{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T22:48:29Z","timestamp":1791672509742,"version":"4.3.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Med"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Current consensus-driven diagnostic systems poorly capture the marked heterogeneity and high comorbidity of psychiatric disorders, necessitating brain-based transdiagnostic frameworks to advance mechanistic understanding and clinical translation. Connectome-wide association studies have consistently identified widespread large-scale dysconnectivity across diagnostic boundaries, yet normative modeling and meta-analytic evidence highlights substantial inter-individual variability in the spatial topography and magnitude of connectomic deviations. In this Perspective, we bridge this divide between cross-disorder commonalities and individualized pathology by integrating connectomic overlap and computational biotyping into a top-down, four-tiered hierarchical architecture. This framework progresses from macro-scale transdiagnostic topological scaffolds, through meso-scale phenotypic triangulation, to data-driven biotypes and micro-scale clinical translations, delineating bidirectional interplay between shared network constraints and heterogeneous pathological diversification. Building on this architecture, we outline strategies leveraging multi-cohort data harmonization and healthy-anchored normative biotyping to refine robust cross-disorder circuits and reproducible neurobiotypes. Lastly, we propose a translational roadmap to transition connectomic stratification toward prospective, treatment-relevant clinical validation in precision psychiatry.<\/jats:p>","DOI":"10.1038\/s43856-026-01967-y","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:50:32Z","timestamp":1791669032000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Connectomic signatures advance the understandings of transdiagnostic heterogeneity in psychosis"],"prefix":"10.1038","volume":"6","author":[{"given":"Yu","family":"Du","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5265-3546","authenticated-orcid":false,"given":"Chenfei","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,10]]},"reference":[{"key":"1967_CR1","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1002\/wps.20050","volume":"12","author":"DA Regier","year":"2013","unstructured":"Regier, D. A., Kuhl, E. A. & Kupfer, D. J. The DSM-5: classification and criteria changes. World Psychiatry 12, 92\u201398 (2013).","journal-title":"World Psychiatry"},{"key":"1967_CR2","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/BF00788743","volume":"28","author":"A Janca","year":"1993","unstructured":"Janca, A., \u00dcst\u00fcn, Early, T. B., S, T. & Sartorius, N. The ICD-10 Symptom Checklist: a companion to the ICD-10 Classification of Mental and Behavioural Disorders. Soc. Psychiatry Psychiatr. Epidemiol. 28, 239\u2013242 (1993).","journal-title":"Soc. Psychiatry Psychiatr. Epidemiol."},{"key":"1967_CR3","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1038\/nrn1993","volume":"7","author":"A Meyer-Lindenberg","year":"2006","unstructured":"Meyer-Lindenberg, A. & Weinberger, D. R. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat. Rev. Neurosci. 7, 818\u2013827 (2006).","journal-title":"Nat. Rev. Neurosci."},{"key":"1967_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.psychres.2019.07.005","volume":"279","author":"K Allsopp","year":"2019","unstructured":"Allsopp, K., Read, J., Corcoran, R. & Kinderman, P. Heterogeneity in psychiatric diagnostic classification. Psychiatry Res. 279, 15\u201322 (2019).","journal-title":"Psychiatry Res."},{"key":"1967_CR5","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. et al. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn. Sci. 23, 584\u2013601 (2019).","journal-title":"Trends Cogn. Sci."},{"key":"1967_CR6","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1176\/appi.ajp.2010.09091379","volume":"167","author":"T Insel","year":"2010","unstructured":"Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748\u2013751 (2010).","journal-title":"Am. J. Psychiatry"},{"key":"1967_CR7","doi-asserted-by":"crossref","unstructured":"Ye, C., Wu, T. & Faria, A. V. Editorial: advances in connectome-wide association studies (CWAS) along the neurodegeneration trajectory. Front. Aging Neurosci. 15, 1329854 (2023).","DOI":"10.3389\/fnagi.2023.1329854"},{"key":"1967_CR8","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neuron.2011.11.004","volume":"73","author":"MP Milham","year":"2012","unstructured":"Milham, M. P. Open neuroscience solutions for the connectome-wide association era. Neuron 73, 214\u2013218 (2012).","journal-title":"Neuron"},{"key":"1967_CR9","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1038\/s41380-020-0699-3","volume":"26","author":"H Cao","year":"2021","unstructured":"Cao, H., Zhou, H. & Cannon, T. D. Functional connectome-wide associations of schizophrenia polygenic risk. Mol. Psychiatry 26, 2553\u20132561 (2021).","journal-title":"Mol. Psychiatry"},{"key":"1967_CR10","first-page":"1068","volume":"7","author":"AF Alexander-Bloch","year":"2022","unstructured":"Alexander-Bloch, A. F. et al. Connectome-wide functional connectivity abnormalities in youth with obsessive-compulsive symptoms. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 7, 1068\u20131077 (2022).","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"1967_CR11","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neunet.2023.04.025","volume":"164","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Ye, C. & Ma, T. A deep connectome learning network using graph convolution for connectome-disease association study. Neural Netw. 164, 91\u2013104 (2023).","journal-title":"Neural Netw."},{"key":"1967_CR12","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.schres.2024.06.031","volume":"270","author":"H He","year":"2024","unstructured":"He, H. et al. A connectome-wide association study of altered functional connectivity in schizophrenia based on resting-state fMRI. Schizophr. Res. 270, 202\u2013211 (2024).","journal-title":"Schizophr. Res."},{"key":"1967_CR13","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1001\/jamapsychiatry.2018.2467","volume":"75","author":"T Wolfers","year":"2018","unstructured":"Wolfers, T. et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry 75, 1146\u20131155 (2018).","journal-title":"JAMA Psychiatry"},{"key":"1967_CR14","first-page":"802","volume":"6","author":"DL Floris","year":"2021","unstructured":"Floris, D. L. et al. Atypical brain asymmetry in autism\u2014a candidate for clinically meaningful stratification. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 6, 802\u2013812 (2021).","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"1967_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119699","volume":"264","author":"JMM Bayer","year":"2022","unstructured":"Bayer, J. M. M. et al. Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. NeuroImage 264, 119699 (2022).","journal-title":"NeuroImage"},{"key":"1967_CR16","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1017\/S0033291719000084","volume":"50","author":"T Wolfers","year":"2020","unstructured":"Wolfers, T. et al. Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models. Psychol. Med. 50, 314\u2013323 (2020).","journal-title":"Psychol. Med."},{"key":"1967_CR17","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.1002\/hbm.25386","volume":"42","author":"T Wolfers","year":"2021","unstructured":"Wolfers, T. et al. Replicating extensive brain structural heterogeneity in individuals with schizophrenia and bipolar disorder. Hum. Brain Mapp. 42, 2546\u20132555 (2021).","journal-title":"Hum. Brain Mapp."},{"key":"1967_CR18","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1016\/j.neuron.2012.06.002","volume":"74","author":"JW Buckholtz","year":"2012","unstructured":"Buckholtz, J. W. & Meyer-Lindenberg, A. Psychopathology and the human connectome: toward a transdiagnostic model of risk for mental illness. Neuron 74, 990\u20131004 (2012).","journal-title":"Neuron"},{"key":"1967_CR19","doi-asserted-by":"publisher","first-page":"102634","DOI":"10.1016\/j.nicl.2021.102634","volume":"30","author":"LD Vanes","year":"2021","unstructured":"Vanes, L. D. & Dolan, R. J. Transdiagnostic neuroimaging markers of psychiatric risk: a narrative review. NeuroImage Clin. 30, 102634 (2021).","journal-title":"NeuroImage Clin."},{"key":"1967_CR20","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1038\/s41562-019-0659-6","volume":"3","author":"SC de Lange","year":"2019","unstructured":"de Lange, S. C. et al. Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders. Nat. Hum. Behav. 3, 988\u2013998 (2019).","journal-title":"Nat. Hum. Behav."},{"key":"1967_CR21","doi-asserted-by":"publisher","unstructured":"Soleimani, N., Pearlson, G., Iraji, A. & Calhoun, V. D. Double functionally independent primitives provide disorder specific fingerprints of mental illnesses. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI) 1\u20134 https:\/\/doi.org\/10.1109\/ISBI56570.2024.10635116 (IEEE, 2024).","DOI":"10.1109\/ISBI56570.2024.10635116"},{"key":"1967_CR22","unstructured":"Richter, V., Neumann, M. & Ramanarayanan, V. Towards remote differential diagnosis of mental and neurological disorders using automatically extracted speech and facial features. In Proc. Machine Learning for Cognitive and Mental Health Workshop(ML4CMH 2024) Co-located with the Thirty-Eighth AAAI Conference on Artifi cial Intelligence (AAAI 2024) https:\/\/ceur-ws.org\/Vol-3649\/Paper19.pdf (Vancouver, BC, Canada, 2024)."},{"key":"1967_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-022-02134-2","volume":"12","author":"X Tong","year":"2022","unstructured":"Tong, X. et al. Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity. Transl. Psychiatry 12, 367 (2022).","journal-title":"Transl. Psychiatry"},{"key":"1967_CR24","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.biopsych.2021.06.024","volume":"91","author":"AN Goldstein-Piekarski","year":"2022","unstructured":"Goldstein-Piekarski, A. N. et al. Mapping neural circuit biotypes to symptoms and behavioral dimensions of depression and anxiety. Biol. Psychiatry 91, 561\u2013571 (2022).","journal-title":"Biol. Psychiatry"},{"key":"1967_CR25","doi-asserted-by":"publisher","first-page":"102514","DOI":"10.1016\/j.nicl.2020.102514","volume":"28","author":"S Liang","year":"2020","unstructured":"Liang, S. et al. Biotypes of major depressive disorder: neuroimaging evidence from resting-state default mode network patterns. NeuroImage Clin. 28, 102514 (2020).","journal-title":"NeuroImage Clin."},{"key":"1967_CR26","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1038\/nm.4246","volume":"23","author":"AT Drysdale","year":"2017","unstructured":"Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23, 28\u201338 (2017).","journal-title":"Nat. Med."},{"key":"1967_CR27","doi-asserted-by":"publisher","first-page":"e2415","DOI":"10.1002\/brb3.2415","volume":"11","author":"I Fern\u00e1ndez-Linsenbarth","year":"2021","unstructured":"Fern\u00e1ndez-Linsenbarth, I. et al. Search for schizophrenia and bipolar biotypes using functional network properties. Brain Behav. 11, e2415 (2021).","journal-title":"Brain Behav."},{"key":"1967_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-32521-z","volume":"8","author":"T Tokuda","year":"2018","unstructured":"Tokuda, T. et al. Identification of depression subtypes and relevant brain regions using a data-driven approach. Sci. Rep. 8, 14082 (2018).","journal-title":"Sci. Rep."},{"key":"1967_CR29","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.1038\/npp.2017.97","volume":"42","author":"RB Price","year":"2017","unstructured":"Price, R. B., Gates, K., Kraynak, T. E., Thase, M. E. & Siegle, G. J. Data-driven subgroups in depression derived from directed functional connectivity paths at rest. Neuropsychopharmacology 42, 2623\u20132632 (2017).","journal-title":"Neuropsychopharmacology"},{"key":"1967_CR30","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1038\/s41386-020-00926-y","volume":"46","author":"S Liang","year":"2021","unstructured":"Liang, S. et al. Aberrant triple-network connectivity patterns discriminate biotypes of first-episode medication-naive schizophrenia in two large independent cohorts. Neuropsychopharmacology 46, 1502\u20131509 (2021).","journal-title":"Neuropsychopharmacology"},{"key":"1967_CR31","doi-asserted-by":"publisher","unstructured":"Du, Y., Hao, H., Xing, Y., Niu, J. & Calhoun, V. D. A Transdiagnostic biotype detection method for schizophrenia and autism spectrum disorder based on graph kernel. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 3241\u20133244 https:\/\/doi.org\/10.1109\/EMBC46164.2021.9629618 (IEEE, 2021).","DOI":"10.1109\/EMBC46164.2021.9629618"},{"key":"1967_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-024-02797-z","volume":"14","author":"J Zhang","year":"2024","unstructured":"Zhang, J. et al. Personalized functional network mapping for autism spectrum disorder and attention-deficit\/hyperactivity disorder. Transl. Psychiatry 14, 92 (2024).","journal-title":"Transl. Psychiatry"},{"key":"1967_CR33","doi-asserted-by":"publisher","first-page":"2991","DOI":"10.1038\/s41380-020-00892-3","volume":"26","author":"M Chang","year":"2021","unstructured":"Chang, M. et al. Identifying and validating subtypes within major psychiatric disorders based on frontal\u2013posterior functional imbalance via deep learning. Mol. Psychiatry 26, 2991\u20133002 (2021).","journal-title":"Mol. Psychiatry"},{"key":"1967_CR34","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1109\/TMI.2023.3337362","volume":"43","author":"W Jung","year":"2024","unstructured":"Jung, W., Jeon, E., Kang, E. & Suk, H.-I. E. A. G.-R. S. A novel explainability-guided ROI-selection framework for ASD diagnosis via inter-regional relation learning. IEEE Trans. Med. Imaging 43, 1400\u20131411 (2024).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1967_CR35","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1002\/mds.28045","volume":"35","author":"JK Wong","year":"2020","unstructured":"Wong, J. K. et al. A comprehensive review of brain connectomics and imaging to improve deep brain stimulation outcomes. Mov. Disord. 35, 741\u2013751 (2020).","journal-title":"Mov. Disord."},{"key":"1967_CR36","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.3390\/jcm15041666","volume":"15","author":"O Mateo-Sierra","year":"2026","unstructured":"Mateo-Sierra, O. et al. Symptom-oriented, connectome-informed deep brain stimulation for asymmetric dystonic tremor: unilateral ventral intermediate nucleus (VIM) DBS targeting a tremor-dominant network. J. Clin. Med. 15, 1666 (2026).","journal-title":"J. Clin. Med."},{"key":"1967_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-023-02499-y","volume":"13","author":"A Tura","year":"2023","unstructured":"Tura, A. & Goya-Maldonado, R. Brain connectivity in major depressive disorder: a precision component of treatment modalities? Transl. Psychiatry 13, 196 (2023).","journal-title":"Transl. Psychiatry"},{"key":"1967_CR38","doi-asserted-by":"publisher","first-page":"107481","DOI":"10.1016\/j.clineuro.2022.107481","volume":"223","author":"HA Shah","year":"2022","unstructured":"Shah, H. A., Mehta, N. H., Saleem, M. I. & D\u2019Amico, R. S. Connecting the connectome: a bibliometric investigation of the 50 most cited articles. Clin. Neurol. Neurosurg. 223, 107481 (2022).","journal-title":"Clin. Neurol. Neurosurg."},{"key":"1967_CR39","first-page":"805","volume":"8","author":"S Parmigiani","year":"2023","unstructured":"Parmigiani, S. et al. Reliability and validity of transcranial magnetic stimulation\u2013electroencephalography biomarkers. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 8, 805\u2013814 (2023).","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"1967_CR40","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1093\/schbul\/sbab090","volume":"48","author":"BA Clementz","year":"2022","unstructured":"Clementz, B. A. et al. Psychosis biotypes: replication and validation from the B-SNIP Consortium. Schizophr. Bull. 48, 56\u201368 (2022).","journal-title":"Schizophr. Bull."},{"key":"1967_CR41","doi-asserted-by":"publisher","unstructured":"Chand G. B. et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 143, 1027\u20131038 https:\/\/doi.org\/10.1093\/brain\/awaa025 (2020).","DOI":"10.1093\/brain\/awaa025"},{"key":"1967_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-04895-z","volume":"12","author":"S Chopra","year":"2025","unstructured":"Chopra, S. et al. The Transdiagnostic Connectome Project: an open dataset for studying brain-behavior relationships in psychiatry. Sci. Data 12, 923 (2025).","journal-title":"Sci. Data"},{"key":"1967_CR43","doi-asserted-by":"publisher","first-page":"3758","DOI":"10.1016\/j.neuron.2025.08.026","volume":"113","author":"G Shafiei","year":"2025","unstructured":"Shafiei, G. et al. Reproducible Brain Charts: an open data resource for mapping brain development and its associations with mental health. Neuron 113, 3758\u20133779.e6 (2025).","journal-title":"Neuron"},{"key":"1967_CR44","doi-asserted-by":"publisher","DOI":"10.1162\/IMAG.a.1269","volume":"4","author":"FA Mallus","year":"2026","unstructured":"Mallus, F. A. et al. From early to contemporary normative modeling: mapping individual differences in neurophysiological signals. Imaging Neurosci. 4, IMAG.a.1269 (2026).","journal-title":"Imaging Neurosci."},{"key":"1967_CR45","first-page":"imag\u20132","volume":"2","author":"AAA de Boer","year":"2024","unstructured":"de Boer, A. A. A. et al. Non-Gaussian normative modelling with hierarchical Bayesian regression. Imaging Neurosci. 2, imag\u20132\u201300132 (2024).","journal-title":"Imaging Neurosci."},{"key":"1967_CR46","doi-asserted-by":"publisher","unstructured":"Zhu, H. et al. Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression. Cell Rep Med. 6 102151 https:\/\/doi.org\/10.1101\/2024.10.04.616707 (2025).","DOI":"10.1101\/2024.10.04.616707"},{"key":"1967_CR47","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.70383","volume":"46","author":"Y Ge","year":"2025","unstructured":"Ge, Y. et al. Molecular mechanisms explaining neuroanatomical subtypes in major depressive disorder: insights from cortical morphometric inverse divergence. Hum. Brain Mapp. 46, e70383 (2025).","journal-title":"Hum. Brain Mapp."},{"key":"1967_CR48","doi-asserted-by":"publisher","first-page":"2008","DOI":"10.1038\/s41380-023-02069-0","volume":"28","author":"DB Dwyer","year":"2023","unstructured":"Dwyer, D. B. et al. Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium. Mol. Psychiatry 28, 2008\u20132017 (2023).","journal-title":"Mol. Psychiatry"},{"key":"1967_CR49","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.neuroimage.2016.02.041","volume":"145","author":"E Varol","year":"2017","unstructured":"Varol, E., Sotiras, A. & Davatzikos, C. HYDRA: revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. NeuroImage 145, 346\u2013364 (2017).","journal-title":"NeuroImage"},{"key":"1967_CR50","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1176\/appi.ajp.2015.14121571","volume":"173","author":"DK Sarpal","year":"2016","unstructured":"Sarpal, D. K. et al. Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment. Am. J. Psychiatry 173, 69\u201377 (2016).","journal-title":"Am. J. Psychiatry"},{"key":"1967_CR51","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1176\/appi.ajp.20220719","volume":"180","author":"H Cao","year":"2023","unstructured":"Cao, H. et al. A functional connectome-based neural signature for individualized prediction of antipsychotic response in first-episode psychosis. Am. J. Psychiatry 180, 827\u2013835 (2023).","journal-title":"Am. J. Psychiatry"},{"key":"1967_CR52","doi-asserted-by":"publisher","first-page":"e2530008","DOI":"10.1001\/jamanetworkopen.2025.30008","volume":"8","author":"K Zhang","year":"2025","unstructured":"Zhang, K. et al. Functional connectivity predicting transdiagnostic treatment outcomes in internalizing psychopathologies. JAMA Netw. Open 8, e2530008 (2025).","journal-title":"JAMA Netw. Open"},{"key":"1967_CR53","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1038\/s41380-022-01482-1","volume":"27","author":"NR Winter","year":"2022","unstructured":"Winter, N. R. & Hahn, T. Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Mol. Psychiatry 27, 1858\u20131859 (2022).","journal-title":"Mol. Psychiatry"},{"key":"1967_CR54","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s40035-021-00255-0","volume":"10","author":"W Li","year":"2021","unstructured":"Li, W. et al. Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis. Transl. Neurodegener. 10, 35 (2021).","journal-title":"Transl. Neurodegener."},{"key":"1967_CR55","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-35712-5","volume":"14","author":"E Maheux","year":"2023","unstructured":"Maheux, E. et al. Forecasting individual progression trajectories in Alzheimer\u2019s disease. Nat. Commun. 14, 761 (2023).","journal-title":"Nat. Commun."},{"key":"1967_CR56","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.1038\/s41591-024-03057-9","volume":"30","author":"L Tozzi","year":"2024","unstructured":"Tozzi, L. et al. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat. Med. 30, 2076\u20132087 (2024).","journal-title":"Nat. Med."}],"container-title":["Communications Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43856-026-01967-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43856-026-01967-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43856-026-01967-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:50:33Z","timestamp":1791669033000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43856-026-01967-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,10]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1967"],"URL":"https:\/\/doi.org\/10.1038\/s43856-026-01967-y","relation":{},"ISSN":["2730-664X"],"issn-type":[{"value":"2730-664X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,10]]},"assertion":[{"value":"19 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2026","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","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"526"}}