4.6 Article

On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI

期刊

ENTROPY
卷 24, 期 8, 页码 -

出版社

MDPI
DOI: 10.3390/e24081148

关键词

functional MRI; resting state; task engagement; temporal complexity; multiscale entropy; Hurst exponent; task specificity; graph signal processing

资金

  1. Eurotech Postdoc Programme
  2. European Commission under its framework programme Horizon 2020 [754462]
  3. Swiss National Centre of Competence in ResearchEvolving Language [51NF40 180888]
  4. SNSF Ambizione project Fingerprinting the brain: network science to extract features of cognition, behaviour and dysfunction [PZ00P2-185716]
  5. CIBM Center for Biomedical Imaging
  6. Lausanne University Hospital (CHUV)
  7. University of Lausanne (UNIL)
  8. Ecole Polytechnique Federale de Lausanne (EPFL)
  9. University of Geneva (UNIGE)
  10. Geneva University Hospitals (HUG)
  11. 16 National Institutes of Health (NIH) institutes and centres [1U54MH091657]
  12. McDonnell Center for Systems Neuroscience at Washington University

向作者/读者索取更多资源

Measuring the temporal complexity of fMRI can provide insights into how brain activity changes over time. This study examined the spatial distribution of temporal complexity in resting state and task fMRI data from 100 subjects. The findings suggest that there is high spatial similarity between different complexity measures and task-specific complexity. Additionally, certain brain networks exhibit stronger complex behavior, regardless of task engagement.
Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据