{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T16:07:10Z","timestamp":1788019630726,"version":"build-2784847793"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"European Union \u201cNextGenerationEU\u201d"},{"name":"EBRAINS-Italy of PNRR"},{"DOI":"10.13039\/501100003196","name":"Ministero della Salute","doi-asserted-by":"publisher","award":["Ricerca Corrente 2023"],"award-info":[{"award-number":["Ricerca Corrente 2023"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86\u2009\u00b1\u20090.08 (SD) and an area under the curve of 0.93\u2009\u00b1\u20090.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5\u00a0min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.<\/jats:p>","DOI":"10.1038\/s41598-024-64870-3","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T11:02:25Z","timestamp":1718708545000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics"],"prefix":"10.1038","volume":"14","author":[{"given":"Marie-Constance","family":"Corsi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emahnuel","family":"Troisi Lopez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierpaolo","family":"Sorrentino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone","family":"Cuozzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Danieli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Bonanni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gian Marco","family":"Duma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"issue":"8","key":"64870_CR1","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.seizure.2006.08.005","volume":"15","author":"A Juarez-Garcia","year":"2006","unstructured":"Juarez-Garcia, A., Stokes, T., Shaw, B., Camosso-Stefinovic, J. & Baker, R. The costs of epilepsy misdiagnosis in England and Wales. Seizure 15(8), 598\u2013605. https:\/\/doi.org\/10.1016\/j.seizure.2006.08.005 (2006).","journal-title":"Seizure"},{"issue":"3","key":"64870_CR2","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1136\/adc.2004.064477","volume":"91","author":"P Uldall","year":"2006","unstructured":"Uldall, P., Alving, J., Hansen, L. K., Kib\u00e6k, M. & Buchholt, J. The misdiagnosis of epilepsy in children admitted to a tertiary epilepsy centre with paroxysmal events. Arch. Dis. Child. 91(3), 219\u2013221. https:\/\/doi.org\/10.1136\/adc.2004.064477 (2006).","journal-title":"Arch. Dis. Child."},{"issue":"1","key":"64870_CR3","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/S0735-1097(00)00700-2","volume":"36","author":"A Zaidi","year":"2000","unstructured":"Zaidi, A., Clough, P., Cooper, P., Scheepers, B. & Fitzpatrick, A. P. Misdiagnosis of epilepsy: Many seizure-like attacks have a cardiovascular cause. J. Am. Coll. Cardiol. 36(1), 181\u2013184. https:\/\/doi.org\/10.1016\/S0735-1097(00)00700-2 (2000).","journal-title":"J. Am. Coll. Cardiol."},{"key":"64870_CR4","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.seizure.2016.11.029","volume":"44","author":"M Oto","year":"2017","unstructured":"Oto, M. The misdiagnosis of epilepsy: Appraising risks and managing uncertainty. Seizure 44, 143\u2013146. https:\/\/doi.org\/10.1016\/j.seizure.2016.11.029 (2017).","journal-title":"Seizure"},{"key":"64870_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119010","volume":"213","author":"L Ilias","year":"2023","unstructured":"Ilias, L., Askounis, D. & Psarras, J. Multimodal detection of epilepsy with deep neural networks. Expert Syst. Appl. 213, 119010. https:\/\/doi.org\/10.1016\/j.eswa.2022.119010 (2023).","journal-title":"Expert Syst. Appl."},{"key":"64870_CR6","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.709400","volume":"12","author":"L Jin","year":"2021","unstructured":"Jin, L. et al. Multimodal image integration for epilepsy presurgical evaluation: A clinical workflow. Front. Neurol. 12, 709400. https:\/\/doi.org\/10.3389\/fneur.2021.709400 (2021).","journal-title":"Front. Neurol."},{"issue":"5","key":"64870_CR7","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1016\/j.clinph.2018.01.019","volume":"129","author":"WO Tatum","year":"2018","unstructured":"Tatum, W. O. et al. Clinical utility of EEG in diagnosing and monitoring epilepsy in adults. Clin. Neurophysiol. 129(5), 1056\u20131082. https:\/\/doi.org\/10.1016\/j.clinph.2018.01.019 (2018).","journal-title":"Clin. Neurophysiol."},{"issue":"10","key":"64870_CR8","doi-asserted-by":"publisher","first-page":"e200","DOI":"10.1111\/epi.13481","volume":"57","author":"H Schmidt","year":"2016","unstructured":"Schmidt, H. et al. A computational biomarker of idiopathic generalized epilepsy from resting state EEG. Epilepsia 57(10), e200\u2013e204. https:\/\/doi.org\/10.1111\/epi.13481 (2016).","journal-title":"Epilepsia"},{"issue":"6","key":"64870_CR9","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac9c93","volume":"19","author":"P Thangavel","year":"2022","unstructured":"Thangavel, P. et al. Improving automated diagnosis of epilepsy from EEGs beyond IEDs. J. Neural. Eng. 19(6), 066017. https:\/\/doi.org\/10.1088\/1741-2552\/ac9c93 (2022).","journal-title":"J. Neural. Eng."},{"key":"64870_CR10","doi-asserted-by":"publisher","DOI":"10.1101\/2023.08.12.23294018","author":"P Myers","year":"2023","unstructured":"Myers, P. et al. Diagnosing epilepsy with normal interictal EEG using dynamic network models. medRxiv https:\/\/doi.org\/10.1101\/2023.08.12.23294018 (2023).","journal-title":"medRxiv"},{"key":"64870_CR11","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.yebeh.2018.09.030","volume":"88","author":"UR Acharya","year":"2018","unstructured":"Acharya, U. R., Hagiwara, Y. & Adeli, H. Automated seizure prediction. Epilepsy Behav. 88, 251\u2013261. https:\/\/doi.org\/10.1016\/j.yebeh.2018.09.030 (2018).","journal-title":"Epilepsy Behav."},{"issue":"2","key":"64870_CR12","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/JBHI.2019.2933046","volume":"24","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Guo, Y., Yang, P., Chen, W. & Lo, B. Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network. IEEE J. Biomed. Health Inform. 24(2), 465\u2013474. https:\/\/doi.org\/10.1109\/JBHI.2019.2933046 (2020).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"64870_CR13","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2018.00095","author":"M Zhou","year":"2018","unstructured":"Zhou, M. et al. Epileptic seizure detection based on EEG signals and CNN. Front. Neuroinform. https:\/\/doi.org\/10.3389\/fninf.2018.00095 (2018).","journal-title":"Front. Neuroinform."},{"issue":"6","key":"64870_CR14","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1007\/s11517-022-02560-w","volume":"60","author":"B Rijnders","year":"2022","unstructured":"Rijnders, B., Korkmaz, E. E. & Yildirim, F. Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG. Med. Biol. Eng. Comput. 60(6), 1675\u20131689. https:\/\/doi.org\/10.1007\/s11517-022-02560-w (2022).","journal-title":"Med. Biol. Eng. Comput."},{"issue":"1","key":"64870_CR15","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3390\/s22010129","volume":"22","author":"G Varone","year":"2021","unstructured":"Varone, G. et al. A machine learning approach involving functional connectivity features to classify rest-EEG psychogenic non-epileptic seizures from healthy controls. Sensors 22(1), 129. https:\/\/doi.org\/10.3390\/s22010129 (2021).","journal-title":"Sensors"},{"issue":"3","key":"64870_CR16","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1007\/s00415-021-10718-z","volume":"269","author":"B Wang","year":"2022","unstructured":"Wang, B. et al. An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features. J. Neurol. 269(3), 1501\u20131514. https:\/\/doi.org\/10.1007\/s00415-021-10718-z (2022).","journal-title":"J. Neurol."},{"issue":"1","key":"64870_CR17","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1001\/jamaneurol.2019.3485","volume":"77","author":"J Jing","year":"2020","unstructured":"Jing, J. et al. Development of expert-level automated detection of epileptiform discharges during electroencephalogram interpretation. JAMA Neurol. 77(1), 103\u2013108. https:\/\/doi.org\/10.1001\/jamaneurol.2019.3485 (2020).","journal-title":"JAMA Neurol."},{"issue":"05","key":"64870_CR18","doi-asserted-by":"publisher","first-page":"2050074","DOI":"10.1142\/S0129065720500744","volume":"31","author":"J Thomas","year":"2021","unstructured":"Thomas, J. et al. Automated adult epilepsy diagnostic tool based on interictal scalp electroencephalogram characteristics: A six-center study. Int. J. Neur. Syst. 31(05), 2050074. https:\/\/doi.org\/10.1142\/S0129065720500744 (2021).","journal-title":"Int. J. Neur. Syst."},{"issue":"11","key":"64870_CR19","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.3390\/brainsci11111525","volume":"11","author":"M Saeidi","year":"2021","unstructured":"Saeidi, M. et al. Neural decoding of EEG signals with machine learning: A systematic review. Brain Sci. 11(11), 1525. https:\/\/doi.org\/10.3390\/brainsci11111525 (2021).","journal-title":"Brain Sci."},{"key":"64870_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainresbull.2023.110684","volume":"200","author":"S Liu","year":"2023","unstructured":"Liu, S., Li, F. & Wan, F. Distance to criticality undergoes critical transition before epileptic seizure attacks. Brain Res. Bull. 200, 110684. https:\/\/doi.org\/10.1016\/j.brainresbull.2023.110684 (2023).","journal-title":"Brain Res. Bull."},{"issue":"2","key":"64870_CR21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1010852","volume":"19","author":"SA Moosavi","year":"2023","unstructured":"Moosavi, S. A. & Truccolo, W. Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures. PLOS Comput. Biol. 19(2), e1010852. https:\/\/doi.org\/10.1371\/journal.pcbi.1010852 (2023).","journal-title":"PLOS Comput. Biol."},{"issue":"1","key":"64870_CR22","doi-asserted-by":"publisher","first-page":"108734","DOI":"10.1016\/j.isci.2023.108734","volume":"27","author":"MC Corsi","year":"2024","unstructured":"Corsi, M. C. et al. Measuring neuronal avalanches to inform brain-computer interfaces. iScience 27(1), 108734. https:\/\/doi.org\/10.1016\/j.isci.2023.108734 (2024).","journal-title":"iScience"},{"key":"64870_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120260","volume":"277","author":"P Sorrentino","year":"2023","unstructured":"Sorrentino, P. et al. Brain fingerprint is based on the aperiodic, scale-free, neuronal activity. NeuroImage 277, 120260. https:\/\/doi.org\/10.1016\/j.neuroimage.2023.120260 (2023).","journal-title":"NeuroImage"},{"issue":"5","key":"64870_CR24","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1111\/epi.17551","volume":"64","author":"GM Duma","year":"2023","unstructured":"Duma, G. M. et al. Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: A resting-state hdEEG study. Epilepsia 64(5), 1278\u20131288. https:\/\/doi.org\/10.1111\/epi.17551 (2023).","journal-title":"Epilepsia"},{"key":"64870_CR25","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1093\/braincomms\/fcad348","volume":"2023","author":"GM Duma","year":"2023","unstructured":"Duma, G. M. et al. Altered spread of waves of activities at large scale is influenced by cortical thickness organization in temporal lobe epilepsy: A MRI - high-density EEG study. Brain Commun. 2023, 348. https:\/\/doi.org\/10.1093\/braincomms\/fcad348 (2023).","journal-title":"Brain Commun."},{"issue":"47","key":"64870_CR26","doi-asserted-by":"publisher","first-page":"8807","DOI":"10.1523\/JNEUROSCI.0938-22.2022","volume":"42","author":"P Sorrentino","year":"2022","unstructured":"Sorrentino, P. et al. Whole-brain propagation delays in multiple sclerosis, a combined tractography-magnetoencephalography study. J. Neurosci. 42(47), 8807\u20138816. https:\/\/doi.org\/10.1523\/JNEUROSCI.0938-22.2022 (2022).","journal-title":"J. Neurosci."},{"issue":"7","key":"64870_CR27","doi-asserted-by":"publisher","first-page":"4262","DOI":"10.3390\/app13074262","volume":"13","author":"S Shafiezadeh","year":"2023","unstructured":"Shafiezadeh, S. et al. Methodological issues in evaluating machine learning models for EEG seizure prediction: Good cross-validation accuracy does not guarantee generalization to new patients. Appl. Sci. 13(7), 4262. https:\/\/doi.org\/10.3390\/app13074262 (2023).","journal-title":"Appl. Sci."},{"issue":"9","key":"64870_CR28","doi-asserted-by":"publisher","first-page":"1870","DOI":"10.1212\/WNL.52.9.1870","volume":"52","author":"N Bernasconi","year":"1999","unstructured":"Bernasconi, N. et al. Entorhinal cortex in temporal lobe epilepsy: A quantitative MRI study. Neurology 52(9), 1870\u20131870. https:\/\/doi.org\/10.1212\/WNL.52.9.1870 (1999).","journal-title":"Neurology"},{"issue":"8","key":"64870_CR29","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1016\/j.clinph.2013.01.024","volume":"124","author":"Y Kubota","year":"2013","unstructured":"Kubota, Y. et al. In vivo human hippocampal cingulate connectivity: A corticocortical evoked potentials (CCEPs) study. Clin. Neurophysiol. 124(8), 1547\u20131556. https:\/\/doi.org\/10.1016\/j.clinph.2013.01.024 (2013).","journal-title":"Clin. Neurophysiol."},{"key":"64870_CR30","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.00625","author":"L Qin","year":"2020","unstructured":"Qin, L. et al. Alterations functional connectivity in temporal lobe epilepsy and their relationships with cognitive function: A longitudinal resting-state fMRI study. Front. Neurol. https:\/\/doi.org\/10.3389\/fneur.2020.00625 (2020).","journal-title":"Front. Neurol."},{"key":"64870_CR31","doi-asserted-by":"publisher","DOI":"10.3389\/fncir.2015.00027","author":"MS Vismer","year":"2015","unstructured":"Vismer, M. S., Forcelli, P. A., Skopin, M. D., Gale, K. & Koubeissi, M. Z. The piriform, perirhinal, and entorhinal cortex in seizure generation. Front. Neural Circuits https:\/\/doi.org\/10.3389\/fncir.2015.00027 (2015).","journal-title":"Front. Neural Circuits"},{"key":"64870_CR32","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2020.550749","volume":"11","author":"R Rucco","year":"2020","unstructured":"Rucco, R. et al. Neuronal avalanches to study the coordination of large-scale brain activity: Application to Rett syndrome. Front Psychol. 11, 550749. https:\/\/doi.org\/10.3389\/fpsyg.2020.550749 (2020).","journal-title":"Front Psychol."},{"issue":"1","key":"64870_CR33","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1162\/netn_a_00270","volume":"7","author":"P Sorrentino","year":"2023","unstructured":"Sorrentino, P. et al. Dynamical interactions reconfigure the gradient of cortical timescales. Netw. Neurosci. 7(1), 73\u201385. https:\/\/doi.org\/10.1162\/netn_a_00270 (2023).","journal-title":"Netw. Neurosci."},{"issue":"11","key":"64870_CR34","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1016\/S1474-4422(18)30274-6","volume":"17","author":"PJ Karoly","year":"2018","unstructured":"Karoly, P. J. et al. Circadian and circaseptan rhythms in human epilepsy: A retrospective cohort study. Lancet Neurol. 17(11), 977\u2013985. https:\/\/doi.org\/10.1016\/S1474-4422(18)30274-6 (2018).","journal-title":"Lancet Neurol."},{"issue":"12","key":"64870_CR35","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1016\/S1474-4422(18)30335-1","volume":"17","author":"S Khan","year":"2018","unstructured":"Khan, S. et al. Circadian rhythm and epilepsy. Lancet Neurol. 17(12), 1098\u20131108. https:\/\/doi.org\/10.1016\/S1474-4422(18)30335-1 (2018).","journal-title":"Lancet Neurol."},{"issue":"1","key":"64870_CR36","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/s41467-017-02577-y","volume":"9","author":"MO Baud","year":"2018","unstructured":"Baud, M. O. et al. Multi-day rhythms modulate seizure risk in epilepsy. Nat. Commun. 9(1), 88. https:\/\/doi.org\/10.1038\/s41467-017-02577-y (2018).","journal-title":"Nat. Commun."},{"key":"64870_CR37","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.seizure.2016.08.005","volume":"41","author":"Y Xu","year":"2016","unstructured":"Xu, Y. et al. Frequency of a false positive diagnosis of epilepsy: A systematic review of observational studies. Seizure Eur. J. Epilepsy 41, 167\u2013174. https:\/\/doi.org\/10.1016\/j.seizure.2016.08.005 (2016).","journal-title":"Seizure Eur. J. Epilepsy"},{"issue":"9","key":"64870_CR38","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1046\/j.1528-1157.2002.52301.x","volume":"43","author":"M Reuber","year":"2002","unstructured":"Reuber, M., Fern\u00e1ndez, G., Bauer, J., Singh, D. D. & Elger, C. E. Interictal EEG abnormalities in patients with psychogenic nonepileptic seizures. Epilepsia 43(9), 1013\u20131020. https:\/\/doi.org\/10.1046\/j.1528-1157.2002.52301.x (2002).","journal-title":"Epilepsia"},{"key":"64870_CR39","doi-asserted-by":"publisher","first-page":"107126","DOI":"10.1016\/j.eplepsyres.2023.107126","volume":"192","author":"T Dorji","year":"2023","unstructured":"Dorji, T. et al. Challenges in epilepsy diagnosis and management in a low-resource setting: An experience from Bhutan. Epilepsy Res. 192, 107126. https:\/\/doi.org\/10.1016\/j.eplepsyres.2023.107126 (2023).","journal-title":"Epilepsy Res."},{"issue":"1","key":"64870_CR40","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","volume":"134","author":"A Delorme","year":"2004","unstructured":"Delorme, A. & Makeig, S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9\u201321. https:\/\/doi.org\/10.1016\/j.jneumeth.2003.10.009 (2004).","journal-title":"J. Neurosci. Methods"},{"issue":"2","key":"64870_CR41","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.neuroimage.2012.01.024","volume":"62","author":"AC Evans","year":"2012","unstructured":"Evans, A. C., Janke, A. L., Collins, D. L. & Baillet, S. Brain templates and atlases. NeuroImage 62(2), 911\u2013922. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.01.024 (2012).","journal-title":"NeuroImage"},{"key":"64870_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2011\/879716","volume":"2011","author":"F Tadel","year":"2011","unstructured":"Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: a user-friendly application for MEG\/EEG analysis. Comput. Intell. Neurosci. 2011, 1\u201313. https:\/\/doi.org\/10.1155\/2011\/879716 (2011).","journal-title":"Comput. Intell. Neurosci."},{"key":"64870_CR43","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.neuroimage.2012.09.050","volume":"65","author":"R Dahnke","year":"2013","unstructured":"Dahnke, R., Yotter, R. A. & Gaser, C. Cortical thickness and central surface estimation. NeuroImage 65, 336\u2013348. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.09.050 (2013).","journal-title":"NeuroImage"},{"issue":"1","key":"64870_CR44","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/1475-925X-9-45","volume":"9","author":"A Gramfort","year":"2010","unstructured":"Gramfort, A., Papadopoulo, T., Olivi, E. & Clerc, M. OpenMEEG: Opensource software for quasistatic bioelectromagnetics. BioMed. Eng. OnLine 9(1), 45. https:\/\/doi.org\/10.1186\/1475-925X-9-45 (2010).","journal-title":"BioMed. Eng. OnLine"},{"issue":"1","key":"64870_CR45","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/BF02512476","volume":"32","author":"MS H\u00e4m\u00e4l\u00e4inen","year":"1994","unstructured":"H\u00e4m\u00e4l\u00e4inen, M. S. & Ilmoniemi, R. J. Interpreting magnetic fields of the brain: Minimum norm estimates. Med. Biol. Eng. Comput. 32(1), 35\u201342. https:\/\/doi.org\/10.1007\/BF02512476 (1994).","journal-title":"Med. Biol. Eng. Comput."},{"issue":"3","key":"64870_CR46","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3), 968\u2013980. https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.021 (2006).","journal-title":"NeuroImage"},{"key":"64870_CR47","doi-asserted-by":"publisher","first-page":"e67400","DOI":"10.7554\/eLife.67400","volume":"10","author":"P Sorrentino","year":"2021","unstructured":"Sorrentino, P. et al. The structural connectome constrains fast brain dynamics. eLife 10, e67400. https:\/\/doi.org\/10.7554\/eLife.67400 (2021).","journal-title":"eLife"},{"issue":"16","key":"64870_CR48","doi-asserted-by":"publisher","first-page":"7079","DOI":"10.1523\/JNEUROSCI.4286-12.2013","volume":"33","author":"O Shriki","year":"2013","unstructured":"Shriki, O. et al. Neuronal Avalanches in the resting MEG of the human brain. J Neurosci. 33(16), 7079\u20137090. https:\/\/doi.org\/10.1523\/JNEUROSCI.4286-12.2013 (2013).","journal-title":"J Neurosci."},{"issue":"5","key":"64870_CR49","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.94.058101","volume":"94","author":"C Haldeman","year":"2005","unstructured":"Haldeman, C. & Beggs, J. M. Critical branching captures activity in living neural networks and maximizes the number of metastable states. Phys Rev Lett 94(5), 058101. https:\/\/doi.org\/10.1103\/PhysRevLett.94.058101 (2005).","journal-title":"Phys Rev Lett"},{"issue":"4","key":"64870_CR50","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1103\/PhysRevLett.59.381","volume":"59","author":"P Bak","year":"1987","unstructured":"Bak, P., Tang, C. & Wiesenfeld, K. Self-organized criticality: an explanation of the 1\/f noise. Phys. Rev. Lett. 59(4), 381\u2013384. https:\/\/doi.org\/10.1103\/PhysRevLett.59.381 (1987).","journal-title":"Phys. Rev. Lett."},{"key":"64870_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.116967","volume":"218","author":"M Scherer","year":"2020","unstructured":"Scherer, M. et al. Desynchronization of temporal lobe theta-band activity during effective anterior thalamus deep brain stimulation in epilepsy. NeuroImage 218, 116967. https:\/\/doi.org\/10.1016\/j.neuroimage.2020.116967 (2020).","journal-title":"NeuroImage"},{"issue":"2","key":"64870_CR52","doi-asserted-by":"publisher","first-page":"e1008731","DOI":"10.1371\/journal.pcbi.1008731","volume":"17","author":"V Sip","year":"2021","unstructured":"Sip, V., Scholly, J., Guye, M., Bartolomei, F. & Jirsa, V. Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings. PLoS Comput. Biol. 17(2), e1008731. https:\/\/doi.org\/10.1371\/journal.pcbi.1008731 (2021).","journal-title":"PLoS Comput. Biol."},{"issue":"4","key":"64870_CR53","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s10548-016-0498-y","volume":"32","author":"S Haufe","year":"2019","unstructured":"Haufe, S. & Ewald, A. A simulation framework for benchmarking EEG-based brain connectivity estimation methodologies. Brain Topogr. 32(4), 625\u2013642. https:\/\/doi.org\/10.1007\/s10548-016-0498-y (2019).","journal-title":"Brain Topogr."},{"issue":"10","key":"64870_CR54","doi-asserted-by":"publisher","first-page":"2292","DOI":"10.1016\/j.clinph.2004.04.029","volume":"115","author":"G Nolte","year":"2004","unstructured":"Nolte, G. et al. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophys. 115(10), 2292\u20132307. https:\/\/doi.org\/10.1016\/j.clinph.2004.04.029 (2004).","journal-title":"Clin. Neurophys."},{"issue":"3","key":"64870_CR55","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.acha.2004.03.004","volume":"16","author":"IC Moore","year":"2004","unstructured":"Moore, I. C. & Cada, M. Prolate spheroidal wave functions, an introduction to the Slepian series and its properties. Appl. Comput. Harmon. Anal. 16(3), 208\u2013230. https:\/\/doi.org\/10.1016\/j.acha.2004.03.004 (2004).","journal-title":"Appl. Comput. Harmon. Anal."},{"issue":"3","key":"64870_CR56","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola, A. J. & Sch\u00f6lkopf, B. A tutorial on support vector regression. Stat. Comput. 14(3), 199\u2013222. https:\/\/doi.org\/10.1023\/B:STCO.0000035301.49549.88 (2004).","journal-title":"Stat. Comput."},{"key":"64870_CR57","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-1-0716-3195-9_2","volume-title":"Machine Learning for Brain Disorders","author":"J Faouzi","year":"2023","unstructured":"Faouzi, J. & Colliot, O. Classic Machine Learning Methods. In Machine Learning for Brain Disorders (ed. Colliot, O.) 25\u201375 (Neuromethods Springer, 2023). https:\/\/doi.org\/10.1007\/978-1-0716-3195-9_2."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-64870-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-64870-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-64870-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T11:06:35Z","timestamp":1718708795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-64870-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,18]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["64870"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-64870-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,18]]},"assertion":[{"value":"19 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 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":"14039"}}