Hostname: page-component-7479d7b7d-qlrfm Total loading time: 0 Render date: 2024-07-10T01:52:14.711Z Has data issue: false hasContentIssue false
  • English
  • Français

Identification of shared and distinct patterns of brain network abnormality across mental disorders through individualized structural covariance network analysis

Published online by Cambridge University Press:  06 March 2023

Shaoqiang Han Open the ORCID record for Shaoqiang Han [Opens in a new window] ,
Kangkang Xue ,
Yuan Chen ,
Yinhuan Xu ,
Shuying Li ,
Xueqin Song ,
Hui-Rong Guo ,
Keke Fang ,
Ruiping Zheng  and
Bingqian Zhou
...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
Kangkang Xue
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
Yinhuan Xu
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
Xueqin Song
Affiliation:
Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Hui-Rong Guo
Affiliation:
Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Keke Fang
Affiliation:
Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 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
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
Jingli 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
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
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
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: shaoqianghan@163.com; Yong Zhang, E-mail: zzuzhangyong2013@163.com; Jingliang Cheng, E-mail: fccchengjl@zzu.edu.cn
Authors for correspondence: Shaoqiang Han, E-mail: shaoqianghan@163.com; Yong Zhang, E-mail: zzuzhangyong2013@163.com; Jingliang Cheng, E-mail: fccchengjl@zzu.edu.cn
Authors for correspondence: Shaoqiang Han, E-mail: shaoqianghan@163.com; Yong Zhang, E-mail: zzuzhangyong2013@163.com; Jingliang Cheng, E-mail: fccchengjl@zzu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders.

Methods

Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed.

Results

Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network.

Conclusions

These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

Keywords

Depressionheterogeneityindividualized analysisobsessive compulsive disorderschizophrenia
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 14 , October 2023 , pp. 6780 - 6791
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

*

These authors contributed equally to this work.

References

Albert, K. M., & Newhouse, P. A. (2019). Estrogen, stress, and depression: Cognitive and biological interactions. Annual Review of Clinical Psychology, 15, 399–423. doi:10.1146/annurev-clinpsy-050718-095557.CrossRefGoogle 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/nrn3465.CrossRefGoogle ScholarPubMed
Alnæs, D., Kaufmann, T., van der Meer, D., Córdova-Palomera, A., Rokicki, J., Moberget, T., … Westlye, L. T. (2019). Brain heterogeneity in schizophrenia and its association with polygenic risk. JAMA Psychiatry, 76(7), 739–748. doi:10.1001/jamapsychiatry.2019.0257.CrossRefGoogle ScholarPubMed
Antoniades, M., Haas, S. S., Modabbernia, A., Bykowsky, O., Frangou, S., Borgwardt, S., & Schmidt, A. (2021). Personalized estimates of brain structural variability in individuals with early psychosis. 47(4), 1029–1038. doi:10.1093/schbul/sbab005.CrossRefGoogle Scholar
Anttila, V., Bulik-Sullivan, B., & Finucane, H. K. (2018). Analysis of shared heritability in common disorders of the brain, Science 360(6395), eaap8757. doi:10.1126/science.aap8757.Google ScholarPubMed
Ashburner, J. (2009). Computational anatomy with the SPM software. Magnetic Resonance Imaging, 27(8), 1163–1174. doi:10.1016/j.mri.2009.01.006.CrossRefGoogle ScholarPubMed
Barch, D. M., & Sheffield, J. M. (2014). Cognitive impairments in psychotic disorders: Common mechanisms and measurement. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 13(3), 224–232. doi:10.1002/wps.20145.CrossRefGoogle ScholarPubMed
Boedhoe, P. S. W., Schmaal, L., Abe, Y., Alonso, P., Ameis, S. H., Anticevic, A., … van den Heuvel, O. A. (2018). Cortical abnormalities associated with pediatric and adult obsessive-compulsive disorder: Findings from the ENIGMA obsessive-compulsive disorder working group. The American Journal of Psychiatry, 175(5), 453–462. doi:10.1176/appi.ajp.2017.17050485.CrossRefGoogle 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)30060-2.CrossRefGoogle ScholarPubMed
Brugger, S. P., & Howes, O. D. (2017). Heterogeneity and homogeneity of regional brain structure in schizophrenia: A meta-analysis. JAMA Psychiatry, 74(11), 1104–1111. doi:10.1001/jamapsychiatry.2017.2663.CrossRefGoogle ScholarPubMed
Chamberlain, S. R., Fineberg, N. A., Blackwell, A. D., Robbins, T. W., & Sahakian, B. J. (2006). Motor inhibition and cognitive flexibility in obsessive-compulsive disorder and trichotillomania. The American Journal of Psychiatry, 163(7), 1282–1284. doi:10.1176/appi.ajp.163.7.1282.CrossRefGoogle ScholarPubMed
Chand, G. B., Dwyer, D. B., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., … Davatzikos, C. (2020). Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain: A Journal of Neurology, 143(3), 1027–1038. doi:10.1093/brain/awaa025.CrossRefGoogle 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.2016.12.068.CrossRefGoogle ScholarPubMed
Craddock, R. C., James, G. A., Holtzheimer, P. E. 3rd, 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.21333.CrossRefGoogle ScholarPubMed
de Wit, S. J., de Vries, F. E., van der Werf, Y. D., Cath, D. C., Heslenfeld, D. J., Veltman, E. M., … van den Heuvel, O. A. (2012). Presupplementary motor area hyperactivity during response inhibition: A candidate endophenotype of obsessive-compulsive disorder. The American Journal of Psychiatry, 169(10), 1100–1108. doi:10.1176/appi.ajp.2012.12010073.CrossRefGoogle 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:10.1038/427311a.CrossRefGoogle ScholarPubMed
Drysdale, A. T., Grosenick, L., & Downar, J. (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. 23(1), 28–38. doi:10.1038/nm.4246.CrossRefGoogle Scholar
Eker, C., & Gonul, A. S. (2010). Volumetric MRI studies of the hippocampus in major depressive disorder: Meanings of inconsistency and directions for future research. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry, 11(1), 19–35. doi:10.1080/15622970902737998.CrossRefGoogle ScholarPubMed
Evans, A. C. (2013). Networks of anatomical covariance. NeuroImage, 80, 489–504. doi:10.1016/j.neuroimage.2013.05.054.CrossRefGoogle 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.4135.CrossRefGoogle ScholarPubMed
Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews. Neuroscience, 16(3), 159–172. doi:10.1038/nrn3901.CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Deste, G., Smieskova, R., Barlati, S., Yung, A. R., Howes, O., … Borgwardt, S. (2012). Cognitive functioning in prodromal psychosis: A meta-analysis. Archives of General Psychiatry, 69(6), 562–571. doi:10.1001/archgenpsychiatry.2011.1592.CrossRefGoogle ScholarPubMed
Ge, R., Sassi, R., Yatham, L., & Frangou, S. (2022). Neuroimaging profiling identifies distinct brain maturational subtypes of youth with mood and anxiety disorders. Molecular Psychiatry, 28(3), 1072–1078. doi:10.1038/s41380-022-01925-9.CrossRefGoogle ScholarPubMed
Giraldo-Chica, M., Rogers, B. P., Damon, S. M., Landman, B. A., & Woodward, N. D. (2018). Prefrontal-thalamic anatomical connectivity and executive cognitive function in schizophrenia. Biological Psychiatry, 83(6), 509–517. doi:10.1016/j.biopsych.2017.09.022.CrossRefGoogle ScholarPubMed
Gonçalves, O. F., Marques, T. R., Lori, N. F., Sampaio, A., & Branco, M. C. (2010). Obsessive-compulsive disorder as a visual processing impairment. Medical Hypotheses, 74(1), 107–109. doi:10.1016/j.mehy.2009.07.048.CrossRefGoogle ScholarPubMed
Gong, J., Wang, J., Qiu, S., Chen, P., Luo, Z., Wang, J., … Wang, Y. (2020). Common and distinct patterns of intrinsic brain activity alterations in major depression and bipolar disorder: Voxel-based meta-analysis. Translational Psychiatry, 10(1), 353–353. doi:10.1038/s41398-020-01036-5.CrossRefGoogle ScholarPubMed
Goodman, W. K., Price, L. H., Rasmussen, S. A., Mazure, C., Fleischmann, R. L., Hill, C. L., … Charney, D. S. (1989). The Yale-Brown obsessive compulsive scale. I. Development, use, and reliability. Archives of General Psychiatry, 46(11), 1006–1011. doi:10.1001/archpsyc.1989.01810110048007.CrossRefGoogle ScholarPubMed
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23(1), 56–62. doi:10.1136/jnnp.23.1.56.CrossRefGoogle ScholarPubMed
Han, S., Xu, Y., Guo, H. R., Fang, K., & Wei, Y. (2022). Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis. Human Brain Mapping, 43(10), 3037–3046. doi:10.1002/hbm.25833.CrossRefGoogle ScholarPubMed
Han, S., Zheng, R., Li, S., Liu, L., Wang, C., Jiang, Y., … Cheng, J. (2021). Progressive brain structural abnormality in depression assessed with MR imaging by using causal network analysis. Psychological Medicine, Online ahead of print, 1–10. doi:10.1017/s0033291721003986.Google ScholarPubMed
Itahashi, T., Fujino, J., Hashimoto, R. I., Tachibana, Y., Sato, T., Ohta, H., … Aoki, Y. Y. (2020). Transdiagnostic subtyping of males with developmental disorders using cortical characteristics. NeuroImage Clinical, 27, 102288. doi:10.1016/j.nicl.2020.102288.CrossRefGoogle 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.2015.0071.CrossRefGoogle ScholarPubMed
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13(2), 261–276. doi:10.1093/schbul/13.2.261.CrossRefGoogle ScholarPubMed
Koshiyama, D., Fukunaga, M., Okada, N., Morita, K., Nemoto, K., Usui, K., … Narita, H. (2020). White matter microstructural alterations across four major psychiatric disorders: Mega-analysis study in 2937 individuals. 25(4), 883–895. doi:10.1038/s41380-019-0553-7.CrossRefGoogle Scholar
Krishnan, V., & Nestler, E. J. (2008). The molecular neurobiology of depression. Nature, 455(7215), 894–902. doi:10.1038/nature07455.CrossRefGoogle ScholarPubMed
Lamb, Y. N., McKay, N. S., Thompson, C. S., Hamm, J. P., Waldie, K. E., & Kirk, I. J. (2015). Brain-derived neurotrophic factor Val66Met polymorphism, human memory, and synaptic neuroplasticity. Wiley Interdisciplinary Reviews. Cognitive Science, 6(2), 97–108. doi:10.1002/wcs.1334.CrossRefGoogle ScholarPubMed
Lázaro, L., Castro-Fornieles, J., Cullell, C., Andrés, S., Falcón, C., Calvo, R., & Bargalló, N. (2011). A voxel-based morphometric MRI study of stabilized obsessive-compulsive adolescent patients. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 35(8), 1863–1869. doi:10.1016/j.pnpbp.2011.07.016.CrossRefGoogle 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.2006.01.042.CrossRefGoogle ScholarPubMed
Li, C., Dong, M., Womer, F. Y., Han, S., Yin, Y., Jiang, X., … Wang, F. (2021). Transdiagnostic time-varying dysconnectivity across major psychiatric disorders. Human Brain Mapping, 42(4), 1182–1196. doi:10.1002/hbm.25285.CrossRefGoogle ScholarPubMed
Lima-Ojeda, J. M., Rupprecht, R., & Baghai, T. C. (2018). Neurobiology of depression: A neurodevelopmental approach. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry, 19(5), 349–359. doi:10.1080/15622975.2017.1289240.CrossRefGoogle ScholarPubMed
Liu, X., Wang, Y., Ji, H., Aihara, K., & Chen, L. (2016). Personalized characterization of diseases using sample-specific networks. Nucleic Acids Research, 44(22), e164. doi:10.1093/nar/gkw772.CrossRefGoogle 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. 26(12), 7719–7731. doi:10.1038/s41380-021-01229-4.CrossRefGoogle Scholar
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/btz128.Google 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(7), 3512–3523. doi:10.1038/s41380-020-00882-5.CrossRefGoogle Scholar
Malykhin, N. V., Carter, R., Seres, P., & Coupland, N. J. (2010). Structural changes in the hippocampus in major depressive disorder: Contributions of disease and treatment. Journal of Psychiatry & Neuroscience: JPN, 35(5), 337–343. doi:10.1503/jpn.100002.CrossRefGoogle ScholarPubMed
Marín, O. (2012). Interneuron dysfunction in psychiatric disorders. Nature Reviews. Neuroscience, 13(2), 107–120. doi:10.1038/nrn3155.CrossRefGoogle ScholarPubMed
Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science (New York, N.Y.), 296(5569), 910–913. doi:10.1126/science.1065103.CrossRefGoogle ScholarPubMed
Menzies, L., Achard, S., Chamberlain, S. R., Fineberg, N., Chen, C. H., del Campo, N., … Bullmore, E. (2007). Neurocognitive endophenotypes of obsessive-compulsive disorder. Brain: A Journal of Neurology, 130(Pt 12), 3223–3236. doi:10.1093/brain/awm205.CrossRefGoogle ScholarPubMed
Minzenberg, M. J., & Carter, C. S. (2012). Developing treatments for impaired cognition in schizophrenia. Trends in Cognitive Sciences, 16(1), 35–42. doi:10.1016/j.tics.2011.11.017.CrossRefGoogle ScholarPubMed
Mitelman, S. A., Buchsbaum, M. S., Brickman, A. M., & Shihabuddin, L. (2005). Cortical intercorrelations of frontal area volumes in schizophrenia. NeuroImage, 27(4), 753–770. doi:10.1016/j.neuroimage.2005.05.024.CrossRefGoogle ScholarPubMed
Moberget, T., Doan, N. T., Alnæs, D., Kaufmann, T., Córdova-Palomera, A., Lagerberg, T. V., … Westlye, L. T. (2018). Cerebellar volume and cerebellocerebral structural covariance in schizophrenia: A multisite mega-analysis of 983 patients and 1349 healthy controls. Molecular Psychiatry, 23(6), 1512–1520. doi:10.1038/mp.2017.106.CrossRefGoogle ScholarPubMed
Nakamura, Y., Okada, N., Koshiyama, D., Kamiya, K., Abe, O., Kunimatsu, A., … Koike, S. (2020). Differences in functional connectivity networks related to the midbrain dopaminergic system-related area in various psychiatric disorders. Schizophrenia Bulletin, 46(5), 1239–1248. doi:10.1093/schbul/sbz121.CrossRefGoogle Scholar
Okasha, A., Rafaat, M., Mahallawy, N., El Nahas, G., El Dawla, A. S., Sayed, M., & El Kholi, S. (2000). Cognitive dysfunction in obsessive-compulsive disorder. Acta Psychiatrica Scandinavica, 101(4), 281–285.CrossRefGoogle ScholarPubMed
Patel, Y., Parker, N., Shin, J., Howard, D., French, L., Thomopoulos, S. I., … Paus, T. (2021). Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders. JAMA Psychiatry, 78(1), 47–63. doi:10.1001/jamapsychiatry.2020.2694.Google 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: The Official Journal of the Society for Neuroscience, 24(45), 10099–10102. doi:10.1523/jneurosci.2680-04.2004.CrossRefGoogle ScholarPubMed
Phillips, M. L., Drevets, W. C., Rauch, S. L., & Lane, R. (2003). Neurobiology of emotion perception II: Implications for major psychiatric disorders. Biological Psychiatry, 54(5), 515–528. doi:10.1016/s0006-3223(03)00171-9.CrossRefGoogle ScholarPubMed
Phillips, M. L., Ladouceur, C. D., & Drevets, W. C. (2008). A neural model of voluntary and automatic emotion regulation: Implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Molecular Psychiatry, 13(9), 829, 833–857. doi:10.1038/mp.2008.65.CrossRefGoogle ScholarPubMed
Pomarol-Clotet, E., Canales-Rodríguez, E. J., Salvador, R., Sarró, S., Gomar, J. J., Vila, F., … McKenna, P. J. (2010). Medial prefrontal cortex pathology in schizophrenia as revealed by convergent findings from multimodal imaging. Molecular Psychiatry, 15(8), 823–830. doi:10.1038/mp.2009.146.CrossRefGoogle ScholarPubMed
Rodriguez-Murillo, L., Gogos, J. A., & Karayiorgou, M. (2012). The genetic architecture of schizophrenia: New mutations and emerging paradigms. Annual Review of Medicine, 63, 63–80. doi:10.1146/annurev-med-072010-091100.CrossRefGoogle ScholarPubMed
Saxena, S., & Rauch, S. L. (2000). Functional neuroimaging and the neuroanatomy of obsessive-compulsive disorder. The Psychiatric Clinics of North America, 23(3), 563–586. doi:10.1016/s0193-953x(05)70181-7.CrossRefGoogle 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.2016.60.CrossRefGoogle ScholarPubMed
Schmaal, L., Veltman, D. J., van Erp, T. G., Sämann, P. G., Frodl, T., Jahanshad, N., … Tiemeier, H. (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.2015.69.CrossRefGoogle ScholarPubMed
Snitz, B. E., Macdonald, A. W. 3rd, & Carter, C. S. (2006). Cognitive deficits in unaffected first-degree relatives of schizophrenia patients: A meta-analytic review of putative endophenotypes. Schizophrenia Bulletin, 32(1), 179–194. doi:10.1093/schbul/sbi048.CrossRefGoogle ScholarPubMed
Sonuga-Barke, E. (2013). Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis. Lancet (London, England),, 381(9875), 1371–1379. doi:10.1016/s0140-6736(12)62129-1.Google Scholar
Sotiras, A., Toledo, J. B., Gur, R. E., Gur, R. C., Satterthwaite, T. D., & Davatzikos, C. (2017). Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion. Proceedings of the National Academy of Sciences of the United States of America, 114(13), 3527–3532. doi:10.1073/pnas.1620928114.CrossRefGoogle ScholarPubMed
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/sbaa155.CrossRefGoogle ScholarPubMed
Toga, A. W., Thompson, P. M., & Sowell, E. R. (2006). Mapping brain maturation. Trends in Neurosciences, 29(3), 148–159. doi:10.1016/j.tins.2006.01.007.CrossRefGoogle ScholarPubMed
Tomiyama, H., Murayama, K., Nemoto, K., Tomita, M., Hasuzawa, S., Mizobe, T., … Nakao, T. (2022). Increased functional connectivity between presupplementary motor area and inferior frontal gyrus associated with the ability of motor response inhibition in obsessive-compulsive disorder. Human Brain Mapping, 43(3), 974–984. doi:10.1002/hbm.25699.CrossRefGoogle ScholarPubMed
Tu, P. C., Lee, Y. C., Chen, Y. S., Li, C. T., & Su, T. P. (2013). Schizophrenia and the brain's control network: Aberrant within- and between-network connectivity of the frontoparietal network in schizophrenia. Schizophrenia Research, 147(2–3), 339–347. doi:10.1016/j.schres.2013.04.011.CrossRefGoogle ScholarPubMed
Unschuld, P. G., Buchholz, A. S., Varvaris, M., van Zijl, P. C. M., Ross, C. A., Pekar, J. J., … Schretlen, D. J. (2014). Prefrontal brain network connectivity indicates degree of both schizophrenia risk and cognitive dysfunction. Schizophrenia Bulletin, 40(3), 653–664. doi:10.1093/schbul/sbt077.CrossRefGoogle 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.2019.09.004.CrossRefGoogle 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.2018.2467.CrossRefGoogle ScholarPubMed
Xia, M., Womer, F. Y., Chang, M., Zhu, Y., Zhou, Q., Edmiston, E. K., … Wang, F. (2019). Shared and distinct functional architectures of brain networks across psychiatric disorders. Schizophrenia Bulletin, 45(2), 450–463. doi:10.1093/schbul/sby046.CrossRefGoogle 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.1635.CrossRefGoogle 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: A Journal of Neurology, 143(2), 684–700. doi:10.1093/brain/awaa001.Google 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.2015.06.009.CrossRefGoogle 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
Zhou, Y., Liang, M., Jiang, T., Tian, L., Liu, Y., Liu, Z., … Kuang, F. (2007). Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI. Neuroscience Letters, 417(3), 297–302. doi:10.1016/j.neulet.2007.02.081.CrossRefGoogle ScholarPubMed
Supplementary material: File

Han et al. supplementary material

Table S1 and Figures S1-S7

Download Han et al. supplementary material(File)
File 3.2 MB