{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T07:04:27Z","timestamp":1793343867688,"version":"4.5.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Transl Psychiatry"],"DOI":"10.1038\/s41398-025-03441-0","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T11:35:47Z","timestamp":1751456147000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Beyond homogeneity: charting the landscape of heterogeneity in neurodevelopmental and psychiatric electroencephalography"],"prefix":"10.1038","volume":"15","author":[{"given":"Aida","family":"Ebadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahar","family":"Allouch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Mheich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judie","family":"Tabbal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aya","family":"Kabbara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2490-0700","authenticated-orcid":false,"given":"Gabriel","family":"Robert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aline","family":"Lefebvre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4927-2798","authenticated-orcid":false,"given":"Anton","family":"Iftimovici","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9392-7996","authenticated-orcid":false,"given":"Borja","family":"Rodr\u00edguez-Herreros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadia","family":"Chabane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0307-5086","authenticated-orcid":false,"given":"Mahmoud","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"3441_CR1","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s13311-012-0131-z","volume":"9","author":"SK Loo","year":"2012","unstructured":"Loo SK, Makeig S. Clinical utility of EEG in attention-deficit\/hyperactivity disorder: a research update. Neurotherapeutics. 2012;9:569\u201387.","journal-title":"Neurotherapeutics"},{"key":"3441_CR2","doi-asserted-by":"publisher","first-page":"604","DOI":"10.3109\/09540261.2013.816269","volume":"25","author":"S Olbrich","year":"2013","unstructured":"Olbrich S, Arns M. EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response. Int Rev Psychiatry. 2013;25:604\u201318.","journal-title":"Int Rev Psychiatry"},{"key":"3441_CR3","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neubiorev.2019.07.021","volume":"105","author":"FS de Aguiar Neto","year":"2019","unstructured":"de Aguiar Neto FS, Rosa JLG. Depression biomarkers using non-invasive EEG: A review. Neurosci Biobehav Rev. 2019;105:83\u201393.","journal-title":"Neurosci Biobehav Rev"},{"key":"3441_CR4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/1866-1955-5-24","volume":"5","author":"J Wang","year":"2013","unstructured":"Wang J, Barstein J, Ethridge LE, Mosconi MW, Takarae Y, Sweeney JA. Resting state EEG abnormalities in autism spectrum disorders. J Neurodev Disord. 2013;5:24.","journal-title":"J Neurodev Disord"},{"key":"3441_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.25122\/jml-2019-0085","volume":"13","author":"L Livint Popa","year":"2020","unstructured":"Livint Popa L, Dragos H, Pantelemon C, Verisezan Rosu O, Strilciuc S. The role of quantitative EEG in the diagnosis of neuropsychiatric disorders. J Med Life. 2020;13:8\u201315.","journal-title":"J Med Life"},{"key":"3441_CR6","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/s41551-020-00614-8","volume":"5","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Wu W, Toll RT, Naparstek S, Maron-Katz A, Watts M, et al. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng. 2021;5:309\u201323.","journal-title":"Nat Biomed Eng"},{"key":"3441_CR7","doi-asserted-by":"publisher","first-page":"104752","DOI":"10.1016\/j.neubiorev.2022.104752","volume":"139","author":"J Slater","year":"2022","unstructured":"Slater J, Joober R, Koborsy BL, Mitchell S, Sahlas E, Palmer C. Can electroencephalography (EEG) identify ADHD subtypes? A systematic review. Neurosci Biobehav Rev. 2022;139:104752.","journal-title":"Neurosci Biobehav Rev"},{"key":"3441_CR8","doi-asserted-by":"publisher","first-page":"2098","DOI":"10.1016\/S1388-2457(01)00668-X","volume":"112","author":"AR Clarke","year":"2001","unstructured":"Clarke AR, Barry RJ, McCarthy R, Selikowitz M. EEG-defined subtypes of children with attention-deficit\/hyperactivity disorder. Clin Neurophysiol. 2001;112:2098\u2013105.","journal-title":"Clin Neurophysiol"},{"key":"3441_CR9","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1176\/appi.ajp.2018.17121358","volume":"176","author":"AS Widge","year":"2019","unstructured":"Widge AS, Bilge MT, Montana R, Chang W, Rodriguez CI, Deckersbach T, et al. Electroencephalographic biomarkers for treatment response prediction in major depressive illness: a meta-analysis. Am J Psychiatry. 2019;176:44\u201356.","journal-title":"Am J Psychiatry"},{"key":"3441_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-022-02064-z","volume":"12","author":"D Watts","year":"2022","unstructured":"Watts D, Pulice RF, Reilly J, Brunoni AR, Kapczinski F, Passos IC. Predicting treatment response using EEG in major depressive disorder: a machine-learning meta-analysis. Transl Psychiatry. 2022;12:332.","journal-title":"Transl Psychiatry"},{"key":"3441_CR11","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1038\/s41587-019-0397-3","volume":"38","author":"W Wu","year":"2020","unstructured":"Wu W, Zhang Y, Jiang J, Lucas MV, Fonzo GA, Rolle CE, et al. An electroencephalographic signature predicts antidepressant response in major depression. Nat Biotechnol. 2020;38:439\u201347.","journal-title":"Nat Biotechnol"},{"key":"3441_CR12","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1001\/jamapsychiatry.2019.3867","volume":"77","author":"CE Rolle","year":"2020","unstructured":"Rolle CE, Fonzo GA, Wu W, Toll R, Jha MK, Cooper C, et al. Cortical connectivity moderators of antidepressant vs placebo treatment response in major depressive disorder: secondary analysis of a randomized clinical trial. JAMA Psychiatry. 2020;77:397\u2013408.","journal-title":"JAMA Psychiatry"},{"key":"3441_CR13","doi-asserted-by":"publisher","first-page":"521","DOI":"10.3389\/fnhum.2018.00521","volume":"12","author":"JJ Newson","year":"2019","unstructured":"Newson JJ, Thiagarajan TC. EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies. Front Hum Neurosci. 2019;12:521.","journal-title":"Front Hum Neurosci"},{"key":"3441_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-023-02681-2","volume":"13","author":"WS Neo","year":"2023","unstructured":"Neo WS, Foti D, Keehn B, Kelleher B. Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis. Transl Psychiatry. 2023;13:389.","journal-title":"Transl Psychiatry"},{"key":"3441_CR15","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/7854_2022_345","volume":"57","author":"K Gonz\u00e1lez-Madruga","year":"2022","unstructured":"Gonz\u00e1lez-Madruga K, Staginnus M, Fairchild G. Alterations in structural and functional connectivity in ADHD: implications for theories of ADHD. Curr Top Behav Neurosci. 2022;57:445\u201381.","journal-title":"Curr Top Behav Neurosci"},{"key":"3441_CR16","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.jaac.2020.08.014","volume":"60","author":"S Cortese","year":"2021","unstructured":"Cortese S, Aoki YY, Itahashi T, Castellanos FX, Eickhoff SB. Systematic review and meta-analysis: resting-state functional magnetic resonance imaging studies of attention-deficit\/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2021;60:61\u201375.","journal-title":"J Am Acad Child Adolesc Psychiatry"},{"key":"3441_CR17","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.jad.2023.01.126","volume":"328","author":"A Miljevic","year":"2023","unstructured":"Miljevic A, Bailey NW, Murphy OW, Perera MPN, Fitzgerald PB. Alterations in EEG functional connectivity in individuals with depression: a systematic review. J Affect Disord. 2023;328:287\u2013302.","journal-title":"J Affect Disord"},{"key":"3441_CR18","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.tics.2019.03.009","volume":"23","author":"E Feczko","year":"2019","unstructured":"Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn Sci. 2019;23:584\u2013601.","journal-title":"Trends Cogn Sci"},{"key":"3441_CR19","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/s41380-019-0451-z","volume":"24","author":"AF Marquand","year":"2019","unstructured":"Marquand AF, Kia SM, Zabihi M, Wolfers T, Buitelaar JK, Beckmann. CF. Conceptualizing mental disorders as deviations from normative functioning. Mol Psychiatry. 2019;24:1565.","journal-title":"Mol Psychiatry"},{"key":"3441_CR20","doi-asserted-by":"publisher","first-page":"2946","DOI":"10.1093\/brain\/awab165","volume":"144","author":"S Verdi","year":"2021","unstructured":"Verdi S, Marquand AF, Schott JM, Cole JH. Beyond the average patient: how neuroimaging models can address heterogeneity in dementia. Brain. 2021;144:2946\u201353.","journal-title":"Brain"},{"key":"3441_CR21","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.biopsych.2015.12.023","volume":"80","author":"AF Marquand","year":"2016","unstructured":"Marquand AF, Rezek I, Buitelaar J, Beckmann CF. Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biol Psychiatry. 2016;80:552\u201361.","journal-title":"Biol Psychiatry"},{"key":"3441_CR22","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1038\/s41593-023-01404-6","volume":"26","author":"A Segal","year":"2023","unstructured":"Segal A, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, et al. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nat Neurosci. 2023;26:1613\u201329.","journal-title":"Nat Neurosci"},{"key":"3441_CR23","doi-asserted-by":"publisher","first-page":"382","DOI":"10.3109\/03014460.2012.694475","volume":"39","author":"TJ Cole","year":"2012","unstructured":"Cole TJ. The development of growth references and growth charts. Ann Hum Biol. 2012;39:382\u201394.","journal-title":"Ann Hum Biol"},{"key":"3441_CR24","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s41586-022-04554-y","volume":"604","author":"RAI Bethlehem","year":"2022","unstructured":"Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, et al. Brain charts for the human lifespan. Nature. 2022;604:525\u201333.","journal-title":"Nature"},{"key":"3441_CR25","doi-asserted-by":"publisher","first-page":"e72904","DOI":"10.7554\/eLife.72904","volume":"11","author":"S Rutherford","year":"2022","unstructured":"Rutherford S, Fraza C, Dinga R, Kia SM, Wolfers T, Zabihi M, et al. Charting brain growth and aging at high spatial precision. Elife. 2022;11:e72904. https:\/\/doi.org\/10.7554\/eLife.72904.","journal-title":"Elife"},{"key":"3441_CR26","doi-asserted-by":"publisher","first-page":"e85082","DOI":"10.7554\/eLife.85082","volume":"12","author":"S Rutherford","year":"2023","unstructured":"Rutherford S, Barkema P, Tso IF, Sripada C, Beckmann CF, Ruhe HG, et al. Evidence for embracing normative modeling. Elife. 2023;12:e85082. https:\/\/doi.org\/10.7554\/eLife.85082.","journal-title":"Elife"},{"key":"3441_CR27","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1017\/S0033291719000084","volume":"50","author":"T Wolfers","year":"2020","unstructured":"Wolfers T, Beckmann CF, Hoogman M, Buitelaar JK, Franke B, Marquand AF. Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models. Psychol Med. 2020;50:314\u201323.","journal-title":"Psychol Med"},{"key":"3441_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-020-01057-0","volume":"10","author":"M Zabihi","year":"2020","unstructured":"Zabihi M, Floris DL, Kia SM, Wolfers T, Tillmann J, Arenas AL, et al. Fractionating autism based on neuroanatomical normative modeling. Transl Psychiatry. 2020;10:384.","journal-title":"Transl Psychiatry"},{"key":"3441_CR29","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1001\/jamapsychiatry.2018.2467","volume":"75","author":"T Wolfers","year":"2018","unstructured":"Wolfers T, Doan NT, Kaufmann T, Aln\u00e6s D, Moberget T, Agartz I, et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry. 2018;75:1146\u201355.","journal-title":"JAMA Psychiatry"},{"key":"3441_CR30","doi-asserted-by":"publisher","first-page":"e2442","DOI":"10.1212\/WNL.0000000000207298","volume":"100","author":"S Verdi","year":"2023","unstructured":"Verdi S, Kia SM, Yong KXX, Tosun D, Schott JM, Marquand AF, et al. Revealing individual neuroanatomical heterogeneity in alzheimer disease using neuroanatomical normative modeling. Neurology. 2023;100:e2442\u2013e2453.","journal-title":"Neurology"},{"key":"3441_CR31","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1038\/s41596-022-00696-5","volume":"17","author":"S Rutherford","year":"2022","unstructured":"Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M, Dinga R, et al. The normative modeling framework for computational psychiatry. Nat Protoc. 2022;17:1711\u201334.","journal-title":"Nat Protoc"},{"key":"3441_CR32","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1038\/s41593-025-01907-4","volume":"28:","author":"L Sun","year":"2025","unstructured":"Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, et al. Human lifespan changes in the brain\u2019s functional connectome. Nat Neurosci. 2025;28:891\u2013901.","journal-title":"Nat Neurosci"},{"key":"3441_CR33","unstructured":"John ER Neurometrics: Clinical Applications of Quantitative Electrophysiology. 1977; 2. https:\/\/books.google.com\/books?hl=en&lr=&id=X7MazwJC0D8C&oi=fnd&pg=PR15&ots=TjCV81LG3g&sig=HjCxo1UJza4srHY_cIG6EaMXKPU."},{"key":"3441_CR34","doi-asserted-by":"publisher","unstructured":"Thatcher RW Normative EEG databases and EEG biofeedback. Journal of Neurotherapy 1998. https:\/\/doi.org\/10.1300\/J184v02n04_02.","DOI":"10.1300\/J184v02n04_02"},{"key":"3441_CR35","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/MSP.2017.2777518","volume":"35","author":"M Hassan","year":"2018","unstructured":"Hassan M, Wendling F. Electroencephalography source connectivity: aiming for high resolution of brain networks in time and space. IEEE Signal Process Mag. 2018;35:81\u201396.","journal-title":"IEEE Signal Process Mag"},{"key":"3441_CR36","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1016\/j.neuroimage.2011.02.054","volume":"56","author":"MJ Brookes","year":"2011","unstructured":"Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST, Barnes GR, et al. NeuroImage Measuring functional connectivity using MEG : methodology and comparison with fcMRI. Neuroimage. 2011;56:1082\u2013104.","journal-title":"Neuroimage"},{"key":"3441_CR37","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1038\/nn.3101","volume":"15","author":"JF Hipp","year":"2012","unstructured":"Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat Neurosci. 2012;15:884\u201390.","journal-title":"Nat Neurosci"},{"key":"3441_CR38","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1016\/j.neuroimage.2012.03.048","volume":"63","author":"BrookesWoolrich","year":"2012","unstructured":"BrookesWoolrich, Barnes. Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. Neuroimage. 2012;63:910\u201320.","journal-title":"Neuroimage"},{"key":"3441_CR39","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1016\/j.neuroimage.2010.06.041","volume":"53","author":"A Zalesky","year":"2010","unstructured":"Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53:1197\u2013207.","journal-title":"Neuroimage"},{"key":"3441_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neubiorev.2019.02.011","volume":"100","author":"F Samea","year":"2019","unstructured":"Samea F, Soluki S, Nejati V, Zarei M, Cortese S, Eickhoff SB, et al. Brain alterations in children\/adolescents with ADHD revisited: A neuroimaging meta-analysis of 96 structural and functional studies. Neurosci Biobehav Rev. 2019;100:1\u20138.","journal-title":"Neurosci Biobehav Rev"},{"key":"3441_CR41","doi-asserted-by":"publisher","first-page":"117.","DOI":"10.1038\/s41531-025-00957-6","volume":"11","author":"J Tabbal","year":"2025","unstructured":"Tabbal J, Ebadi A, Mheich A, Kabbara A, G\u00fcntekin B, Yener G, et al. Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling. npj Parkinsons Dis. 2025;11:117.","journal-title":"npj Parkinsons Dis"},{"key":"3441_CR42","doi-asserted-by":"publisher","unstructured":"Boxum M, Voetterl H, van Dijk H, Gordon E, DeBeus R, Arnold LE et al. Challenging the diagnostic value of theta\/beta ratio: Insights from an EEG subtyping meta-analytical approach in ADHD. Appl Psychophysiol Biofeedback 2024. https:\/\/doi.org\/10.1007\/s10484-024-09649-y.","DOI":"10.1007\/s10484-024-09649-y"},{"key":"3441_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.181","volume":"4","author":"LM Alexander","year":"2017","unstructured":"Alexander LM, Escalera J, Ai L, Andreotti C, Febre K, Mangone A, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017;4:170181.","journal-title":"Sci Data"},{"key":"3441_CR44","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.40","volume":"4","author":"N Langer","year":"2017","unstructured":"Langer N, Ho EJ, Alexander LM, Xu HY, Jozanovic RK, Henin S, et al. A resource for assessing information processing in the developing brain using EEG and eye tracking. Sci Data. 2017;4:170040.","journal-title":"Sci Data"},{"key":"3441_CR45","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3389\/fnint.2020.00016","volume":"14","author":"JC McPartland","year":"2020","unstructured":"McPartland JC, Bernier RA, Jeste SS, Dawson G, Nelson CA, Chawarska K, et al. The Autism Biomarkers Consortium for Clinical Trials (ABC-CT): Scientific Context, Study Design, and Progress Toward Biomarker Qualification. Front Integr Neurosci. 2020;14:16.","journal-title":"Front Integr Neurosci"},{"key":"3441_CR46","unstructured":"Pelphrey K Multimodal developmental neurogenetics of females with ASD. 2014."},{"key":"3441_CR47","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3389\/fninf.2015.00016","volume":"9","author":"N Bigdely-Shamlo","year":"2015","unstructured":"Bigdely-Shamlo N, Mullen T, Kothe C, Su K-M, Robbins KA. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Front Neuroinform. 2015;9:16.","journal-title":"Front Neuroinform"},{"key":"3441_CR48","unstructured":"Appelhoff S, Hurst AJ, Lawrence A, Li A, Mantilla Ramos YJ, O\u2019Reilly C et al. PyPREP: A Python implementation of the preprocessing pipeline (PREP) for EEG data. 2022."},{"key":"3441_CR49","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.neuroimage.2019.05.026","volume":"198","author":"L Pion-Tonachini","year":"2019","unstructured":"Pion-Tonachini L, Kreutz-Delgado K, Makeig S. ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. Neuroimage. 2019;198:181\u201397.","journal-title":"Neuroimage"},{"key":"3441_CR50","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.neuroimage.2017.06.030","volume":"159","author":"M Jas","year":"2017","unstructured":"Jas M, Engemann DA, Bekhti Y, Raimondo F, Gramfort A. Autoreject: Automated artifact rejection for MEG and EEG data. Neuroimage. 2017;159:417\u201329.","journal-title":"Neuroimage"},{"key":"3441_CR51","unstructured":"Pascual-Marqui RD Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. 2007. http:\/\/arxiv.org\/abs\/0710.3341."},{"key":"3441_CR52","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neuroimage.2010.07.033","volume":"54","author":"V Fonov","year":"2011","unstructured":"Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, et al. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 2011;54:313\u201327.","journal-title":"Neuroimage"},{"key":"3441_CR53","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan RS, S\u00e9gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968\u201380.","journal-title":"Neuroimage"},{"key":"3441_CR54","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1111\/j.1467-9876.2005.00510.x","volume":"54","author":"RA Rigby","year":"2005","unstructured":"Rigby RA, Stasinopoulos DM. Generalized additive models for location, scale and shape. J R Stat Soc Ser C Appl Stat. 2005;54:507\u201354.","journal-title":"J R Stat Soc Ser C Appl Stat"},{"key":"3441_CR55","doi-asserted-by":"publisher","DOI":"10.1201\/b21973","volume-title":"Flexible Regression and Smoothing: Using GAMLSS in R.","author":"MD Stasinopoulos","year":"2017","unstructured":"Stasinopoulos MD, Rigby RA, Heller GZ, Voudouris V, De Bastiani F. Flexible Regression and Smoothing: Using GAMLSS in R. CRC Press; 2017."},{"key":"3441_CR56","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/10618600.1996.10474708","volume":"5","author":"PK Dunn","year":"1996","unstructured":"Dunn PK, Smyth GK. Randomized quantile residuals. J Comput Graph Stat. 1996;5:236\u201344.","journal-title":"J Comput Graph Stat"},{"key":"3441_CR57","first-page":"1","volume":"23","author":"D Mikis Stasinopoulos","year":"2008","unstructured":"Mikis Stasinopoulos D, Rigby RA. Generalized additive models for location scale and shape (GAMLSS) in R. J Stat Softw. 2008;23:1\u201346.","journal-title":"J Stat Softw"},{"key":"3441_CR58","doi-asserted-by":"publisher","first-page":"e68910","DOI":"10.1371\/journal.pone.0068910","volume":"8","author":"M Xia","year":"2013","unstructured":"Xia M, Wang J, He Y. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS One. 2013;8:e68910. https:\/\/doi.org\/10.1371\/journal.pone.0068910.","journal-title":"PLoS One"}],"container-title":["Translational Psychiatry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41398-025-03441-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41398-025-03441-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41398-025-03441-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T12:03:01Z","timestamp":1751457781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41398-025-03441-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3441"],"URL":"https:\/\/doi.org\/10.1038\/s41398-025-03441-0","relation":{},"ISSN":["2158-3188"],"issn-type":[{"value":"2158-3188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"11 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.E., S.A. and M.H. are all full-time employees in MINDIG company.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"223"}}