{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:28:07Z","timestamp":1775068087569,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81971683"],"award-info":[{"award-number":["81971683"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["L182010"],"award-info":[{"award-number":["L182010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["81971683"],"award-info":[{"award-number":["81971683"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L182010"],"award-info":[{"award-number":["L182010"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer\u2019s disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects\u2019 features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs\u2019 input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or\/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph\u2019s imaging features and edge-assigning functions can both significantly affect classification accuracy.<\/jats:p>","DOI":"10.3390\/s23041914","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T01:37:07Z","timestamp":1675906627000},"page":"1914","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification"],"prefix":"10.3390","volume":"23","author":[{"given":"Lan","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Min","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Ge","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Wenjie","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Shen","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Shuicai","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}]},{"name":"Initiative Alzheimer\u2019s Disease Neuroimaging","sequence":"additional","affiliation":[]}],"member":"1968","published-online":{"date-parts":[[2023,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s12929-018-0404-x","article-title":"Impact of Social Relationships on Alzheimer\u2019s Memory Impairment: Mechanistic Studies","volume":"25","author":"Hsiao","year":"2018","journal-title":"J. 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