paper-with-me

홈 › Papers

Diurnal variations of resting-state fMRI data: A graph-based analysis

2021-10-12 · Farzad V. Farahani, Waldemar Karwowski, Mark D Esposito, Richard F. Betzel, Magdalena Fafrowicz, Bartosz Bohaterewicz, Tadeusz Marek, Pamela K. Douglas

Circadian rhythms synchronize a variety of physiological processes ranging from neural activity and hormone secretion to sleep cycles and feeding habits. Despite significant diurnal variation, time-of-day (TOD) is rarely recorded or analyzed in human brain research. Moreover, sleep-wake patterns, diurnal preferences, and daytime alertness vary across individuals, known as sleep chronotypes. Here, we performed graph-theory network analysis on resting-state functional MRI (rs-fMRI) data to explore topological differences in whole-brain functional networks between morning and evening sessions (TOD effect), and between extreme morning-type and evening-type chronotypes. To that end, 62 individuals (31 extreme morning, 31 evening-type) underwent two fMRI sessions: about 1 hour after the wake-up time (morning), and 10 hours thereafter, scheduled in accord with their declared habitual sleep-wake pattern. TOD significantly altered functional connectivity (FC) patterns, but there was no significant difference in chronotypic categories. Compared to the morning session, we found relatively increased small-worldness, modularity, assortativity, and synchronization in the evening session, indicating more efficient functional topology. Local measures were changed during the day predominantly across the areas involved in somatomotor, ventral attention, as well as default mode networks. Also, connectivity and hub analyses showed that the somatomotor, ventral attention, and visual networks are the most densely-connected brain areas in both sessions, respectively, with the first being more active in the evening session and the two latter in the morning session. Collectively, these findings suggest TOD can impact classic analyses used in human neuroimaging such as functional connectivity, and should be recorded and included in the analysis of functional neuroimaging data.

📄 PDF Abstract BibTeX arXiv:2110.05766

Code (0)

등록된 구현이 없습니다.

Tasks

Functional Connectivity

Similar Papers 제목 키워드 기반

Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies

2021-05-13 · NeurIPS 2021 12 · Dushyant Sahoo, Christos Davatzikos

Resting-state fMRI has been shown to provide surrogate biomarkers for the analysis of various diseases. In addition, fMRI data helps in understanding the brain's functional working during resting state and task-induced a…

Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI

2022-04-25 · Xirui Hou, Pengfei Guo, Puyang Wang, Peiying Liu 외

Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early strat…

Deep LearningManagementPrognosis

BOLDSimNet: Examining Brain Network Similarity between Task and Resting-State fMRI

2025-04-02 · Boseong Kim, Debashis Das Chakladar, Haejun Chung, Ikbeom Jang

Traditional causal connectivity methods in task-based and resting-state functional magnetic resonance imaging (fMRI) face challenges in accurately capturing directed information flow due to their sensitivity to noise and…

NeoRS: a neonatal resting state fMRI data preprocessing pipeline

2022-04-08 · V. Enguix, J. Kenley, D. Luck, J. Cohen-Adad 외

Resting state fMRI (rsfMRI) has been shown to be a promising tool to study intrinsic functional connectivity and assess its integrity in cerebral development. In neonates, where fMRI is limited to few paradigms, rsfMRI w…

Functional ConnectivitySpecificity

Machine learning in resting-state fMRI analysis

2018-12-30 · Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski 외

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learn…

BIG-bench Machine Learning