Hostname: page-component-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-05-31T17:22:10.681Z Has data issue: false hasContentIssue false
  • English
  • Français

Altered topology of individual brain structural covariance networks in major depressive disorder

Published online by Cambridge University Press:  10 July 2023

Liangliang Ping Open the ORCID record for Liangliang Ping [Opens in a new window] ,
Shan Sun ,
Cong Zhou ,
Jianyu Que ,
Zhiyi You ,
Xiufeng Xu ,
Yuqi Cheng Open the ORCID record for Yuqi Cheng [Opens in a new window]  and
the DIRECT Consortium
Liangliang Ping
Affiliation:
Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
Shan Sun
Affiliation:
Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
Cong Zhou
Affiliation:
School of Mental Health, Jining Medical University, Jining, China
Jianyu Que
Affiliation:
Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
Zhiyi You
Affiliation:
Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
Xiufeng Xu
Affiliation:
Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
Yuqi Cheng*
Affiliation:
Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
*
Corresponding author: Yuqi Cheng; Email: yuqicheng@126.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks.

Methods

We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics.

Results

Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex.

Conclusions

The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.

Keywords

individual structural covariance networkmajor depressive disordertopological metrics
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 14 , October 2023 , pp. 6921 - 6932
Check for updates
Copyright
Copyright © The Author(s), 2023. 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

The original version of this article was published with an error in the Financial Support section. A notice detailing this has been published and the errors rectified in the online PDF and HTML version.

*

Contributed equally to this work.

References

Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20. doi: 10.1016/j.tics.2017.10.001CrossRefGoogle ScholarPubMed
Bartlett, E. A., DeLorenzo, C., Sharma, P., Yang, J., Zhang, M., Petkova, E., … Parsey, R. V. (2018). Pretreatment and early-treatment cortical thickness is associated with SSRI treatment response in major depressive disorder. Neuropsychopharmacology, 43(11), 2221–2230. doi: 10.1038/s41386-018-0122-9CrossRefGoogle ScholarPubMed
Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., … Schonberg, T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582(7810), 84–88. doi: 10.1038/s41586-020-2314-9CrossRefGoogle ScholarPubMed
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376. doi: 10.1038/nrn3475CrossRefGoogle ScholarPubMed
Chen, C., Liu, Z., Xi, C., Tan, W., Fan, Z., Cheng, Y., … Yang, J. (2022a). Multimetric structural covariance in first-episode major depressive disorder: A graph theoretical analysis. Journal of Psychiatry and Neuroscience, 47(3), E176–E185. doi: 10.1503/jpn.210204CrossRefGoogle ScholarPubMed
Chen, T., Kendrick, K. M., Wang, J., Wu, M., Li, K., Huang, X., … Gong, Q. (2017). Anomalous single-subject based morphological cortical networks in drug-naive, first-episode major depressive disorder. Human Brain Mapping, 38(5), 2482–2494. doi: 10.1002/hbm.23534CrossRefGoogle ScholarPubMed
Chen, X., Lu, B., Li, H.-X., Li, X.-Y., Wang, Y.-W., Castellanos, F. X., … Yan, C.-G. (2022b). The DIRECT consortium and the REST-meta-MDD project: Towards neuroimaging biomarkers of major depressive disorder. Psychoradiology, 2(1), 32–42. doi: 10.1093/psyrad/kkac005CrossRefGoogle Scholar
de la Salud, O. M. (2017). Depression and other common mental disorders: Global health estimates. World Health Organization, 1–24. Retrieved from https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf?sequence=1%0Ahttp://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf;jsessionid=0886B5297E6F5A04AA4F2F2FD5FE36F9?sequence=1%0Ahttp://apps.who.int/Google Scholar
Enneking, V., Leehr, E. J., Dannlowski, U., & Redlich, R. (2019). Brain structural effects of treatments for depression and biomarkers of response: A systematic review of neuroimaging studies. Psychological Medicine, 50(2), 187–209. doi: 10.1017/S0033291719003660CrossRefGoogle ScholarPubMed
Gao, J., Chen, M., Xiao, D., Li, Y., Zhu, S., Li, Y., … Wang, J. (2022). Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network. Cerebral Cortex, 33(6), 2415–2425. doi: 10.1093/cercor/bhac217CrossRefGoogle Scholar
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
Gray, J. P., Müller, V. I., Eickhoff, S. B., & Fox, P. T. (2020). Multimodal abnormalities of brain structure and function in major depressive disorder: A meta-analysis of neuroimaging studies. American Journal of Psychiatry, 177(5), 422–434. doi: 10.1176/appi.ajp.2019.19050560CrossRefGoogle ScholarPubMed
Grimm, S., Beck, J., Schuepbach, D., Hell, D., Boesiger, P., Bermpohl, F., … Northoff, G. (2008). Imbalance between left and right dorsolateral prefrontal cortex in major depression is linked to negative emotional judgment: An fMRI study in severe major depressive disorder. Biological Psychiatry, 63(4), 369–376. doi: 10.1016/j.biopsych.2007.05.033CrossRefGoogle ScholarPubMed
Han, S., Chen, Y., Zheng, R., Li, S., Jiang, Y., Wang, C., … Cheng, J. (2021). 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., Xue, K., Chen, Y., Xu, Y., Li, S., Song, X., … Cheng, J. (2023). Identification of shared and distinct patterns of brain network abnormality across mental disorders through individualized structural covariance network analysis. Psychological Medicine, 1–12. doi: 10.1017/s0033291723000302Google ScholarPubMed
He, Y., Chen, Z. J., & Evans, A. C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17(10), 2407–2419. doi: 10.1093/cercor/bhl149CrossRefGoogle ScholarPubMed
Hiser, J., & Koenigs, M. (2018). The multifaceted role of the ventromedial prefrontal cortex in emotion, decision making, social cognition, and psychopathology. Biological Psychiatry, 83(8), 638–647. doi: 10.1016/j.biopsych.2017.10.030CrossRefGoogle ScholarPubMed
Huang, S. Y., Hsu, J. L., Lin, K. J., & Hsiao, I. T. (2020). A novel individual metabolic brain network for 18F-FDG PET imaging. Frontiers in Neuroscience, 14(May), 1–11. doi: 10.3389/fnins.2020.00344CrossRefGoogle Scholar
Jiang, Y., Yao, D., Zhou, J., Tan, Y., Huang, H., Wang, M. L., … Luo, C. (2022). Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia. Psychological Medicine, 52(7), 1333–1343. doi: 10.1017/S0033291720003141CrossRefGoogle ScholarPubMed
Kang, S. G., & Cho, S. E. (2020). Neuroimaging biomarkers for predicting treatment response and recurrence of major depressive disorder. International Journal of Molecular Sciences, 21(6), 2148. doi: 10.3390/ijms21062148CrossRefGoogle ScholarPubMed
Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., … Borgwardt, S. (2018). Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: A multimodal, multisite machine learning analysis. JAMA Psychiatry, 75(11), 1156–1172. doi: 10.1001/jamapsychiatry.2018.2165CrossRefGoogle ScholarPubMed
Larivière, S., Royer, J., Rodríguez-Cruces, R., Paquola, C., Caligiuri, M. E., Gambardella, A., … Bernhardt, B. C. (2022). Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression. Nature Communications, 13(1), 4320. doi: 10.1038/s41467-022-31730-5CrossRefGoogle Scholar
Lemke, H., Klute, H., Skupski, J., Thiel, K., Waltemate, L., Winter, A., … Dannlowski, U. (2022). Brain structural correlates of recurrence following the first episode in patients with major depressive disorder. Translational Psychiatry, 12(1), 349. doi: 10.1038/s41398-022-02113-7CrossRefGoogle ScholarPubMed
LeMoult, J., Battaglini, A. M., Grocott, B., Jopling, E., Rnic, K., & Yang, L. (2022). Advances in stress and depression research. Current Opinion in Psychiatry, 36(1), 8–13. doi: 10.1097/yco.0000000000000831CrossRefGoogle ScholarPubMed
Li, H., Yang, J., Yin, L., Zhang, H., Zhang, F., Chen, Z., … Gong, Q. (2021a). Alteration of single-subject gray matter networks in major depressed patients with suicidality. Journal of Magnetic Resonance Imaging, 54(1), 215–224. doi: 10.1002/jmri.27499CrossRefGoogle ScholarPubMed
Li, J., Seidlitz, J., Suckling, J., Fan, F., Ji, G. J., Meng, Y., … Liao, W. (2021b). 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, W., Yang, C., Shi, F., Wu, S., Wang, Q., Nie, Y., & Zhang, X. (2017). Construction of individual morphological brain networks with multiple morphometric features. Frontiers in Neuroanatomy, 11(April), 1–14. doi: 10.3389/FNANA.2017.00034CrossRefGoogle ScholarPubMed
Li, Y., Wang, N., Wang, H., Lv, Y., Zou, Q., & Wang, J. (2021c). Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability. NeuroImage, 235(August 2020), 118018. doi: 10.1016/j.neuroimage.2021.118018CrossRefGoogle ScholarPubMed
Liao, X., Vasilakos, A. V., & He, Y. (2017). Small-world human brain networks: Perspectives and challenges. Neuroscience and Biobehavioral Reviews, 77, 286–300. doi: 10.1016/j.neubiorev.2017.03.018CrossRefGoogle ScholarPubMed
Libedinsky, I., Helwegen, K., Dannlowski, U., Fornito, A., Repple, J., Zalesky, A., … van den Heuvel, M. P. (2022). Reproducibility of neuroimaging studies of brain disorders with hundreds – not thousands – of participants. BioRxiv, 2022.07.05.498443. doi: 10.1101/2022.07.05.498443CrossRefGoogle Scholar
Lin, Y. H., Young, I. M., Conner, A. K., Glenn, C. A., Chakraborty, A. R., Nix, C. E., … Sughrue, M. E. (2020). Anatomy and white matter connections of the inferior temporal gyrus. World Neurosurgery, 143, e656–e666. doi: 10.1016/j.wneu.2020.08.058CrossRefGoogle ScholarPubMed
Liu, W., Mao, Y., Wei, D., Yang, J., Du, X., Xie, P., & Qiu, J. (2016). Structural asymmetry of dorsolateral prefrontal cortex correlates with depressive symptoms: Evidence from healthy individuals and patients with major depressive disorder. Neuroscience Bulletin, 32(3), 217–226. doi: 10.1007/s12264-016-0025-xCrossRefGoogle ScholarPubMed
Liu, Z., Palaniyappan, L., Wu, X., Zhang, K., Du, J., Zhao, Q., … Feng, J. (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
Ma, Y. (2015). Neuropsychological mechanism underlying antidepressant effect: A systematic meta-analysis. Molecular Psychiatry, 20(3), 311–319. doi: 10.1038/mp.2014.24CrossRefGoogle ScholarPubMed
Malhi, G. S., & Mann, J. J. (2018). Depression. Lancet, 392(10161), 2299–2312. doi: 10.1016/S0140-6736(18)31948-2CrossRefGoogle ScholarPubMed
Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., … Dosenbach, N. U. F. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603(7902), 654–660. doi: 10.1038/s41586-022-04492-9CrossRefGoogle ScholarPubMed
Neufeld, N. H., Kaczkurkin, A. N., Sotiras, A., Mulsant, B. H., Dickie, E. W., Flint, A. J., … Voineskos, A. N. (2020). Structural brain networks in remitted psychotic depression. Neuropsychopharmacology, 45(7), 1223–1231. doi: 10.1038/s41386-020-0646-7CrossRefGoogle ScholarPubMed
Nogovitsyn, N., Muller, M., Souza, R., Hassel, S., Arnott, S. R., Davis, A. D., … MacQueen, G. M. (2020). Hippocampal tail volume as a predictive biomarker of antidepressant treatment outcomes in patients with major depressive disorder: A CAN-BIND report. Neuropsychopharmacology, 45(2), 283–291. doi: 10.1038/s41386-019-0542-1CrossRefGoogle ScholarPubMed
Rolls, E. T., Joliot, M., & Tzourio-Mazoyer, N. (2015). Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. NeuroImage, 122, 1–5. doi: 10.1016/j.neuroimage.2015.07.075CrossRefGoogle ScholarPubMed
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. doi: 10.1016/j.neuroimage.2009.10.003CrossRefGoogle ScholarPubMed
Schmaal, L., Hibar, D. P., Sämann, 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), 900–909. doi: 10.1038/mp.2016.60CrossRefGoogle ScholarPubMed
Schmaal, L., Pozzi, E., C Ho, T., 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. M., Smann, 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), 806–812. doi: 10.1038/mp.2015.69CrossRefGoogle ScholarPubMed
Seidlitz, J., Váša, F., Shinn, M., Romero-Garcia, R., Whitaker, K. J., Vértes, P. E., … Bullmore, E. T. (2018). Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron, 97(1), 231–247, e7. doi: 10.1016/j.neuron.2017.11.039CrossRefGoogle ScholarPubMed
Sha, Z., Xia, M., Lin, Q., Cao, M., Tang, Y., Xu, K., … He, Y. (2018). Meta-connectomic analysis reveals commonly disrupted functional architectures in network modules and connectors across brain disorders. Cerebral Cortex, 28(12), 4179–4194. doi: 10.1093/cercor/bhx273CrossRefGoogle ScholarPubMed
Shen, X., Reus, L. M., Cox, S. R., Adams, M. J., Liewald, D. C., Bastin, M. E., … McIntosh, A. M. (2017). Subcortical volume and white matter integrity abnormalities in major depressive disorder: Findings from UK Biobank imaging data. Scientific Reports, 7(1), 5547. doi: 10.1038/s41598-017-05507-6CrossRefGoogle ScholarPubMed
Singh, M. K., Kesler, S. R., Hadi Hosseini, S. M., Kelley, R. G., Amatya, D., Hamilton, J. P., … Gotlib, I. H. (2013). Anomalous gray matter structural networks in major depressive disorder. Biological Psychiatry, 74(10), 777–785. doi: 10.1016/j.biopsych.2013.03.005CrossRefGoogle ScholarPubMed
Stroud, C. B., Davila, J., Hammen, C., & Vrshek-Schallhorn, S. (2011). Severe and nonsevere events in first onsets versus recurrences of depression: Evidence for stress sensitization. Journal of Abnormal Psychology, 120(1), 142–154. doi: 10.1037/a0021659CrossRefGoogle ScholarPubMed
Suárez, 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), 302–315. doi: 10.1016/j.tics.2020.01.008CrossRefGoogle ScholarPubMed
Suo, X., Lei, D., Li, L., Li, W., Dai, J., Wang, S., … Gong, Q. (2018). Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders. Journal of Psychiatry and Neuroscience, 43(6), 416–427. doi: 10.1503/jpn.170214CrossRefGoogle Scholar
Thiebaut de Schotten, M., & Forkel, S. J. (2022). The emergent properties of the connected brain. Science, 378(6619), 505–510. doi: 10.1126/science.abq2591CrossRefGoogle ScholarPubMed
Tijms, B. M., Seris, P., Willshaw, D. J., & Lawrie, S. M. (2012). Similarity-based extraction of individual networks from gray matter MRI scans. Cerebral Cortex, 22(7), 1530–1541. doi: 10.1093/cercor/bhr221CrossRefGoogle ScholarPubMed
Treadway, M. T., Waskom, M. L., Dillon, D. G., Holmes, A. J., Park, M. T. M., Chakravarty, M. M., … Pizzagalli, D. A. (2015). Illness progression, recent stress, and morphometry of hippocampal subfields and medial prefrontal cortex in major depression. Biological Psychiatry, 77(3), 285–294. doi: 10.1016/j.biopsych.2014.06.018CrossRefGoogle ScholarPubMed
van Velzen, L. S., Kelly, S., Isaev, D., Aleman, A., Aftanas, L. I., Bauer, J., … Schmaal, L. (2020). White matter disturbances in major depressive disorder: A coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. Molecular Psychiatry, 25(7), 1511–1525. doi: 10.1038/s41380-019-0477-2CrossRefGoogle ScholarPubMed
Vos De Wael, R., Royer, J., Tavakol, S., Wang, Y., Paquola, C., Benkarim, O., … Bernhardt, B. C. (2021). Structural connectivity gradients of the temporal lobe serve as multiscale axes of brain organization and cortical evolution. Cerebral Cortex, 31(11), 5151–5164. doi: 10.1093/cercor/bhab149CrossRefGoogle ScholarPubMed
Wang, H., Jin, X., Zhang, Y., & Wang, J. (2016). Single-subject morphological brain networks: Connectivity mapping, topological characterization and test-retest reliability. Brain and Behavior, 6(4), 1–21. doi: 10.1002/brb3.448CrossRefGoogle ScholarPubMed
Wang, R., Zhen, S., Zhou, C., & Yu, R. (2022). Acute stress promotes brain network integration and reduces state transition variability. Proceedings of the National Academy of Sciences of the USA, 119(24), 1–3. doi: 10.1073/pnas.2204144119Google 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(July 2019), 111083. doi: 10.1016/j.pscychresns.2020.111083CrossRefGoogle ScholarPubMed
Winter, N. R., Leenings, R., Ernsting, J., Sarink, K., Fisch, L., Emden, D., … Hahn, T. (2022). Quantifying deviations of brain structure and function in major depressive disorder across neuroimaging modalities. JAMA Psychiatry, 79(9), 879–888. doi: 10.1001/jamapsychiatry.2022.1780CrossRefGoogle ScholarPubMed
Xia, M., Si, T., Sun, X., Ma, Q., Liu, B., Wang, L., … He, Y. (2019). Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1434 individuals. NeuroImage, 189, 700–714. doi: 10.1016/j.neuroimage.2019.01.074CrossRefGoogle ScholarPubMed
Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: A network visualization tool for human brain connectomics. PLoS ONE, 8(7), e68910. doi: 10.1371/journal.pone.0068910CrossRefGoogle ScholarPubMed
Xiong, G., Dong, D., Cheng, C., Jiang, Y., Sun, X., He, J., … Yao, S. (2021). Potential structural trait markers of depression in the form of alterations in the structures of subcortical nuclei and structural covariance network properties. NeuroImage: Clinical, 32(November), 102871. doi: 10.1016/j.nicl.2021.102871CrossRefGoogle ScholarPubMed
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), 9078–9083. 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, H., Chen, X., Chen, Z. B., Li, L., Li, X. Y., Castellanos, F. X., … Yan, C. G. (2021). Disrupted intrinsic functional brain topology in patients with major depressive disorder. Molecular Psychiatry, 26(12), 7363–7371. doi: 10.1038/s41380-021-01247-2CrossRefGoogle ScholarPubMed
Yang, Y., Chen, Y., Sang, F., Zhao, S., Wang, J., Li, X., … Zhang, Z. (2022). Successful or pathological cognitive aging? Converging into a ‘frontal preservation, temporal impairment (FPTI)’ hypothesis. Science Bulletin, 67(22), 2285–2290. doi: 10.1016/j.scib.2022.11.004CrossRefGoogle ScholarPubMed
Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., & Jiang, T. (2010). Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Computational Biology, 6(11), e1001006. doi: 10.1371/journal.pcbi.1001006CrossRefGoogle ScholarPubMed
Yu, K., Wang, X., Li, Q., Zhang, X., Li, X., & Li, S. (2018). Individual morphological brain network construction based on multivariate Euclidean distances between brain regions. Frontiers in Human Neuroscience, 12(May), 1–13. doi: 10.3389/fnhum.2018.00204CrossRefGoogle ScholarPubMed
Yun, J. Y., Boedhoe, P. S. W., Vriend, C., Jahanshad, N., Abe, Y., Ameis, S. H., … Kwon, J. S. (2020). Brain structural covariance networks in obsessive-compulsive disorder: A graph analysis from the ENIGMA consortium. Brain, 143(2), 684–700. doi: 10.1093/brain/awaa001Google 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_1.CrossRefGoogle ScholarPubMed
Zaremba, D., Dohm, K., Redlich, R., Grotegerd, D., Strojny, R., Meinert, S., … Dannlowski, U. (2018). Association of brain cortical changes with relapse in patients with major depressive disorder. JAMA Psychiatry, 75(5), 484–492. doi: 10.1001/jamapsychiatry.2018.0123CrossRefGoogle ScholarPubMed
Supplementary material: File

Ping et al. supplementary material

Ping et al. supplementary material

Download Ping et al. supplementary material(File)
File 1.5 MB