{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T07:15:26Z","timestamp":1773990926268,"version":"3.50.1"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T00:00:00Z","timestamp":1704412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T00:00:00Z","timestamp":1704412800000},"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":["82172528"],"award-info":[{"award-number":["82172528"]}],"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":["81972147"],"award-info":[{"award-number":["81972147"]}],"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":["62271477"],"award-info":[{"award-number":["62271477"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Parkinson\u2019s disease (PD) exhibits significant clinical heterogeneity, presenting challenges in the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning techniques have been integrated with resting-state EEG for PD diagnosis, but their practicality is constrained by the interpretable features and the stochastic nature of resting-state EEG. The present study proposes a novel and interpretable deep learning model, graph signal processing-graph convolutional networks (GSP-GCNs), using event-related EEG data obtained from a specific task involving vocal pitch regulation for PD diagnosis. By incorporating both local and global information from single-hop and multi-hop networks, our proposed GSP-GCNs models achieved an averaged classification accuracy of 90.2%, exhibiting a significant improvement of 9.5% over other deep learning models. Moreover, the interpretability analysis revealed discriminative distributions of large-scale EEG networks and topographic map of microstate MS5 learned by our models, primarily located in the left ventral premotor cortex, superior temporal gyrus, and Broca\u2019s area that are implicated in PD-related speech disorders, reflecting our GSP-GCN models\u2019 ability to provide interpretable insights identifying distinctive EEG biomarkers from large-scale networks. These findings demonstrate the potential of interpretable deep learning models coupled with voice-related EEG signals for distinguishing PD patients from healthy controls with accuracy and elucidating the underlying neurobiological mechanisms.<\/jats:p>","DOI":"10.1038\/s41746-023-00983-9","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T16:03:09Z","timestamp":1704470589000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["An interpretable model based on graph learning for diagnosis of Parkinson\u2019s disease with voice-related EEG"],"prefix":"10.1038","volume":"7","author":[{"given":"Shuzhi","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Guangyan","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Jingting","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaoxia","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Xiyan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yongxue","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mingdan","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Lan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Fang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0544-8723","authenticated-orcid":false,"given":"Xi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Yan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1466-4193","authenticated-orcid":false,"given":"Hanjun","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,5]]},"reference":[{"key":"983_CR1","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.gendis.2019.01.004","volume":"6","author":"S Selvaraj","year":"2019","unstructured":"Selvaraj, S. & Piramanayagam, S. Impact of gene mutation in the development of Parkinson\u2019s disease. Genes Dis. 6, 120\u2013128 (2019).","journal-title":"Genes Dis."},{"key":"983_CR2","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1001\/jama.2019.22360","volume":"323","author":"MJ Armstrong","year":"2020","unstructured":"Armstrong, M. J. & Okun, M. S. Diagnosis and treatment of parkinson disease: a review. JAMA 323, 548\u2013560 (2020).","journal-title":"JAMA"},{"key":"983_CR3","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/S1474-4422(19)30287-X","volume":"19","author":"C Blauwendraat","year":"2020","unstructured":"Blauwendraat, C., Nalls, M. A. & Singleton, A. B. The genetic architecture of Parkinson\u2019s disease. Lancet Neurol. 19, 170\u2013178 (2020).","journal-title":"Lancet Neurol."},{"key":"983_CR4","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","volume":"397","author":"BR Bloem","year":"2021","unstructured":"Bloem, B. R., Okun, M. S. & Klein, C. Parkinson\u2019s disease. Lancet 397, 2284\u20132303 (2021).","journal-title":"Lancet"},{"key":"983_CR5","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.artmed.2016.01.004","volume":"67","author":"P Drotar","year":"2016","unstructured":"Drotar, P. et al. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson\u2019s disease. Artif. Intell. Med. 67, 39\u201346 (2016).","journal-title":"Artif. Intell. Med."},{"key":"983_CR6","doi-asserted-by":"publisher","first-page":"S40","DOI":"10.1016\/j.metabol.2014.10.030","volume":"64","author":"DB Miller","year":"2015","unstructured":"Miller, D. B. & O\u2019Callaghan, J. P. Biomarkers of Parkinson\u2019s disease: present and future. Metabolism. 64, S40\u2013S46 (2015).","journal-title":"Metabolism."},{"key":"983_CR7","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1002\/mds.25684","volume":"28","author":"FB Horak","year":"2013","unstructured":"Horak, F. B. & Mancini, M. Objective biomarkers of balance and gait for Parkinson\u2019s disease using body-worn sensors. Mov. Disord. 28, 1544\u20131551 (2013).","journal-title":"Mov. Disord."},{"key":"983_CR8","doi-asserted-by":"crossref","unstructured":"Wroge, T. J. et al. Parkinson\u2019s disease diagnosis using machine learning and voice. In: 2018 IEEE signal processing in medicine and biology symposium (SPMB)). IEEE (2018).","DOI":"10.1109\/SPMB.2018.8615607"},{"key":"983_CR9","doi-asserted-by":"publisher","first-page":"102132","DOI":"10.1016\/j.nicl.2019.102132","volume":"25","author":"C Chu","year":"2020","unstructured":"Chu, C. et al. Spatiotemporal EEG microstate analysis in drug-free patients with Parkinson\u2019s disease. NeuroImage. Clin. 25, 102132 (2020).","journal-title":"NeuroImage. Clin."},{"key":"983_CR10","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s11571-016-9415-z","volume":"11","author":"GS Yi","year":"2017","unstructured":"Yi, G. S., Wang, J., Deng, B. & Wei, X. L. Complexity of resting-state EEG activity in the patients with early-stage Parkinson\u2019s disease. Cogn. Neurodyn. 11, 147\u2013160 (2017).","journal-title":"Cogn. Neurodyn."},{"key":"983_CR11","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1016\/j.neuroimage.2008.03.032","volume":"41","author":"M Moazami-Goudarzi","year":"2008","unstructured":"Moazami-Goudarzi, M., Sarnthein, J., Michels, L., Moukhtieva, R. & Jeanmonod, D. Enhanced frontal low and high frequency power and synchronization in the resting EEG of parkinsonian patients. NeuroImage 41, 985\u2013997 (2008).","journal-title":"NeuroImage"},{"key":"983_CR12","doi-asserted-by":"crossref","unstructured":"Song, Y., Zheng, Q., Liu, B. & Gao, X. EEG conformer: Convolutional transformer for EEG decoding and visualization. IEEE Trans. Neural Syst. Rehabil. Eng. (2022).","DOI":"10.1109\/TNSRE.2022.3230250"},{"key":"983_CR13","doi-asserted-by":"publisher","first-page":"118236","DOI":"10.1016\/j.eswa.2022.118236","volume":"209","author":"E Lillo","year":"2022","unstructured":"Lillo, E., Mora, M. & Lucero, B. Automated diagnosis of schizophrenia using EEG microstates and deep convolutional neural network. Expert. Syst. Appl. 209, 118236 (2022).","journal-title":"Expert. Syst. Appl."},{"key":"983_CR14","doi-asserted-by":"publisher","first-page":"10927","DOI":"10.1007\/s00521-018-3689-5","volume":"32","author":"SL Oh","year":"2020","unstructured":"Oh, S. L. et al. A deep learning approach for Parkinson\u2019s disease diagnosis from EEG signals. Neural. Comput. Appl. 32, 10927\u201310933 (2020).","journal-title":"Neural. Comput. Appl."},{"key":"983_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3389\/fnagi.2017.00003","volume":"9","author":"M Chaturvedi","year":"2017","unstructured":"Chaturvedi, M. et al. Quantitative EEG (QEEG) measures differentiate Parkinson\u2019s disease (PD) patients from healthy controls (HC). Front. Aging Neurosci. 9, 3 (2017).","journal-title":"Front. Aging Neurosci."},{"key":"983_CR16","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1002\/mds.24954","volume":"27","author":"A Siderowf","year":"2012","unstructured":"Siderowf, A. & Lang, A. E. Premotor Parkinson\u2019s disease: concepts and definitions. Mov. Disord. 27, 608\u2013616 (2012).","journal-title":"Mov. Disord."},{"key":"983_CR17","doi-asserted-by":"publisher","first-page":"S656","DOI":"10.1002\/mds.22672","volume":"24","author":"C Gaig","year":"2009","unstructured":"Gaig, C. & Tolosa, E. When does Parkinson\u2019s disease begin? Mov. Disord. 24, S656\u2013S664 (2009).","journal-title":"Mov. Disord."},{"key":"983_CR18","doi-asserted-by":"publisher","first-page":"e33629","DOI":"10.1371\/journal.pone.0033629","volume":"7","author":"H Liu","year":"2012","unstructured":"Liu, H., Wang, E. Q., Verhagen Metman, L. & Larson, C. R. Vocal responses to perturbations in voice auditory feedback in individuals with Parkinson\u2019s disease. PLoS ONE 7, e33629 (2012).","journal-title":"PLoS ONE"},{"key":"983_CR19","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.brainres.2013.06.030","volume":"1527","author":"X Chen","year":"2013","unstructured":"Chen, X. et al. Sensorimotor control of vocal pitch production in Parkinson\u2019s disease. Brain Res. 1527, 99\u2013107 (2013).","journal-title":"Brain Res."},{"key":"983_CR20","doi-asserted-by":"publisher","first-page":"1668","DOI":"10.1002\/mds.25588","volume":"28","author":"F Mollaei","year":"2013","unstructured":"Mollaei, F., Shiller, D. M. & Gracco, V. L. Sensorimotor adaptation of speech in Parkinson\u2019s disease. Mov. Disord. 28, 1668\u20131674 (2013).","journal-title":"Mov. Disord."},{"key":"983_CR21","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.brainres.2016.06.013","volume":"1646","author":"F Mollaei","year":"2016","unstructured":"Mollaei, F., Shiller, D. M., Baum, S. R. & Gracco, V. L. Sensorimotor control of vocal pitch and formant frequencies in Parkinson\u2019s disease. Brain Res. 1646, 269\u2013277 (2016).","journal-title":"Brain Res."},{"key":"983_CR22","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1044\/2014_JSLHR-S-13-0039","volume":"57","author":"S Sapir","year":"2014","unstructured":"Sapir, S. Multiple factors are involved in the dysarthria associated with Parkinson\u2019s disease: a review with implications for clinical practice and research. J. Speech Lang. Hear. Res. 57, 1330\u20131343 (2014).","journal-title":"J. Speech Lang. Hear. Res."},{"key":"983_CR23","doi-asserted-by":"publisher","first-page":"4248","DOI":"10.1002\/hbm.23306","volume":"37","author":"X Huang","year":"2016","unstructured":"Huang, X. et al. The impact of Parkinson\u2019s disease on the cortical mechanisms that support auditory-motor integration for voice control. Hum. Brain Mapp. 37, 4248\u20134261 (2016).","journal-title":"Hum. Brain Mapp."},{"key":"983_CR24","doi-asserted-by":"publisher","first-page":"624801","DOI":"10.3389\/fnins.2021.624801","volume":"15","author":"Y Li","year":"2021","unstructured":"Li, Y. et al. Neurobehavioral effects of LSVT\u00ae LOUD on auditory-vocal Integration in Parkinson\u2019s disease: a preliminary study. Front. Neurosci. 15, 624801 (2021).","journal-title":"Front. Neurosci."},{"key":"983_CR25","doi-asserted-by":"publisher","first-page":"948696","DOI":"10.3389\/fnagi.2022.948696","volume":"14","author":"G Dai","year":"2022","unstructured":"Dai, G. et al. Continuous theta burst stimulation over left supplementary motor area facilitates auditory-vocal integration in individuals with Parkinson\u2019s disease. Front. Aging Neurosci. 14, 948696 (2022).","journal-title":"Front. Aging Neurosci."},{"key":"983_CR26","doi-asserted-by":"crossref","unstructured":"Shi, X., Wang, T., Wang, L., Liu, H. & Yan, N. Hybrid convolutional recurrent neural networks outperform CNN and RNN in task-state EEG detection for Parkinson\u2019s disease. In: 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)). IEEE (2019).","DOI":"10.1109\/APSIPAASC47483.2019.9023190"},{"key":"983_CR27","doi-asserted-by":"publisher","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","volume":"79","author":"BHM van der Velden","year":"2022","unstructured":"van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A. & Viergever, M. A. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med. Image Anal. 79, 102470 (2022).","journal-title":"Med. Image Anal."},{"key":"983_CR28","doi-asserted-by":"publisher","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","volume":"4","author":"D Gunning","year":"2019","unstructured":"Gunning, D. et al. XAI-Explainable artificial intelligence. Sci. Robot 4, eaay7120 (2019).","journal-title":"Sci. Robot"},{"key":"983_CR29","doi-asserted-by":"publisher","first-page":"779","DOI":"10.3389\/fnins.2020.00779","volume":"14","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Wang, M., Liu, M. & Zhang, D. A survey on deep learning for neuroimaging-based brain disorder analysis. Front. Neurosci. 14, 779 (2020).","journal-title":"Front. Neurosci."},{"key":"983_CR30","unstructured":"Springenberg, J. T., Dosovitskiy, A., Brox, T. & Riedmiller, M. Striving for simplicity: The all convolutional net. arXiv arXiv, 1412.6806 (2014)."},{"key":"983_CR31","unstructured":"Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. arXiv arXiv, 1609.02907 (2016)."},{"key":"983_CR32","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu. X.-J. & Kittler, J. Spatial residual layer and dense connection block enhanced spatial temporal graph convolutional network for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops) (2019).","DOI":"10.1109\/ICCVW.2019.00216"},{"key":"983_CR33","doi-asserted-by":"publisher","first-page":"109758","DOI":"10.1016\/j.knosys.2022.109758","volume":"256","author":"B Wu","year":"2022","unstructured":"Wu, B., Zhong, L., Li, H. & Ye, Y. Efficient complementary graph convolutional network without negative sampling for item recommendation. Knowledge-Based Syst. 256, 109758 (2022).","journal-title":"Knowledge-Based Syst."},{"key":"983_CR34","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15, 056013 (2018).","journal-title":"J. Neural Eng."},{"key":"983_CR35","doi-asserted-by":"crossref","unstructured":"Yue, L. et al. Intention recognition from spatio-temporal representation of EEG signals. In: Australasian Database Conference). Springer (2021).","DOI":"10.1007\/978-3-030-69377-0_1"},{"key":"983_CR36","unstructured":"Vaswani, A. et al. Attention is all you need. Adv. Neur. Inf. Process. Syst. 30 (2017)."},{"key":"983_CR37","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1007\/s00521-016-2756-z","volume":"30","author":"R Yuvaraj","year":"2018","unstructured":"Yuvaraj, R., Acharya, U. R. & Hagiwara, Y. A novel Parkinson\u2019s Disease Diagnosis Index using higher-order spectra features in EEG signals. Neural. Comput. Appl. 30, 1225\u20131235 (2018).","journal-title":"Neural. Comput. Appl."},{"key":"983_CR38","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.neunet.2020.06.018","volume":"130","author":"SAA Shah","year":"2020","unstructured":"Shah, S. A. A., Zhang, L. & Bais, A. Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals. Neural Netw. 130, 75\u201384 (2020).","journal-title":"Neural Netw."},{"key":"983_CR39","doi-asserted-by":"publisher","first-page":"109282","DOI":"10.1016\/j.jneumeth.2021.109282","volume":"361","author":"S Lee","year":"2021","unstructured":"Lee, S., Hussein, R., Ward, R., Jane Wang, Z. & McKeown, M. J. A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson\u2019s disease. J. Neurosci. Methods 361, 109282 (2021).","journal-title":"J. Neurosci. Methods"},{"key":"983_CR40","doi-asserted-by":"publisher","first-page":"e0261947","DOI":"10.1371\/journal.pone.0261947","volume":"17","author":"S Hassin-Baer","year":"2022","unstructured":"Hassin-Baer, S. et al. Identification of an early-stage Parkinson\u2019s disease neuromarker using event-related potentials, brain network analytics and machine-learning. PLoS ONE 17, e0261947 (2022).","journal-title":"PLoS ONE"},{"key":"983_CR41","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.bandl.2005.06.001","volume":"96","author":"FH Guenther","year":"2006","unstructured":"Guenther, F. H., Ghosh, S. S. & Tourville, J. A. Neural modeling and imaging of the cortical interactions underlying syllable production. Brain Lang. 96, 280\u2013301 (2006).","journal-title":"Brain Lang."},{"key":"983_CR42","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1162\/jocn.2009.21306","volume":"22","author":"JW Bohland","year":"2010","unstructured":"Bohland, J. W., Bullock, D. & Guenther, F. H. Neural representations and mechanisms for the performance of simple speech sequences. J. Cogn. Neurosci. 22, 1504\u20131529 (2010).","journal-title":"J. Cogn. Neurosci."},{"key":"983_CR43","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1073\/pnas.1216827110","volume":"110","author":"EF Chang","year":"2013","unstructured":"Chang, E. F., Niziolek, C. A., Knight, R. T., Nagarajan, S. S. & Houde, J. F. Human cortical sensorimotor network underlying feedback control of vocal pitch. Proc. Natl Acad. Sci. USA 110, 2653\u20132658 (2013).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"983_CR44","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1002\/hbm.23114","volume":"37","author":"NS Kort","year":"2016","unstructured":"Kort, N. S., Cuesta, P., Houde, J. F. & Nagarajan, S. S. Bihemispheric network dynamics coordinating vocal feedback control. Hum. Brain Mapp. 37, 1474\u20131485 (2016).","journal-title":"Hum. Brain Mapp."},{"key":"983_CR45","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.neuroimage.2012.02.068","volume":"61","author":"AL Parkinson","year":"2012","unstructured":"Parkinson, A. L. et al. Understanding the neural mechanisms involved in sensory control of voice production. NeuroImage 61, 314\u2013322 (2012).","journal-title":"NeuroImage"},{"key":"983_CR46","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.neuroimage.2015.01.040","volume":"109","author":"R Behroozmand","year":"2015","unstructured":"Behroozmand, R. et al. Sensory-motor networks involved in speech production and motor control: an fMRI study. NeuroImage 109, 418\u2013428 (2015).","journal-title":"NeuroImage"},{"key":"983_CR47","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-74790-7","volume":"10","author":"L Tait","year":"2020","unstructured":"Tait, L. et al. EEG microstate complexity for aiding early diagnosis of Alzheimer\u2019s disease. Sci. Rep. 10, 17627 (2020).","journal-title":"Sci. Rep."},{"key":"983_CR48","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10548-020-00803-3","volume":"34","author":"AL Jouen","year":"2021","unstructured":"Jouen, A. L., Lancheros, M. & Laganaro, M. Microstate ERP analyses to pinpoint the articulatory onset in speech production. Brain Topogr. 34, 29\u201340 (2021).","journal-title":"Brain Topogr."},{"key":"983_CR49","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1016\/j.clinph.2010.10.042","volume":"122","author":"J Kindler","year":"2011","unstructured":"Kindler, J., Hubl, D., Strik, W. K., Dierks, T. & Koenig, T. Resting-state EEG in schizophrenia: auditory verbal hallucinations are related to shortening of specific microstates. Clin. Neurophysiol. 122, 1179\u20131182 (2011).","journal-title":"Clin. Neurophysiol."},{"key":"983_CR50","doi-asserted-by":"publisher","first-page":"102839","DOI":"10.1016\/j.neucli.2022.102839","volume":"53","author":"TDC Costa","year":"2023","unstructured":"Costa, T. D. C. et al. Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson\u2019s disease? Neurophysiol. Clin. 53, 102839 (2023).","journal-title":"Neurophysiol. Clin."},{"key":"983_CR51","doi-asserted-by":"publisher","first-page":"10323","DOI":"10.1523\/JNEUROSCI.1329-17.2017","volume":"37","author":"Z Guo","year":"2017","unstructured":"Guo, Z. et al. Top-down modulation of auditory-motor integration during speech production: the role of working memory. J. Neurosci. 37, 10323\u201310333 (2017).","journal-title":"J. Neurosci."},{"key":"983_CR52","doi-asserted-by":"publisher","first-page":"4515","DOI":"10.1093\/cercor\/bhaa054","volume":"30","author":"D Liu","year":"2020","unstructured":"Liu, D. et al. Top-down inhibitory mechanisms underlying auditory-motor integration for voice control: evidence by TMS. Cereb. Cortex 30, 4515\u20134527 (2020).","journal-title":"Cereb. Cortex"},{"key":"983_CR53","doi-asserted-by":"publisher","first-page":"5625","DOI":"10.1093\/cercor\/bhac447","volume":"33","author":"T Li","year":"2023","unstructured":"Li, T. et al. The left inferior frontal gyrus is causally linked to vocal feedback control: evidence from high-definition transcranial alternating current stimulation. Cereb. Cortex 33, 5625\u20135635 (2023).","journal-title":"Cereb. Cortex"},{"key":"983_CR54","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.brs.2021.03.010","volume":"14","author":"L Brabenec","year":"2021","unstructured":"Brabenec, L. et al. Non-invasive brain stimulation for speech in Parkinson\u2019s disease: a randomized controlled trial. Brain Stimul. 14, 571\u2013578 (2021).","journal-title":"Brain Stimul."},{"key":"983_CR55","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.parkreldis.2018.10.011","volume":"61","author":"L Brabenec","year":"2019","unstructured":"Brabenec, L. et al. Non-invasive stimulation of the auditory feedback area for improved articulation in Parkinson\u2019s disease. Parkinsonism. Relat. Disord. 61, 187\u2013192 (2019).","journal-title":"Parkinsonism. Relat. Disord."},{"key":"983_CR56","doi-asserted-by":"publisher","first-page":"103031","DOI":"10.1016\/j.ipm.2022.103031","volume":"59","author":"S Wang","year":"2022","unstructured":"Wang, S., Zhang, P., Wang, H., Yu, H. & Zhang, F. Detecting shilling groups in online recommender systems based on graph convolutional network. Inf. Process. Manag. 59, 103031 (2022).","journal-title":"Inf. Process. Manag."},{"key":"983_CR57","doi-asserted-by":"crossref","unstructured":"Wang, X. et al. Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the web conference 2020) (2020).","DOI":"10.1145\/3366423.3380186"},{"key":"983_CR58","doi-asserted-by":"publisher","first-page":"108038","DOI":"10.1016\/j.compag.2023.108038","volume":"212","author":"A Parmiggiani","year":"2023","unstructured":"Parmiggiani, A., Liu, D., Psota, E., Fitzgerald, R. & Norton, T. Don\u2019t get lost in the crowd: graph convolutional network for online animal tracking in dense groups. Comput. Electron. Agric. 212, 108038 (2023).","journal-title":"Comput. Electron. Agric."},{"key":"983_CR59","doi-asserted-by":"publisher","first-page":"031002","DOI":"10.1088\/1741-2552\/abc902","volume":"18","author":"X Zhang","year":"2021","unstructured":"Zhang, X. et al. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J. Neural Eng. 18, 031002 (2021).","journal-title":"J. Neural Eng."},{"key":"983_CR60","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-030-05668-1_3","volume":"7","author":"S Aliakbaryhosseinabadi","year":"2019","unstructured":"Aliakbaryhosseinabadi, S., Kamavuako, E. N., Jiang, N., Farina, D. & Mrachacz-Kersting, N. Online adaptive synchronous BCI system with attention variations. Brain-Computer Interface Research: A State-of-the-Art Summary 7, 31\u201341 (2019).","journal-title":"Brain-Computer Interface Research: A State-of-the-Art Summary"},{"key":"983_CR61","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1136\/jnnp.55.3.181","volume":"55","author":"AJ Hughes","year":"1992","unstructured":"Hughes, A. J., Daniel, S. E., Kilford, L. & Lees, A. J. Accuracy of clinical diagnosis of idiopathic Parkinson\u2019s disease: a clinico-pathological study of 100 cases. J. Neurol. Neurosurg. Psychiatr. 55, 181\u2013184 (1992).","journal-title":"J. Neurol. Neurosurg. Psychiatr."},{"key":"983_CR62","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1121\/1.2404624","volume":"121","author":"SH Chen","year":"2007","unstructured":"Chen, S. H., Liu, H., Xu, Y. & Larson, C. R. Voice F0 responses to pitch-shifted voice feedback during English speech. J. Acoust. Soc. Am. 121, 1157\u20131163 (2007).","journal-title":"J. Acoust. Soc. Am."},{"key":"983_CR63","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-30869-w","volume":"8","author":"M Lai","year":"2018","unstructured":"Lai, M., Demuru, M., Hillebrand, A. & Fraschini, M. A comparison between scalp- and source-reconstructed EEG networks. Sci. Rep. 8, 12269 (2018).","journal-title":"Sci. Rep."},{"key":"983_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijpsycho.2014.02.001","volume":"92","author":"B Toth","year":"2014","unstructured":"Toth, B. et al. EEG network connectivity changes in mild cognitive impairment - Preliminary results. Int. J. Psychophysiol. 92, 1\u20137 (2014).","journal-title":"Int. J. Psychophysiol."},{"key":"983_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3408313","volume":"14","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Tong, H. H., Tang, J., Xu, J. J. & Fan, W. Incomplete Network Alignment: Problem Definitions and Fast Solutions. ACM Trans. Knowl. Discov. Data 14, 1\u201326 (2020).","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"983_CR66","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s10618-005-0003-9","volume":"11","author":"M Kuramochi","year":"2005","unstructured":"Kuramochi, M. & Karypis, G. Finding frequent patterns in a large sparse graph. Data. Min. Knowl. Disc. 11, 243\u2013271 (2005).","journal-title":"Data. Min. Knowl. Disc."},{"key":"983_CR67","unstructured":"Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv arXiv, 1312.6034 (2013)."},{"key":"983_CR68","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TAI.2021.3076021","volume":"2","author":"F Xia","year":"2021","unstructured":"Xia, F. et al. Graph learning: a survey. IEEE Trans. Artif. Intell. 2, 109\u2013127 (2021).","journal-title":"IEEE Trans. Artif. Intell."},{"key":"983_CR69","first-page":"5879","volume":"35","author":"Y Liu","year":"2022","unstructured":"Liu, Y. et al. Graph self-supervised learning: a survey. IEEE Trans. Knowl. Data Eng. 35, 5879\u20135900 (2022).","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"983_CR70","doi-asserted-by":"crossref","unstructured":"Koutra, D., Tong, H. & Lubensky, D. Big-align: Fast bipartite graph alignment. In: 2013 IEEE 13th international conference on data mining). IEEE (2013).","DOI":"10.1109\/ICDM.2013.152"},{"key":"983_CR71","doi-asserted-by":"crossref","unstructured":"Zhang, J. & Philip, S. Y. Multiple anonymized social networks alignment. In: 2015 IEEE International Conference on Data Mining). IEEE (2015).","DOI":"10.1109\/ICDM.2015.114"},{"key":"983_CR72","unstructured":"Singh, R., Xu, J. & Berger, B. Pairwise global alignment of protein interaction networks by matching neighborhood topology. In: Annual international conference on research in computational molecular biology). Springer (2007)."},{"key":"983_CR73","doi-asserted-by":"publisher","first-page":"i253","DOI":"10.1093\/bioinformatics\/btp203","volume":"25","author":"CS Liao","year":"2009","unstructured":"Liao, C. S., Lu, K., Baym, M., Singh, R. & Berger, B. IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics 25, i253\u2013i258 (2009).","journal-title":"Bioinformatics"},{"key":"983_CR74","unstructured":"Defferrard, M., Bresson, X. & Vandergheynst, P. Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural Inf. Process. Syst. 29, (2016)."},{"key":"983_CR75","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R. et al. Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision). IEEE (2017).","DOI":"10.1109\/ICCV.2017.74"},{"key":"983_CR76","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.neuroimage.2017.11.062","volume":"180","author":"CM Michel","year":"2018","unstructured":"Michel, C. M. & Koenig, T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. Neuroimage 180, 577\u2013593 (2018).","journal-title":"Neuroimage"},{"key":"983_CR77","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1109\/10.391164","volume":"42","author":"RD Pascual-Marqui","year":"1995","unstructured":"Pascual-Marqui, R. D., Michel, C. M. & Lehmann, D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans. Biomed. Eng. 42, 658\u2013665 (1995).","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00983-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00983-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00983-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T17:08:31Z","timestamp":1704474511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00983-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,5]]},"references-count":77,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["983"],"URL":"https:\/\/doi.org\/10.1038\/s41746-023-00983-9","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,5]]},"assertion":[{"value":"23 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3"}}