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Early detection of MCI is critical for timely intervention.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our model outperformed existing techniques, achieving 83.9% accuracy and 83.1% AUC in distinguishing AD from healthy controls.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>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\u2019s pathology. These findings suggest that dynamic connectivity analysis holds promise for non-invasive and interpretable early-stage diagnosis of AD.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnins.2025.1597777","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T05:50:48Z","timestamp":1749793848000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis"],"prefix":"10.3389","volume":"19","author":[{"given":"Shipeng","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingru","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianglian","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuqing","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.jalz.2017.02.001","article-title":"2017 Alzheimer\u2019s disease facts and figures.","volume":"13","year":"2017","journal-title":"Alzheimer\u2019s Dement."},{"key":"B2","first-page":"1","article-title":"Dynamic functional connectivity and graph convolution network for Alzheimer\u2019s disease classification","author":"An","year":"2020","journal-title":"Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics Engineering"},{"key":"B3","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1162\/netn_a_00238","article-title":"mixed-modeling framework for whole-brain dynamic network analysis.","volume":"6","author":"Bahrami","year":"2022","journal-title":"Netw. 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