Hostname: page-component-5cf477f64f-tgq86 Total loading time: 0 Render date: 2025-04-02T23:51:51.252Z Has data issue: false hasContentIssue false
  • English
  • Français

Resolving heterogeneity in depression using individualized structural covariance network analysis

Published online by Cambridge University Press:  12 August 2022

Shaoqiang Han Open the ORCID record for Shaoqiang Han [Opens in a new window] ,
Ruiping Zheng ,
Shuying Li ,
Bingqian Zhou ,
Yu Jiang ,
Keke Fang ,
Yarui Wei ,
Jianyue Pang ,
Hengfen Li  and
Yong Zhang
...Show all authors
Shaoqiang Han*
Affiliation:
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
Ruiping Zheng
Affiliation:
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
Shuying Li
Affiliation:
Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Bingqian Zhou
Affiliation:
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
Yu Jiang
Affiliation:
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
Keke Fang
Affiliation:
Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
Yarui Wei
Affiliation:
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
Jianyue Pang
Affiliation:
Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Hengfen Li
Affiliation:
Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Yong Zhang
Affiliation:
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
Yuan Chen*
Affiliation:
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
Jingliang Cheng*
Affiliation:
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
*
Authors for correspondence: Shaoqiang Han, E-mail: [email protected]; Yuan Chen, E-mail: [email protected]; Jingliang Cheng, E-mail: [email protected]
Authors for correspondence: Shaoqiang Han, E-mail: [email protected]; Yuan Chen, E-mail: [email protected]; Jingliang Cheng, E-mail: [email protected]
Authors for correspondence: Shaoqiang Han, E-mail: [email protected]; Yuan Chen, E-mail: [email protected]; Jingliang Cheng, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

Methods

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

Results

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

Conclusions

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Keywords

Depressiongray matter volumeheterogeneityindividualized analysissubtypes of depression
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 11 , August 2023 , pp. 5312 - 5321
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ajnakina, O., Das, T., Lally, J., Di Forti, M., Pariante, C. M., Marques, T. R., & Mondelli, V. (2021). Structural covariance of cortical gyrification at illness onset in treatment resistance: A longitudinal study of first-episode psychoses. Schizophrenia Bulletin, 47(6), 1729–1739. doi: 10.1093/schbul/sbab035CrossRefGoogle ScholarPubMed
Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013). Imaging structural co-variance between human brain regions. Nature Reviews. Neuroscience, 14(5), 322–336. doi: 10.1038/nrn3465CrossRefGoogle ScholarPubMed
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex, 24(3), 663–676. doi: 10.1093/cercor/bhs352CrossRefGoogle ScholarPubMed
Beijers, L., Wardenaar, K. J., van Loo, H. M., & Schoevers, R. A. (2019). Data-driven biological subtypes of depression: Systematic review of biological approaches to depression subtyping. Molecular Psychiatry, 24(6), 888–900. doi: 10.1038/s41380-019-0385-5CrossRefGoogle ScholarPubMed
Boden, J. M., & Fergusson, D. M. (2011). Alcohol and depression. Addiction (Abingdon, England), 106(5), 906–914. doi: 10.1111/j.1360-0443.2010.03351.xCrossRefGoogle ScholarPubMed
Bondar, J., Caye, A., Chekroud, A. M., & Kieling, C. (2020). Symptom clusters in adolescent depression and differential response to treatment: A secondary analysis of the treatment for adolescents with depression study randomised trial. The Lancet. Psychiatry, 7(4), 337–343. doi: 10.1016/s2215-0366(20)30060-2CrossRefGoogle ScholarPubMed
Buyukdura, J. S., McClintock, S. M., & Croarkin, P. E. (2011). Psychomotor retardation in depression: Biological underpinnings, measurement, and treatment. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 35(2), 395–409. doi: 10.1016/j.pnpbp.2010.10.019CrossRefGoogle ScholarPubMed
Chen, J., Rashid, B., Yu, Q., Liu, J., Lin, D., Du, Y., … Calhoun, V. D. (2018). Variability in resting state network and functional network connectivity associated with schizophrenia genetic risk: A pilot study. Frontiers in Neuroscience, 12, 114. doi: 10.3389/fnins.2018.00114CrossRefGoogle ScholarPubMed
Cheng, W., Rolls, E. T., Zhang, J., Sheng, W., Ma, L., Wan, L., … Feng, J. (2017). Functional connectivity decreases in autism in emotion, self, and face circuits identified by knowledge-based enrichment analysis. NeuroImage, 148, 169–178. doi: 10.1016/j.neuroimage.2016.12.068CrossRefGoogle ScholarPubMed
Cheng, Y., Xu, J., Yu, H., Nie, B., Li, N., Luo, C., … Xu, X. (2014). Delineation of early and later adult onset depression by diffusion tensor imaging. PLoS One, 9(11), e112307. doi: 10.1371/journal.pone.0112307CrossRefGoogle ScholarPubMed
Cole, M. W., Anticevic, A., Repovs, G., & Barch, D. (2011). Variable global dysconnectivity and individual differences in schizophrenia. Biological Psychiatry, 70(1), 43–50. doi: 10.1016/j.biopsych.2011.02.010CrossRefGoogle ScholarPubMed
Craddock, R. C., James, G. A., Holtzheimer, P. E. III., Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8), 1914–1928. doi: 10.1002/hbm.21333CrossRefGoogle ScholarPubMed
Das, T., Borgwardt, S., Hauke, D. J., Harrisberger, F., Lang, U. E., Riecher-Rössler, A., … Schmidt, A. (2018). Disorganized gyrification network properties during the transition to psychosis. JAMA Psychiatry, 75(6), 613–622. doi: 10.1001/jamapsychiatry.2018.0391CrossRefGoogle ScholarPubMed
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427(6972), 311–312. doi: 10.1038/427311aCrossRefGoogle ScholarPubMed
Drysdale, A. T., Grosenick, L., & Downar, J. (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine, 23(1), 28–38. doi: 10.1038/nm.4246CrossRefGoogle ScholarPubMed
Evans, A. C. (2013). Networks of anatomical covariance. NeuroImage, 80, 489–504. doi: 10.1016/j.neuroimage.2013.05.054CrossRefGoogle ScholarPubMed
Feder, S., Sundermann, B., Wersching, H., Teuber, A., Kugel, H., Teismann, H., … Pfleiderer, B. (2017). Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects. Journal of Affective Disorders, 222, 79–87. doi: 10.1016/j.jad.2017.06.055CrossRefGoogle ScholarPubMed
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., … Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671. doi: 10.1038/nn.4135CrossRefGoogle ScholarPubMed
Goldberg, D. (2011). The heterogeneity of ‘major depression’. World Psychiatry, 10(3), 226–228. doi: 10.1002/j.2051-5545.2011.tb00061.xCrossRefGoogle ScholarPubMed
Gong, Q., & He, Y. (2015). Depression, neuroimaging and connectomics: A selective overview. Biological Psychiatry, 77(3), 223–235. doi: 10.1016/j.biopsych.2014.08.009CrossRefGoogle ScholarPubMed
Gopal, S., Miller, R. L., Michael, A., Adali, T., Cetin, M., Rachakonda, S., … Calhoun, V. D. (2016). Spatial variance in resting fMRI networks of schizophrenia patients: An independent vector analysis. Schizophrenia Bulletin, 42(1), 152–160. doi: 10.1093/schbul/sbv085Google ScholarPubMed
Han, S., Chen, Y., Zheng, R., Li, S., Jiang, Y., Wang, C., … Cheng, J. (2021a). The stage-specifically accelerated brain aging in never-treated first-episode patients with depression. Human Brain Mapping, 42(11), 3656–3666. doi: 10.1002/hbm.25460CrossRefGoogle ScholarPubMed
Han, S., Cui, Q., Wang, X., Chen, Y., Li, D., Li, L., … Chen, H. (2020). The anhedonia is differently modulated by structural covariance network of NAc in bipolar disorder and major depressive disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 99, 109865. doi: 10.1016/j.pnpbp.2020.109865CrossRefGoogle ScholarPubMed
Han, S., Xu, Y., Guo, H. R., Fang, K., Wei, Y., Liu, L., … Cheng, J. (2022). Resolving heterogeneity in obsessive-compulsive disorder through individualized differential structural covariance network analysis. Cerebral Cortex. doi: 10.1093/cercor/bhac163Google Scholar
Han, S., Zheng, R., Li, S., Liu, L., Wang, C., Jiang, Y., … Cheng, J. (2021b). Progressive brain structural abnormality in depression assessed with MR imaging by using causal network analysis. Psychological Medicine, 1–10. doi: 10.1017/s0033291721003986, (https://www.cambridge.org/core/journals/psychological-medicine/article/abs/progressive-brain-structural-abnormality-in-depressionassessed-with-mr-imaging-by-using-causal-network-analysis/2F9AFE9CCDD4D6DA9E983460780D5991).Google ScholarPubMed
Harald, B., & Gordon, P. (2012). Meta-review of depressive subtyping models. Journal of Affective Disorders, 139(2), 126–140. doi: 10.1016/j.jad.2011.07.015CrossRefGoogle ScholarPubMed
Hasler, G. (2010). Pathophysiology of depression: Do we have any solid evidence of interest to clinicians? World Psychiatry, 9(3), 155–161. doi: 10.1002/j.2051-5545.2010.tb00298.xCrossRefGoogle ScholarPubMed
Hu Be Rt, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218.10.1007/BF01908075CrossRefGoogle Scholar
Jantaratnotai, N., Mosikanon, K., Lee, Y., & McIntyre, R. S. (2017). The interface of depression and obesity. Obesity Research & Clinical Practice, 11(1), 1–10. doi: 10.1016/j.orcp.2016.07.003CrossRefGoogle ScholarPubMed
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. (2015). Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity. JAMA Psychiatry, 72(6), 603–611. doi: 10.1001/jamapsychiatry.2015.0071CrossRefGoogle ScholarPubMed
Kendell, R., & Jablensky, A. (2003). Distinguishing between the validity and utility of psychiatric diagnoses. The American Journal of Psychiatry, 160(1), 4–12. doi: 10.1176/appi.ajp.160.1.4CrossRefGoogle ScholarPubMed
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., … Wang, P. S. (2003). The epidemiology of major depressive disorder: Results from the National Comorbidity Survey Replication (NCS-R). JAMA, 289(23), 3095–3105. doi: 10.1001/jama.289.23.3095CrossRefGoogle ScholarPubMed
Krishnan, V., & Nestler, E. J. (2008). The molecular neurobiology of depression. Nature, 455(7215), 894–902. doi: 10.1038/nature07455CrossRefGoogle ScholarPubMed
Lee, A., Poh, J. S., Wen, D. J., Guillaume, B., Chong, Y. S., Shek, L. P., … Qiu, A. (2019). Long-term influences of prenatal maternal depressive symptoms on the amygdala-prefrontal circuitry of the offspring from birth to early childhood. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 4(11), 940–947. doi: 10.1016/j.bpsc.2019.05.006CrossRefGoogle ScholarPubMed
Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R. K., Giedd, J., & Evans, A. C. (2006). Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. NeuroImage, 31(3), 993–1003. doi: 10.1016/j.neuroimage.2006.01.042CrossRefGoogle ScholarPubMed
Lima-Ojeda, J. M., Rupprecht, R., & Baghai, T. C. (2018). Neurobiology of depression: A neurodevelopmental approach. The World Journal of Biological Psychiatry, 19(5), 349–359. doi: 10.1080/15622975.2017.1289240CrossRefGoogle ScholarPubMed
Liu, Z., Palaniyappan, L., Wu, X., Zhang, K., Du, J., Zhao, Q., … Lin, C. P. (2021). Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: Individualized structural covariance network analysis. Molecular Psychiatry, 26(12), 7719–7731. doi: 10.1038/s41380-021-01229-4CrossRefGoogle ScholarPubMed
Liu, Z., Rolls, E. T., Liu, Z., Zhang, K., Yang, M., Du, J., … Feng, J. (2019). Brain annotation toolbox: Exploring the functional and genetic associations of neuroimaging results. Bioinformatics (Oxford, England), 35(19), 3771–3778. doi: 10.1093/bioinformatics/btz128Google ScholarPubMed
Lv, J., Di Biase, M., Cash, R. F. H., & Cocchi, L. (2020). Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort. Molecular Psychiatry, 26, 3512–3523. doi: 10.1038/s41380-020-00882-5CrossRefGoogle Scholar
Lynch, C. J., Gunning, F. M., & Liston, C. (2020). Causes and consequences of diagnostic heterogeneity in depression: Paths to discovering novel biological depression subtypes. Biological Psychiatry, 88(1), 83–94. doi: 10.1016/j.biopsych.2020.01.012CrossRefGoogle ScholarPubMed
Mak, E., Colloby, S. J., Thomas, A., & O'Brien, J. T. (2016). The segregated connectome of late-life depression: A combined cortical thickness and structural covariance analysis. Neurobiology of Aging, 48, 212–221. doi: 10.1016/j.neurobiolaging.2016.08.013CrossRefGoogle ScholarPubMed
Mathew, A. R., Hogarth, L., Leventhal, A. M., Cook, J. W., & Hitsman, B. (2017). Cigarette smoking and depression comorbidity: Systematic review and proposed theoretical model. Addiction (Abingdon, England), 112(3), 401–412. doi: 10.1111/add.13604CrossRefGoogle ScholarPubMed
Miller, , & Rupert, G. (1974). The jackknife-a review. Biometrika, 61(1), 1–15.Google Scholar
Neufeld, N. H., & Kaczkurkin, A. N. (2020). Structural brain networks in remitted psychotic depression. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology, 45(7), 1223–1231. doi: 10.1038/s41386-020-0646-7CrossRefGoogle ScholarPubMed
Okada, K., Nakao, T., Sanematsu, H., Murayama, K., Honda, S., Tomita, M., … Kanba, S. (2015). Biological heterogeneity of obsessive-compulsive disorder: A voxel-based morphometric study based on dimensional assessment. Psychiatry and Clinical Neurosciences, 69(7), 411–421. doi: 10.1111/pcn.12269CrossRefGoogle ScholarPubMed
Otte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., … Schatzberg, A. F. (2016). Major depressive disorder. Nature Reviews. Disease Primers, 2, 16065. doi: 10.1038/nrdp.2016.65CrossRefGoogle ScholarPubMed
Pezawas, L., Verchinski, B. A., Mattay, V. S., Callicott, J. H., Kolachana, B. S., Straub, R. E., … Weinberger, D. R. (2004). The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. The Journal of Neuroscience, 24(45), 10099–10102. doi: 10.1523/jneurosci.2680-04.2004CrossRefGoogle ScholarPubMed
Price, R. B., Gates, K., Kraynak, T. E., Thase, M. E., & Siegle, G. J. (2017a). Data-driven subgroups in depression derived from directed functional connectivity paths at rest. Neuropsychopharmacology, 42(13), 2623–2632. doi: 10.1038/npp.2017.97CrossRefGoogle ScholarPubMed
Price, R. B., Lane, S., Gates, K., Kraynak, T. E., Horner, M. S., Thase, M. E., & Siegle, G. J. (2017b). Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biological Psychiatry, 81(4), 347–357. doi: 10.1016/j.biopsych.2016.06.023CrossRefGoogle ScholarPubMed
Rashidi-Ranjbar, N., Rajji, T. K., Kumar, S., Herrmann, N., Mah, L., Flint, A. J., … Dickie, E. W. (2020). Frontal-executive and corticolimbic structural brain circuitry in older people with remitted depression, mild cognitive impairment, Alzheimer's dementia, and normal cognition. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology, 45(9), 1567–1578. doi: 10.1038/s41386-020-0715-yCrossRefGoogle ScholarPubMed
Ravindran, A., Richter, M., Jain, T., Ravindran, L., Rector, N., & Farb, N. (2020). Functional connectivity in obsessive-compulsive disorder and its subtypes. Psychological Medicine, 50(7), 1173–1181. doi: 10.1017/s0033291719001090CrossRefGoogle ScholarPubMed
Schmaal, L., Hibar, D. P., Sämann, P. G., Hall, G. B., Baune, B. T., Jahanshad, N., … Tiemeier, H. (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. Molecular Psychiatry, 22(6), 900–909. doi: 10.1038/mp.2016.60CrossRefGoogle ScholarPubMed
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage, 82, 403–415. doi: 10.1016/j.neuroimage.2013.05.081CrossRefGoogle Scholar
Sun, X., Liu, J., Ma, Q., Duan, J., Wang, X., Xu, Y., … Xia, M. (2021). Disrupted intersubject variability architecture in functional connectomes in schizophrenia. Schizophrenia Bulletin, 47(3), 837–848. doi: 10.1093/schbul/sbaa155CrossRefGoogle ScholarPubMed
Voineskos, A. N., Jacobs, G. R., & Ameis, S. H. (2020). Neuroimaging heterogeneity in psychosis: Neurobiological underpinnings and opportunities for prognostic and therapeutic innovation. Biological Psychiatry, 88(1), 95–102. doi: 10.1016/j.biopsych.2019.09.004CrossRefGoogle ScholarPubMed
Watanabe, K., Kakeda, S., Katsuki, A., Ueda, I., Ikenouchi, A., Yoshimura, R., & Korogi, Y. (2020). Whole-brain structural covariance network abnormality in first-episode and drug-naïve major depressive disorder. Psychiatry Research. Neuroimaging, 300, 111083. doi: 10.1016/j.pscychresns.2020.111083CrossRefGoogle ScholarPubMed
Wolfers, T., Doan, N. T., Kaufmann, T., Alnæs, D., Moberget, T., Agartz, I., … Marquand, A. F. (2018). Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry, 75(11), 1146–1155. doi: 10.1001/jamapsychiatry.2018.2467CrossRefGoogle ScholarPubMed
Xia, J., Fan, J., Liu, W., Du, H., Zhu, J., Yi, J., … Zhu, X. (2020). Functional connectivity within the salience network differentiates autogenous – From reactive-type obsessive-compulsive disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 98, 109813. doi: 10.1016/j.pnpbp.2019.109813CrossRefGoogle ScholarPubMed
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670. doi: 10.1038/nmeth.1635CrossRefGoogle ScholarPubMed
Yoo, S. Y., Roh, M. S., Choi, J. S., Kang, D. H., Ha, T. H., Lee, J. M., … Kwon, J. S. (2008). Voxel-based morphometry study of gray matter abnormalities in obsessive-compulsive disorder. Journal of Korean Medical Science, 23(1), 24–30. doi: 10.3346/jkms.2008.23.1.24CrossRefGoogle ScholarPubMed
Yu, M., Linn, K. A., Shinohara, R. T., Oathes, D. J., Cook, P. A., Duprat, R., & Moore, T. M. (2019). Childhood trauma history is linked to abnormal brain connectivity in major depression. Proceedings of the National Academy of Sciences of the United States of America, 116(17), 8582–8590. doi: 10.1073/pnas.1900801116CrossRefGoogle ScholarPubMed
Yun, J. Y., Jang, J. H., Kim, S. N., Jung, W. H., & Kwon, J. S. (2015). Neural correlates of response to pharmacotherapy in obsessive-compulsive disorder: Individualized cortical morphology-based structural covariance. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 63, 126–133. doi: 10.1016/j.pnpbp.2015.06.009CrossRefGoogle ScholarPubMed
Yun, J. Y., & Kim, Y. K. (2021). Phenotype network and brain structural covariance network of major depression. Advances in Experimental Medicine and Biology, 1305, 3–18. doi: 10.1007/978-981-33-6044-0_1CrossRefGoogle ScholarPubMed
Zhao, Y. J., Du, M. Y., Huang, X. Q., Lui, S., Chen, Z. Q., Liu, J., … Gong, Q. Y. (2014). Brain grey matter abnormalities in medication-free patients with major depressive disorder: A meta-analysis. Psychological Medicine, 44(14), 2927–2937. doi: 10.1017/s0033291714000518CrossRefGoogle ScholarPubMed
Supplementary material: File

Han et al. supplementary material

Figures S1-S3

Download Han et al. supplementary material(File)
File 299.1 KB