Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease

Si-Ping Zhang; Si-Ping Zhang; Bi Mao; Bi Mao; Tianlin Zhou; Tianlin Zhou; Chun-Wang Su; Chun-Wang Su; Chenxi Li; Junjie Jiang; Junjie Jiang; Simeng An; Simeng An; Nan Yao; Nan Yao; Youjun Li; Youjun Li; Zi-Gang Huang; Zi-Gang Huang; Zi-Gang Huang; Alzheimer’s Disease Neuroimaging Initiative

doi:10.3389/fnins.2023.1198839

Frontiers in Neuroscience (Oct 2023)

Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease

Si-Ping Zhang,
Si-Ping Zhang,
Bi Mao,
Bi Mao,
Tianlin Zhou,
Tianlin Zhou,
Chun-Wang Su,
Chun-Wang Su,
Chenxi Li,
Junjie Jiang,
Junjie Jiang,
Simeng An,
Simeng An,
Nan Yao,
Nan Yao,
Youjun Li,
Youjun Li,
Zi-Gang Huang,
Zi-Gang Huang,
Zi-Gang Huang,
Alzheimer’s Disease Neuroimaging Initiative

Affiliations

Si-Ping Zhang: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
Si-Ping Zhang: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Bi Mao: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
Bi Mao: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Tianlin Zhou: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
Tianlin Zhou: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Chun-Wang Su: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
Chun-Wang Su: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Chenxi Li: Department of Military Medical Psychology, Air Force Medical University, Xi’an, Shaanxi, China
Junjie Jiang: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
Junjie Jiang: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Simeng An: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
Simeng An: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Nan Yao: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Nan Yao: Department of Applied Physics, Xi'an University of Technology, Xi'an, China
Youjun Li: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
Youjun Li: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Zi-Gang Huang: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
Zi-Gang Huang: Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Zi-Gang Huang: The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
Alzheimer’s Disease Neuroimaging Initiative

DOI: https://doi.org/10.3389/fnins.2023.1198839
Journal volume & issue: Vol. 17

Abstract

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BackgroundThe brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer’s disease (AD).ObjectiveCoactivation pattern (CAP) analysis can identify different brain states. This paper aimed to investigate the dynamic characteristics of frequency dependent whole-brain CAPs in AD.MethodsWe utilized a multiband CAP approach to model the state space and study brain dynamics in both AD and NC. The correlation between the dynamic characteristics and the subjects’ clinical index was further analyzed.ResultsThe results showed similar CAP patterns at different frequency bands, but the occurrence of patterns was different. In addition, CAPs associated with the default mode network (DMN) and the ventral/dorsal visual network (dorsal/ventral VN) were altered significantly between the AD and NC groups. This study also found the correlation between the altered dynamic characteristics of frequency dependent CAPs and the patients’ clinical Mini-Mental State Examination assessment scale scores.ConclusionThis study revealed that while similar CAP spatial patterns appear in different frequency bands, their dynamic characteristics in subbands vary. In addition, delineating subbands was more helpful in distinguishing AD from NC in terms of CAP.

Published in Frontiers in Neuroscience

ISSN: 1662-4548 (Print); 1662-453X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: http://www.frontiersin.org/neuroscience

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