{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:03:13Z","timestamp":1790697793575,"version":"4.1.0"},"reference-count":64,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["20230484469"],"award-info":[{"award-number":["20230484469"]}],"id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Provincial Scientific Research Program for Traditional Chinese Medicine and Integrated Chinese-Western Medicine","award":["PDJH2026005"],"award-info":[{"award-number":["PDJH2026005"]}]},{"name":"Nanjing Municipal Science and Technology Bureau\u2013Jiangsu Hengrui Pharmaceuticals Co., Ltd. Clinical Basic Research Project","award":["No. 202511083"],"award-info":[{"award-number":["No. 202511083"]}]},{"name":"Peak Academic Talent Program of Jiangsu Provincial Hospital of Chinese Medicine","award":["No. k2026yrc22"],"award-info":[{"award-number":["No. k2026yrc22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Objective: To address the limitations of existing deep learning methods for brain networks in jointly modeling directional interregional connectivity, multiband information, and short-term dynamic features, this study proposes a multiscale directed temporal graph convolutional network (MSD-STGNN) for the three-class classification of healthy controls (HCs), patients with left temporal lobe epilepsy (lTLE), and patients with right temporal lobe epilepsy (rTLE). Methods: Resting-state magnetoencephalography (MEG) data were obtained from 43 subjects, including 14 HCs, 13 patients with lTLE, and 16 patients with rTLE. Based on 26 predefined default mode network (DMN)-related brain regions, directed effective connectivity networks were constructed in six frequency bands using the directed transfer function (DTF), and indices including information-flow strength, directional preference, and hemispheric asymmetry were used as node features. MSD-STGNN separately modeled incoming and outgoing connectivity information through directed graph convolution, fused frequency-band information using a hierarchical multiband attention mechanism with gated residual correction, and employed gated recurrent units (GRUs) to extract short-term dynamic features from consecutive brain-network slices. Model performance was evaluated using subject-level stratified fivefold cross-validation, with predictions from multiple temporal groups of each subject aggregated to obtain the final subject-level prediction. Frequency-band masking and node-level fusion-weight analyses were further performed to evaluate the model\u2019s dependence on different frequency bands and information from the predefined brain regions. Results: In the subject-level fivefold cross-validation, MSD-STGNN achieved an accuracy of 0.836 \u00b1 0.067, a macro-F1 of 0.830 \u00b1 0.065, and a macro-AUC of 0.900 \u00b1 0.055 using the one-vs-rest strategy, with the highest fivefold mean values across all evaluation metrics among the baseline models and ablation configurations investigated in this study. Post-training frequency-band masking showed that the model exhibited relatively high dependence on the low-gamma (30\u201380 Hz) and beta (13\u201330 Hz) bands. Node-level fusion-weight analysis showed that, within the 26 predefined DMN-related brain regions, orbitofrontal, cingulate, medial temporal, and parietal regions exhibited relatively high overall fusion weights across different frequency bands, although the exact top 5 regional rankings varied across folds. Significance: Within a unified framework, MSD-STGNN integrates directional connectivity, multiband information, and short-term dynamic features derived from MEG-based directed brain networks, providing a modeling approach with a certain degree of interpretability for the three-class classification of HCs, patients with lTLE, and patients with rTLE. The current findings are based on internal cross-validation of a small, single-center cohort; therefore, the classification performance and the observed frequency-band and brain-region attention patterns require further validation in larger, independent multicenter datasets.<\/jats:p>","DOI":"10.3390\/s26196185","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:14:37Z","timestamp":1790694877000},"page":"6185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Multiscale Directed Temporal Graph Learning for MEG-Based Identification and Lateralization of Temporal Lobe Epilepsy"],"prefix":"10.3390","volume":"26","author":[{"given":"Yilin","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"He","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Shuicai","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2362-4694","authenticated-orcid":false,"given":"Ting","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7576-8549","authenticated-orcid":false,"given":"Chunlan","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1111\/epi.12550","article-title":"ILAE official report: A practical clinical definition of epilepsy","volume":"55","author":"Fisher","year":"2014","journal-title":"Epilepsia"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1159\/000503831","article-title":"The epidemiology of epilepsy","volume":"54","author":"Beghi","year":"2020","journal-title":"Neuroepidemiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1093\/brain\/awl241","article-title":"Seizure prediction: The long and winding road","volume":"130","author":"Mormann","year":"2007","journal-title":"Brain"},{"key":"ref_4","first-page":"630853","article-title":"A review of the epidemiology of temporal lobe epilepsy","volume":"2012","year":"2012","journal-title":"Epilepsy Res. 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