ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1597777
This article is part of the Research TopicUnraveling Cognitive Impairment: A Multimodal MRI Approach to Brain NetworksView all 9 articles
Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis
Provisionally accepted
- 1Changzhou University, Changzhou, Jiangsu Province, China
- 2Changzhou Institute of Technology, Changzhou, China
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Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder, with Mild Cognitive Impairment (MCI) often serving as a prodromal stage. Early detection of MCI is critical for timely intervention. Dynamic Functional Connectivity analysis reveals temporal dynamics obscured by static functional connectivity, making it valuable for analyzing and classifying psychiatric disorders. This study proposes a novel spatio-temporal approach for analyzing dynamic brain networks using resting-state fMRI. The method was evaluated on data from 85 subjects (33 healthy controls, 29 Early Mild Cognitive Impairment (EMCI), 23 AD) from the ADNI dataset. Our model outperformed existing techniques, achieving 83.9% accuracy and 83.1% AUC in distinguishing AD from healthy controls. In addition to improved classification performance, key affected regions such as left hippocampus, the right amygdala, the left inferior parietal lobe, the left olfactory cortex, the right precuneus, and the insula, were identified-areas known to be associated with memory function and early Alzheimer’s pathology. These findings suggest that dynamic connectivity analysis holds promise for non-invasive and interpretable early-stage diagnosis of AD.
Keywords: Dynamic Functional Connectivity, Attention, RS-fMRI, Early Alzheimer's disease, DMNs
Received: 21 Mar 2025; Accepted: 16 May 2025.
Copyright: © 2025 Wen, Wang, Liu, Meng and Jiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xianglian Meng, Changzhou Institute of Technology, Changzhou, China
Zhuqing Jiao, Changzhou University, Changzhou, Jiangsu Province, China
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