Skip to main content Accessibility help
×
Home
Hostname: page-component-7f7b94f6bd-82ts8 Total loading time: 0.668 Render date: 2022-06-29T01:17:38.454Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium

Published online by Cambridge University Press:  15 February 2022

KangCheng Wang
Affiliation:
School of Psychology, Shandong Normal University, Jinan, Shandong, China
YuFei Hu
Affiliation:
School of Psychology, Shandong Normal University, Jinan, Shandong, China
ChaoGan Yan
Affiliation:
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China Department of Psychology, University of Chinese Academy of Sciences, Beijing, China Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
MeiLing Li
Affiliation:
Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA02129, USA
YanJing Wu
Affiliation:
Faculty of Foreign Languages, Ningbo University, Ningbo, Zhejiang, China
Jiang Qiu*
Affiliation:
Faculty of Psychology, Southwest University, Chongqing400716, China
XingXing Zhu*
Affiliation:
Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
*
Author for correspondence: Jiang Qiu, E-mail: qiuj318@swu.edu.cn; XingXing Zhu, E-mail: x.zhu.4@research.gla.ac.uk
Author for correspondence: Jiang Qiu, E-mail: qiuj318@swu.edu.cn; XingXing Zhu, E-mail: x.zhu.4@research.gla.ac.uk

Abstract

Background

Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices.

Methods

Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations.

Results

VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network.

Conclusions

Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

Type
Original Article
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.)

Footnotes

*

These authors contributed equally to this work.

References

Ancelin, M. L., Carriere, I., Artero, S., Maller, J., Meslin, C., Ritchie, K., … Chaudieu, I. (2019). Lifetime major depression and grey-matter volume. Journal of Psychiatry & Neuroscience, 44(1), 4553. doi: 10.1503/jpn.180026CrossRefGoogle ScholarPubMed
Aron, A. R., Robbins, T. W., & Poldrack, R. A. (2014). Inhibition and the right inferior frontal cortex: One decade on. Trends in Cognitive Sciences, 18(4), 177185. doi: 10.1016/j.tics.2013.12.003CrossRefGoogle Scholar
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95113. doi: 10.1016/j.neuroimage.2007.07.007CrossRefGoogle ScholarPubMed
Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 11291159. doi: 10.1162/neco.1995.7.6.1129CrossRefGoogle ScholarPubMed
Bewernick, B. H., Hurlemann, R., Matusch, A., Kayser, S., Grubert, C., Hadrysiewicz, B., … Schlaepfer, T. E. (2010). Nucleus accumbens deep brain stimulation decreases ratings of depression and anxiety in treatment-resistant depression. Biological Psychiatry, 67(2), 110116. doi: 10.1016/j.biopsych.2009.09.013CrossRefGoogle ScholarPubMed
Binnewies, J., Nawijn, L., van Tol, M. J., van der Wee, N. J. A., Veltman, D. J., & Penninx, B. (2021). Associations between depression, lifestyle and brain structure: A longitudinal MRI study. Neuroimage, 231, 117834. doi: 10.1016/j.neuroimage.2021.117834CrossRefGoogle ScholarPubMed
Bogoian, H. R., King, T. Z., Turner, J. A., Semmel, E. S., & Dotson, V. M. (2020). Linking depressive symptom dimensions to cerebellar subregion volumes in later life. Translational Psychiatry, 10(1), 201. doi: 10.1038/s41398-020-00883-6CrossRefGoogle ScholarPubMed
Burcusa, S. L., & Iacono, W. G. (2007). Risk for recurrence in depression. Clinical Psychology Review, 27(8), 959985. doi: 10.1016/j.cpr.2007.02.005CrossRefGoogle ScholarPubMed
Bush, G., Vogt, B. A., Holmes, J., Dale, A. M., Greve, D., Jenike, M. A., & Rosen, B. R. (2002). Dorsal anterior cingulate cortex: A role in reward-based decision making. Proceedings of the National Academy of Sciences of the USA, 99(1), 523528. doi: 10.1073/pnas.012470999CrossRefGoogle ScholarPubMed
Castro, E., Hjelm, R. D., Plis, S. M., Dinh, L., Turner, J. A., & Calhoun, V. D. (2016). Deep independence network analysis of structural brain imaging: Application to schizophrenia. IEEE Transactions on Medical Imaging, 35(7), 17291740. doi: 10.1109/TMI.2016.2527717CrossRefGoogle Scholar
Cheng, W., Rolls, E. T., Qiu, J., Yang, D. Y., Ruan, H. T., Wei, D. T., … Feng, J. F. (2018). Functional connectivity of the precuneus in unmedicated patients with depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(12), 10401049. doi: 10.1016/j.bpsc.2018.07.008Google ScholarPubMed
Depping, M. S., Wolf, N. D., Vasic, N., Sambataro, F., Thomann, P. A., & Wolf, R. C. (2016). Common and distinct structural network abnormalities in major depressive disorder and borderline personality disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 65, 127133. doi: 10.1016/j.pnpbp.2015.09.007CrossRefGoogle ScholarPubMed
Desseilles, M., Balteau, E., Sterpenich, V., Dang-Vu, T. T., Darsaud, A., Vandewalle, G., … Schwartz, S. (2009). Abnormal neural filtering of irrelevant visual information in depression. Journal of Neuroscience, 29(5), 13951403. doi: 10.1523/JNEUROSCI.3341-08.2009CrossRefGoogle ScholarPubMed
Disner, S. G., Beevers, C. G., Haigh, E. A., & Beck, A. T. (2011). Neural mechanisms of the cognitive model of depression. Nature Reviews Neuroscience, 12(8), 467477. doi: 10.1038/nrn3027CrossRefGoogle ScholarPubMed
Dixon, M. L., Thiruchselvam, R., Todd, R., & Christoff, K. (2017). Emotion and the prefrontal cortex: An integrative review. Psychological Bulletin, 143(10), 10331081. doi: 10.1037/bul0000096CrossRefGoogle Scholar
D'Mello, A. M., Gabrieli, J. D. E., & Nee, D. E. (2020). Evidence for hierarchical cognitive control in the human cerebellum. Current Biology, 30(10), 18811892. doi: 10.1016/j.cub.2020.03.028CrossRefGoogle ScholarPubMed
Enneking, V., Leehr, E. J., Dannlowski, U., & Redlich, R. (2020). Brain structural effects of treatments for depression and biomarkers of response: A systematic review of neuroimaging studies. Psychological Medicine, 50(2), 187209. doi: 10.1017/S0033291719003660CrossRefGoogle ScholarPubMed
Fitzgerald, P. B., Laird, A. R., Maller, J., & Daskalakis, Z. J. (2008). A meta-analytic study of changes in brain activation in depression. Human Brain Mapping, 29(6), 683695. doi: 10.1002/hbm.20426CrossRefGoogle ScholarPubMed
Gong, Q. Y., & He, Y. (2015). Depression, neuroimaging and connectomics: A selective overview. Biological Psychiatry, 77(3), 223235. doi: 10.1016/j.biopsych.2014.08.009CrossRefGoogle ScholarPubMed
Gupta, C. N., Calhoun, V. D., Rachakonda, S., Chen, J., Patel, V., Liu, J., … Turner, J. A. (2015). Patterns of gray matter abnormalities in schizophrenia based on an international mega-analysis. Schizophrenia Bulletin, 41(5), 11331142. doi: 10.1093/schbul/sbu177CrossRefGoogle Scholar
Gupta, C. N., Turner, J. A., & Calhoun, V. D. (2019). Source-based morphometry: A decade of covarying structural brain patterns. Brain Structure and Function, 224(9), 30313044. doi: 10.1007/s00429-019-01969-8CrossRefGoogle ScholarPubMed
Haber, S. (2008). Parallel and integrative processing through the basal ganglia reward circuit: Lessons from addiction. Biological Psychiatry, 64(3), 173174. doi: 10.1016/j.biopsych.2008.05.033CrossRefGoogle 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
Harenski, C. L., Harenski, K. A., Calhoun, V. D., & Kiehl, K. A. (2020). Source-based morphometry reveals gray matter differences related to suicidal behavior in criminal offenders. Brain Imaging and Behavior, 14(1), 19. doi: 10.1007/s11682-018-9957-2CrossRefGoogle ScholarPubMed
Jahn, A., Nee, D. E., Alexander, W. H., & Brown, J. W. (2016). Distinct regions within medial prefrontal cortex process pain and cognition. Journal of Neuroscience, 36(49), 1238512392. doi: 10.1523/JNEUROSCI.2180-16.2016CrossRefGoogle 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), 603611. doi: 10.1001/jamapsychiatry.2015.0071CrossRefGoogle ScholarPubMed
Kakeda, S., Watanabe, K., Nguyen, H., Katsuki, A., Sugimoto, K., Igata, N., … Korogi, Y. (2020). An independent component analysis reveals brain structural networks related to TNF-alpha in drug-naive, first-episode major depressive disorder: A source-based morphometric study. Translational Psychiatry, 10(1), 187. doi: 10.1038/s41398-020-00873-8CrossRefGoogle ScholarPubMed
Kandilarova, S., Stoyanov, D., Sirakov, N., Maes, M., & Specht, K. (2019). Reduced grey matter volume in frontal and temporal areas in depression: Contributions from voxel-based morphometry study. Acta Neuropsychiatrica, 31(5), 252257. doi: 10.1017/neu.2019.20CrossRefGoogle ScholarPubMed
Kessler, R. C., Angermeyer, M., Anthony, J. C., De Graaf, R., Demyttenaere, K., Gasquet, I., … Ustun, T. B. (2007). Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization's World Mental Health Survey Initiative. World Psychiatry, 6(3), 168176.Google ScholarPubMed
Kocsis, K., Holczer, A., Kazinczi, C., Boross, K., Horvath, R., Nemeth, L. V., … Must, A. (2021). Voxel-based asymmetry of the regional gray matter over the inferior temporal gyrus correlates with depressive symptoms in medicated patients with major depressive disorder. Psychiatry Research: Neuroimaging, 317, 111378. doi: 10.1016/j.pscychresns.2021.111378CrossRefGoogle ScholarPubMed
Kunst, J., Marecek, R., Klobusiakova, P., Balazova, Z., Anderkova, L., Nemcova-Elfmarkova, N., & Rektorova, I. (2019). Patterns of grey matter atrophy at different stages of Parkinson's and Alzheimer's diseases and relation to cognition. Brain Topography, 32(1), 142160. doi: 10.1007/s10548-018-0675-2CrossRefGoogle Scholar
Li, B. J., Friston, K., Mody, M., Wang, H. N., Lu, H. B., & Hu, D. W. (2018). A brain network model for depression: From symptom understanding to disease intervention. CNS Neuroscience & Therapeutics, 24(11), 10041019. doi: 10.1111/cns.12998CrossRefGoogle ScholarPubMed
Li, J., Seidlitz, J., Suckling, J., Fan, F., Ji, G. J., Meng, Y., … Liao, W. (2021). Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures. Nature Communications, 12(1), 1647. doi: 10.1038/s41467-021-21943-5CrossRefGoogle ScholarPubMed
Li, Q., Zhao, Y. J., Chen, Z. Q., Long, J. Y., Dai, J., Huang, X. Q., … Gong, Q. Y. (2020). Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder. Neuropsychopharmacology, 45(4), 703712. doi: 10.1038/s41386-019-0563-9CrossRefGoogle ScholarPubMed
Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15(10), 483506. doi: 10.1016/j.tics.2011.08.003CrossRefGoogle ScholarPubMed
Miller, E. K. (2000). The prefrontal cortex and cognitive control. Nature Review Neuroscience, 1(1), 5965. doi: 10.1038/35036228CrossRefGoogle ScholarPubMed
Murty, V. P., Ritchey, M., Adcock, R. A., & LaBar, K. S. (2010). fMRI studies of successful emotional memory encoding: A quantitative meta-analysis. Neuropsychologia, 48(12), 34593469. doi: 10.1016/j.neuropsychologia.2010.07.030CrossRefGoogle ScholarPubMed
Nguyen, L., Kakeda, S., Watanabe, K., Katsuki, A., Sugimoto, K., Igata, N., … Yoshimura, R. (2020). Brain structural network alterations related to serum cortisol levels in drug-naive, first-episode major depressive disorder patients: A source-based morphometric study. Scientific Reports, 10(1), 22096. doi: 10.1038/s41598-020-79220-2CrossRefGoogle ScholarPubMed
Nobbelin, L., Bogren, M., Mattisson, C., & Bradvik, L. (2018). Risk factors for recurrence in depression in the Lundby population, 1947-1997. Journal of Affective Disorders, 228, 125131. doi: 10.1016/j.jad.2017.11.038CrossRefGoogle Scholar
Okamoto, N., Watanabe, K., Ngyuyen, L., Ikenouchi, A., Kishi, T., Iwata, N., … Yoshimura, R. (2020). Association of serum kynurenine levels and neural networks in patients with first-episode, drug-naive major depression: A source-based morphometry study. Neuropsychiatric Disease and Treatment, 16, 25692577. doi: 10.2147/NDT.S279622CrossRefGoogle ScholarPubMed
Pappaianni, E., Siugzdaite, R., Vettori, S., Venuti, P., Job, R., & Grecucci, A. (2018). Three shades of grey: Detecting brain abnormalities in children with autism using source-, voxel- and surface-based morphometry. European Journal of Neuroscience, 47(6), 690700. doi: 10.1111/ejn.13704CrossRefGoogle ScholarPubMed
Paquola, C., Bennett, M. R., & Lagopoulos, J. (2018). Structural and functional connectivity underlying gray matter covariance: Impact of developmental insult. Brain Connectivity, 8(5), 299310. doi: 10.1089/brain.2018.0584CrossRefGoogle ScholarPubMed
Pierce, J. E., & Peron, J. (2020). The basal ganglia and the cerebellum in human emotion. Social Cognitive and Affective Neuroscience, 15(5), 599613. doi: 10.1093/scan/nsaa076CrossRefGoogle ScholarPubMed
Rice, F., Riglin, L., Lomax, T., Souter, E., Potter, R., Smith, D. J., … Thapar, A. (2019). Adolescent and adult differences in major depression symptom profiles. Journal of Affective Disorders, 243, 175181. doi: 10.1016/j.jad.2018.09.015CrossRefGoogle ScholarPubMed
Rohr, C. S., Vinette, S. A., Parsons, K. A. L., Cho, I. Y. K., Dimond, D., Benischek, A., … Bray, S. (2017). Functional connectivity of the dorsal attention network predicts selective attention in 4–7 year-old girls. Cerebral Cortex, 27(9), 43504360. doi: 10.1093/cercor/bhw236Google ScholarPubMed
Rolls, E. T., Cheng, W., Du, J., Wei, D., Qiu, J., Dai, D., … Feng, J. (2020). Functional connectivity of the right inferior frontal gyrus and orbitofrontal cortex in depression. Social Cognitive and Affective Neuroscience, 15(1), 7586. doi: 10.1093/scan/nsaa014CrossRefGoogle ScholarPubMed
Scangos, K. W., Khambhati, A. N., Daly, P. M., Makhoul, G. S., Sugrue, L. P., Zamanian, H., … Chang, E. F. (2021). Closed-loop neuromodulation in an individual with treatment-resistant depression. Nature Medicine, 27(10), 16961700. doi: 10.1038/s41591-021-01480-wCrossRefGoogle Scholar
Schmaal, L., Hibar, D. P., Samann, P. G., Hall, G. B., Baune, B. T., Jahanshad, N., … Veltman, D. J. (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), 900909. doi: 10.1038/mp.2016.60CrossRefGoogle ScholarPubMed
Schmaal, L., Pozzi, E., Tiffany, C. H., van Velzen, L. S., Veer, I. M., Opel, N., … Veltman, D. J. (2020). ENIGMA MDD: Seven years of global neuroimaging studies of major depression through worldwide data sharing. Translational Psychiatry, 10(1), 172. doi: 10.1038/s41398-020-0842-6CrossRefGoogle ScholarPubMed
Schmaal, L., Veltman, D. J., van Erp, T. G., Samann, P. G., Frodl, T., Jahanshad, N., … Hibar, D. P. (2016). Subcortical brain alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group. Molecular Psychiatry, 21(6), 806812. doi: 10.1038/mp.2015.69CrossRefGoogle ScholarPubMed
Schneier, F. R., Slifstein, M., Whitton, A. E., Pizzagalli, D. A., Reinen, J., McGrath, P. J., … Abi-Dargham, A. (2018). Dopamine release in antidepressant-naive major depressive disorder: A multimodal [11C]-(+)-PHNO positron emission tomography and functional magnetic resonance imaging study. Biological Psychiatry, 84(8), 563573. doi: 10.1016/j.biopsych.2018.05.014CrossRefGoogle ScholarPubMed
Schultz, W., Tremblay, L., & Hollerman, J. R. (2000). Reward processing in primate orbitofrontal cortex and basal ganglia. Cerebral Cortex, 10(3), 272284. doi: 10.1093/cercor/10.3.272CrossRefGoogle ScholarPubMed
Serra-Blasco, M., Radua, J., Soriano-Mas, C., Gomez-Benlloch, A., Porta-Casteras, D., Carulla-Roig, M., … Cardoner, N. (2021). Structural brain correlates in major depression, anxiety disorders and post-traumatic stress disorder: A voxel-based morphometry meta-analysis. Neuroscience & Biobehavioral Reviews, 129, 269281. doi: 10.1016/j.neubiorev.2021.07.002CrossRefGoogle ScholarPubMed
Shen, X., MacSweeney, N., Chan, S. W. Y., Barbu, M. C., Adams, M. J., Lawrie, S. M., … Whalley, H. C. (2021). Brain structural associations with depression in a large early adolescent sample (the ABCD study(R)). eClinicalMedicine, 42, 101204. doi: 10.1016/j.eclinm.2021.101204CrossRefGoogle Scholar
Singh, A., Arya, A., Agarwal, V., Shree, R., & Kumar, U. (2022). Grey and white matter alteration in euthymic children with bipolar disorder: A combined source-based morphometry (SBM) and voxel-based morphometry (VBM) study. Brain Imaging and Behavior, 16(1), 2230. doi: 10.1007/s11682-021-00473-0.CrossRefGoogle ScholarPubMed
Sokolov, A. A., Miall, R. C., & Ivry, R. B. (2017). The cerebellum: Adaptive prediction for movement and cognition. Trends in Cognitive Sciences, 21(5), 313332. doi: 10.1016/j.tics.2017.02.005CrossRefGoogle ScholarPubMed
Spreng, R. N., & Turner, G. R. (2013). Structural covariance of the default network in healthy and pathological aging. Journal of Neuroscience, 33(38), 1522615234. doi: 10.1523/JNEUROSCI.2261-13.2013CrossRefGoogle ScholarPubMed
Suarez, L. E., Markello, R. D., Betzel, R. F., & Misic, B. (2020). Linking structure and function in macroscale brain networks. Trends in Cognitive Sciences, 24(4), 302315. doi: 10.1016/j.tics.2020.01.008CrossRefGoogle ScholarPubMed
Sullivan, C. R. P., Olsen, S., & Widge, A. S. (2021). Deep brain stimulation for psychiatric disorders: From focal brain targets to cognitive networks. Neuroimage, 225, 117515. doi: 10.1016/j.neuroimage.2020.117515CrossRefGoogle ScholarPubMed
Wang, L., LaBar, K. S., Smoski, M., Rosenthal, M. Z., Dolcos, F., Lynch, T. R., … McCarthy, G. (2008). Prefrontal mechanisms for executive control over emotional distraction are altered in major depression. Psychiatry Research, 163(2), 143155. doi: 10.1016/j.pscychresns.2007.10.004CrossRefGoogle ScholarPubMed
Wang, T., Wang, K., Qu, H., Zhou, J., Li, Q., Deng, Z., … Xie, P. (2016). Disorganized cortical thickness covariance network in major depressive disorder implicated by aberrant hubs in large-scale networks. Scientific Reports, 6, 27964. doi: 10.1038/srep27964CrossRefGoogle 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-naive major depressive disorder. Psychiatry Research: Neuroimaging, 300, 111083. doi: 10.1016/j.pscychresns.2020.111083CrossRefGoogle ScholarPubMed
Wolf, R. C., Nolte, H. M., Hirjak, D., Hofer, S., Seidl, U., Depping, M. S., … Thomann, P. A. (2016). Structural network changes in patients with major depression and schizophrenia treated with electroconvulsive therapy. European Neuropsychopharmacology, 26(9), 14651474. doi: 10.1016/j.euroneuro.2016.06.008CrossRefGoogle ScholarPubMed
Wu, H. W., Sun, H., Wang, C., Yu, L., Li, Y. L., Peng, H. J., … Wang, J. J. (2017). Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder. Journal of Psychiatric Research, 84, 237242. doi: 10.1016/j.jpsychires.2016.10.001CrossRefGoogle ScholarPubMed
Xu, L., Groth, K. M., Pearlson, G., Schretlen, D. J., & Calhoun, V. D. (2009). Source-based morphometry: The use of independent component analysis to identify gray matter differences with application to schizophrenia. Human Brain Mapping, 30(3), 711724. doi: 10.1002/hbm.20540CrossRefGoogle Scholar
Yan, C. G., Chen, X., Li, L., Castellanos, F. X., Bai, T. J., Bo, Q. J., … Zang, Y. F. (2019). Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proceedings of the National Academy of Sciences of the USA, 116(18), 90789083. doi: 10.1073/pnas.1900390116CrossRefGoogle ScholarPubMed
Yan, C. G., & Zang, Y. F. (2010). DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13. doi: 10.3389/fnsys.2010.00013Google Scholar
Yang, X., Kumar, P., Nickerson, L. D., Du, Y., Wang, M., Chen, Y., … Ma, X. (2021). Identifying subgroups of major depressive disorder using brain structural covariance networks and mapping of associated clinical and cognitive variables. Biological Psychiatry Global Open Science, 1(2), 135145.CrossRefGoogle Scholar
Yoon, Y. B., Shin, W. G., Lee, T. Y., Hur, J. W., Cho, K. I. K., Sohn, W. S., … Kwon, J. S. (2017). Brain structural networks associated with intelligence and visuomotor ability. Scientific Reports, 7(1), 2177. doi: 10.1038/s41598-017-02304-zCrossRefGoogle ScholarPubMed
Zhou, H. X., Chen, X., Shen, Y. Q., Li, L., Chen, N. X., Zhu, Z. C., … Yan, C. G. (2020). Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression. Neuroimage, 206, 116287. doi: 10.1016/j.neuroimage.2019.116287CrossRefGoogle ScholarPubMed
Zielinski, B. A., Gennatas, E. D., Zhou, J., & Seeley, W. W. (2010). Network-level structural covariance in the developing brain. Proceedings of the National Academy of Sciences of the USA, 107(42), 1819118196. doi: 10.1073/pnas.1003109107CrossRefGoogle ScholarPubMed
Supplementary material: File

Wang et al. supplementary material

Wang et al. supplementary material

Download Wang et al. supplementary material(File)
File 1 MB

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *