Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis
IntroductionAlzheimer’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.MethodsDynamic Functional Connectivity analysis reveals temporal dynamics obscured by...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1597777/full |
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| author | Shipeng Wen Jingru Wang Wenjie Liu Xianglian Meng Xianglian Meng Zhuqing Jiao Zhuqing Jiao |
| author_facet | Shipeng Wen Jingru Wang Wenjie Liu Xianglian Meng Xianglian Meng Zhuqing Jiao Zhuqing Jiao |
| author_sort | Shipeng Wen |
| collection | DOAJ |
| description | IntroductionAlzheimer’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.MethodsDynamic 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.ResultsOur model outperformed existing techniques, achieving 83.9% accuracy and 83.1% AUC in distinguishing AD from healthy controls.DiscussionIn 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. |
| format | Article |
| id | doaj-art-18fdfca1ad5e4802b56d711904e2bad3 |
| institution | OA Journals |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-18fdfca1ad5e4802b56d711904e2bad32025-08-20T02:23:51ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-06-011910.3389/fnins.2025.15977771597777Spatio-temporal dynamic functional brain network for mild cognitive impairment analysisShipeng Wen0Jingru Wang1Wenjie Liu2Xianglian Meng3Xianglian Meng4Zhuqing Jiao5Zhuqing Jiao6Wangzheng School of Microelectronics, Changzhou University, Changzhou, ChinaWangzheng School of Microelectronics, Changzhou University, Changzhou, ChinaSchool of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, ChinaWangzheng School of Microelectronics, Changzhou University, Changzhou, ChinaSchool of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, ChinaWangzheng School of Microelectronics, Changzhou University, Changzhou, ChinaSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, ChinaIntroductionAlzheimer’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.MethodsDynamic 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.ResultsOur model outperformed existing techniques, achieving 83.9% accuracy and 83.1% AUC in distinguishing AD from healthy controls.DiscussionIn 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.https://www.frontiersin.org/articles/10.3389/fnins.2025.1597777/fulldynamic functional connectivityattentionrS-fMRIearly Alzheimer’s diseaseDMNs |
| spellingShingle | Shipeng Wen Jingru Wang Wenjie Liu Xianglian Meng Xianglian Meng Zhuqing Jiao Zhuqing Jiao Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis Frontiers in Neuroscience dynamic functional connectivity attention rS-fMRI early Alzheimer’s disease DMNs |
| title | Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis |
| title_full | Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis |
| title_fullStr | Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis |
| title_full_unstemmed | Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis |
| title_short | Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis |
| title_sort | spatio temporal dynamic functional brain network for mild cognitive impairment analysis |
| topic | dynamic functional connectivity attention rS-fMRI early Alzheimer’s disease DMNs |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1597777/full |
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