{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T14:47:37Z","timestamp":1791989257298,"version":"4.4.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"iBehave network sponsored by the Ministry of Culture and Science of the State of North Rhine-Westphalia, Germany"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Arch Psychiatry Clin Neurosci"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The heterogeneity of attention-deficit\/hyperactivity disorder (ADHD) and its symptom overlap with other mental disorders limit the clinical utility of isolated diagnostic markers. Virtual reality (VR)-based paradigms offer potential to aid individual diagnostic decisions. This study investigated the diagnostic value of classification models based on multimodal data derived from a virtual email sorting task (VEST). Data were pooled from two studies and used to distinguish unmedicated participants with ADHD (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200943) from medicated participants with ADHD (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200923), participants with major depressive disorder (MDD,\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200916), and control participants (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200921). During the VEST, objective measures of task performance, head actigraphy, and eye tracking were recorded. Subjective data were obtained by assessing task-related ADHD symptoms. Using nested cross-validation, support vector machines were trained and tested, comparing model performance between objective, subjective and combined parameters. The highest balanced accuracy for the classification of participants with ADHD versus control participants (0.85) was achieved by a single-feature model using self-reported inattentiveness. Participants with ADHD and MDD were differentiated with 0.75 balanced accuracy using a 13-feature model combining subjective and objective measures. A 13-feature model derived exclusively from objective measures yielded 0.64 balanced accuracy for the differentiation of medicated versus unmedicated participants with ADHD yet did not perform significantly above chance level. These findings underscore the potential of VR-based multimodal assessments and machine learning to advance precision diagnostics and highlight the utility of self-report measures to detect ADHD while demonstrating the diagnostic potential of objective measures to distinguish disorders and capture medication effects.\n                  <\/jats:p>","DOI":"10.1007\/s00406-026-02401-z","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T14:11:08Z","timestamp":1791987068000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Virtual reality (bio-)markers differentiate ADHD from depression and controls"],"prefix":"10.1007","author":[{"given":"Julian","family":"Pakos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Asch\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luisa","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johanna","family":"Schuder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah Marie","family":"Schrage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl","family":"Heine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Popova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario","family":"Sanchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annika","family":"Wiebe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Behrem","family":"Aslan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra","family":"Philipsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niclas","family":"Braun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,14]]},"reference":[{"key":"2401_CR1","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1016\/j.neubiorev.2021.01.022","volume":"128","author":"SV Faraone","year":"2021","unstructured":"Faraone SV, Banaschewski T, Coghill D et al (2021) The world federation of ADHD international consensus statement: 208 evidence-based conclusions about the disorder. Neurosci Biobehav Rev 128:789\u2013818. https:\/\/doi.org\/10.1016\/j.neubiorev.2021.01.022","journal-title":"Neurosci Biobehav Rev"},{"key":"2401_CR2","doi-asserted-by":"publisher","unstructured":"Diagnostic and Statistical Manual of Mental Disorders: DSM-5\u2122, 5th Ed. American Psychiatric Publishing, Inc.; 2013:xliv, 947. https:\/\/doi.org\/10.1176\/appi.books.9780890425596","DOI":"10.1176\/appi.books.9780890425596"},{"issue":"1","key":"2401_CR3","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1002\/wps.21037","volume":"22","author":"S Cortese","year":"2023","unstructured":"Cortese S, Solmi M, Michelini G et al (2023) Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review. World Psychiatry 22(1):129\u2013149. https:\/\/doi.org\/10.1002\/wps.21037","journal-title":"World Psychiatry"},{"issue":"2","key":"2401_CR4","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10862-011-9217-x","volume":"33","author":"RA Barkley","year":"2011","unstructured":"Barkley RA, Murphy KR (2011) The Nature of Executive Function (EF) deficits in daily life activities in adults with ADHD and their relationship to performance on EF tests. J Psychopathol Behav Assess 33(2):137\u2013158. https:\/\/doi.org\/10.1007\/s10862-011-9217-x","journal-title":"J Psychopathol Behav Assess"},{"issue":"6","key":"2401_CR5","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1177\/1087054718822060","volume":"24","author":"S Baggio","year":"2020","unstructured":"Baggio S, Hasler R, Giacomini V et al (2020) Does the continuous performance test predict ADHD symptoms severity and ADHD presentation in adults? J Atten Disord 24(6):840\u2013848. https:\/\/doi.org\/10.1177\/1087054718822060","journal-title":"J Atten Disord"},{"issue":"2","key":"2401_CR6","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1111\/jnp.12304","volume":"17","author":"L Pieri","year":"2023","unstructured":"Pieri L, Tosi G, Romano D (2023) Virtual reality technology in neuropsychological testing: a systematic review. J Neuropsychol 17(2):382\u2013399. https:\/\/doi.org\/10.1111\/jnp.12304","journal-title":"J Neuropsychol"},{"key":"2401_CR7","doi-asserted-by":"publisher","unstructured":"Parsons TD, Rizzo A (2019) Skip. A review of virtual classroom environments for neuropsychological assessment. In: Rizzo A, Skip, Bouchard S (eds) Virtual reality for psychological and neurocognitive interventions. Virtual reality technologies for health and clinical applications. Springer New York, pp 247\u2013265. https:\/\/doi.org\/10.1007\/978-1-4939-9482-3_11","DOI":"10.1007\/978-1-4939-9482-3_11"},{"issue":"5","key":"2401_CR8","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1002\/cpp.2863","volume":"30","author":"A Wiebe","year":"2023","unstructured":"Wiebe A, Aslan B, Brockmann C et al (2023) Multimodal assessment of adult attention-deficit hyperactivity disorder: a controlled virtual seminar room study. Clin Psychol Psychother 30(5):1111\u20131129. https:\/\/doi.org\/10.1002\/cpp.2863","journal-title":"Clin Psychol Psychother"},{"issue":"1","key":"2401_CR9","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1038\/s41398-024-03217-y","volume":"14","author":"A Wiebe","year":"2024","unstructured":"Wiebe A, Selaskowski B, Paskin M et al (2024) Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples. Transl Psychiatry 14(1):508. https:\/\/doi.org\/10.1038\/s41398-024-03217-y","journal-title":"Transl Psychiatry"},{"issue":"1","key":"2401_CR10","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s44247-025-00209-w","volume":"3","author":"J Pakos","year":"2025","unstructured":"Pakos J, Asch\u00e9 L, Popova A et al (2025) Evaluation of a virtual reality-based office room for a multimodal assessment of adult ADHD. BMC Digit Health 3(1):71. https:\/\/doi.org\/10.1186\/s44247-025-00209-w","journal-title":"BMC Digit Health"},{"issue":"1","key":"2401_CR11","doi-asserted-by":"publisher","first-page":"e85351","DOI":"10.2196\/85351","volume":"13","author":"L Asch\u00e9","year":"2026","unstructured":"Asch\u00e9 L, Pakos J, Schrage H et al (2026) Multimodal virtual reality assessment of medication effects in attention-deficit\/hyperactivity disorder and its distinction from depression: cross-sectional study. JMIR Hum Factors 13(1):e85351. https:\/\/doi.org\/10.2196\/85351","journal-title":"JMIR Hum Factors"},{"issue":"2","key":"2401_CR12","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1080\/09297040802169077","volume":"15","author":"R Adams","year":"2009","unstructured":"Adams R, Finn P, Moes E, Flannery K, Rizzo A (2009) Skip. Distractibility in Attention\/Deficit\/ Hyperactivity Disorder (ADHD): the virtual reality classroom. Child Neuropsychol 15(2):120\u2013135. https:\/\/doi.org\/10.1080\/09297040802169077","journal-title":"Child Neuropsychol"},{"issue":"7","key":"2401_CR13","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s00787-015-0798-x","volume":"25","author":"CL Hall","year":"2016","unstructured":"Hall CL, Valentine AZ, Groom MJ et al (2016) The clinical utility of the continuous performance test and objective measures of activity for diagnosing and monitoring ADHD in children: a systematic review. Eur Child Adolesc Psychiatry 25(7):677\u2013699. https:\/\/doi.org\/10.1007\/s00787-015-0798-x","journal-title":"Eur Child Adolesc Psychiatry"},{"issue":"1","key":"2401_CR14","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s11689-024-09578-1","volume":"16","author":"IC Chen","year":"2024","unstructured":"Chen IC, Chang CL, Chang MH, Ko LW (2024) The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit\/hyperactivity disorder. J Neurodev Disord 16(1):62. https:\/\/doi.org\/10.1186\/s11689-024-09578-1","journal-title":"J Neurodev Disord"},{"issue":"1","key":"2401_CR15","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1186\/s12888-024-05987-7","volume":"24","author":"D Andrikopoulos","year":"2024","unstructured":"Andrikopoulos D, Vassiliou G, Fatouros P, Tsirmpas C, Pehlivanidis A, Papageorgiou C (2024) Machine learning-enabled detection of attention-deficit\/hyperactivity disorder with multimodal physiological data: a case-control study. BMC Psychiatry 24(1):547. https:\/\/doi.org\/10.1186\/s12888-024-05987-7","journal-title":"BMC Psychiatry"},{"key":"2401_CR16","doi-asserted-by":"publisher","unstructured":"Feretzakis G, Boufeas I, Fotakidis S et al (2025) Enhanced machine learning approach to ADHD classification using actigraphy data. In: Mantas J, Hasman A, Gallos P, Zoulias E, Karitis K (eds) Studies in health technology and informatics. IOS. https:\/\/doi.org\/10.3233\/SHTI250688","DOI":"10.3233\/SHTI250688"},{"issue":"1","key":"2401_CR17","doi-asserted-by":"publisher","first-page":"12934","DOI":"10.1038\/s41598-022-17126-x","volume":"12","author":"P Mikolas","year":"2022","unstructured":"Mikolas P, Vahid A, Bernardoni F et al (2022) Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records. Sci Rep 12(1):12934. https:\/\/doi.org\/10.1038\/s41598-022-17126-x","journal-title":"Sci Rep"},{"key":"2401_CR18","doi-asserted-by":"publisher","DOI":"10.13154\/RUB.102.91","author":"J Margraf","year":"2017","unstructured":"Margraf J, Cwik JC (2017) Mini-DIPS Open Access: Diagnostisches Kurzinterview bei psychischen St\u00f6rungen. Published online. https:\/\/doi.org\/10.13154\/RUB.102.91","journal-title":"Published online"},{"issue":"07","key":"2401_CR19","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1055\/s-0033-1335740","volume":"81","author":"W Retz","year":"2013","unstructured":"Retz W, Retz-Junginger P, R\u00f6mer K, R\u00f6sler M (2013) Standardisierte Skalen zur strukturierten Diagnostik der ADHS im Erwachsenenalter. Fortschr Neurol \u00b7 Psychiatr 81(07):381\u2013389. https:\/\/doi.org\/10.1055\/s-0033-1335740","journal-title":"Fortschr Neurol \u00b7 Psychiatr"},{"key":"2401_CR20","unstructured":"RED Medical Systems GmbH (2022) RED connect [Computer software]. https:\/\/www.redmedical.de\/red-connect-videosprechstunde\/"},{"key":"2401_CR21","unstructured":"Leiner DJ (2022) SoSci Survey [Computer software]. https:\/\/www.soscisurvey.de"},{"key":"2401_CR22","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1007\/s00115-003-1622-2","volume":"75","author":"M R\u00f6sler","year":"2004","unstructured":"R\u00f6sler M, Retz W, Retz-Junginger P et al (2004) Instrumente zur Diagnostik der Aufmerksamkeitsdefizit-\/Hyperaktivit\u00e4tsst\u00f6rung (ADHS) im Erwachsenenalter: Selbstbeurteilungsskala (ADHS-SB) und Diagnosecheckliste (ADHS-DC). Nervenarzt 75:888\u2013895. https:\/\/doi.org\/10.1007\/s00115-003-1622-2","journal-title":"Nervenarzt"},{"issue":"1","key":"2401_CR23","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1159\/000173701","volume":"42","author":"M Bohus","year":"2009","unstructured":"Bohus M, Kleindienst N, Limberger MF et al (2009) The short version of the borderline symptom list (BSL-23): development and initial data on psychometric properties. Psychopathology 42(1):32\u201339. https:\/\/doi.org\/10.1159\/000173701","journal-title":"Psychopathology"},{"issue":"3","key":"2401_CR24","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1017\/S0033291798006667","volume":"28","author":"The Whoqol Group","year":"1998","unstructured":"The Whoqol Group (1998) Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychol Med 28(3):551\u2013558. https:\/\/doi.org\/10.1017\/S0033291798006667","journal-title":"Psychol Med"},{"key":"2401_CR25","unstructured":"Unity Technologies (2020) Unity (Version 2019.4.11f1) [Computer software].https:\/\/unity.com\/"},{"key":"2401_CR26","unstructured":"The Blender Foundation. Blender [Computer software]. https:\/\/www.blender.org"},{"key":"2401_CR27","unstructured":"Demes L (2021) ambientCG [Computer software]. https:\/\/ambientcg.com\/"},{"key":"2401_CR28","doi-asserted-by":"publisher","unstructured":"Kothe C, Shirazi SY, Stenner T et al (2024) The lab streaming layer for synchronized multimodal recording. Neuroscience Preprint posted online Febr 14. https:\/\/doi.org\/10.1101\/2024.02.13.580071","DOI":"10.1101\/2024.02.13.580071"},{"issue":"1","key":"2401_CR29","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/s12888-023-04551-z","volume":"23","author":"B Selaskowski","year":"2023","unstructured":"Selaskowski B, Asch\u00e9 LM, Wiebe A et al (2023) Gaze-based attention refocusing training in virtual reality for adult attention-deficit\/hyperactivity disorder. BMC Psychiatry 23(1):74. https:\/\/doi.org\/10.1186\/s12888-023-04551-z","journal-title":"BMC Psychiatry"},{"issue":"5","key":"2401_CR30","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1177\/10731911221089193","volume":"30","author":"A Wiebe","year":"2023","unstructured":"Wiebe A, Kannen K, Li M et al (2023) Multimodal virtual reality-based assessment of adult ADHD: a feasibility study in healthy subjects. Assessment 30(5):1435\u20131453. https:\/\/doi.org\/10.1177\/10731911221089193","journal-title":"Assessment"},{"key":"2401_CR31","unstructured":"Tobii Technology. Eye trackers [Internet]. https:\/\/www.tobii.com\/products\/eye-trackers"},{"key":"2401_CR32","unstructured":"Tobii Technology (2020) Tobii XR SDK (Version 1.8.0) [Computer software]. https:\/\/www.tobii.com\/products\/software\/applications-and-developer-kits\/xr-developer"},{"issue":"2","key":"2401_CR33","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1038\/s41380-023-02334-2","volume":"29","author":"TP Quinn","year":"2024","unstructured":"Quinn TP, Hess JL, Marshe VS et al (2024) A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 29(2):387\u2013401","journal-title":"Mol Psychiatry"},{"key":"#cr-split#-2401_CR34.1","doi-asserted-by":"crossref","unstructured":"Pisner DA, Schnyer DM (2020) Support vector machine. In: Machine learning. Elsevier","DOI":"10.1016\/B978-0-12-815739-8.00006-7"},{"key":"#cr-split#-2401_CR34.2","unstructured":"pp 101-121. Accessed February 27, 2026. https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780128157398000067"},{"issue":"5","key":"2401_CR35","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1177\/0962280213502437","volume":"25","author":"M Khondoker","year":"2016","unstructured":"Khondoker M, Dobson R, Skirrow C, Simmons A, Stahl D (2016) A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies. Stat Methods Med Res 25(5):1804\u20131823. https:\/\/doi.org\/10.1177\/0962280213502437","journal-title":"Stat Methods Med Res"},{"key":"2401_CR36","doi-asserted-by":"publisher","unstructured":"Larracy R, Phinyomark A, Scheme E (2021) Machine learning model validation for early stage studies with small sample sizes. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; pp 2314\u20132319. https:\/\/doi.org\/10.1109\/EMBC46164.2021.9629697","DOI":"10.1109\/EMBC46164.2021.9629697"},{"key":"2401_CR37","doi-asserted-by":"publisher","unstructured":"Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. Hernandez-Lemus E, ed. PLOS ONE 14(11):e0224365. https:\/\/doi.org\/10.1371\/journal.pone.0224365","DOI":"10.1371\/journal.pone.0224365"},{"key":"2401_CR38","doi-asserted-by":"publisher","unstructured":"Varoquaux G, Colliot O (2023) Evaluating Machine Learning Models and Their Diagnostic Value. In: Colliot O (ed) Machine Learning for Brain Disorders, vol 197. Springer US, pp 601\u2013630. doi:https:\/\/doi.org\/10.1007\/978-1-0716-3195-9_20","DOI":"10.1007\/978-1-0716-3195-9_20"},{"issue":"2","key":"2401_CR39","doi-asserted-by":"publisher","first-page":"e1484","DOI":"10.1002\/widm.1484","volume":"13","author":"B Bischl","year":"2023","unstructured":"Bischl B, Binder M, Lang M et al (2023) Hyperparameter optimization: foundations, algorithms, best practices, and open challenges. WIREs Data Min Knowl Discov 13(2):e1484. https:\/\/doi.org\/10.1002\/widm.1484","journal-title":"WIREs Data Min Knowl Discov"},{"key":"2401_CR40","doi-asserted-by":"publisher","first-page":"120253","DOI":"10.1016\/j.neuroimage.2023.120253","volume":"277","author":"P Th\u00f6lke","year":"2023","unstructured":"Th\u00f6lke P, Mantilla-Ramos YJ, Abdelhedi H et al (2023) Class imbalance should not throw you off balance: choosing the right classifiers and performance metrics for brain decoding with imbalanced data. NeuroImage 277:120253","journal-title":"NeuroImage"},{"issue":"1","key":"2401_CR41","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/1758-2946-6-10","volume":"6","author":"D Krstajic","year":"2014","unstructured":"Krstajic D, Buturovic LJ, Leahy DE, Thomas S (2014) Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminformatics 6(1):10. https:\/\/doi.org\/10.1186\/1758-2946-6-10","journal-title":"J Cheminformatics"},{"key":"2401_CR42","doi-asserted-by":"publisher","unstructured":"Parvandeh S, Yeh HW, Paulus MP, McKinney BA (2020) Consensus features nested cross-validation. Valencia A, ed. Bioinformatics 36(10):3093\u20133098. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa046","DOI":"10.1093\/bioinformatics\/btaa046"},{"key":"2401_CR43","doi-asserted-by":"publisher","unstructured":"Lewis MJ, Spiliopoulou A, Goldmann K, Pitzalis C, McKeigue P, Barnes MR (2023) nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data. Lengauer T, ed. Bioinforma Adv 3(1):vbad048. https:\/\/doi.org\/10.1093\/bioadv\/vbad048","DOI":"10.1093\/bioadv\/vbad048"},{"issue":"2","key":"2401_CR44","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.bpsc.2018.06.003","volume":"4","author":"AA Pulini","year":"2019","unstructured":"Pulini AA, Kerr WT, Loo SK, Lenartowicz A (2019) Classification accuracy of neuroimaging biomarkers in attention-deficit\/hyperactivity disorder: effects of sample size and circular analysis. Biol Psychiatry Cogn Neurosci Neuroimaging 4(2):108\u2013120. https:\/\/doi.org\/10.1016\/j.bpsc.2018.06.003","journal-title":"Biol Psychiatry Cogn Neurosci Neuroimaging"},{"issue":"02","key":"2401_CR45","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"03","author":"C Ding","year":"2005","unstructured":"Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene, expression data. J Bioinform Comput Biol 03(02):185\u2013205. https:\/\/doi.org\/10.1142\/S0219720005001004","journal-title":"J Bioinform Comput Biol"},{"key":"2401_CR46","unstructured":"Ojala M, Garriga GC (2010) Permutation tests for studying classifier performance. J Mach Learn Res 11(6): 1833-1863"},{"key":"2401_CR47","doi-asserted-by":"publisher","unstructured":"Phipson B, Smyth GK (2010) Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat Appl Genet Mol Biol. https:\/\/doi.org\/10.2202\/1544-6115.1585","DOI":"10.2202\/1544-6115.1585"},{"key":"2401_CR48","doi-asserted-by":"publisher","first-page":"1706216","DOI":"10.3389\/fpsyt.2025.1706216","volume":"16","author":"HM Shakeel","year":"2025","unstructured":"Shakeel HM, Antoniou G, Adamou M (2025) Integrating explainable AI with clinical features to enhance ADHD diagnostic understanding. Front Psychiatry 16:1706216","journal-title":"Front Psychiatry"},{"issue":"5","key":"2401_CR49","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1177\/1087054712461178","volume":"19","author":"GJ Privitera","year":"2015","unstructured":"Privitera GJ, Agnello JE, Walters SA, Bender SL (2015) Randomized feedback about diagnosis influences statistical and clinical significance of self-report ADHD assessment in adults. J Atten Disord 19(5):447\u2013451. https:\/\/doi.org\/10.1177\/1087054712461178","journal-title":"J Atten Disord"},{"issue":"5","key":"2401_CR50","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3810\/pgm.2009.09.2049","volume":"121","author":"DW Goodman","year":"2009","unstructured":"Goodman DW, Thase ME (2009) Recognizing ADHD in adults with comorbid mood disorders: implications for identification and management. Postgrad Med 121(5):20\u201330. https:\/\/doi.org\/10.3810\/pgm.2009.09.2049","journal-title":"Postgrad Med"},{"issue":"2","key":"2401_CR51","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1111\/bdi.13396","volume":"26","author":"B Little","year":"2024","unstructured":"Little B, Anwyll M, Norsworthy L, Corbett L, Schultz-Froggatt M, Gallagher P (2024) Processing speed and sustained attention in bipolar disorder and major depressive disorder: a systematic review and meta-analysis. Bipolar Disord 26(2):109\u2013128. https:\/\/doi.org\/10.1111\/bdi.13396","journal-title":"Bipolar Disord"},{"issue":"4","key":"2401_CR52","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s12402-019-00303-w","volume":"11","author":"J Bernstein","year":"2019","unstructured":"Bernstein J, Roye S, Calamia M, De Vito A (2019) Utility of a novel simulator paradigm in the assessment of driving ability in individuals with and without attention-deficit hyperactivity disorder. ADHD Atten Deficit Hyperact Disord 11(4):403\u2013411. https:\/\/doi.org\/10.1007\/s12402-019-00303-w","journal-title":"ADHD Atten Deficit Hyperact Disord"},{"key":"2401_CR53","unstructured":"National Collaborating Centre for Mental Health (UK) (2010) Depression: The Treatment and Management of Depression in Adults (Updated Edition). British Psychological Society. Accessed April 14, 2026. http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK63748\/"}],"container-title":["European Archives of Psychiatry and Clinical Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00406-026-02401-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00406-026-02401-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00406-026-02401-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T14:11:17Z","timestamp":1791987077000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00406-026-02401-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,14]]},"references-count":54,"alternative-id":["2401"],"URL":"https:\/\/doi.org\/10.1007\/s00406-026-02401-z","relation":{},"ISSN":["0940-1334","1433-8491"],"issn-type":[{"value":"0940-1334","type":"print"},{"value":"1433-8491","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,14]]},"assertion":[{"value":"1 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"NB and AP receive funding by the Ministry of Culture and Science of the State of North Rhine-Westphalia (iBehave network, grant number: NW21-049), by which JP\u2019s, LA\u2019s and LJ\u2019s positions are funded. AP and NB received funding from MEDICE Arzneimittel P\u00fctter GmbH & Co KG, by which AW\u2019s PhD position was funded. NB received funding from BONFOR, by which NB was partly funded. LA\u2019s and JP\u2019s positions are partially funded by Neurocast for work on a mobile sensing study unrelated to this manuscript. JP received compensation from MEDICE Arzneimittel P\u00fctter GmbH & Co KG for conducting a workshop. AP has received funding by the German Federal Ministry of Education and Research, Horizon2020, and DFG, North Rhine-Westphalia, and Medice Arzneimittel P\u00fctter GmbH & Co KG; she has been on advisory boards for Takeda, Medice, and Boehringer; and delivered lectures sponsored by Medice, Takeda. All other authors declare no conflicts of interest. Open Access funding enabled and organized by Projekt DEAL.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Both studies received approval from the Ethics Committee of the Faculty of Medicine at the Friedrich-Wilhelms-University of Bonn (protocol numbers: 362\/22 and 105\/23). Informed consent was obtained from all individual participants included in both studies. All procedures performed in both studies involving human participants were in accordance with the Helsinki declaration as revised in 2013 and its later amendments.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}}]}}