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- Publisher Website: 10.1007/s11682-022-00748-0
- Scopus: eid_2-s2.0-85142452017
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Article: Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI
Title | Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI |
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Authors | |
Keywords | Convolutional neural networks Cross-sample entropy Disease classification Late-life depression Machine learning |
Issue Date | 24-Nov-2022 |
Publisher | Springer |
Citation | Brain Imaging and Behavior, 2022, v. 17, n. 1, p. 125-135 How to Cite? |
Abstract | Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD. |
Persistent Identifier | http://hdl.handle.net/10722/337698 |
ISSN | 2023 Impact Factor: 2.4 2023 SCImago Journal Rankings: 0.977 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lin, Chemin | - |
dc.contributor.author | Lee, Shwu-Hua | - |
dc.contributor.author | Huang, Chih-Mao | - |
dc.contributor.author | Chen, Guan-Yen | - |
dc.contributor.author | Chang, Wei | - |
dc.contributor.author | Liu, Ho-Ling | - |
dc.contributor.author | Ng, Shu-Hang | - |
dc.contributor.author | Lee, Tatia Mei-Chun | - |
dc.contributor.author | Wu, Shun-Chi | - |
dc.date.accessioned | 2024-03-11T10:23:11Z | - |
dc.date.available | 2024-03-11T10:23:11Z | - |
dc.date.issued | 2022-11-24 | - |
dc.identifier.citation | Brain Imaging and Behavior, 2022, v. 17, n. 1, p. 125-135 | - |
dc.identifier.issn | 1931-7557 | - |
dc.identifier.uri | http://hdl.handle.net/10722/337698 | - |
dc.description.abstract | <p>Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.</p> | - |
dc.language | eng | - |
dc.publisher | Springer | - |
dc.relation.ispartof | Brain Imaging and Behavior | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Convolutional neural networks | - |
dc.subject | Cross-sample entropy | - |
dc.subject | Disease classification | - |
dc.subject | Late-life depression | - |
dc.subject | Machine learning | - |
dc.title | Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s11682-022-00748-0 | - |
dc.identifier.scopus | eid_2-s2.0-85142452017 | - |
dc.identifier.volume | 17 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 125 | - |
dc.identifier.epage | 135 | - |
dc.identifier.eissn | 1931-7565 | - |
dc.identifier.isi | WOS:000886805900002 | - |
dc.identifier.issnl | 1931-7557 | - |