{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:45:17Z","timestamp":1760489117066,"version":"build-2065373602"},"reference-count":69,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>\n            Psychiatric disorders involve complex neural activity changes, with functional magnetic resonance imaging (fMRI) data serving as key diagnostic evidence. However, data scarcity and the diverse nature of fMRI information pose significant challenges. While graph-based self-supervised learning (SSL) methods have shown promise in brain network analysis, they primarily focus on time-domain representations, often overlooking the rich information embedded in the frequency domain. To overcome these limitations, we propose\n            <jats:italic toggle=\"yes\">F<\/jats:italic>\n            requency-\n            <jats:italic toggle=\"yes\">E<\/jats:italic>\n            nhanced\n            <jats:italic toggle=\"yes\">Net<\/jats:italic>\n            work (FENet), a novel SSL framework specially designed for fMRI data that integrates time-domain and frequency-domain information to improve psychiatric disorder detection in small-sample datasets. FENet constructs multi-view brain networks based on the inherent properties of fMRI data, explicitly incorporating frequency information into the learning process of representation. Additionally, it employs domain-specific encoders to capture temporal-spectral characteristics, including an efficient frequency-domain encoder that highlights disease-relevant frequency features. Finally, FENet introduces a domain consistency-guided learning objective, which balances the utilization of diverse information and generates frequency-enhanced brain graph representations. Experiments on two real-world medical datasets demonstrate that FENet outperforms state-of-the-art methods while maintaining strong performance in minimal data conditions. Furthermore, we analyze the correlation between various frequency-domain features and psychiatric disorders, emphasizing the critical role of high-frequency information in disorder detection.\n          <\/jats:p>","DOI":"10.1145\/3766907","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T12:14:26Z","timestamp":1757420066000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Data-Efficient Psychiatric Disorder Detection via Self-Supervised Learning on Frequency-Enhanced Brain Networks"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0879-7168","authenticated-orcid":false,"given":"Mujie","family":"Liu","sequence":"first","affiliation":[{"name":"Federation University Australia, Ballarat, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3438-4394","authenticated-orcid":false,"given":"Mengchu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang Gongshang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5986-0124","authenticated-orcid":false,"given":"Qichao","family":"Dong","sequence":"additional","affiliation":[{"name":"Zhejiang Gongshang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3806-1493","authenticated-orcid":false,"given":"Ting","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8449-7610","authenticated-orcid":false,"given":"Jiangang","family":"Ma","sequence":"additional","affiliation":[{"name":"Federation University Australia, Ballarat, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0169-1491","authenticated-orcid":false,"given":"Jing","family":"Ren","sequence":"additional","affiliation":[{"name":"RMIT University, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8324-1859","authenticated-orcid":false,"given":"Feng","family":"Xia","sequence":"additional","affiliation":[{"name":"RMIT University, Melbourne, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1247","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Andrew Galen","year":"2013","unstructured":"Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu. 2013. Deep canonical correlation analysis. In Proceedings of the International Conference on Machine Learning. PMLR, 1247\u20131255."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0166-2236(00)01995-0"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2024.11.025"},{"key":"e_1_3_2_5_2","first-page":"1959","article-title":"Community graph convolution neural network for Alzheimer\u2019s disease classification and pathogenetic factors identification","author":"Bi Xia-An","year":"2023","unstructured":"Xia-An Bi, Ke Chen, Siyu Jiang, Sheng Luo, Wenyan Zhou, Zhaoxu Xing, Luyun Xu, Zhengliang Liu, and Tianming Liu. 2023. Community graph convolution neural network for Alzheimer\u2019s disease classification and pathogenetic factors identification. IEEE Transactions on Neural Networks and Learning Systems 36, 2 (2023), 1959\u20131973.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_6_2","first-page":"17766","article-title":"Spectral temporal graph neural network for multivariate time-series forecasting","volume":"33","author":"Cao Defu","year":"2020","unstructured":"Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, et al. 2020. Spectral temporal graph neural network for multivariate time-series forecasting. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 17766\u201317778.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00161"},{"key":"e_1_3_2_8_2","first-page":"1","article-title":"Pearson correlation coefficient","author":"Cohen Israel","year":"2009","unstructured":"Israel Cohen, Yiteng Huang, Jingdong Chen, Jacob Benesty, Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009. Pearson correlation coefficient. In Noise Reduction in Speech Processing. Springer Berlin, Berlin, 1\u20134.","journal-title":"Noise Reduction in Speech Processing"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3218745"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2024.3516216"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tins.2024.05.011"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67389-9_42"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.04.087"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25547"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00811-0"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2016.2600859"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02103"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.079"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2019-3448"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105643"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1002\/aur.2846"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-022-00245-6"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102233"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1101\/2024.10.27.24316244"},{"key":"e_1_3_2_25_2","first-page":"2972","article-title":"Revisiting graph contrastive learning from the perspective of graph spectrum","volume":"35","author":"Liu Nian","year":"2022","unstructured":"Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, and Jian Pei. 2022. Revisiting graph contrastive learning from the perspective of graph spectrum. In Proceedings of the Advances in Neural Information Processing Systems 35 (2022), 2972\u20132983.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_26_2","first-page":"2733","article-title":"Frequency domain-oriented complex graph neural networks for graph classification","author":"Liu Youfa","year":"2024","unstructured":"Youfa Liu and Bo Du. 2024. Frequency domain-oriented complex graph neural networks for graph classification. IEEE Transactions on Neural Networks and Learning Systems 36, 2 (2024), 2733\u20132746.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"6","key":"e_1_3_2_27_2","first-page":"5879","article-title":"Graph self-supervised learning: A survey","volume":"35","author":"Liu Yixin","year":"2022","unstructured":"Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and S. Yu Philip. 2022. Graph self-supervised learning: A survey. IEEE Transactions on Knowledge and Data Engineering 35, 6 (2022), 5879\u20135900.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_28_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Loshchilov Ilya","year":"2019","unstructured":"Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635695"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3341802"},{"key":"e_1_3_2_31_2","first-page":"8170","volume-title":"Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI \u201924)","author":"Luo Xuexiong","year":"2024","unstructured":"Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Quan Z. Sheng, David McAlpine, Paul Sowman, Alexis Giral, and S. Yu Philip. 2024. Graph neural networks for brain graph learning: A survey. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI \u201924). International Joint Conferences on Artificial Intelligence, 8170\u20138178."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-42090-4"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3681794"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.11.052"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3704740"},{"key":"e_1_3_2_36_2","unstructured":"Ciyuan Peng Jiayuan He and Feng Xia. 2024. Learning on multimodal graphs: A survey. arXiv:2402.05322. Retrieved from https:\/\/arxiv.org\/abs\/2402.05322"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Ciyuan Peng Mujie Liu Chenxuan Meng Sha Xue Kathleen Keogh and Feng Xia. 2024. Stage-aware brain graph learning for Alzheimer\u2019s disease. In 2024 IEEE Conference on Artificial Intelligence (CAI). IEEE 1347\u20131349.","DOI":"10.1109\/CAI59869.2024.00239"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3201974"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671928"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1002\/wps.20050"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3570906"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.10.003"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3712710"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eurpsy.2017.01.079"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117375"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2235192"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2024.3352972"},{"key":"e_1_3_2_48_2","first-page":"7363","article-title":"Contrastive brain network learning via hierarchical signed graph pooling model","author":"Tang Haoteng","year":"2022","unstructured":"Haoteng Tang, Guixiang Ma, Lei Guo, Xiyao Fu, Heng Huang, and Liang Zhan. 2022. Contrastive brain network learning via hierarchical signed graph pooling model. IEEE Transactions on Neural Networks and Learning Systems 35, 6 (2022), 7363\u20137375.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_49_2","article-title":"Bootstrapped representation learning on graphs","author":"Thakoor Shantanu","year":"2021","unstructured":"Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, R\u00e9mi Munos, Petar Veli\u010dkovi\u0107, and Michal Valko. 2021. Bootstrapped representation learning on graphs. In Proceedings of the ICLR 2021 Workshop on Geometrical and Topological Representation Learning.","journal-title":"Proceedings of the ICLR 2021 Workshop on Geometrical and Topological Representation Learning"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3681795"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.26469"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679732"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3076021"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3149888"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.02.031"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3392988"},{"key":"e_1_3_2_57_2","first-page":"25038","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Yang Ling","year":"2022","unstructured":"Ling Yang and Shenda Hong. 2022. Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion. In Proceedings of the International Conference on Machine Learning. PMLR, 25038\u201325054."},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583593"},{"key":"e_1_3_2_59_2","article-title":"FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective","volume":"36","author":"Yi Kun","year":"2023","unstructured":"Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbing Cao, and Zhendong Niu. 2023. FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_60_2","article-title":"Frequency-domain MLPs are more effective learners in time series forecasting","volume":"36","author":"Yi Kun","year":"2024","unstructured":"Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Ning An, Defu Lian, Longbing Cao, and Zhendong Niu. 2024. Frequency-domain MLPs are more effective learners in time series forecasting. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_61_2","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 5812\u20135823.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_62_2","first-page":"76","article-title":"From canonical correlation analysis to self-supervised graph neural networks","volume":"34","author":"Zhang Hengrui","year":"2021","unstructured":"Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, and Philip S. Yu. 2021. From canonical correlation analysis to self-supervised graph neural networks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 34, 76\u201389.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102932"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40649-019-0069-y"},{"key":"e_1_3_2_65_2","first-page":"3988","article-title":"Self-supervised contrastive pre-training for time series via time-frequency consistency","volume":"35","author":"Zhang Xiang","year":"2022","unstructured":"Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. 2022. Self-supervised contrastive pre-training for time series via time-frequency consistency. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 3988\u20134003.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26336"},{"key":"e_1_3_2_67_2","first-page":"27268","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhou Tian","year":"2022","unstructured":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. In Proceedings of the International Conference on Machine Learning. PMLR, 27268\u201327286."},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25090"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3442811"}],"container-title":["ACM Transactions on Computing for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3766907","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T23:28:20Z","timestamp":1760398100000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3766907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"references-count":69,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10,31]]}},"alternative-id":["10.1145\/3766907"],"URL":"https:\/\/doi.org\/10.1145\/3766907","relation":{},"ISSN":["2691-1957","2637-8051"],"issn-type":[{"type":"print","value":"2691-1957"},{"type":"electronic","value":"2637-8051"}],"subject":[],"published":{"date-parts":[[2025,10,13]]},"assertion":[{"value":"2025-04-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}