Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

Herrman, 2022, Time for united action on depression: A Lancet-World Psychiatric Association Commission, Lancet, 399, 957, 10.1016/S0140-6736(21)02141-3 Wen, 2022, Characterizing heterogeneity in neuroimaging, cognition, clinical symptoms, and genetics among patients with late-life depression, JAMA Psychiatry, 79, 464, 10.1001/jamapsychiatry.2022.0020 Fried, 2015, Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR∗D study, J Affect Disord, 172, 96, 10.1016/j.jad.2014.10.010 Kennedy, 2016, Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 3. Pharmacological treatments, Can J Psychiatry, 61, 540, 10.1177/0706743716659417 Beijers, 2019, Data-driven biological subtypes of depression: Systematic review of biological approaches to depression subtyping, Mol Psychiatry, 24, 888, 10.1038/s41380-019-0385-5 Goldberg, 2011, The heterogeneity of “major depression”, World Psychiatry, 10, 226, 10.1002/j.2051-5545.2011.tb00061.x Drysdale, 2017, Resting-state connectivity biomarkers define neurophysiological subtypes of depression, Nat Med, 23, 28, 10.1038/nm.4246 Li, 2020, Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder, Neuropsychopharmacology, 45, 703, 10.1038/s41386-019-0563-9 Suh, 2019, Cortical thickness in major depressive disorder: A systematic review and meta-analysis, Prog Neuropsychopharmacol Biol Psychiatry, 88, 287, 10.1016/j.pnpbp.2018.08.008 Schmaal, 2017, Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group, Mol Psychiatry, 22, 900, 10.1038/mp.2016.60 Peng, 2015, Surface vulnerability of cerebral cortex to major depressive disorder, PLoS One, 10, 10.1371/journal.pone.0128947 Zorlu, 2017, Effects of cigarette smoking on cortical thickness in major depressive disorder, J Psychiatr Res, 84, 1, 10.1016/j.jpsychires.2016.09.009 Wang, 2021, Data-driven clustering differentiates subtypes of major depressive disorder with distinct brain connectivity and symptom features, Br J Psychiatry, 219, 606, 10.1192/bjp.2021.103 Feder, 2017, Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects, J Affect Disord, 222, 79, 10.1016/j.jad.2017.06.055 Zhu, 2011, Altered white matter integrity in first-episode, treatment-naive young adults with major depressive disorder: A tract-based spatial statistics study, Brain Res, 1369, 223, 10.1016/j.brainres.2010.10.104 Remiszewski, 2022, Contrasting case-control and normative reference approaches to capture clinically relevant structural brain abnormalities in patients with first-episode psychosis who are antipsychotic naive, JAMA Psychiatry, 79, 1133, 10.1001/jamapsychiatry.2022.3010 Gong, 2021, Phenotype discovery from population brain imaging, Med Image Anal, 71, 10.1016/j.media.2021.102050 McIntosh, 2019, Uncovering the genetic architecture of major depression, Neuron, 102, 91, 10.1016/j.neuron.2019.03.022 van der Meer, 2021, The genetic architecture of human cortical folding, Sci Adv, 7, 10.1126/sciadv.abj9446 Grasby, 2020, The genetic architecture of the human cerebral cortex, Science, 367, 10.1126/science.aay6690 Zhao, 2021, Common genetic variation influencing human white matter microstructure, Science, 372, 10.1126/science.abf3736 Zhao, 2021, Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706), Mol Psychiatry, 26, 3943, 10.1038/s41380-019-0569-z Anderson, 2020, Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder, Proc Natl Acad Sci USA, 117, 25138, 10.1073/pnas.2008004117 Li, 2021, Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures, Nat Commun, 12, 1647, 10.1038/s41467-021-21943-5 Arnatkeviciute, 2021, Genetic influences on hub connectivity of the human connectome, Nat Commun, 12, 4237, 10.1038/s41467-021-24306-2 Fulcher, 2016, A transcriptional signature of hub connectivity in the mouse connectome, Proc Natl Acad Sci USA, 113, 1435, 10.1073/pnas.1513302113 Patania, 2019, Topological gene expression networks recapitulate brain anatomy and function, Netw Neurosci, 3, 744, 10.1162/netn_a_00094 Yang, 2021, Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome, Transl Psychiatry, 11, 526, 10.1038/s41398-021-01646-7 Shafiei, 2020, Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture, Biol Psychiatry, 87, 727, 10.1016/j.biopsych.2019.09.031 Hansen, 2022, Local molecular and global connectomic contributions to cross-disorder cortical abnormalities, Nat Commun, 13, 4682, 10.1038/s41467-022-32420-y Chopra, 2022, Network constraints on longitudinal grey matter changes in first episode psychosis, medRxiv Frangou, 2020, The enduring allure of explanatory reductionism in schizophrenia, Biol Psychiatry, 87, 690, 10.1016/j.biopsych.2020.01.008 Kaiser, 2015, Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity, JAMA Psychiatry, 72, 603, 10.1001/jamapsychiatry.2015.0071 Rodriguez, 2014, Machine learning. Clustering by fast search and find of density peaks, Science, 344, 1492, 10.1126/science.1242072 Hawrylycz, 2012, An anatomically comprehensive atlas of the adult human brain transcriptome, Nature, 489, 391, 10.1038/nature11405 Romero-Garcia, 2012, Effects of network resolution on topological properties of human neocortex, Neuroimage, 59, 3522, 10.1016/j.neuroimage.2011.10.086 Tetreault, 2020, Network localization of clinical, cognitive, and neuropsychiatric symptoms in Alzheimer’s disease, Brain, 143, 1249, 10.1093/brain/awaa058 Markello, 2021, Standardizing workflows in imaging transcriptomics with the abagen toolbox, eLife, 10, 10.7554/eLife.72129 Shafiei, 2023, Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia, Brain, 146, 321, 10.1093/brain/awac069 Hansen, 2023, Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease, PLoS Biol, 21, e3002314, 10.1371/journal.pbio.3002314 Siddiqi, 2020, Distinct symptom-specific treatment targets for circuit-based neuromodulation, Am J Psychiatry, 177, 435, 10.1176/appi.ajp.2019.19090915 Tremblay, 2020, Sex effects on brain structure in de novo Parkinson’s disease: A multimodal neuroimaging study, Brain, 143, 3052, 10.1093/brain/awaa234 von Economo, 2008, Atlas of Cytoarchitectonics of the Adult van den Heuvel, 2019, Multiscale neuroscience of psychiatric disorders, Biol Psychiatry, 86, 512, 10.1016/j.biopsych.2019.05.015 Romero-Garcia, 2018, Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex, Neuroimage, 171, 256, 10.1016/j.neuroimage.2017.12.060 Sweeney, 1999, Pursuit tracking impairments in schizophrenia and mood disorders: Step-ramp studies with unmedicated patients, Biol Psychiatry, 46, 671, 10.1016/S0006-3223(99)00132-8 Lynch, 2020, Causes and consequences of diagnostic heterogeneity in depression: Paths to discovering novel biological depression subtypes, Biol Psychiatry, 88, 83, 10.1016/j.biopsych.2020.01.012 Buch, 2021, Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics, Neuropsychopharmacology, 46, 156, 10.1038/s41386-020-00789-3 Winter, 2022, Quantifying deviations of brain structure and function in major depressive disorder across neuroimaging modalities, JAMA Psychiatry, 79, 879, 10.1001/jamapsychiatry.2022.1780 Zhang, 2023, Biological subtyping of psychiatric syndromes as a pathway for advances in drug discovery and personalized medicine, Nat Mental Health, 1, 88, 10.1038/s44220-023-00019-x Weigand, 2018, Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites, Biol Psychiatry, 84, 28, 10.1016/j.biopsych.2017.10.028 Nestor, 2020, Mapping symptom clusters to circuits: Toward personalizing tms targets to improve treatment outcomes in depression, Am J Psychiatry, 177, 373, 10.1176/appi.ajp.2020.20030271 Yeung, 2021, Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability, Eur J Neurosci, 54, 6281, 10.1111/ejn.15423 Chand, 2020, Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning, Brain, 143, 1027, 10.1093/brain/awaa025 Chang, 2021, Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning, Mol Psychiatry, 26, 2991, 10.1038/s41380-020-00892-3 Lalousis, 2022, Neurobiologically based stratification of recent-onset depression and psychosis: Identification of two distinct transdiagnostic phenotypes, Biol Psychiatry, 92, 552, 10.1016/j.biopsych.2022.03.021 Korgaonkar, 2020, Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder, Mol Psychiatry, 25, 1537, 10.1038/s41380-019-0574-2 Elliott, 2018, A connectome-wide functional signature of transdiagnostic risk for mental illness, Biol Psychiatry, 84, 452, 10.1016/j.biopsych.2018.03.012 Repple, 2020, Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder, Mol Psychiatry, 25, 1550, 10.1038/s41380-019-0603-1 Hearne, 2021, Activity flow underlying abnormalities in brain activations and cognition in schizophrenia, Sci Adv, 7, 10.1126/sciadv.abf2513 Larivière, 2020, Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study, Sci Adv, 6, 10.1126/sciadv.abc6457 Richiardi, 2015, Brain networks. Correlated gene expression supports synchronous activity in brain networks, Science, 348, 1241, 10.1126/science.1255905 Howard, 2019, Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions, Nat Neurosci, 22, 343, 10.1038/s41593-018-0326-7 Wray, 2018, Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression, Nat Genet, 50, 668, 10.1038/s41588-018-0090-3 Panizzon, 2009, Distinct genetic influences on cortical surface area and cortical thickness, Cereb Cortex, 19, 2728, 10.1093/cercor/bhp026 Montal, 2022, Network Tau spreading is vulnerable to the expression gradients of APOE and glutamatergic-related genes, Sci Transl Med, 14, 10.1126/scitranslmed.abn7273 Vogel, 2021, Four distinct trajectories of tau deposition identified in Alzheimer’s disease, Nat Med, 27, 871, 10.1038/s41591-021-01309-6 Li, 2019, Hippocampus-driving progressive structural alterations in medication-naive major depressive disorder, J Affect Disord, 256, 148, 10.1016/j.jad.2019.05.053 Hare, 2020, Prefrontal cortex circuits in depression and anxiety: Contribution of discrete neuronal populations and target regions, Mol Psychiatry, 25, 2742, 10.1038/s41380-020-0685-9 Marrus, 2015, Ventromedial prefrontal cortex thinning in preschool-onset depression, J Affect Disord, 180, 79, 10.1016/j.jad.2015.03.033 Liu, 2017, The role of neural plasticity in depression: From hippocampus to prefrontal cortex, Neural Plast, 2017, 10.1155/2017/6871089 Pizzagalli, 2022, Prefrontal cortex and depression, Neuropsychopharmacology, 47, 225, 10.1038/s41386-021-01101-7 Pitcher, 2021, Evidence for a third visual pathway specialized for social perception, Trends Cogn Sci, 25, 100, 10.1016/j.tics.2020.11.006 Golomb, 2009, Enhanced visual motion perception in major depressive disorder, J Neurosci, 29, 9072, 10.1523/JNEUROSCI.1003-09.2009 Le, 2017, Alterations in visual cortical activation and connectivity with prefrontal cortex during working memory updating in major depressive disorder, Neuroimage Clin, 14, 43, 10.1016/j.nicl.2017.01.004