Buch AM, Liston C. Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics. Neuropsychopharmacology. 2021;46:156–75.
Google Scholar
Lynall M-E, McIntosh AM. The heterogeneity of depression. Am J Psychiatry. 2023;180:703–4.
Google Scholar
Hasler G. Pathophysiology of depression: do we have any solid evidence of interest to clinicians? World Psychiatry. 2010;9:155–61.
Google Scholar
Warden D, Rush AJ, Trivedi MH, Fava M, Wisniewski SR. The STAR*D Project results: a comprehensive review of findings. Curr Psychiatry Rep. 2007;9:449–59.
Google Scholar
Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163:1905–17.
Google Scholar
Drevets WC, Wittenberg GM, Bullmore ET, Manji HK. Immune targets for therapeutic development in depression: towards precision medicine. Nat Rev Drug Discov. 2022;21:224–44.
Google Scholar
Kennard BD, Silva SG, Tonev S, Rohde P, Hughes JL, Vitiello B, et al. Remission and recovery in the treatment for adolescents with depression study (TADS): acute and long-term outcomes. J Am Acad Child Adolesc Psychiatry. 2009;48:186–95.
Google Scholar
Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Erratum: Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23:264.
Google Scholar
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.
Google Scholar
Beijers L, Wardenaar KJ, van Loo HM, Schoevers RA. Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping. Mol Psychiatry. 2019;24:888–900.
Google Scholar
Zhang Y, Wu W, Toll RT, Naparstek S, Maron-Katz A, Watts M, et al. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng. 2021;5:309–23.
Google Scholar
Fagiolini A, Kupfer DJ. Is treatment-resistant depression a unique subtype of depression? Biol Psychiatry. 2003;53:640–8.
Google Scholar
Haroon E, Chen X, Li Z, Patel T, Woolwine BJ, Hu XP, et al. Increased inflammation and brain glutamate define a subtype of depression with decreased regional homogeneity, impaired network integrity, and anhedonia. Transl Psychiatry. 2018;8:189.
Google Scholar
Nguyen TD, Harder A, Xiong Y, Kowalec K, Hägg S, Cai N, et al. Genetic heterogeneity and subtypes of major depression. Mol Psychiatry. 2022;27:1667–75.
Google Scholar
Yu C, Arcos-Burgos M, Licinio J, Wong ML. A latent genetic subtype of major depression identified by whole-exome genotyping data in a Mexican-American cohort. Transl Psychiatry. 2017;7:e1134.
Google Scholar
Meyer-Lindenberg A, Weinberger DR. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat Rev Neurosci. 2006;7:818–27.
Google Scholar
Pearlson GD. Etiologic, phenomenologic, and endophenotypic overlap of schizophrenia and bipolar disorder. Annu Rev Clin Psychol. 2015;11:251–81.
Google Scholar
Chen D, Wang X, Voon V, Jiang Y, Lo C-YZ, Wang L, et al. Neurophysiological stratification of major depressive disorder by distinct trajectories. Nat Ment Health. 2023;1:863–75.
Google Scholar
Sun X, Sun J, Lu X, Dong Q, Zhang L, Wang W, et al. Mapping neurophysiological subtypes of major depressive disorder using normative models of the functional connectome. Biol Psychiatry. 2023;94:936–47.
Google Scholar
Küblböck M, Woletz M, Höflich A, Sladky R, Kranz GS, Hoffmann A, et al. Stability of low-frequency fluctuation amplitudes in prolonged resting-state fMRI. Neuroimage. 2014;103:249–57.
Google Scholar
Liu J, Ren L, Womer FY, Wang J, Fan G, Jiang W, et al. Alterations in amplitude of low frequency fluctuation in treatment-naïve major depressive disorder measured with resting-state fMRI. Hum Brain Mapp. 2014;35:4979–88.
Google Scholar
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.
Google Scholar
Teng C, Zhou J, Ma H, Tan Y, Wu X, Guan C, et al. Abnormal resting state activity of left middle occipital gyrus and its functional connectivity in female patients with major depressive disorder. BMC Psychiatry. 2018;18:370.
Google Scholar
Meyer JH, Ginovart N, Boovariwala A, Sagrati S, Hussey D, Garcia A, et al. Elevated monoamine oxidase a levels in the brain: an explanation for the monoamine imbalance of major depression. Arch Gen Psychiatry. 2006;63:1209–16.
Google Scholar
Kiecolt-Glaser JK, Derry HM, Fagundes CP. Inflammation: depression fans the flames and feasts on the heat. Am J Psychiatry. 2015;172:1075–91.
Google Scholar
Scaini G, Mason BL, Diaz AP, Jha MK, Soares JC, Trivedi MH, et al. Dysregulation of mitochondrial dynamics, mitophagy and apoptosis in major depressive disorder: does inflammation play a role? Mol Psychiatry. 2022;27:1095–102.
Google Scholar
Amin N, Liu J, Bonnechere B, MahmoudianDehkordi S, Arnold M, Batra R, et al. Interplay of metabolome and gut microbiome in individuals with major depressive disorder vs control individuals. JAMA Psychiatry. 2023;80:597–609.
Google Scholar
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.
Google Scholar
Guo H, Zhang R, Wang P, Zhang L, Yin Z, Zhang Y, et al. Brain functional and structural alterations in women with bipolar disorder and suicidality. Front Psychiatry. 2021;12:630849.
Google Scholar
Duan J, Gong X, Womer FY, Sun K, Tang L, Liu J, et al. Neurodevelopmental trajectories, polygenic risk, and lipometabolism in vulnerability and resilience to schizophrenia. BMC Psychiatry. 2023;23:153.
Google Scholar
Worsley KJ, Chen JI, Lerch J, Evans AC. Comparing functional connectivity via thresholding correlations and singular value decomposition. Philos Trans R Soc Lond B Biol Sci. 2005;360:913–20.
Google Scholar
MacQueen J. Some methods for classification and analysis of multivariate observations. Fifth Berkeley Symposium on Mathematical Statistics and Probability. 1967;II:281–97.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn:machine learning in python. J. Machine Learn. Res. 2011;12:2825–30.
Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17:208.
Google Scholar
Khan N, Bano A, Rahman MA, Rathinasabapathi B, Babar MA. UPLC-HRMS-based untargeted metabolic profiling reveals changes in chickpea (Cicer arietinum) metabolome following long-term drought stress. Plant Cell Environ. 2019;42:115–32.
Google Scholar
Zheng J, Womer FY, Tang L, Guo H, Zhang X, Tang Y, et al. Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder. Transl Psychiatry. 2024;14:17.
Google Scholar
Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33:3982–4.
Google Scholar
Ligthart S, Marzi C, Aslibekyan S, Mendelson MM, Conneely KN, Tanaka T, et al. DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome Biol. 2016;17:255.
Google Scholar
Stevenson AJ, McCartney DL, Hillary RF, Campbell A, Morris SW, Bermingham ML, et al. Characterisation of an inflammation-related epigenetic score and its association with cognitive ability. Clin Epigenetics. 2020;12:113.
Google Scholar
Rahmani E, Yedidim R, Shenhav L, Schweiger R, Weissbrod O, Zaitlen N, et al. GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data. Bioinformatics. 2017;33:1870–2.
Google Scholar
Koestler DC, Jones MJ, Usset J, Christensen BC, Butler RA, Kobor MS, et al. Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL). BMC Bioinformatics. 2016;17:120.
Google Scholar
Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlén SE, Greco D, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One. 2012;7:e41361.
Google Scholar
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 2018;57:289–300.
Google Scholar
Wang Y, Lu J, Yu J, Gibbs RA, Yu F. An integrative variant analysis pipeline for accurate genotype/haplotype inference in population NGS data. Genome Res. 2013;23:833–42.
Google Scholar
Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.
Google Scholar
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.
Google Scholar
Green C, Shen X, Stevenson AJ, Conole ELS, Harris MA, Barbu MC, et al. Structural brain correlates of serum and epigenetic markers of inflammation in major depressive disorder. Brain Behav Immun. 2021;92:39–48.
Google Scholar
Barker ED, Cecil CAM, Walton E, Houtepen LC, O’Connor TG, Danese A, et al. Inflammation-related epigenetic risk and child and adolescent mental health: a prospective study from pregnancy to middle adolescence. Dev Psychopathol. 2018;30:1145–56.
Google Scholar
Talens RP, Boomsma DI, Tobi EW, Kremer D, Jukema JW, Willemsen G, et al. Variation, patterns, and temporal stability of DNA methylation: considerations for epigenetic epidemiology. FASEB J. 2010;24:3135–44.
Google Scholar
Pérez-Figueroa E, Álvarez-Carrasco P, Ortega E, Maldonado-Bernal C. Neutrophils: many ways to die. Front Immunol. 2021;12:631821.
Google Scholar
Mantovani A, Cassatella MA, Costantini C, Jaillon S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol. 2011;11:519–31.
Google Scholar
Northoff G, Wiebking C, Feinberg T, Panksepp J. The ‘resting-state hypothesis’ of major depressive disorder-a translational subcortical-cortical framework for a system disorder. Neurosci Biobehav Rev. 2011;35:1929–45.
Google Scholar
Phillips ML, Chase HW, Sheline YI, Etkin A, Almeida JR, Deckersbach T, et al. Identifying predictors, moderators, and mediators of antidepressant response in major depressive disorder: neuroimaging approaches. Am J Psychiatry. 2015;172:124–38.
Google Scholar
Wu F, Lu Q, Kong Y, Zhang Z. A comprehensive overview of the role of visual cortex malfunction in depressive disorders: opportunities and challenges. Neurosci Bull. 2023;39:1426–38.
Google Scholar
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.
Google Scholar
Jiang S, Huang H, Zhou J, Li H, Duan M, Yao D, et al. Progressive trajectories of schizophrenia across symptoms, genes, and the brain. BMC Med. 2023;21:237.
Google Scholar
Guo H, Xiao Y, Sun D, Yang J, Wang J, Wang H, et al. Early-stage repetitive transcranial magnetic stimulation altered posterior-anterior cerebrum effective connectivity in methylazoxymethanol acetate rats. Front Neurosci. 2021;15:652715.
Google Scholar
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.
Google Scholar
Wang D, Tang L, Xi C, Luo D, Liang Y, Huang Q, et al. Targeted visual cortex stimulation (TVCS): a novel neuro-navigated repetitive transcranial magnetic stimulation mode for improving cognitive function in bipolar disorder. Transl Psychiatry. 2023;13:193.
Google Scholar
Berk M, Williams LJ, Jacka FN, O’Neil A, Pasco JA, Moylan S, et al. So depression is an inflammatory disease, but where does the inflammation come from? BMC Med. 2013;11:200.
Google Scholar
Wohleb ES, Franklin T, Iwata M, Duman RS. Integrating neuroimmune systems in the neurobiology of depression. Nat Rev Neurosci. 2016;17:497–511.
Google Scholar
Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim EK, et al. A meta-analysis of cytokines in major depression. Biol Psychiatry. 2010;67:446–57.
Google Scholar
Kim YK, Na KS, Myint AM, Leonard BE. The role of pro-inflammatory cytokines in neuroinflammation, neurogenesis and the neuroendocrine system in major depression. Prog Neuropsychopharmacol Biol Psychiatry. 2016;64:277–84.
Google Scholar
Enache D, Pariante CM, Mondelli V. Markers of central inflammation in major depressive disorder: a systematic review and meta-analysis of studies examining cerebrospinal fluid, positron emission tomography and post-mortem brain tissue. Brain Behav Immun. 2019;81:24–40.
Google Scholar
Singh D, Guest PC, Dobrowolny H, Vasilevska V, Meyer-Lotz G, Bernstein HG, et al. Changes in leukocytes and CRP in different stages of major depression. J Neuroinflammation. 2022;19:74.
Google Scholar
Sørensen NV, Frandsen BH, Orlovska-Waast S, Buus TB, Ødum N, Christensen RH, et al. Immune cell composition in unipolar depression: a comprehensive systematic review and meta-analysis. Mol Psychiatry. 2023;28:391–401.
Google Scholar
Capuron L, Dantzer R. Cytokines and depression: the need for a new paradigm. Brain Behav Immun. 2003;17:S119–24.
Google Scholar
Schaefer M, Schwaiger M, Garkisch AS, Pich M, Hinzpeter A, Uebelhack R, et al. Prevention of interferon-alpha associated depression in psychiatric risk patients with chronic hepatitis C. J Hepatol. 2005;42:793–8.
Google Scholar
Dunn AJ, Swiergiel AH, de, Beaurepaire R. Cytokines as mediators of depression: what can we learn from animal studies? Neurosci Biobehav Rev. 2005;29:891–909.
Google Scholar
Su WJ, Hu T, Jiang CL. Cool the inflamed brain: a novel anti-inflammatory strategy for the treatment of major depressive disorder. Curr Neuropharmacol. 2024;22:810–42.
Google Scholar
Lin K, Shao R, Wang R, Lu W, Zou W, Chen K, et al. Inflammation, brain structure and cognition interrelations among individuals with differential risks for bipolar disorder. Brain Behav Immun. 2020;83:192–9.
Google Scholar
Goldsmith DR, Bekhbat M, Mehta ND, Felger JC. Inflammation-related functional and structural dysconnectivity as a pathway to psychopathology. Biol Psychiatry. 2023;93:405–18.
Google Scholar
