{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T06:27:17Z","timestamp":1792650437976,"version":"4.5.0"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a prevalent neurodegenerative disease that primarily affects the elderly population. The early detection of mild cognitive impairment (MCI) holds significant clinical importance for prompt intervention and treatment of AD. Currently, functional connectivity (FC) networks-based diagnostic methods for early MCI (eMCI) detection are widely employed. FC considers the interaction patterns between brain regions as a topological structure. Recently, graph neural network (GNN) approaches have been utilized for disease diagnosis using FC networks, leveraging their ability to extract features from topological structures. However, existing methods typically treat FC features as GNN node attributes, disregarding the fact that FC features represent the weights of connection edges in FC networks with topological characteristics. In this paper, we propose a dynamically weighted GNN-based approach for early eMCI detection. Our method takes into account the topological structure and dynamic properties of FC networks by utilizing temporal FC features as the weighted adjacency matrix for dynamic GNNs. Moreover, the weighted graph local clustering coefficient of brain regions is employed as the node feature for GNNs. We extensively evaluated our approach using the ADNI database and achieved accuracies of 91.67% and 78.33% on low- and high-order FC networks, respectively. These results demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.<\/jats:p>","DOI":"10.1371\/journal.pone.0323894","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T17:33:42Z","timestamp":1749058422000},"page":"e0323894","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamically weighted graph neural network for detection of early mild cognitive impairment"],"prefix":"10.1371","volume":"20","author":[{"given":"Li","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5022-4845","authenticated-orcid":true,"given":"Kai","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"issue":"9","key":"pone.0323894.ref001","doi-asserted-by":"crossref","first-page":"3282","DOI":"10.1002\/hbm.23240","article-title":"High-order resting-state functional connectivity network for MCI classification","volume":"37","author":"X Chen","year":"2016","journal-title":"Hum Brain Mapp"},{"key":"pone.0323894.ref002","doi-asserted-by":"crossref","first-page":"1081788","DOI":"10.3389\/fnins.2022.1081788","article-title":"A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection","volume":"16","author":"X Zhang","year":"2022","journal-title":"Front Neurosci"},{"issue":"8","key":"pone.0323894.ref003","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TBME.2019.2957921","article-title":"Spatial-temporal dependency modeling and network hub detection for functional MRI analysis via convolutional-recurrent network","volume":"67","author":"M Wang","year":"2020","journal-title":"IEEE Trans Biomed Eng"},{"issue":"2","key":"pone.0323894.ref004","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/TMI.2019.2928790","article-title":"Deep learning of static and dynamic brain functional networks for early MCI detection","volume":"39","author":"T-E Kam","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"pone.0323894.ref005","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1109\/JBHI.2023.3304388","article-title":"Brain status transferring generative adversarial network for decoding individualized atrophy in Alzheimer\u2019s disease","volume":"27","author":"X Gao","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"pone.0323894.ref006","doi-asserted-by":"crossref","first-page":"115383","DOI":"10.1109\/ACCESS.2020.3003424","article-title":"Resting state fMRI and improved deep learning algorithm for earlier detection of Alzheimer\u2019s disease","volume":"8","author":"H Guo","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0323894.ref007","doi-asserted-by":"crossref","first-page":"108603","DOI":"10.1109\/ACCESS.2023.3321220","article-title":"sMRI-PatchNet: a novel efficient explainable patch-based deep learning network for alzheimer\u2019s disease diagnosis with structural MRI","volume":"11","author":"X Zhang","year":"2023","journal-title":"IEEE Access"},{"issue":"4","key":"pone.0323894.ref008","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1097\/WCO.0b013e328306f2c5","article-title":"Resting-state functional connectivity in neuropsychiatric disorders","volume":"21","author":"M Greicius","year":"2008","journal-title":"Curr Opin Neurol"},{"issue":"2","key":"pone.0323894.ref009","doi-asserted-by":"crossref","first-page":"218","DOI":"10.3390\/diagnostics13020218","article-title":"A similarity measure-based approach using RS-fMRI data for autism spectrum disorder diagnosis","volume":"13","author":"X Zhang","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"3","key":"pone.0323894.ref010","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1109\/TBME.2021.3122813","article-title":"Constructing multi-view high-order functional connectivity networks for diagnosis of autism spectrum disorder","volume":"69","author":"F Zhao","year":"2022","journal-title":"IEEE Trans Biomed Eng"},{"key":"pone.0323894.ref011","doi-asserted-by":"crossref","first-page":"103032","DOI":"10.1016\/j.media.2023.103032","article-title":"Joint learning framework of cross-modal synthesis and diagnosis for Alzheimer\u2019s disease by mining underlying shared modality information","volume":"91","author":"C Wang","year":"2024","journal-title":"Med Image Anal"},{"key":"pone.0323894.ref012","doi-asserted-by":"crossref","first-page":"918969","DOI":"10.3389\/fnhum.2022.918969","article-title":"Multi-view feature enhancement based on self-attention mechanism graph convolutional network for autism spectrum disorder diagnosis","volume":"16","author":"F Zhao","year":"2022","journal-title":"Front Hum Neurosci"},{"key":"pone.0323894.ref013","doi-asserted-by":"crossref","unstructured":"Chen L, Huang Y, Liao B, Nie K, Dong S, Hu J. Graph learning approaches for graph with noise: application to disease prediction in population graph. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2020. p. 2724\u20139.","DOI":"10.1109\/BIBM49941.2020.9313348"},{"key":"pone.0323894.ref014","unstructured":"Gilmer J, Schoenholz S, Riley P, Vinyals O, Dahl G. Neural message passing for quantum chemistry. In: International Conference on Machine Learning. PMLR. 2017. p. 1263\u201372."},{"issue":"4","key":"pone.0323894.ref015","doi-asserted-by":"crossref","first-page":"2434","DOI":"10.1109\/TII.2021.3093115","article-title":"Design and analysis of a hybrid GNN-ZNN model with a fuzzy adaptive factor for matrix inversion","volume":"18","author":"J Dai","year":"2022","journal-title":"IEEE Trans Ind Inf"},{"issue":"3","key":"pone.0323894.ref016","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","article-title":"Complex network measures of brain connectivity: uses and interpretations","volume":"52","author":"M Rubinov","year":"2010","journal-title":"Neuroimage"},{"key":"pone.0323894.ref017","article-title":"Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder","volume":"9","author":"Q Xie","year":"2021","journal-title":"PeerJ"},{"key":"pone.0323894.ref018","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3389\/fnhum.2018.00184","article-title":"Diagnosis of autism spectrum disorders using multi-level high-order functional networks derived from resting-state functional MRI","volume":"12","author":"F Zhao","year":"2018","journal-title":"Front Hum Neurosci"},{"issue":"7","key":"pone.0323894.ref019","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/JSTSP.2016.2600400","article-title":"Transmodal learning of functional networks for Alzheimer\u2019s disease prediction","volume":"10","author":"M Rahim","year":"2016","journal-title":"IEEE J Sel Top Signal Process"},{"issue":"8","key":"pone.0323894.ref020","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1109\/TNSRE.2020.3007589","article-title":"Automated thresholding method for fnirs-based functional connectivity analysis: validation with a case study on Alzheimer\u2019s disease","volume":"28","author":"YL Chan","year":"2020","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"pone.0323894.ref021","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1109\/TNSRE.2022.3204913","article-title":"EEG-based graph neural network classification of Alzheimer\u2019s disease: an empirical evaluation of functional connectivity methods","volume":"30","author":"D Klepl","year":"2022","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"10","key":"pone.0323894.ref022","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1109\/TMI.2020.2987817","article-title":"Multi-class ASD classification based on functional connectivity and functional correlation tensor via multi-source domain adaptation and multi-view sparse representation","volume":"39","author":"J Wang","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"pone.0323894.ref023","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1109\/TCYB.2018.2839693","article-title":"Sparse multiview task-centralized ensemble learning for ASD diagnosis based on age- and sex-related functional connectivity patterns","volume":"49","author":"J Wang","year":"2019","journal-title":"IEEE Trans Cybern"},{"key":"pone.0323894.ref024","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.1109\/TNSRE.2021.3120024","article-title":"A convolutional neural network combined with prototype learning framework for brain functional network classification of autism spectrum disorder","volume":"29","author":"Y Liang","year":"2021","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"2","key":"pone.0323894.ref025","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1007\/s11682-015-9408-2","article-title":"Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification","volume":"10","author":"C-Y Wee","year":"2016","journal-title":"Brain Imaging Behav"},{"issue":"1","key":"pone.0323894.ref026","doi-asserted-by":"crossref","first-page":"4330","DOI":"10.1038\/s41598-020-60702-2","article-title":"Alterations of functional connectivities associated with autism spectrum disorder symptom severity: a multi-site study using multivariate pattern analysis","volume":"10","author":"X Liu","year":"2020","journal-title":"Sci Rep"},{"key":"pone.0323894.ref027","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhang H, Chen X, Shen D. Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment. In: Connectomics in Neuroimaging: First International Workshop, CNI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings 1. Springer; 2017. p. 9\u201316.","DOI":"10.1007\/978-3-319-67159-8_2"},{"key":"pone.0323894.ref028","unstructured":"Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint 2018. https:\/\/arxiv.org\/abs\/1806.01261"},{"key":"pone.0323894.ref029","first-page":"1381","article-title":"Interpretable temporal graph neural network for prognostic prediction of Alzheimer\u2019s disease using longitudinal neuroimaging data","volume":"2021","author":"M Kim","year":"2021","journal-title":"Proceedings (IEEE Int Conf Bioinformatics Biomed)"},{"issue":"1","key":"pone.0323894.ref030","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1162\/netn_a_00171","article-title":"Graph convolutional network for fMRI analysis based on connectivity neighborhood","volume":"5","author":"L Wang","year":"2021","journal-title":"Netw Neurosci"},{"key":"pone.0323894.ref031","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.inffus.2021.07.013","article-title":"Interpretable learning based dynamic graph convolutional networks for Alzheimer\u2019s disease analysis","volume":"77","author":"Y Zhu","year":"2022","journal-title":"Information Fusion"},{"issue":"2","key":"pone.0323894.ref032","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1007\/s12559-023-10224-6","article-title":"Classification of developmental and brain disorders via graph convolutional aggregation","volume":"16","author":"I Salim","year":"2023","journal-title":"Cogn Comput"},{"key":"pone.0323894.ref033","doi-asserted-by":"crossref","first-page":"104096","DOI":"10.1016\/j.compbiomed.2020.104096","article-title":"Hi-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction","volume":"127","author":"H Jiang","year":"2020","journal-title":"Comput Biol Med"},{"key":"pone.0323894.ref034","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"M Defferrard","year":"2016","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"pone.0323894.ref035","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","article-title":"Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","journal-title":"Neuroimage"},{"key":"pone.0323894.ref036","doi-asserted-by":"crossref","first-page":"102063","DOI":"10.1016\/j.media.2021.102063","article-title":"Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI","volume":"71","author":"M Wang","year":"2021","journal-title":"Med Image Anal"},{"key":"pone.0323894.ref037","doi-asserted-by":"crossref","unstructured":"Zhang J, Feng F, Han T, Gong X, Duan F. Detection of autism spectrum disorder using fMRI functional connectivity with feature selection and deep learning. Cogn Comput. 2022. p. 1\u201312.","DOI":"10.1007\/s12559-021-09981-z"},{"issue":"7","key":"pone.0323894.ref038","article-title":"BrainNet Viewer: a network visualization tool for human brain connectomics","volume":"8","author":"M Xia","year":"2013","journal-title":"PLoS One"},{"key":"pone.0323894.ref039","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3389\/fnhum.2018.00184","article-title":"Diagnosis of autism spectrum disorders using multi-level high-order functional networks derived from resting-state functional MRI","volume":"12","author":"F Zhao","year":"2018","journal-title":"Front Hum Neurosci"},{"issue":"25","key":"pone.0323894.ref040","doi-asserted-by":"crossref","first-page":"10503","DOI":"10.1523\/JNEUROSCI.1103-13.2013","article-title":"The autonomic brain: an activation likelihood estimation meta-analysis for central processing of autonomic function","volume":"33","author":"F Beissner","year":"2013","journal-title":"J Neurosci"},{"issue":"1","key":"pone.0323894.ref041","doi-asserted-by":"crossref","first-page":"4330","DOI":"10.1038\/s41598-020-60702-2","article-title":"Alterations of functional connectivities associated with autism spectrum disorder symptom severity: a multi-site study using multivariate pattern analysis","volume":"10","author":"X Liu","year":"2020","journal-title":"Sci Rep"},{"issue":"6","key":"pone.0323894.ref042","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1001\/jamaneurol.2022.0723","article-title":"Addition of vision impairment to a life-course model of potentially modifiable dementia risk factors in the US","volume":"79","author":"JR Ehrlich","year":"2022","journal-title":"JAMA Neurol"},{"issue":"3","key":"pone.0323894.ref043","doi-asserted-by":"crossref","first-page":"781","DOI":"10.3233\/JAD-2010-1275","article-title":"Antisaccades: a probe into the dorsolateral prefrontal cortex in Alzheimer\u2019s disease. A critical review","volume":"19","author":"LD Kaufman","year":"2010","journal-title":"J Alzheimers Dis"},{"issue":"5","key":"pone.0323894.ref044","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.neurobiolaging.2008.06.015","article-title":"Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer\u2019s disease","volume":"31","author":"IH Choo","year":"2010","journal-title":"Neurobiol Aging"},{"key":"pone.0323894.ref045","doi-asserted-by":"crossref","first-page":"535816","DOI":"10.4061\/2011\/535816","article-title":"The default mode network in healthy aging and Alzheimer\u2019s disease","volume":"2011","author":"K Mevel","year":"2011","journal-title":"Int J Alzheimers Dis"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0323894","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T17:33:54Z","timestamp":1749058434000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0323894"}},"subtitle":[],"editor":[{"given":"Simone","family":"Varrasi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,6,4]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6,4]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0323894","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,4]]}}}