{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T12:49:02Z","timestamp":1777207742294,"version":"3.51.4"},"reference-count":191,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The diagnostic criteria of psychiatric disorders rest solely on symptoms rather than etiologies; this led to the lack of objective biomarkers for diagnosis, differential diagnosis and treatment of major psychiatric disorders (MPDs) including schizophrenia, bipolar disorder, and major depressive disorder. Facing this bottleneck problem, we firstly started our review from the evolution of psychiatric nosology for the diagnosis of MPDs which refined from categorical to dimensional classification. Specifically, the neuroimaging was sought as an intermediate phenotype to solve the dilemma introduced from a symptom-based diagnostic classification framework. Secondly, we reviewed findings applying traditional mass-univariate methods as well as machine learning methods to identify the diagnostic and treatment neurobiomarkers for MPDs from group level to individual level, and suggested the frontal-posterior functional imbalanced pattern as a potential transdiagnostic neurobiomarker for MPDs. We further initialized the necessity of using unsupervised learning approach for MPDs subtyping prior to performing supervised learning for classification, as seeking one single neurobiological feature should be incapable of coping with the strong heterogeneity in MPDs. We concluded that the ultimate goal of MPDs subtyping is to identify an objective neurobiomarker to guide clinical practice; based on frontal-posterior functional imbalance, we developed a subtyping and precise neuromodulation strategy to achieve the establishment of a precision medicine framework. Future studies should perform more etiological investigations based on this neurobiomarker, and conduct randomized controlled trials in larger MPDs populations for further verification.<\/jats:p>","DOI":"10.1515\/jtim-2025-0045","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T14:58:26Z","timestamp":1764946706000},"page":"175-191","source":"Crossref","is-referenced-by-count":0,"title":["Identifying transdiagnostic neurobiomarkers for precision major psychiatric disorders\n                    <i>via<\/i>\n                    machine learning: Causes, findings, and future directions"],"prefix":"10.1515","volume":"14","author":[{"given":"Fay Y.","family":"Womer","sequence":"first","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center , Nashville, TN , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Xiao","sequence":"additional","affiliation":[{"name":"Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University , Nanjing , Jiangsu Province , China"},{"name":"Functional Brain Imaging Institute of Nanjing Medical University , Nanjing , Jiangsu Province , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Ngo","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Neuroscience, Saint Louis University School of Medicine , St. Louis, MO , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Zheng","sequence":"additional","affiliation":[{"name":"Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University , Nanjing , Jiangsu Province , China"},{"name":"Functional Brain Imaging Institute of Nanjing Medical University , Nanjing , Jiangsu Province , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University , Nanjing , Jiangsu Province , China"},{"name":"Functional Brain Imaging Institute of Nanjing Medical University , Nanjing , Jiangsu Province , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"2026042612202556700_j_jtim-2025-0045_ref_001","doi-asserted-by":"crossref","unstructured":"Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, et al. No health without mental health. Lancet 2007;370:859-877.","DOI":"10.1016\/S0140-6736(07)61238-0"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_002","doi-asserted-by":"crossref","unstructured":"Kessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health 2013;34:119-138.","DOI":"10.1146\/annurev-publhealth-031912-114409"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_003","doi-asserted-by":"crossref","unstructured":"Weye N, Santomauro DF, Agerbo E, Christensen MK, Iburg KM, Momen NC, et al. Register-based metrics of years lived with disability associated with mental and substance use disorders: a register-based cohort study in Denmark. Lancet Psychiatry 2021;8:310-319.","DOI":"10.1016\/S2215-0366(21)00029-8"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_004","doi-asserted-by":"crossref","unstructured":"Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 2013;382:1575-1586.","DOI":"10.1016\/S0140-6736(13)61611-6"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_005","doi-asserted-by":"crossref","unstructured":"Greenberg PE, Fournier AA, Sisitsky T, Pike CT, Kessler RC. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry 2015;76:155-162.","DOI":"10.4088\/JCP.14m09298"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_006","doi-asserted-by":"crossref","unstructured":"Weber S, Scott JG, Chatterton ML. Healthcare costs and resource use associated with negative symptoms of schizophrenia: A systematic literature review. Schizophr Res 2022;241:251-259.","DOI":"10.1016\/j.schres.2022.01.051"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_007","unstructured":"Woods SW. The economic burden of bipolar disease. J Clin Psychiatry 2000;61:38-41."},{"key":"2026042612202556700_j_jtim-2025-0045_ref_008","doi-asserted-by":"crossref","unstructured":"Bessonova L, Ogden K, Doane MJ, O\u2019Sullivan AK, Tohen M. The Economic Burden of Bipolar Disorder in the United States: A Systematic Literature Review. Clinicoecon Outcomes Res 2020;12:481-497.","DOI":"10.2147\/CEOR.S259338"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_009","doi-asserted-by":"crossref","unstructured":"Gratton C, Mittal VA. Embracing the complexity of heterogeneity in schizophrenia: a new perspective from latent clinical-anatomical dimensions. Schizophr Bull 2020;46:1337-1338.","DOI":"10.1093\/schbul\/sbaa122"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_010","doi-asserted-by":"crossref","unstructured":"Coombes BJ, Markota M, Mann JJ, Colby C, Stahl E, Talati A, et al. Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores. Transl Psychiatry 2020;10:314.","DOI":"10.1038\/s41398-020-00996-y"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_011","doi-asserted-by":"crossref","unstructured":"Wardenaar KJ, de Jonge P. Diagnostic heterogeneity in psychiatry:towards an empirical solution. BMC Med 2013;11:201.","DOI":"10.1186\/1741-7015-11-201"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_012","doi-asserted-by":"crossref","unstructured":"Lynch CJ, Gunning FM, Liston C. Causes and consequences of diagnostic heterogeneity in depression: Paths to discovering novel biological depression subtypes. Biol Psychiatry 2020;88:83-94.","DOI":"10.1016\/j.biopsych.2020.01.012"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_013","doi-asserted-by":"crossref","unstructured":"Blashfield RK, Keeley JW, Flanagan EH, Miles SR. The cycle of classification: DSM-I through DSM-5. Annu Rev Clin Psychol 2014;10:25-51.","DOI":"10.1146\/annurev-clinpsy-032813-153639"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_014","doi-asserted-by":"crossref","unstructured":"Sur\u00eds A, Holliday R, North CS. The Evolution of the Classification of Psychiatric Disorders. Behav Sci (Basel) 2016;6:5.","DOI":"10.3390\/bs6010005"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_015","doi-asserted-by":"crossref","unstructured":"Andreasen NC, Shore D, Burke JD Jr, Grove WM, Lieberman JA, Oltmanns TF, et al. Clinical phenomenology. Schizophr Bull 1988;14:345363.","DOI":"10.1093\/schbul\/14.3.345"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_016","unstructured":"American Psychiatric Association. Diagnostic and statistical manual of mental disorders (3rd Ed.). American Psychiatric Association. 1980."},{"key":"2026042612202556700_j_jtim-2025-0045_ref_017","unstructured":"American Psychiatric Association. Diagnostic and statistical manual of mental disorders (3rd Ed., Rev.). Am J Psychiatry 1987."},{"key":"2026042612202556700_j_jtim-2025-0045_ref_018","unstructured":"American Psychiatric Association. Diagnostic and statistical manual of mental disorders (4th Ed.). American Psychiatric Association. 1994."},{"key":"2026042612202556700_j_jtim-2025-0045_ref_019","unstructured":"American Psychiatric Association. Diagnostic and statistical manual of mental disorders (4th Ed., Text Rev.). American Psychiatric Association. 2000. 886 p."},{"key":"2026042612202556700_j_jtim-2025-0045_ref_020","unstructured":"American Psychiatric Association. Highlight of changes from DSM-IV-TR to DSM 5. c2013. Available from: https:\/\/www.psychiatry.org\/File%20Library\/Psychiatrists\/Practice\/DSM\/APA_DSM_Changes_from_DSM-IV-TR_-to_DSM-5.pdf."},{"key":"2026042612202556700_j_jtim-2025-0045_ref_021","doi-asserted-by":"crossref","unstructured":"Clarke DE, Narrow WE, Regier DA, Kuramoto SJ, Kupfer DJ, Kuhl EA, et al. DSM-5 field trials in the United States and Canada, Part I: study design, sampling strategy, implementation, and analytic approaches. Am J Psychiatry 2013;170:43-58.","DOI":"10.1176\/appi.ajp.2012.12070998"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_022","doi-asserted-by":"crossref","unstructured":"Kraemer HC, Kupfer DJ, Clarke DE, Narrow WE, Regier DA. DSM-5: how reliable is reliable enough? Am J Psychiatry 2012;169:13-15.","DOI":"10.1176\/appi.ajp.2011.11010050"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_023","doi-asserted-by":"crossref","unstructured":"Spitzer RL, Williams JB, Endicott J. Standards for DSM-5 reliability. Am J Psychiatry 2012;169:537; author reply 537-537; author reply 538.","DOI":"10.1176\/appi.ajp.2012.12010083"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_024","doi-asserted-by":"crossref","unstructured":"Regier DA, Narrow WE, Clarke DE, Kraemer HC, Kuramoto SJ, Kuhl EA, et al. DSM-5 field trials in the United States and Canada, Part II: test-retest reliability of selected categorical diagnoses. Am J Psychiatry 2013;170:59-70.","DOI":"10.1176\/appi.ajp.2012.12070999"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_025","doi-asserted-by":"crossref","unstructured":"Marshall M. The hidden links between mental disorders. Nature 2020;581:19-21.","DOI":"10.1038\/d41586-020-00922-8"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_026","doi-asserted-by":"crossref","unstructured":"Kapadia M, Desai M, Parikh R. Fractures in the framework: limitations of classification systems in psychiatry. Dialogues Clin Neurosci 2020;22:17-26.","DOI":"10.31887\/DCNS.2020.22.1\/rparikh"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_027","doi-asserted-by":"crossref","unstructured":"Nesse RM, Stein DJ. Towards a genuinely medical model for psychiatric nosology. BMC Med 2012;10:5.","DOI":"10.1186\/1741-7015-10-5"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_028","doi-asserted-by":"crossref","unstructured":"Chen H, Wang T, Wang D, Gao X. Time delay in seeking treatment for first-episode schizophrenia: a retrospective study. Early Interv Psychiatry 2020;14:553-558.","DOI":"10.1111\/eip.12879"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_029","doi-asserted-by":"crossref","unstructured":"Fusar-Poli P, McGorry PD, Kane JM. Improving outcomes of first-episode psychosis: an overview. World Psychiatry 2017;16:251-265.","DOI":"10.1002\/wps.20446"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_030","doi-asserted-by":"crossref","unstructured":"Drake RJ, Husain N, Marshall M, Lewis SW, Tomenson B, Chaudhry IB, et al. Effect of delaying treatment of first-episode psychosis on symptoms and social outcomes: a longitudinal analysis and modelling study. Lancet Psychiatry 2020;7:602-610.","DOI":"10.1016\/S2215-0366(20)30147-4"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_031","doi-asserted-by":"crossref","unstructured":"Greene T. The Kraepelinian dichotomy:the twin pillars crumbling? Hist Psychiatry 2007;18:361-379.","DOI":"10.1177\/0957154X07078977"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_032","doi-asserted-by":"crossref","unstructured":"Craddock N, Owen MJ. The Kraepelinian dichotomy - going, going... but still not gone. Br J Psychiatry 2010;196:92-95.","DOI":"10.1192\/bjp.bp.109.073429"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_033","doi-asserted-by":"crossref","unstructured":"Hafner H, an der Heiden W, Maurer K. Evidence for separate diseases?:Stages of one disease or different combinations of symptom dimensions? Eur Arch Psychiatry Clin Neurosci 2008;258 Suppl 2:85-96.","DOI":"10.1007\/s00406-008-2011-4"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_034","doi-asserted-by":"crossref","unstructured":"Adam D. Mental health:On the spectrum. Nature 2013;496:416-418.","DOI":"10.1038\/496416a"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_035","doi-asserted-by":"crossref","unstructured":"Rasetti R, Weinberger DR. Intermediate phenotypes in psychiatric disorders. Curr Opin Genet Dev 2011;21:340-348.","DOI":"10.1016\/j.gde.2011.02.003"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_036","doi-asserted-by":"crossref","unstructured":"Leuchter AF, Hunter AM, Krantz DE, Cook IA. Intermediate phenotypes and biomarkers of treatment outcome in major depressive disorder. Dialogues Clin Neurosci 2014;16:525-537.","DOI":"10.31887\/DCNS.2014.16.4\/aleuchter"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_037","doi-asserted-by":"crossref","unstructured":"Donaldson ZR, Hen R. From psychiatric disorders to animal models:a bidirectional and dimensional approach. Biol Psychiatry 2015;77:15-21.","DOI":"10.1016\/j.biopsych.2014.02.004"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_038","doi-asserted-by":"crossref","unstructured":"Flint J, Timpson N, Munaf\u00f2 M. Assessing the utility of intermediate phenotypes for genetic mapping of psychiatric disease. Trends Neurosci 2014;37:733-741.","DOI":"10.1016\/j.tins.2014.08.007"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_039","doi-asserted-by":"crossref","unstructured":"Insel TR. The NIMH Research Domain Criteria (RDoC) Project:precision medicine for psychiatry. Am J Psychiatry 2014;171:395-397.","DOI":"10.1176\/appi.ajp.2014.14020138"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_040","doi-asserted-by":"crossref","unstructured":"Ruderfer DM, Fanous AH, Ripke S, McQuillin A, Amdur RL, Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al. Mol Psychiatry 2014;19:1017-1024.","DOI":"10.1038\/mp.2013.138"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_041","doi-asserted-by":"crossref","unstructured":"Chang M, Womer FY, Edmiston EK, Bai C, Zhou Q, Jiang X, et al. Neurobiological Commonalities and Distinctions Among Three Major Psychiatric Diagnostic Categories:A Structural MRI Study. Schizophr Bull 2018;44:65-74.","DOI":"10.1093\/schbul\/sbx028"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_042","doi-asserted-by":"crossref","unstructured":"Wei S, Womer F, Geng H, Jiang X, Zhou Q, Chang M, et al. Similarities and differences of functional connectivity in drug-na\u00efve, first-episode adolescent and young adult with major depressive disorder and schizophrenia. Sci Rep 2017;7:44316.","DOI":"10.1038\/srep44316"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_043","doi-asserted-by":"crossref","unstructured":"Xia M, Womer FY, Chang M, Zhu Y, Zhou Q, Edmiston EK, et al. Shared and Distinct Functional Architectures of Brain Networks Across Psychiatric Disorders. Schizophr Bull 2019;45:450-463.","DOI":"10.1093\/schbul\/sby046"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_044","doi-asserted-by":"crossref","unstructured":"Yun JY, Kim YK. Graph theory approach for the structural-functional brain connectome of depression. Prog Neuropsychopharmacol Biol Psychiatry 2021;111:110401.","DOI":"10.1016\/j.pnpbp.2021.110401"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_045","doi-asserted-by":"crossref","unstructured":"Perry A, Roberts G, Mitchell PB, Breakspear M. Connectomics of bipolar disorder:a critical review, and evidence for dynamic instabilities within interoceptive networks. Mol Psychiatry 2019;24:1296-1318.","DOI":"10.1038\/s41380-018-0267-2"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_046","doi-asserted-by":"crossref","unstructured":"van den Heuvel MP, Fornito A. Brain networks in schizophrenia. Neuropsychol Rev 2014;24:32-48.","DOI":"10.1007\/s11065-014-9248-7"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_047","doi-asserted-by":"crossref","unstructured":"Phillips ML, Swartz HA. A critical appraisal of neuroimaging studies of bipolar disorder:toward a new conceptualization of underlying neural circuitry and a road map for future research. Am J Psychiatry 2014;171:829-843.","DOI":"10.1176\/appi.ajp.2014.13081008"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_048","doi-asserted-by":"crossref","unstructured":"Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, et al. Precision Functional Mapping of Individual Human Brains. Neuron 2017;95:791-807.e7.","DOI":"10.1016\/j.neuron.2017.07.011"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_049","doi-asserted-by":"crossref","unstructured":"Mueller S, Wang D, Fox MD, Yeo BT, Sepulcre J, Sabuncu MR, et al. Individual variability in functional connectivity architecture of the human brain. Neuron 2013;77:586-595.","DOI":"10.1016\/j.neuron.2012.12.028"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_050","doi-asserted-by":"crossref","unstructured":"Nawaz U, Lee I, Beermann A, Eack S, Keshavan M, Brady R. Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology. Schizophr Bull 2021;47:180-188.","DOI":"10.1093\/schbul\/sbaa088"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_051","doi-asserted-by":"crossref","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;75:1146-1155.","DOI":"10.1001\/jamapsychiatry.2018.2467"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_052","doi-asserted-by":"crossref","unstructured":"Santo-Angles A, Salvador R, Gomar JJ, Guerrero-Pedraza A, Ramiro N, et al. Interindividual variability of functional connectome in schizophrenia. Schizophr Res 2021;235:65-73.","DOI":"10.1016\/j.schres.2021.07.010"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_053","doi-asserted-by":"crossref","unstructured":"Ivleva EI, Clementz BA, Dutcher AM, Arnold SJM, Jeon-Slaughter H, Aslan S, et al. Brain structure biomarkers in the psychosis biotypes:Findings from the bipolar-schizophrenia network for intermediate phenotypes. Biol Psychiatry 2017;82:26-39.","DOI":"10.1016\/j.biopsych.2016.08.030"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_054","doi-asserted-by":"crossref","unstructured":"Whitwell JL. Voxel-based morphometry:an automated technique for assessing structural changes in the brain. J Neurosci 2009;29:9661-9664.","DOI":"10.1523\/JNEUROSCI.2160-09.2009"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_055","doi-asserted-by":"crossref","unstructured":"Alexander AL, Lee JE, Lazar M, Field AS. Diffusion tensor imaging of the brain. Neurotherapeutics 2007;4:316-329.","DOI":"10.1016\/j.nurt.2007.05.011"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_056","doi-asserted-by":"crossref","unstructured":"Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics:voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31:1487-1505.","DOI":"10.1016\/j.neuroimage.2006.02.024"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_057","doi-asserted-by":"crossref","unstructured":"Zou QH, Zhu CZ, Yang Y, Zuo XN, Long XY, Cao QJ, et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI:fractional ALFF. J Neurosci Methods 2008;172:137-141.","DOI":"10.1016\/j.jneumeth.2008.04.012"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_058","doi-asserted-by":"crossref","unstructured":"Lv H, Wang Z, Tong E, Williams LM, Zaharchuk G, Zeineh M, et al. Resting-State Functional MRI:Everything That Nonexperts Have Always Wanted to Know. AJNR Am J Neuroradiol 2018;39:1390-1399.","DOI":"10.3174\/ajnr.A5527"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_059","doi-asserted-by":"crossref","unstructured":"Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995;34:537-541.","DOI":"10.1002\/mrm.1910340409"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_060","doi-asserted-by":"crossref","unstructured":"Jiang L, Zuo XN. Regional Homogeneity: A Multimodal, Multiscale Neuroimaging Marker of the Human Connectome. Neuroscientist 2016;22:486-505.","DOI":"10.1177\/1073858415595004"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_061","doi-asserted-by":"crossref","unstructured":"Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, et al. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021;244:118649.","DOI":"10.1016\/j.neuroimage.2021.118649"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_062","doi-asserted-by":"crossref","unstructured":"Joo SW, Kim H, Jo YT, Yoon W, Kim Y, Lee J. Shared and distinct white matter abnormalities in schizophrenia and bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021;108:110175.","DOI":"10.1016\/j.pnpbp.2020.110175"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_063","doi-asserted-by":"crossref","unstructured":"Mamah D, Ji A, Rutlin J, Shimony JS. White matter integrity in schizophrenia and bipolar disorder: Tract- and voxel-based analyses of diffusion data from the Connectom scanner. Neuroimage Clin 2019;21:101649.","DOI":"10.1016\/j.nicl.2018.101649"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_064","doi-asserted-by":"crossref","unstructured":"Womer FY, Wang L, Alpert KI, Smith MJ, Csernansky JG, Barch DM, et al. Basal ganglia and thalamic morphology in schizophrenia and bipolar disorder. Psychiatry Res 2014;223:75-83.","DOI":"10.1016\/j.pscychresns.2014.05.017"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_065","doi-asserted-by":"crossref","unstructured":"Mitelman SA. Transdiagnostic neuroimaging in psychiatry: A review. Psychiatry Res 2019;277:23-38.","DOI":"10.1016\/j.psychres.2019.01.026"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_066","doi-asserted-by":"crossref","unstructured":"Womer FY, Kalmar JH, Wang F, Blumberg HP. A Ventral Prefrontal-Amygdala Neural System in Bipolar Disorder: A View from Neuroimaging Research. Acta Neuropsychiatr 2009;21:228-238.","DOI":"10.1111\/j.1601-5215.2009.00414.x"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_067","doi-asserted-by":"crossref","unstructured":"Womer FY, Wang F, Chepenik LG, Kalmar JH, Spencer L, Edmiston E, et al. Sexually dimorphic features of vermis morphology in bipolar disorder. Bipolar Disord 2009;11:753-758.","DOI":"10.1111\/j.1399-5618.2009.00745.x"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_068","doi-asserted-by":"crossref","unstructured":"Womer FY, Tang Y, Harms MP, Bai C, Chang M, Jiang X, Wei S, et al. Sexual dimorphism of the cerebellar vermis in schizophrenia. Schizophr Res 2016;176:164-170.","DOI":"10.1016\/j.schres.2016.06.028"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_069","doi-asserted-by":"crossref","unstructured":"Zhang W, Lei D, Keedy SK, Ivleva EI, Eum S, Yao L, et al. Brain gray matter network organization in psychotic disorders. Neuropsychopharmacology 2020;45:666-674.","DOI":"10.1038\/s41386-019-0586-2"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_070","doi-asserted-by":"crossref","unstructured":"Chang M, Womer FY, Bai C, Zhou Q, Wei S, Jiang X, et al. Voxel-Based Morphometry in Individuals at Genetic High Risk for Schizophrenia and Patients with Schizophrenia during Their First Episode of Psychosis. PLoS One 2016;11:e0163749.","DOI":"10.1371\/journal.pone.0163749"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_071","doi-asserted-by":"crossref","unstructured":"Lener MS, Iosifescu DV. In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature. Ann N Y Acad Sci 2015;1344:50-65.","DOI":"10.1111\/nyas.12759"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_072","doi-asserted-by":"crossref","unstructured":"Lee DK, Lee H, Park K, Joh E, Kim CE, Ryu S. Common gray and white matter abnormalities in schizophrenia and bipolar disorder. PLoS One 2020;15:e0232826.","DOI":"10.1371\/journal.pone.0232826"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_073","doi-asserted-by":"crossref","unstructured":"van Velzen LS, Kelly S, Isaev D, Aleman A, Aftanas LI, Bauer J, et al. White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. Mol Psychiatry 2020;25:1511-1525.","DOI":"10.1038\/s41380-019-0477-2"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_074","doi-asserted-by":"crossref","unstructured":"Koshiyama D, Fukunaga M, Okada N, Morita K, Nemoto K, Usui K, et al. White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals. Mol Psychiatry 2020;25:883-895.","DOI":"10.1038\/s41380-019-0553-7"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_075","doi-asserted-by":"crossref","unstructured":"Ji E, Guevara P, Guevara M, Grigis A, Labra N, Sarrazin S, et al. Increased and Decreased Superficial White Matter Structural Connectivity in Schizophrenia and Bipolar Disorder. Schizophr Bull 2019;45:1367-1378.","DOI":"10.1093\/schbul\/sbz015"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_076","doi-asserted-by":"crossref","unstructured":"Gong Q, He Y. Depression, neuroimaging and connectomics: a selective overview. Biol Psychiatry 2015;77:223-235.","DOI":"10.1016\/j.biopsych.2014.08.009"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_077","doi-asserted-by":"crossref","unstructured":"Narr KL, Leaver AM. Connectome and schizophrenia. Curr Opin Psychiatry 2015;28:229-235.","DOI":"10.1097\/YCO.0000000000000157"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_078","doi-asserted-by":"crossref","unstructured":"Cui Y, Dong J, Yang Y, Yu H, Li W, Liu Y, et al. White matter micro-structural differences across major depressive disorder, bipolar disorder and schizophrenia: A tract-based spatial statistics study. J Affect Disord 2020;260:281-286.","DOI":"10.1016\/j.jad.2019.09.029"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_079","doi-asserted-by":"crossref","unstructured":"Li J, Kale Edmiston E, Chen K, Tang Y, Ouyang X, et al. A comparative diffusion tensor imaging study of corpus callosum subregion integrity in bipolar disorder and schizophrenia. Psychiatry Res 2014;221:58-62.","DOI":"10.1016\/j.pscychresns.2013.10.007"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_080","doi-asserted-by":"crossref","unstructured":"Kochunov P, Hong LE, Dennis EL, Morey RA, Tate DF, Wilde EA, et al. ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research. Hum Brain Mapp 2022;43:194-206.","DOI":"10.1002\/hbm.24998"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_081","doi-asserted-by":"crossref","unstructured":"Ho NF, Li Hui Chong P, Lee DR, Chew QH, Chen G, Sim K. The amygdala in schizophrenia and bipolar disorder: A synthesis of structural MRI, diffusion tensor imaging, and resting-state functional connectivity findings. Harv Rev Psychiatry 2019;27:150-164.","DOI":"10.1097\/HRP.0000000000000207"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_082","doi-asserted-by":"crossref","unstructured":"Lee MH, Smyser CD, Shimony JS. Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol 2013;34:1866-1872.","DOI":"10.3174\/ajnr.A3263"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_083","doi-asserted-by":"crossref","unstructured":"Wang X, Cheng B, Roberts N, Wang S, Luo Y, Tian F, Yue S. Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder. Hum Brain Mapp 2021;42:5458-5476.","DOI":"10.1002\/hbm.25618"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_084","doi-asserted-by":"crossref","unstructured":"Argyelan M, Ikuta T, DeRosse P, Braga RJ, Burdick KE, John M, et al. Resting-state fMRI connectivity impairment in schizophrenia and bipolar disorder. Schizophr Bull 2014;40:100-110.","DOI":"10.1093\/schbul\/sbt092"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_085","doi-asserted-by":"crossref","unstructured":"Zhang L, Ai H, Opmeer EM, Marsman JC, van der Meer L, Ruh\u00e9 HG, et al. Distinct temporal brain dynamics in bipolar disorder and schizophrenia during emotion regulation. Psychol Med 2020;50:413-421.","DOI":"10.1017\/S0033291719000217"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_086","doi-asserted-by":"crossref","unstructured":"Yang Y, Liu S, Jiang X, Yu H, Ding S, Lu Y, et al. Common and Specific Functional Activity Features in Schizophrenia, Major Depressive Disorder, and Bipolar Disorder. Front Psychiatry 2019;10:52.","DOI":"10.3389\/fpsyt.2019.00052"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_087","doi-asserted-by":"crossref","unstructured":"Gong J, Wang J, Qiu S, Chen P, Luo Z, Wang J, et al. Common and distinct patterns of intrinsic brain activity alterations in major depression and bipolar disorder: voxel-based meta-analysis. Transl Psychiatry 2020;10:353.","DOI":"10.1038\/s41398-020-01036-5"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_088","doi-asserted-by":"crossref","unstructured":"He H, Yu Q, Du Y, Vergara V, Victor TA, Drevets WC, et al. Resting-state functional network connectivity in prefrontal regions differs between unmedicated patients with bipolar and major depressive disorders. J Affect Disord 2016;190:483-493.","DOI":"10.1016\/j.jad.2015.10.042"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_089","doi-asserted-by":"crossref","unstructured":"Yaple ZA, Tolomeo S, Yu R. Mapping working memory-specific dysfunction using a transdiagnostic approach. Neuroimage Clin 2021;31:102747.","DOI":"10.1016\/j.nicl.2021.102747"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_090","doi-asserted-by":"crossref","unstructured":"Frangou S. A systems neuroscience perspective of schizophrenia and bipolar disorder. Schizophr Bull 2014;40:523-531.","DOI":"10.1093\/schbul\/sbu017"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_091","doi-asserted-by":"crossref","unstructured":"Liu H, Tang Y, Womer F, Fan G, Lu T, Driesen N, et al. Differentiating patterns of amygdala-frontal functional connectivity in schizophrenia and bipolar disorder. Schizophr Bull 2014;40:469-477.","DOI":"10.1093\/schbul\/sbt044"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_092","doi-asserted-by":"crossref","unstructured":"Sk\u00e5tun KC, Kaufmann T, Brandt CL, Doan NT, Aln\u00e6s D, T\u00f8nnesen S, et al. Thalamo-cortical functional connectivity in schizophrenia and bipolar disorder. Brain Imaging Behav 2018;12:640-652.","DOI":"10.1007\/s11682-017-9714-y"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_093","doi-asserted-by":"crossref","unstructured":"Morris RW, Sparks A, Mitchell PB, Weickert CS, Green MJ. Lack of cortico-limbic coupling in bipolar disorder and schizophrenia during emotion regulation. Transl Psychiatry 2012;2:e90.","DOI":"10.1038\/tp.2012.16"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_094","doi-asserted-by":"crossref","unstructured":"Chen L, Wang Y, Niu C, Zhong S, Hu H, Chen P, et al. Common and distinct abnormal frontal-limbic system structural and functional patterns in patients with major depression and bipolar disorder. Neuroimage Clin 2018;20:42-50.","DOI":"10.1016\/j.nicl.2018.07.002"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_095","doi-asserted-by":"crossref","unstructured":"Anticevic A, Cole MW, Repovs G, Murray JD, Brumbaugh MS, Winkler AM, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex 2014;24:3116-3130.","DOI":"10.1093\/cercor\/bht165"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_096","doi-asserted-by":"crossref","unstructured":"Zhang L, Opmeer EM, van der Meer L, Aleman A, \u0106ur\u010di\u0107-Blake B, Ruh\u00e9 HG. Altered frontal-amygdala effective connectivity during effortful emotion regulation in bipolar disorder. Bipolar Disord 2018;20:349-358.","DOI":"10.1111\/bdi.12611"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_097","doi-asserted-by":"crossref","unstructured":"Li J, Tang Y, Womer F, Fan G, Zhou Q, Sun W, et al. Two patterns of anterior insular cortex functional connectivity in bipolar disorder and schizophrenia. World J Biol Psychiatry 2018;19:S115-S123.","DOI":"10.1080\/15622975.2016.1274051"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_098","doi-asserted-by":"crossref","unstructured":"Mitchell AC, Jiang Y, Peter C, Akbarian S. Transcriptional regulation of GAD1 GABA synthesis gene in the prefrontal cortex of subjects with schizophrenia. Schizophr Res 2015;167:28-34.","DOI":"10.1016\/j.schres.2014.10.020"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_099","doi-asserted-by":"crossref","unstructured":"Tendilla-Beltr\u00e1n H, Sanchez-Islas NDC, Marina-Ramos M, Leza JC, Flores G. The prefrontal cortex as a target for atypical antipsychotics in schizophrenia, lessons of neurodevelopmental animal models. Prog Neurobiol 2021;199:101967.","DOI":"10.1016\/j.pneurobio.2020.101967"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_100","doi-asserted-by":"crossref","unstructured":"Zhou Y, Fan L, Qiu C, Jiang T. Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia. Neurosci Bull 2015;31:207-219.","DOI":"10.1007\/s12264-014-1502-8"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_101","doi-asserted-by":"crossref","unstructured":"Savitz JB, Price JL, Drevets WC. Neuropathological and neuromorphometric abnormalities in bipolar disorder: view from the medial prefrontal cortical network. Neurosci Biobehav Rev 2014;42:132-147.","DOI":"10.1016\/j.neubiorev.2014.02.008"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_102","doi-asserted-by":"crossref","unstructured":"Xu P, Chen A, Li Y, Xing X, Lu H. Medial prefrontal cortex in neurological diseases. Physiol Genomics 2019;51:432-442.","DOI":"10.1152\/physiolgenomics.00006.2019"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_103","doi-asserted-by":"crossref","unstructured":"Silva RC, Maffioletti E, Gennarelli M, Baune BT, Minelli A. Biological correlates of early life stressful events in major depressive disorder. Psychoneuroendocrinology 2021;125:105103.","DOI":"10.1016\/j.psyneuen.2020.105103"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_104","doi-asserted-by":"crossref","unstructured":"Duan J, Xia M, Womer FY, Chang M, Yin Z, Zhou Q, et al. Dynamic changes of functional segregation and integration in vulnerability and resilience to schizophrenia. Hum Brain Mapp 2019;40:2200-2211.","DOI":"10.1002\/hbm.24518"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_105","doi-asserted-by":"crossref","unstructured":"Chang M, Womer FY, Gong X, Chen X, Tang L, Feng R, et al. Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning. Mol Psychiatry 2021;26:2991-3002.","DOI":"10.1038\/s41380-020-00892-3"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_106","doi-asserted-by":"crossref","unstructured":"Chang M, Edmiston EK, Womer FY, Zhou Q, Wei S, Jiang X, et al. Spontaneous low-frequency fluctuations in the neural system for emotional perception in major psychiatric disorders: amplitude similarities and differences across frequency bands. J Psychiatry Neurosci 2019;44:132-141.","DOI":"10.1503\/jpn.170226"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_107","doi-asserted-by":"crossref","unstructured":"Chen Y, Womer FY, Feng R, Zhang X, Zhang Y, Duan J, et al. A Real-World Observation of Antipsychotic Effects on Brain Volumes and Intrinsic Brain Activity in Schizophrenia. Front Neurosci 2021;15:749316.","DOI":"10.3389\/fnins.2021.749316"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_108","doi-asserted-by":"crossref","unstructured":"Jiang X, Fu S, Yin Z, Kang J, Wang X, Zhou Y, Wei S, et al. Common and distinct neural activities in frontoparietal network in first-episode bipolar disorder and major depressive disorder: Preliminary findings from a follow-up resting state fMRI study. J Affect Disord 2020;260:653-659.","DOI":"10.1016\/j.jad.2019.09.063"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_109","doi-asserted-by":"crossref","unstructured":"Lui S, Yao L, Xiao Y, Keedy SK, Reilly JL, Keefe RS, et al. Resting-state brain function in schizophrenia and psychotic bipolar probands and their first-degree relatives. Psychol Med 2015;45:97-108.","DOI":"10.1017\/S003329171400110X"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_110","doi-asserted-by":"crossref","unstructured":"Yao X, Yin Z, Liu F, Wei S, Zhou Y, Jiang X, et al. Shared and distinct regional homogeneity changes in bipolar and unipolar depression. Neurosci Lett 2018;673:28-32.","DOI":"10.1016\/j.neulet.2018.02.033"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_111","doi-asserted-by":"crossref","unstructured":"Zhao Y, Wang L, Edmiston EK, Womer FY, Jiang X, Wu F, et al. Alterations in gray matter volumes and intrinsic activity in the prefrontal cortex are associated with suicide attempts in patients with bipolar disorder. Psychiatry Res Neuroimaging 2021;307:111229.","DOI":"10.1016\/j.pscychresns.2020.111229"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_112","doi-asserted-by":"crossref","unstructured":"Wei D, Chen Z, Rose A. Evaluating the role of resilience in reducing economic losses from disasters: A multi\u2010regional analysis of a seaport disruption. Papers in Regional Science 2020;99:1691-1723.","DOI":"10.1111\/pirs.12553"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_113","doi-asserted-by":"crossref","unstructured":"Lencer R, Yao L, Reilly JL, Keedy SK, McDowell JE, Keshavan MS, et al. Alterations in intrinsic fronto-thalamo-parietal connectivity are associated with cognitive control deficits in psychotic disorders. Hum Brain Mapp 2019;40:163-174.","DOI":"10.1002\/hbm.24362"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_114","doi-asserted-by":"crossref","unstructured":"Huang Y, Wang W, Hei G, Yang Y, Long Y, Wang X, et al. Altered regional homogeneity and cognitive impairments in first-episode schizophrenia: A resting-state fMRI study. Asian J Psychiatr 2022;71:103055.","DOI":"10.1016\/j.ajp.2022.103055"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_115","doi-asserted-by":"crossref","unstructured":"Kang J, Jiao Z, Qin Y, Wang Y, Wang J, Jin L, et al. Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia. J Psychiatr Res 2022;147:4-12.","DOI":"10.1016\/j.jpsychires.2021.12.043"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_116","doi-asserted-by":"crossref","unstructured":"Sun D, Guo H, Womer FY, Yang J, Tang J, Liu J, et al. Frontal-posterior functional imbalance and aberrant function developmental patterns in schizophrenia. Transl Psychiatry 2021;11:495.","DOI":"10.1038\/s41398-021-01617-y"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_117","doi-asserted-by":"crossref","unstructured":"Malhi GS, Mann JJ. Depression. Lancet 2018;392:2299-2312.","DOI":"10.1016\/S0140-6736(18)31948-2"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_118","doi-asserted-by":"crossref","unstructured":"Jauhar S, Johnstone M, McKenna PJ. Schizophrenia. Lancet 2022;399:473-486.","DOI":"10.1016\/S0140-6736(21)01730-X"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_119","doi-asserted-by":"crossref","unstructured":"Geddes JR, Miklowitz DJ. Treatment of bipolar disorder. Lancet 2013;381:1672-1682.","DOI":"10.1016\/S0140-6736(13)60857-0"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_120","doi-asserted-by":"crossref","unstructured":"Bellani M, Dusi N, Yeh PH, Soares JC, Brambilla P. The effects of antidepressants on human brain as detected by imaging studies. Focus on major depression. Prog Neuropsychopharmacol Biol Psychiatry 2011;35:1544-1552.","DOI":"10.1016\/j.pnpbp.2010.11.040"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_121","doi-asserted-by":"crossref","unstructured":"Hafeman DM, Chang KD, Garrett AS, Sanders EM, Phillips ML. Effects of medication on neuroimaging findings in bipolar disorder: an updated review. Bipolar Disord 2012;14:375-410.","DOI":"10.1111\/j.1399-5618.2012.01023.x"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_122","doi-asserted-by":"crossref","unstructured":"Yang C, Tang J, Liu N, Yao L, Xu M, Sun H, et al. The effects of antipsychotic treatment on the brain of patients with first-episode schizophrenia: A selective review of longitudinal MRI studies. Front Psychiatry 2021;12:593703.","DOI":"10.3389\/fpsyt.2021.593703"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_123","doi-asserted-by":"crossref","unstructured":"Feng R, Womer FY, Edmiston EK, Chen Y, Wang Y, Chang M, et al. Antipsychotic effects on cortical morphology in schizophrenia and bipolar disorders. Front Neurosci 2020;14:579139.","DOI":"10.3389\/fnins.2020.579139"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_124","doi-asserted-by":"crossref","unstructured":"Huang XF, Song X. Effects of antipsychotic drugs on neurites relevant to schizophrenia treatment. Med Res Rev 2019;39:386-403.","DOI":"10.1002\/med.21512"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_125","doi-asserted-by":"crossref","unstructured":"Cyranoski D. Antipsychotic drugs could shrink patients\u2019 brains. Nature 2011. https:\/\/doi.org\/10.1038\/news.2011.75.","DOI":"10.1038\/news.2011.75"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_126","doi-asserted-by":"crossref","unstructured":"Janssen RJ, Mour\u00e3o-Miranda J, Schnack HG. Making individual prognoses in psychiatry using neuroimaging and machine learning. Biol Psychiatry Cogn Neurosci Neuroimaging 2018;3:798-808.","DOI":"10.1016\/j.bpsc.2018.04.004"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_127","doi-asserted-by":"crossref","unstructured":"Koutsouleris N, Wobrock T, Guse B, Langguth B, Landgrebe M, Eichhammer P, et al. Predicting response to repetitive transcranial magnetic stimulation in patients with schizophrenia using structural magnetic resonance imaging: A multisite machine learning analysis. Schizophr Bull 2018;44:1021-1034.","DOI":"10.1093\/schbul\/sbx114"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_128","doi-asserted-by":"crossref","unstructured":"Li A, Zalesky A, Yue W, Howes O, Yan H, Liu Y, et al. A neuroimaging biomarker for striatal dysfunction in schizophrenia. Nat Med 2020;26:558-565.","DOI":"10.1038\/s41591-020-0793-8"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_129","doi-asserted-by":"crossref","unstructured":"Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020;41:3468-3535.","DOI":"10.1002\/hbm.25013"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_130","doi-asserted-by":"crossref","unstructured":"Janousova E, Montana G, Kasparek T, Schwarz D. Supervised, multivariate, whole-brain reduction did not help to achieve high classification performance in schizophrenia research. Front Neurosci 2016;10:392.","DOI":"10.3389\/fnins.2016.00392"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_131","doi-asserted-by":"crossref","unstructured":"Winterburn JL, Voineskos AN, Devenyi GA, Plitman E, de la Fuente-Sandoval C, Bhagwat N, et al. Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study. Schizophr Res 2019;214:3-10.","DOI":"10.1016\/j.schres.2017.11.038"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_132","doi-asserted-by":"crossref","unstructured":"Vieira S, Gong QY, Pinaya WHL, Scarpazza C, Tognin S, Crespo-Facorro B, et al. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull 2020;46:17-26.","DOI":"10.1093\/schbul\/sby189"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_133","doi-asserted-by":"crossref","unstructured":"Demirci O, Clark VP, Calhoun VD. A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia. Neuroimage 2008;39:1774-1782.","DOI":"10.1016\/j.neuroimage.2007.10.012"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_134","doi-asserted-by":"crossref","unstructured":"Ardekani BA, Tabesh A, Sevy S, Robinson DG, Bilder RM, Szeszko PR. Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers. Hum Brain Mapp 2011;32:1-9.","DOI":"10.1002\/hbm.20995"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_135","doi-asserted-by":"crossref","unstructured":"Mikolas P, Hlinka J, Skoch A, Pitra Z, Frodl T, Spaniel F, et al. Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy. BMC Psychiatry 2018;18:97.","DOI":"10.1186\/s12888-018-1678-y"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_136","doi-asserted-by":"crossref","unstructured":"Caan MW, Vermeer KA, van Vliet LJ, Majoie CB, Peters BD, den Heeten GJ, et al. Shaving diffusion tensor images in discriminant analysis: a study into schizophrenia. Med Image Anal 2006;10:841-849.","DOI":"10.1016\/j.media.2006.07.006"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_137","doi-asserted-by":"crossref","unstructured":"Caprihan A, Pearlson GD, Calhoun VD. Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements. Neuroimage 2008;42:675-682.","DOI":"10.1016\/j.neuroimage.2008.04.255"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_138","doi-asserted-by":"crossref","unstructured":"Sui J, He H, Yu Q, Chen J, Rogers J, Pearlson GD, et al. Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA. Front Hum Neurosci 2013;7:235.","DOI":"10.3389\/fnhum.2013.00235"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_139","doi-asserted-by":"crossref","unstructured":"Sui J, Qi S, van Erp TGM, Bustillo J, Jiang R, Lin D, et al. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat Commun 2018;9:3028.","DOI":"10.1038\/s41467-018-05432-w"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_140","doi-asserted-by":"crossref","unstructured":"Lee J, Chon MW, Kim H, Rathi Y, Bouix S, Shenton ME, Kubicki M. Diagnostic value of structural and diffusion imaging measures in schizophrenia. Neuroimage Clin 2018;18:467-474.","DOI":"10.1016\/j.nicl.2018.02.007"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_141","doi-asserted-by":"crossref","unstructured":"Liu J, Wang X, Zhang X, Pan Y, Wang X, Wang J. MMM: classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning. Multimed Tools Appl 2018;77:29651-29667.","DOI":"10.1007\/s11042-017-5470-7"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_142","doi-asserted-by":"crossref","unstructured":"Costafreda SG, Chu C, Ashburner J, Fu CH. Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 2009;4:e6353.","DOI":"10.1371\/journal.pone.0006353"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_143","doi-asserted-by":"crossref","unstructured":"Sankar A, Zhang T, Gaonkar B, Doshi J, Erus G, Costafreda SG, Marangell L, Davatzikos C, Fu CH. Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample. BJPsych Open 2016;2:247-254.","DOI":"10.1192\/bjpo.bp.115.002493"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_144","doi-asserted-by":"crossref","unstructured":"Mwangi B, Ebmeier KP, Matthews K, Steele JD. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 2012;135:1508-1521.","DOI":"10.1093\/brain\/aws084"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_145","doi-asserted-by":"crossref","unstructured":"Yang J, Zhang M, Ahn H, Zhang Q, Jin TB, Li I, et al. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp 2018;39:4420-4439.","DOI":"10.1002\/hbm.24282"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_146","doi-asserted-by":"crossref","unstructured":"Marquand AF, Mour\u00e3o-Miranda J, Brammer MJ, Cleare AJ, Fu CH. Neuroanatomy of verbal working memory as a diagnostic biomarker for depression. Neuroreport 2008;19:1507-1511.","DOI":"10.1097\/WNR.0b013e328310425e"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_147","doi-asserted-by":"crossref","unstructured":"Fu CH, Mourao-Miranda J, Costafreda SG, Khanna A, Marquand AF, Williams SC, et al. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry 2008;63:656-662.","DOI":"10.1016\/j.biopsych.2007.08.020"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_148","doi-asserted-by":"crossref","unstructured":"Johnston BA, Tolomeo S, Gradin V, Christmas D, Matthews K, Steele JD. Failure of hippocampal deactivation during loss events in treatment-resistant depression. Brain 2015;138:2766-2776.","DOI":"10.1093\/brain\/awv177"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_149","doi-asserted-by":"crossref","unstructured":"Shimizu Y, Yoshimoto J, Toki S, Takamura M, Yoshimura S, Okamoto Y, et al. Toward Probabilistic Diagnosis and Understanding of Depression Based on Functional MRI Data Analysis with Logistic Group LASSO. PLoS One 2015;10:e0123524.","DOI":"10.1371\/journal.pone.0123524"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_150","doi-asserted-by":"crossref","unstructured":"Craddock RC, Holtzheimer PE 3rd, Hu XP, Mayberg HS. Disease state prediction from resting state functional connectivity. Magn Reson Med 2009;62:1619-1628.","DOI":"10.1002\/mrm.22159"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_151","doi-asserted-by":"crossref","unstructured":"Ma Z, Li R, Yu J, He Y, Li J. Alterations in regional homogeneity of spontaneous brain activity in late-life subthreshold depression. PLoS One 2013;8:e53148.","DOI":"10.1371\/journal.pone.0053148"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_152","doi-asserted-by":"crossref","unstructured":"Cao L, Guo S, Xue Z, Hu Y, Liu H, Mwansisya TE, et al. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci 2014;68:110-119.","DOI":"10.1111\/pcn.12106"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_153","doi-asserted-by":"crossref","unstructured":"Deng F, Wang Y, Huang H, Niu M, Zhong S, Zhao L, et al. Abnormal segments of right uncinate fasciculus and left anterior thalamic radiation in major and bipolar depression. Prog Neuropsychopharmacol Biol Psychiatry 2018;81:340-349.","DOI":"10.1016\/j.pnpbp.2017.09.006"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_154","doi-asserted-by":"crossref","unstructured":"Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front Psychiatry 2015;6:21.","DOI":"10.3389\/fpsyt.2015.00021"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_155","doi-asserted-by":"crossref","unstructured":"Schnyer DM, Clasen PC, Gonzalez C, Beevers CG. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res Neuroimaging 2017;264:1-9.","DOI":"10.1016\/j.pscychresns.2017.03.003"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_156","doi-asserted-by":"crossref","unstructured":"Ota M, Ishikawa M, Sato N, Hori H, Sasayama D, Hattori K, et al. Discrimination between schizophrenia and major depressive disorder by magnetic resonance imaging of the female brain. J Psychiatr Res 2013;47:1383-1388.","DOI":"10.1016\/j.jpsychires.2013.06.010"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_157","doi-asserted-by":"crossref","unstructured":"Redlich R, Almeida JJ, Grotegerd D, Opel N, Kugel H, Heindel W, et al. Brain morphometric biomarkers distinguishing unipolar and bipolar depression. A voxel-based morphometry-pattern classification approach. JAMA Psychiatry 2014;71:1222-1230.","DOI":"10.1001\/jamapsychiatry.2014.1100"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_158","doi-asserted-by":"crossref","unstructured":"He H, Sui J, Du Y, Yu Q, Lin D, Drevets WC, et al. Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders. Brain Struct Funct 2017;222:4051-4064.","DOI":"10.1007\/s00429-017-1451-x"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_159","doi-asserted-by":"crossref","unstructured":"B\u00fcrger C, Redlich R, Grotegerd D, Meinert S, Dohm K, Schneider I, et al. Differential abnormal pattern of anterior cingulate gyrus activation in unipolar and bipolar depression: an fMRI and pattern classification approach. Neuropsychopharmacology 2017;42:1399-1408.","DOI":"10.1038\/npp.2017.36"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_160","doi-asserted-by":"crossref","unstructured":"Borgwardt S, Koutsouleris N, Aston J, Studerus E, Smieskova R, Riecher-R\u00f6ssler A, et al. Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition. Schizophr Bull 2013;39:1105-1114.","DOI":"10.1093\/schbul\/sbs095"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_161","doi-asserted-by":"crossref","unstructured":"Kim YK, Na KS. Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective. Prog Neuropsychopharmacol Biol Psychiatry 2018;80:71-80.","DOI":"10.1016\/j.pnpbp.2017.06.024"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_162","doi-asserted-by":"crossref","unstructured":"Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology 2021;46:176-190.","DOI":"10.1038\/s41386-020-0767-z"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_163","doi-asserted-by":"crossref","unstructured":"Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry 2019;24:1583-1598.","DOI":"10.1038\/s41380-019-0365-9"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_164","doi-asserted-by":"crossref","unstructured":"Vieira S, Pinaya WH, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci Biobehav Rev 2017;74:58-75.","DOI":"10.1016\/j.neubiorev.2017.01.002"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_165","doi-asserted-by":"crossref","unstructured":"Pinaya WHL, Mechelli A, Sato JR. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study. Hum Brain Mapp 2019;40:944-954.","DOI":"10.1002\/hbm.24423"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_166","doi-asserted-by":"crossref","unstructured":"Matsubara T, Tashiro T, Uehara K. Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis. IEEE Trans Biomed Eng 2019;66:2768-2779.","DOI":"10.1109\/TBME.2019.2895663"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_167","doi-asserted-by":"crossref","unstructured":"Zeng LL, Wang H, Hu P, Yang B, Pu W, Shen H, et al. Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI. EBioMedicine 2018;30:74-85.","DOI":"10.1016\/j.ebiom.2018.03.017"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_168","doi-asserted-by":"crossref","unstructured":"Yan W, Calhoun V, Song M, Cui Y, Yan H, Liu S, et al. Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data. EBioMedicine 2019;47:543-552.","DOI":"10.1016\/j.ebiom.2019.08.023"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_169","doi-asserted-by":"crossref","unstructured":"Winter NR, Blanke J, Leenings R, Ernsting J, Fisch L, Sarink K, et al. A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder. JAMA Psychiatry 2024;81:386-395.","DOI":"10.1001\/jamapsychiatry.2023.5083"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_170","doi-asserted-by":"crossref","unstructured":"Chekroud AM, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, et al. Illusory generalizability of clinical prediction models. Science 2024;383:164-167.","DOI":"10.1126\/science.adg8538"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_171","doi-asserted-by":"crossref","unstructured":"Wen J, Varol E, Yang Z, Hwang G, Dwyer D, Kazerooni AF, et al. Subtyping Brain Diseases from Imaging Data. 2023 Jul 23. In: Machine Learning for Brain Disorders.","DOI":"10.1007\/978-1-0716-3195-9_16"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_172","doi-asserted-by":"crossref","unstructured":"Downar J, Geraci J, Salomons TV, Dunlop K, Wheeler S, McAndrews MP, et al. Anhedonia and reward-circuit connectivity distinguish nonresponders from responders to dorsomedial prefrontal repetitive transcranial magnetic stimulation in major depression. Biol Psychiatry 2014;76:176-185.","DOI":"10.1016\/j.biopsych.2013.10.026"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_173","doi-asserted-by":"crossref","unstructured":"Der-Avakian A, Markou A. The neurobiology of anhedonia and other reward-related deficits. Trends Neurosci 2012;35:68-77.","DOI":"10.1016\/j.tins.2011.11.005"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_174","doi-asserted-by":"crossref","unstructured":"Ostergaard SD, Jensen SO, Bech P. The heterogeneity of the depressive syndrome: when numbers get serious. Acta Psychiatr Scand 2011;124:495-496.","DOI":"10.1111\/j.1600-0447.2011.01744.x"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_175","unstructured":"Von Luxburg U, Williamson RC, Guyon I. Clustering: science or art?. Available from: https:\/\/dl.acm.org\/doi\/10.5555\/3045796.3045803."},{"key":"2026042612202556700_j_jtim-2025-0045_ref_176","doi-asserted-by":"crossref","unstructured":"Price RB, Lane S, Gates K, Kraynak TE, Horner MS, Thase ME, et al. Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood. Biol Psychiatry 2017;81:347-357.","DOI":"10.1016\/j.biopsych.2016.06.023"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_177","doi-asserted-by":"crossref","unstructured":"Price RB, Gates K, Kraynak TE, Thase ME, Siegle GJ. Data-Driven Subgroups in Depression Derived from Directed Functional Connectivity Paths at Rest. Neuropsychopharmacology 2017;42:2623-2632.","DOI":"10.1038\/npp.2017.97"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_178","doi-asserted-by":"crossref","unstructured":"Chand GB, Dwyer DB, Erus G, Sotiras A, Varol E, Srinivasan D, et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 2020;143:1027-1038.","DOI":"10.1093\/brain\/awaa025"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_179","doi-asserted-by":"crossref","unstructured":"Dwyer DB, Cabral C, Kambeitz-Ilankovic L, Sanfelici R, Kambeitz J, Calhoun V, et al. Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia. Schizophr Bull 2018;44:1060-1069.","DOI":"10.1093\/schbul\/sby008"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_180","doi-asserted-by":"crossref","unstructured":"Stanley ML, Moussa MN, Paolini BM, Lyday RG, Burdette JH, Laurienti PJ. Defining nodes in complex brain networks. Front Comput Neurosci 2013;7:169.","DOI":"10.3389\/fncom.2013.00169"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_181","doi-asserted-by":"crossref","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;23:584-601.","DOI":"10.1016\/j.tics.2019.03.009"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_182","doi-asserted-by":"crossref","unstructured":"Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017;23:28-38.","DOI":"10.1038\/nm.4246"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_183","doi-asserted-by":"crossref","unstructured":"Dinga R, Schmaal L, Penninx BWJH, van Tol MJ, Veltman DJ, van Velzen L, et al. Evaluating the evidence for biotypes of depression: Methodological replication and extension of. Neuroimage Clin 2019;22:101796.","DOI":"10.1016\/j.nicl.2019.101796"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_184","doi-asserted-by":"crossref","unstructured":"Dunlop K, Grosenick L, Downar J, Vila-Rodriguez F, Gunning FM, Daskalakis ZJ, et al. Dimensional and categorical solutions to parsing depression heterogeneity in a large single-site sample. Biol Psychiatry 2024;96:422-434.","DOI":"10.1016\/j.biopsych.2024.01.012"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_185","doi-asserted-by":"crossref","unstructured":"Winter NR, Hahn T. Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Mol Psychiatry 2022;27:1858-1859.","DOI":"10.1038\/s41380-022-01482-1"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_186","doi-asserted-by":"crossref","unstructured":"Zhang X, Wang F, Zhang W. Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022). Mol Psychiatry 2022;27:3569-3570.","DOI":"10.1038\/s41380-022-01613-8"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_187","doi-asserted-by":"crossref","unstructured":"Insel TR, Cuthbert BN. Medicine. Brain disorders? Precisely. Science 2015;348:499-500.","DOI":"10.1126\/science.aab2358"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_188","doi-asserted-by":"crossref","unstructured":"Xiao Y, Womer FY, Dong S, Zhu R, Zhang R, Yang J, et al. A neuroimaging-based precision medicine framework for depression. Asian J Psychiatr 2024;91:103803.","DOI":"10.1016\/j.ajp.2023.103803"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_189","doi-asserted-by":"crossref","unstructured":"Liu J, Guo H, Yang J, Xiao Y, Cai A, Zhao T, et al. Visual cortex repetitive transcranial magnetic stimulation (rTMS) reversing neurodevelopmental impairments in adolescents with major psychiatric disorders (MPDs): A cross-species translational study. CNS Neurosci Ther 2024;30:e14427.","DOI":"10.1111\/cns.14427"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_190","doi-asserted-by":"crossref","unstructured":"Yang J, Guo H, Cai A, Zheng J, Liu J, Xiao Y, et al. Aberrant Hippocampal Development in Early-onset Mental Disorders and Promising Interventions: Evidence from a Translational Study. Neurosci Bull 2024;40:683-694.","DOI":"10.1007\/s12264-023-01162-2"},{"key":"2026042612202556700_j_jtim-2025-0045_ref_191","doi-asserted-by":"crossref","unstructured":"Zhao T, Guo H, Yang J, Cai A, Liu J, Zheng J, et al. Repetitive transcranial magnetic stimulation reversing abnormal brain function in mood disorders with early life stress: from preclinical models to clinical applications. Asian J Psychiatr 2024;97:104092.","DOI":"10.1016\/j.ajp.2024.104092"}],"container-title":["Journal of Translational Internal Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jtim-2025-0045\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jtim-2025-0045\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T12:21:35Z","timestamp":1777206095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jtim-2025-0045\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,5]]},"references-count":191,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,12,5]]},"published-print":{"date-parts":[[2026,4,21]]}},"alternative-id":["10.1515\/jtim-2025-0045"],"URL":"https:\/\/doi.org\/10.1515\/jtim-2025-0045","relation":{},"ISSN":["2224-4018"],"issn-type":[{"value":"2224-4018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,5]]}}}