Hostname: page-component-669899f699-qzcqf Total loading time: 0 Render date: 2025-04-29T11:38:33.594Z Has data issue: false hasContentIssue false
  • English
  • Français

Cerebral blood flow changes and their genetic mechanisms in major depressive disorder: a combined neuroimaging and transcriptome study

Published online by Cambridge University Press:  05 January 2023

Xuetian Sun ,
Weisheng Huang ,
Jie Wang ,
Ruoxuan Xu ,
Xiaohan Zhang ,
Jianhui Zhou ,
Jiajia Zhu  and
Yinfeng Qian
Xuetian Sun
Affiliation:
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
Weisheng Huang
Affiliation:
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
Jie Wang
Affiliation:
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
Ruoxuan Xu
Affiliation:
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
Xiaohan Zhang
Affiliation:
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
Jianhui Zhou
Affiliation:
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
Jiajia Zhu*
Affiliation:
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
Yinfeng Qian*
Affiliation:
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
*
Authors for correspondence: Yinfeng Qian, E-mail: [email protected]; Jiajia Zhu, E-mail: [email protected]
Authors for correspondence: Yinfeng Qian, E-mail: [email protected]; Jiajia Zhu, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Extensive research has shown abnormal cerebral blood flow (CBF) in patients with major depressive disorder (MDD) that is a heritable disease. The objective of this study was to investigate the genetic mechanisms of CBF abnormalities in MDD.

Methods

To achieve a more thorough characterization of CBF changes in MDD, we performed a comprehensive neuroimaging meta-analysis of previous literature as well as examined group CBF differences in an independent sample of 133 MDD patients and 133 controls. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial association analyses were conducted to identify genes whose expression correlated with CBF changes in MDD, followed by a set of gene functional feature analyses.

Results

We found increased CBF in the reward circuitry and default-mode network and decreased CBF in the visual system in MDD patients. Moreover, these CBF changes were spatially associated with expression of 1532 genes, which were enriched for important molecular functions, biological processes, and cellular components of the cerebral cortex as well as several common mental disorders. Concurrently, these genes were specifically expressed in the brain tissue, in immune cells and neurons, and during nearly all developmental stages. Regarding behavioral relevance, these genes were associated with domains involving emotion and sensation. In addition, these genes could construct a protein-protein interaction network supported by 60 putative hub genes with functional significance.

Conclusions

Our findings suggest a cerebral perfusion redistribution in MDD, which may be a consequence of complex interactions of a wide range of genes with diverse functional features.

Keywords

Cerebral blood flowgene expressionmajor depressive disordermeta-analysisneuroimaging
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 14 , October 2023 , pp. 6468 - 6480
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.)

Article purchase

Temporarily unavailable

Footnotes

*

These authors contributed equally to this work.

References

Albajes-Eizagirre, A., Solanes, A., Fullana, M. A., Ioannidis, J. P. A., Fusar-Poli, P., Torrent, C., … Radua, J. (2019a). Meta-analysis of voxel-based neuroimaging studies using seed-based d mapping with permutation of subject images (SDM-PSI). Journal of Visualized Experiments, 153, e59841. doi:10.3791/59841.Google Scholar
Albajes-Eizagirre, A., Solanes, A., Vieta, E., & Radua, J. (2019b). Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM. Neuroimage, 186, 174–184. doi:10.1016/j.neuroimage.2018.10.077.CrossRefGoogle ScholarPubMed
Althubaity, N., Schubert, J., Martins, D., Yousaf, T., Nettis, M. A., Mondelli, V., … Veronese, M. (2022). Choroid plexus enlargement is associated with neuroinflammation and reduction of blood brain barrier permeability in depression. Neuroimage Clinical, 33, 102926. doi:10.1016/j.nicl.2021.102926.CrossRefGoogle ScholarPubMed
Anderson, K. M., Collins, M. A., Kong, R., Fang, K., Li, J., He, T., … Holmes, A. J. (2020). Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder. Proceedings of the National Academy of Sciences of the United States of America, 117(40), 25138–25149. doi:10.1073/pnas.2008004117.CrossRefGoogle ScholarPubMed
Arnatkeviciute, A., Fulcher, B. D., & Fornito, A. (2019). A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage, 189, 353–367. doi:10.1016/j.neuroimage.2019.01.011.CrossRefGoogle ScholarPubMed
Bench, C. J., Friston, K. J., Brown, R. G., Scott, L. C., Frackowiak, R. S., & Dolan, R. J. (1992). The anatomy of melancholia--focal abnormalities of cerebral blood flow in major depression. Psychological Medicine, 22(3), 607–615. doi:10.1017/s003329170003806x.CrossRefGoogle ScholarPubMed
Bonte, F. J., Trivedi, M. H., Devous, M. D. Sr., Harris, T. S., Payne, J. K., Weinberg, W. A., & Haley, R. W. (2001). Occipital brain perfusion deficits in children with major depressive disorder. Journal of Nuclear Medicine, 42(7), 1059–1061. Retrieved from https://pubmed.ncbi.nlm.nih.gov/11438629/.Google ScholarPubMed
Borroto-Escuela, D. O., Ambrogini, P., Narvaez, M., Di Liberto, V., Beggiato, S., Ferraro, L., … Fuxe, K. (2021). Serotonin heteroreceptor complexes and their integration of signals in neurons and astroglia-relevance for mental diseases. Cells, 10(8), 1902. doi:10.3390/cells10081902.CrossRefGoogle ScholarPubMed
Bromet, E., Andrade, L. H., Hwang, I., Sampson, N. A., Alonso, J., de Girolamo, G., … Kessler, R. C. (2011). Cross-national epidemiology of DSM-IV major depressive episode. BMC Medicine, 9, 90. doi:10.1186/1741-7015-9-90.CrossRefGoogle ScholarPubMed
Buckner, R. L., & DiNicola, L. M. (2019). The brain's default network: Updated anatomy, physiology and evolving insights. Nature Reviews Neuroscience, 20(10), 593–608. doi:10.1038/s41583-019-0212-7.CrossRefGoogle ScholarPubMed
Buxton, R. B. (2021). The thermodynamics of thinking: Connections between neural activity, energy metabolism and blood flow. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 376(1815), 20190624. doi:10.1098/rstb.2019.0624.CrossRefGoogle ScholarPubMed
Buxton, R. B., Frank, L. R., Wong, E. C., Siewert, B., Warach, S., & Edelman, R. R. (1998). A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magnetic Resonance in Medicine, 40(3), 383–396. doi:10.1002/mrm.1910400308.CrossRefGoogle ScholarPubMed
Cantisani, A., Stegmayer, K., Bracht, T., Federspiel, A., Wiest, R., Horn, H., … Walther, S. (2016). Distinct resting-state perfusion patterns underlie psychomotor retardation in unipolar vs. bipolar depression. Acta Psychiatrica Scandinavica, 134(4), 329–338. doi:10.1111/acps.12625.CrossRefGoogle ScholarPubMed
Chen, G., Bian, H., Jiang, D., Cui, M., Ji, S., Liu, M., … Zhuo, C. (2016). Pseudo-continuous arterial spin labeling imaging of cerebral blood perfusion asymmetry in drug-naive patients with first-episode major depression. Biomedical Reports, 5(6), 675–680. doi:10.3892/br.2016.796.CrossRefGoogle ScholarPubMed
Chen, J., Bardes, E. E., Aronow, B. J., & Jegga, A. G. (2009). ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Research, 37(Web Server issue), W305–W311. doi:10.1093/nar/gkp427.CrossRefGoogle ScholarPubMed
Chen, J., Zhang, C., Wang, R., Jiang, P., Cai, H., Zhao, W., … Yu, Y. (2022). Molecular basis underlying functional connectivity of fusiform gyrus subregions: A transcriptome-neuroimaging spatial correlation study. Cortex, 152, 59–73. doi:10.1016/j.cortex.2022.03.016.CrossRefGoogle ScholarPubMed
Chen, Z. Q., Du, M. Y., Zhao, Y. J., Huang, X. Q., Li, J., Lui, S., … Gong, Q. Y. (2015). Voxel-wise meta-analyses of brain blood flow and local synchrony abnormalities in medication-free patients with major depressive disorder. Journal of Psychiatry & Neuroscience, 40(6), 401–411. doi:10.1503/jpn.140119.CrossRefGoogle ScholarPubMed
Chithiramohan, T., Parekh, J. N., Kronenberg, G., Haunton, V. J., Minhas, J. S., Panerai, R. B., … Beishon, L. (2022). Investigating the association between depression and cerebral haemodynamics-A systematic review and meta-analysis. Journal of Affective Disorders, 299, 144–158. doi:10.1016/j.jad.2021.11.037.CrossRefGoogle ScholarPubMed
Choudary, P. V., Molnar, M., Evans, S. J., Tomita, H., Li, J. Z., Vawter, M. P., … Jones, E. G. (2005). Altered cortical glutamatergic and GABAergic signal transmission with glial involvement in depression. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15653–15658. doi:10.1073/pnas.0507901102.CrossRefGoogle ScholarPubMed
Clery-Melin, M. L., Jollant, F., & Gorwood, P. (2019). Reward systems and cognitions in major depressive disorder. CNS Spectrums, 24(1), 64–77. doi:10.1017/S1092852918001335.CrossRefGoogle ScholarPubMed
Consortium, C. (2015). Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature, 523(7562), 588–591. doi:10.1038/nature14659.CrossRefGoogle Scholar
Cooper, C. M., Chin Fatt, C. R., Liu, P., Grannemann, B. D., Carmody, T., Almeida, J. R. C., … Trivedi, M. H. (2020). Discovery and replication of cerebral blood flow differences in major depressive disorder. Molecular Psychiatry, 25(7), 1500–1510. doi:10.1038/s41380-019-0464-7.CrossRefGoogle ScholarPubMed
Corfield, E. C., Yang, Y., Martin, N. G., & Nyholt, D. R. (2017). A continuum of genetic liability for minor and major depression. Translational Psychiatry, 7(5), e1131–e1131. doi:10.1038/tp.2017.99.CrossRefGoogle ScholarPubMed
Dougherty, J. D., Schmidt, E. F., Nakajima, M., & Heintz, N. (2010). Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells. Nucleic Acids Research, 38(13), 4218–4230. doi:10.1093/nar/gkq130.CrossRefGoogle ScholarPubMed
Duhameau, B., Ferre, J. C., Jannin, P., Gauvrit, J. Y., Verin, M., Millet, B., & Drapier, D. (2010). Chronic and treatment-resistant depression: A study using arterial spin labeling perfusion MRI at 3Tesla. Psychiatry Research, 182(2), 111–116. doi:10.1016/j.pscychresns.2010.01.009.CrossRefGoogle ScholarPubMed
Duman, R. S., & Aghajanian, G. K. (2012). Synaptic dysfunction in depression: Potential therapeutic targets. Science (New York, N.Y.), 338(6103), 68–72. doi:10.1126/science.1222939.CrossRefGoogle ScholarPubMed
Fam, J., Rush, A. J., Haaland, B., Barbier, S., & Luu, C. (2013). Visual contrast sensitivity in major depressive disorder. Journal of Psychosomatic Research, 75(1), 83–86. doi:10.1016/j.jpsychores.2013.03.008.CrossRefGoogle ScholarPubMed
Fornito, A., Arnatkeviciute, A., & Fulcher, B. D. (2019). Bridging the gap between connectome and transcriptome. Trends in Cognitive Sciences, 23(1), 34–50. doi:10.1016/j.tics.2018.10.005.CrossRefGoogle ScholarPubMed
Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, G., Hartl, C., … Geschwind, D. H. (2018). Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science (New York, N.Y.), 359(6376), 693–697. doi:10.1126/science.aad6469.CrossRefGoogle ScholarPubMed
Garaschuk, O., & Verkhratsky, A. (2019). Physiology of microglia. Methods in Molecular Biology (Clifton, N.J.), 2034, 27–40. doi:10.1007/978-1-4939-9658-2_3.CrossRefGoogle ScholarPubMed
Grace, A. A. (2016). Dysregulation of the dopamine system in the pathophysiology of schizophrenia and depression. Nature Reviews Neuroscience, 17(8), 524–532. doi:10.1038/nrn.2016.57.CrossRefGoogle ScholarPubMed
Greenberg, P. E., Fournier, A. A., Sisitsky, T., Simes, M., Berman, R., Koenigsberg, S. H., & Kessler, R. C. (2021). The economic burden of adults with major depressive disorder in the United States (2010 and 2018). Pharmacoeconomics, 39(6), 653–665. doi:10.1007/s40273-021-01019-4.CrossRefGoogle ScholarPubMed
Gunduz-Bruce, H., Silber, C., Kaul, I., Rothschild, A. J., Riesenberg, R., Sankoh, A. J., … Kanes, S. J. (2019). Trial of SAGE-217 in patients with major depressive disorder. The New England Journal of Medicine, 381(10), 903–911. doi:10.1056/NEJMoa1815981.CrossRefGoogle ScholarPubMed
Haller, S., Zaharchuk, G., Thomas, D. L., Lovblad, K. O., Barkhof, F., & Golay, X. (2016). Arterial spin labeling perfusion of the brain: Emerging clinical applications. Radiology, 281(2), 337–356. doi:10.1148/radiol.2016150789.CrossRefGoogle ScholarPubMed
Hamilton, J. P., Etkin, A., Furman, D. J., Lemus, M. G., Johnson, R. F., & Gotlib, I. H. (2012). Functional neuroimaging of major depressive disorder: A meta-analysis and new integration of base line activation and neural response data. The American Journal of Psychiatry, 169(7), 693–703. doi:10.1176/appi.ajp.2012.11071105.CrossRefGoogle ScholarPubMed
Hashimoto, K. (2019). Rapid-acting antidepressant ketamine, its metabolites and other candidates: A historical overview and future perspective. Psychiatry and Clinical Neurosciences, 73(10), 613–627. doi:10.1111/pcn.12902.CrossRefGoogle ScholarPubMed
Hawrylycz, M., Miller, J. A., Menon, V., Feng, D., Dolbeare, T., Guillozet-Bongaarts, A. L., … Lein, E. (2015). Canonical genetic signatures of the adult human brain. Nature Neuroscience, 18(12), 1832–1844. doi:10.1038/nn.4171.CrossRefGoogle ScholarPubMed
Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng, L., Miller, J. A., … Jones, A. R. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391–399. doi:10.1038/nature11405.CrossRefGoogle ScholarPubMed
Hernandez-Garcia, L., Lahiri, A., & Schollenberger, J. (2019). Recent progress in ASL. Neuroimage, 187, 3–16. doi:10.1016/j.neuroimage.2017.12.095.CrossRefGoogle ScholarPubMed
Ho, T. C., Wu, J., Shin, D. D., Liu, T. T., Tapert, S. F., Yang, G., … Yang, T. T. (2013). Altered cerebral perfusion in executive, affective, and motor networks during adolescent depression. Journal of the American Academy of Child & Adolescent Psychiatry, 52(10), 1076–1091.e2. doi:10.1016/j.jaac.2013.07.008.CrossRefGoogle ScholarPubMed
Hodes, G. E., Kana, V., Menard, C., Merad, M., & Russo, S. J. (2015). Neuroimmune mechanisms of depression. Nature Neuroscience, 18(10), 1386–1393. doi:10.1038/nn.4113.CrossRefGoogle ScholarPubMed
Hoflich, A., Michenthaler, P., Kasper, S., & Lanzenberger, R. (2019). Circuit mechanisms of reward, anhedonia, and depression. The International Journal of Neuropsychopharmacology, 22(2), 105–118. doi:10.1093/ijnp/pyy081.CrossRefGoogle ScholarPubMed
Howard, D. M., Adams, M. J., Clarke, T. K., Hafferty, J. D., Gibson, J., Shirali, M., … McIntosh, A. M. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience, 22(3), 343–352. doi:10.1038/s41593-018-0326-7.CrossRefGoogle ScholarPubMed
Howard, D. M., Adams, M. J., Shirali, M., Clarke, T. K., Marioni, R. E., Davies, G., … McIntosh, A. M. (2018). Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nature Communications, 9(1), 1470. doi:10.1038/s41467-018-03819-3.CrossRefGoogle ScholarPubMed
Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., … Winslow, A. R. (2016). Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics, 48(9), 1031–1036. doi:10.1038/ng.3623.CrossRefGoogle ScholarPubMed
Iadecola, C. (2017). The neurovascular unit coming of age: A journey through neurovascular coupling in health and disease. Neuron, 96(1), 17–42. doi:10.1016/j.neuron.2017.07.030.CrossRefGoogle ScholarPubMed
Jarnum, H., Eskildsen, S. F., Steffensen, E. G., Lundbye-Christensen, S., Simonsen, C. W., Thomsen, I. S., … Larsson, E. M. (2011). Longitudinal MRI study of cortical thickness, perfusion, and metabolite levels in major depressive disorder. Acta Psychiatrica Scandinavica, 124(6), 435–446. doi:10.1111/j.1600-0447.2011.01766.x.CrossRefGoogle ScholarPubMed
Ji, Y., Zhang, X., Wang, Z., Qin, W., Liu, H., Xue, K., … Yu, C. (2021). Genes associated with gray matter volume alterations in schizophrenia. Neuroimage, 225, 117526. doi:10.1016/j.neuroimage.2020.117526.CrossRefGoogle ScholarPubMed
Kaichi, Y., Okada, G., Takamura, M., Toki, S., Akiyama, Y., Higaki, T., … Awai, K. (2016). Changes in the regional cerebral blood flow detected by arterial spin labeling after 6-week escitalopram treatment for major depressive disorder. Journal of Affective Disorders, 194, 135–143. doi:10.1016/j.jad.2015.12.062.CrossRefGoogle ScholarPubMed
Kessler, R. C., & Bromet, E. J. (2013). The epidemiology of depression across cultures. Annual Review of Public Health, 34, 119–138. doi:10.1146/annurev-publhealth-031912-114409.CrossRefGoogle ScholarPubMed
Klempan, T. A., Sequeira, A., Canetti, L., Lalovic, A., Ernst, C., ffrench-Mullen, J., & Turecki, G. (2009). Altered expression of genes involved in ATP biosynthesis and GABAergic neurotransmission in the ventral prefrontal cortex of suicides with and without major depression. Molecular Psychiatry, 14(2), 175–189. doi:10.1038/sj.mp.4002110.CrossRefGoogle ScholarPubMed
Krausz, Y., Freedman, N., Lester, H., Barkai, G., Levin, T., Bocher, M., … Bonne, O. (2007). Brain SPECT study of common ground between hypothyroidism and depression. The International Journal of Neuropsychopharmacology, 10(1), 99–106. doi:10.1017/S1461145706006481.CrossRefGoogle ScholarPubMed
Krishnan, V., & Nestler, E. J. (2008). The molecular neurobiology of depression. Nature, 455(7215), 894–902. doi:10.1038/nature07455.CrossRefGoogle ScholarPubMed
Kumar, P., Kumar, D., Jha, S. K., Jha, N. K., & Ambasta, R. K. (2016). Ion channels in neurological disorders. Advances in Protein Chemistry and Structural Biology, 103, 97–136. doi:10.1016/bs.apcsb.2015.10.006.CrossRefGoogle ScholarPubMed
Lancaster, J. L., Tordesillas-Gutierrez, D., Martinez, M., Salinas, F., Evans, A., Zilles, K., … Fox, P. T. (2007). Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Human Brain Mapping, 28(11), 1194–1205. doi:10.1002/hbm.20345.CrossRefGoogle ScholarPubMed
Lecrux, C., Bourourou, M., & Hamel, E. (2019). How reliable is cerebral blood flow to map changes in neuronal activity? Autonomic Neuroscience, 217, 71–79. doi:10.1016/j.autneu.2019.01.005.CrossRefGoogle 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/s41467-021-21943-5.CrossRefGoogle ScholarPubMed
Li, J., Xu, C., Cao, X., Gao, Q., Wang, Y., Wang, Y., … Zhang, K. (2013). Abnormal activation of the occipital lobes during emotion picture processing in major depressive disorder patients. Neural Regeneration Research, 8(18), 1693–1701. doi:10.3969/j.issn.1673-5374.2013.18.007.CrossRefGoogle ScholarPubMed
Li, J., Yang, Y., Zhu, Y., Zhou, L., Han, Y., Yin, T., … Chen, J. (2018). Towards characterizing the regional cerebral perfusion in evaluating the severity of major depression disorder with SPECT/CT. BMC Psychiatry, 18(1), 70. doi:10.1186/s12888-018-1654-6.CrossRefGoogle ScholarPubMed
Li, W., Chen, Z., Wu, M., Zhu, H., Gu, L., Zhao, Y., … Gong, Q. (2017). Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: A multimodal meta-analysis. Journal of Affective Disorders, 210, 303–311. doi:10.1016/j.jad.2016.12.032.CrossRefGoogle ScholarPubMed
Lieberman, M. D., & Cunningham, W. A. (2009). Type I and Type II error concerns in fMRI research: Re-balancing the scale. Social Cognitive and Affective Neuroscience, 4(4), 423–428. doi:10.1093/scan/nsp052.CrossRefGoogle ScholarPubMed
Liu, F., Tian, H., Li, J., Li, S., & Zhuo, C. (2019). Altered voxel-wise gray matter structural brain networks in schizophrenia: Association with brain genetic expression pattern. Brain Imaging and Behavior, 13(2), 493–502. doi:10.1007/s11682-018-9880-6.CrossRefGoogle ScholarPubMed
Liu, S., Zhang, C., Meng, C., Wang, R., Jiang, P., Cai, H., … Zhu, J. (2022). Frequency-dependent genetic modulation of neuronal oscillations: A combined transcriptome and resting-state functional MRI study. Cerebral cortex, 32(22), 5132–5144. doi:10.1093/cercor/bhac003.CrossRefGoogle ScholarPubMed
Liu, Z., Xu, C., Xu, Y., Wang, Y., Zhao, B., Lv, Y., … Du, C. (2010). Decreased regional homogeneity in insula and cerebellum: A resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry Research, 182(3), 211–215. doi:10.1016/j.pscychresns.2010.03.004.CrossRefGoogle ScholarPubMed
Lui, S., Parkes, L. M., Huang, X., Zou, K., Chan, R. C., Yang, H., … Gong, Q. Y. (2009). Depressive disorders: Focally altered cerebral perfusion measured with arterial spin-labeling MR imaging. Radiology, 251(2), 476–484. doi:10.1148/radiol.2512081548.CrossRefGoogle ScholarPubMed
Malhi, G. S., & Mann, J. J. (2018). Depression. Lancet (London, England), 392(10161), 2299–2312. doi:10.1016/s0140-6736(18)31948-2.CrossRefGoogle ScholarPubMed
Mantas, I., Saarinen, M., Xu, Z. D., & Svenningsson, P. (2022). Update on GPCR-based targets for the development of novel antidepressants. Molecular Psychiatry, 27(1), 534–558. doi:10.1038/s41380-021-01040-1.CrossRefGoogle ScholarPubMed
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214(5–6), 655–667. doi:10.1007/s00429-010-0262-0.CrossRefGoogle ScholarPubMed
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. doi:10.1371/journal.pmed.1000097.CrossRefGoogle ScholarPubMed
Monkul, E. S., Silva, L. A., Narayana, S., Peluso, M. A., Zamarripa, F., Nery, F. G., … Soares, J. C. (2012). Abnormal resting state corticolimbic blood flow in depressed unmedicated patients with major depression: A (15)O-H(2)O PET study. Human Brain Mapping, 33(2), 272–279. doi:10.1002/hbm.21212.CrossRefGoogle Scholar
Moody, W. J., & Bosma, M. M. (2005). Ion channel development, spontaneous activity, and activity-dependent development in nerve and muscle cells. Physiological Reviews, 85(3), 883–941. doi:10.1152/physrev.00017.2004.CrossRefGoogle ScholarPubMed
Muller, V. I., Cieslik, E. C., Laird, A. R., Fox, P. T., Radua, J., Mataix-Cols, D., … Eickhoff, S. B. (2018). Ten simple rules for neuroimaging meta-analysis. Neuroscience and Biobehavioral Reviews, 84, 151–161. doi:10.1016/j.neubiorev.2017.11.012.CrossRefGoogle ScholarPubMed
Nagafusa, Y., Okamoto, N., Sakamoto, K., Yamashita, F., Kawaguchi, A., Higuchi, T., & Matsuda, H. (2012). Assessment of cerebral blood flow findings using 99mTc-ECD single-photon emission computed tomography in patients diagnosed with major depressive disorder. Journal of Affective Disorders, 140(3), 296–299. doi:10.1016/j.jad.2012.03.026.CrossRefGoogle ScholarPubMed
Nestler, E. J., & Carlezon, W. A. Jr. (2006). The mesolimbic dopamine reward circuit in depression. Biological Psychiatry, 59(12), 1151–1159. doi:10.1016/j.biopsych.2005.09.018.CrossRefGoogle ScholarPubMed
Ota, M., Noda, T., Sato, N., Hattori, K., Teraishi, T., Hori, H., … Kunugi, H. (2014). Characteristic distributions of regional cerebral blood flow changes in major depressive disorder patients: A pseudo-continuous arterial spin labeling (pCASL) study. Journal of Affective Disorders, 165, 59–63. doi:10.1016/j.jad.2014.04.032.CrossRefGoogle ScholarPubMed
Pereda, A. E. (2014). Electrical synapses and their functional interactions with chemical synapses. Nature Reviews Neuroscience, 15(4), 250–263. doi:10.1038/nrn3708.CrossRefGoogle ScholarPubMed
Périco, C. A.-M., Skaf, C. R., Yamada, A., Duran, F., Buchpiguel, C. A., Castro, C. C., … Busatto, G. F. (2005). Relationship between regional cerebral blood flow and separate symptom clusters of major depression: A single photon emission computed tomography study using statistical parametric mapping. Neuroscience Letters, 384(3), 265–270. doi:10.1016/j.neulet.2005.04.088.CrossRefGoogle ScholarPubMed
Pizzagalli, D. A., Holmes, A. J., Dillon, D. G., Goetz, E. L., Birk, J. L., Bogdan, R., … Fava, M. (2009). Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. The American Journal of Psychiatry, 166(6), 702–710. doi:10.1176/appi.ajp.2008.08081201.CrossRefGoogle ScholarPubMed
Prasad, S., & Galetta, S. L. (2011). Anatomy and physiology of the afferent visual system. Handbook of Clinical Neurology, 102, 3–19. doi:10.1016/B978-0-444-52903-9.00007-8.CrossRefGoogle ScholarPubMed
Radua, J., Mataix-Cols, D., Phillips, M. L., El-Hage, W., Kronhaus, D. M., Cardoner, N., & Surguladze, S. (2012). A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry, 27(8), 605–611. doi:10.1016/j.eurpsy.2011.04.001.CrossRefGoogle ScholarPubMed
Raichle, M. E. (2015). The brain's default mode network. Annual Review of Neuroscience, 38, 433–447. doi:10.1146/annurev-neuro-071013-014030.CrossRefGoogle ScholarPubMed
Rappaport, N., Twik, M., Plaschkes, I., Nudel, R., Iny Stein, T., Levitt, J., … Lancet, D. (2017). MalaCards: An amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Research, 45(D1), D877–D887. doi:10.1093/nar/gkw1012.CrossRefGoogle ScholarPubMed
Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M. Breen, G., … Sullivan, P. F. (2013). A mega-analysis of genome-wide association studies for major depressive disorder. Molecular Psychiatry, 18(4), 497–511. doi:10.1038/mp.2012.21.Google ScholarPubMed
Roelfsema, P. R., & de Lange, F. P. (2016). Early visual cortex as a multiscale cognitive blackboard. Annual Review of Vision Science, 2, 131–151. doi:10.1146/annurev-vision-111815-114443.CrossRefGoogle ScholarPubMed
Romero-Garcia, R., Warrier, V., Bullmore, E. T., Baron-Cohen, S., & Bethlehem, R. A. I. (2019). Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Molecular Psychiatry, 24(7), 1053–1064. doi:10.1038/s41380-018-0023-7.CrossRefGoogle ScholarPubMed
Rottenberg, J. (2017). Emotions in depression: What do we really know? Annual Review of Clinical Psychology, 13, 241–263. doi:10.1146/annurev-clinpsy-032816-045252.CrossRefGoogle ScholarPubMed
Russo, S. J., & Nestler, E. J. (2013). The brain reward circuitry in mood disorders. Nature Reviews Neuroscience, 14(9), 609–625. doi:10.1038/nrn3381.CrossRefGoogle ScholarPubMed
Sahib, A. K., Loureiro, J. R. A., Vasavada, M. M., Kubicki, A., Joshi, S. H., Wang, K., … Narr, K. L. (2020). Single and repeated ketamine treatment induces perfusion changes in sensory and limbic networks in major depressive disorder. European Neuropsychopharmacology, 33, 89–100. doi:10.1016/j.euroneuro.2020.01.017.CrossRefGoogle ScholarPubMed
Salmela, V., Socada, L., Soderholm, J., Heikkila, R., Lahti, J., Ekelund, J., & Isometsa, E. (2021). Reduced visual contrast suppression during major depressive episodes. Journal of Psychiatry and Neuroscience, 46(2), E222–E231. doi:10.1503/jpn.200091.CrossRefGoogle ScholarPubMed
Savitz, J., Nugent, A. C., Cannon, D. M., Carlson, P. J., Davis, R., Neumeister, A., … Drevets, W. C. (2012). Effects of arterial cannulation stress on regional cerebral blood flow in major depressive disorder. Scientific Reports, 2, 308. doi:10.1038/srep00308.CrossRefGoogle ScholarPubMed
Segarra, M., Aburto, M. R., Hefendehl, J., & Acker-Palmer, A. (2019). Neurovascular interactions in the nervous system. Annual Review of Cell and Developmental Biology, 35, 615–635. doi:10.1146/annurev-cellbio-100818-125142.CrossRefGoogle ScholarPubMed
Smith, R. S., & Walsh, C. A. (2020). Ion channel functions in early brain development. Trends in Neurosciences, 43(2), 103–114. doi:10.1016/j.tins.2019.12.004.CrossRefGoogle ScholarPubMed
Stratmann, M., Konrad, C., Kugel, H., Krug, A., Schoning, S., Ohrmann, P., … Dannlowski, U. (2014). Insular and hippocampal gray matter volume reductions in patients with major depressive disorder. PLoS One, 9(7), e102692. doi:10.1371/journal.pone.0102692.CrossRefGoogle ScholarPubMed
Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. The American Journal of Psychiatry, 157(10), 1552–1562. doi:10.1176/appi.ajp.157.10.1552.CrossRefGoogle ScholarPubMed
Tao, H., Guo, S., Ge, T., Kendrick, K. M., Xue, Z., Liu, Z., … Feng, J. (2013). Depression uncouples brain hate circuit. Molecular Psychiatry, 18(1), 101–111. doi:10.1038/mp.2011.127.CrossRefGoogle ScholarPubMed
Vardi, N., Freedman, N., Lester, H., Gomori, J. M., Chisin, R., Lerer, B., & Bonne, O. (2011). Hyperintensities on T2-weighted images in the basal ganglia of patients with major depression: Cerebral perfusion and clinical implications. Psychiatry Research, 192(2), 125–130. doi:10.1016/j.pscychresns.2010.11.010.CrossRefGoogle ScholarPubMed
Vasic, N., Wolf, N. D., Gron, G., Sosic-Vasic, Z., Connemann, B. J., Sambataro, F., … Wolf, R. C. (2015). Baseline brain perfusion and brain structure in patients with major depression: A multimodal magnetic resonance imaging study. Journal of Psychiatry & Neuroscience, 40(6), 412–421. doi:10.1503/jpn.140246.CrossRefGoogle ScholarPubMed
Wang, Y. M., & Yang, Z. Y. (2022). Aberrant pattern of cerebral blood flow in patients with major depressive disorder: A meta-analysis of arterial spin labelling studies. Psychiatry Research Neuroimaging, 321, 111458. doi:10.1016/j.pscychresns.2022.111458.CrossRefGoogle ScholarPubMed
Whiteford, H. A., Degenhardt, L., Rehm, J., Baxter, A. J., Ferrari, A. J., Erskine, H. E., … Vos, T. (2013). Global burden of disease attributable to mental and substance use disorders: Findings from the Global Burden of Disease Study 2010. Lancet (London, England), 382(9904), 1575–1586. doi:10.1016/s0140-6736(13)61611-6.CrossRefGoogle ScholarPubMed
Whitmer, A. J., & Gotlib, I. H. (2013). An attentional scope model of rumination. Psychological Bulletin, 139(5), 1036–1061. doi:10.1037/a0030923.CrossRefGoogle ScholarPubMed
Willner, P., Scheel-Krüger, J., & Belzung, C. (2013). The neurobiology of depression and antidepressant action. Neuroscience and Biobehavioral Reviews, 37(10 Pt 1), 2331–2371. doi:10.1016/j.neubiorev.2012.12.007.CrossRefGoogle ScholarPubMed
Wintermark, M., Sesay, M., Barbier, E., Borbély, K., Dillon, W. P., Eastwood, J. D., … Yonas, H. (2005). Comparative overview of brain perfusion imaging techniques. Journal of Neuroradiology, 32(5), 294–314. doi:10.1016/s0150-9861(05)83159-1.CrossRefGoogle ScholarPubMed
World Health Organization. (2008). The global burden of disease: 2004 update. Geneva: World Health Organization. Retrieved from https://www.who.int/evidence/bod.Google Scholar
Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., … Sullivan, P. F. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681. doi:10.1038/s41588-018-0090-3.CrossRefGoogle ScholarPubMed
Xie, Y., Zhang, X., Liu, F., Qin, W., Fu, J., Xue, K., & Yu, C. (2020). Brain mRNA expression associated with cortical volume alterations in autism spectrum disorder. Cell Reports, 32(11), 108137. doi:10.1016/j.celrep.2020.108137.CrossRefGoogle ScholarPubMed
Xu, G., Rowley, H. A., Wu, G., Alsop, D. C., Shankaranarayanan, A., Dowling, M., … Johnson, S. C. (2010). Reliability and precision of pseudo-continuous arterial spin labeling perfusion MRI on 3.0T and comparison with 15O-water PET in elderly subjects at risk for Alzheimer's disease. NMR in Biomedicine, 23(3), 286–293. doi:10.1002/nbm.1462.CrossRefGoogle Scholar
Xu, X., Wells, A. B., O'Brien, D. R., Nehorai, A., & Dougherty, J. D. (2014). Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. The Journal of Neuroscience, 34(4), 1420–1431. doi:10.1523/JNEUROSCI.4488-13.2014.CrossRefGoogle ScholarPubMed
Xue, K., Liang, S., Yang, B., Zhu, D., Xie, Y., Qin, W., … Yu, C. (2020). Local dynamic spontaneous brain activity changes in first-episode, treatment-naive patients with major depressive disorder and their associated gene expression profiles. Psychological Medicine, 52(11), 2052–2061. doi:10.1017/S0033291720003876.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
Yeshurun, Y., Nguyen, M., & Hasson, U. (2021). The default mode network: Where the idiosyncratic self meets the shared social world. Nature Reviews Neuroscience, 22(3), 181–192. doi:10.1038/s41583-020-00420-w.CrossRefGoogle ScholarPubMed
Zeng, H., Shen, E. H., Hohmann, J. G., Oh, S. W., Bernard, A., Royall, J. J., … Jones, A. R. (2012). Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures. Cell, 149(2), 483–496. doi:10.1016/j.cell.2012.02.052.CrossRefGoogle ScholarPubMed
Zhang, C., Cai, H., Xu, X., Li, Q., Li, X., Zhao, W., … Yu, Y. (2021). Genetic architecture underlying differential resting-state functional connectivity of subregions within the human visual cortex. Cerebral Cortex, 32(10), 2063–2078. doi:10.1093/cercor/bhab335.CrossRefGoogle Scholar
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.2019.116287.CrossRefGoogle ScholarPubMed
Zhu, J., Zhuo, C., Qin, W., Xu, Y., Xu, L., Liu, X., & Yu, C. (2015). Altered resting-state cerebral blood flow and its connectivity in schizophrenia. Journal of Psychiatric Research, 63, 28–35. doi:10.1016/j.jpsychires.2015.03.002.CrossRefGoogle ScholarPubMed
Zhu, J., Zhuo, C., Xu, L., Liu, F., Qin, W., & Yu, C. (2017). Altered coupling between resting-state cerebral blood flow and functional connectivity in schizophrenia. Schizophrenia Bulletin, 43(6), 1363–1374. doi:10.1093/schbul/sbx051.CrossRefGoogle ScholarPubMed
Zhuo, C., Zhu, J., Qin, W., Qu, H., Ma, X., & Yu, C. (2017). Cerebral blood flow alterations specific to auditory verbal hallucinations in schizophrenia. The British Journal of Psychiatry, 210(3), 209–215. doi:10.1192/bjp.bp.115.174961.CrossRefGoogle ScholarPubMed
Supplementary material: File

Sun et al. supplementary material

Sun et al. supplementary material

Download Sun et al. supplementary material(File)
File 3.1 MB