto,

Burkhardt,

Thiel

et al. 2024

Preprint

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The large number of different analytical choices researchers use may be partly responsible for the replication challenge in neuroimaging studies. For robustness analysis, knowledge of the full space of options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable options. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the options within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) distinct sequencing of steps, and 3) parameter tuning within steps. To facilitate access to the data, we developed a decision support app with high educational value called METEOR, which allows researchers to explore the space of choices as reference for well-informed robustness (multiverse) analysis.

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Cross-module switching diversity of brain network nodes in resting and cognitive states

Cited by 3 publications

References 40 publications

Exploring the Interplay between BOLD Signal Variability, Complexity, and Static and Dynamic Functional Brain Network Features during Movie Viewing

Exploring the Interplay between BOLD Signal Variability, Complexity, and Static and Dynamic Functional Brain Network Features during Movie Viewing

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

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