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Progressive brain structural abnormality in depression assessed with MR imaging by using causal network analysis

Published online by Cambridge University Press:  29 September 2021

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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
Shuying Li
Affiliation:
Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Liang Liu
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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
Caihong Wang
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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
Yu Jiang
Affiliation:
Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
Mengmeng Wen
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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
Jianyue Pang
Affiliation:
Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Hengfen Li
Affiliation:
Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Yong Zhang
Affiliation:
Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
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, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, 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
*
Authors for correspondence: Shaoqiang Han, E-mail: shaoqianghan@163.com, Jingliang Cheng, E-mail: fccchengjl@zzu.edu.cn, Yuan Chen, E-mail: chenyuanshizt@163.com
Authors for correspondence: Shaoqiang Han, E-mail: shaoqianghan@163.com, Jingliang Cheng, E-mail: fccchengjl@zzu.edu.cn, Yuan Chen, E-mail: chenyuanshizt@163.com
Authors for correspondence: Shaoqiang Han, E-mail: shaoqianghan@163.com, Jingliang Cheng, E-mail: fccchengjl@zzu.edu.cn, Yuan Chen, E-mail: chenyuanshizt@163.com

Abstract

Background

As a neuroprogressive illness, depression is accompanied by brain structural abnormality that extends to many brain regions. However, the progressive structural alteration pattern remains unknown.

Methods

To elaborate the progressive structural alteration of depression according to illness duration, we recruited 195 never-treated first-episode patients with depression and 130 healthy controls (HCs) undergoing T1-weighted MRI scans. Voxel-based morphometry method was adopted to measure gray matter volume (GMV) for each participant. Patients were first divided into three stages according to the length of illness duration, then we explored stage-specific GMV alterations and the causal effect relationship between them using causal structural covariance network (CaSCN) analysis.

Results

Overall, patients with depression presented stage-specific GMV alterations compared with HCs. Regions including the hippocampus, the thalamus and the ventral medial prefrontal cortex (vmPFC) presented GMV alteration at onset of illness. Then as the illness advanced, others regions began to present GMV alterations. These results suggested that GMV alteration originated from the hippocampus, the thalamus and vmPFC then expanded to other brain regions. The results of CaSCN analysis revealed that the hippocampus and the vmPFC corporately exerted causal effect on regions such as nucleus accumbens, the precuneus and the cerebellum. In addition, GMV alteration in the hippocampus was also potentially causally related to that in the dorsolateral frontal gyrus.

Conclusions

Consistent with the neuroprogressive hypothesis, our results reveal progressive morphological alteration originating from the vmPFC and the hippocampus and further elucidate possible details about disease progression of depression.

Type
Original Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Adler, C. M., Levine, A. D., DelBello, M. P., & Strakowski, S. M. (2005). Changes in gray matter volume in patients with bipolar disorder. Biological Psychiatry, 58(2), 151157. doi: 10.1016/j.biopsych.2005.03.022CrossRefGoogle ScholarPubMed
Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013). Imaging structural co-variance between human brain regions. Nature Reviews Neuroscience, 14(5), 322336. doi: 10.1038/nrn3465CrossRefGoogle ScholarPubMed
Ancelin, M. L., Carrière, I., Artero, S., Maller, J., Meslin, C., Ritchie, K., & Chaudieu, I. (2019). Lifetime major depression and grey-matter volume. Journal of Psychiatry & Neuroscience : JPN, 44(1), 4553. doi: 10.1503/jpn.180026CrossRefGoogle ScholarPubMed
Ansell, E. B., Rando, K., Tuit, K., Guarnaccia, J., & Sinha, R. (2012). Cumulative adversity and smaller gray matter volume in medial prefrontal, anterior cingulate, and insula regions. Biological Psychiatry, 72(1), 5764. doi: 10.1016/j.biopsych.2011.11.022CrossRefGoogle ScholarPubMed
Barbas, H., & Blatt, G. J. (1995). Topographically specific hippocampal projections target functionally distinct prefrontal areas in the rhesus monkey. Hippocampus, 5(6), 511533. doi: 10.1002/hipo.450050604CrossRefGoogle ScholarPubMed
Belleau, E. L., Treadway, M. T., & Pizzagalli, D. A. (2019). The impact of stress and major depressive disorder on hippocampal and medial prefrontal Cortex morphology. Biological Psychiatry, 85(6), 443453. doi: 10.1016/j.biopsych.2018.09.031CrossRefGoogle ScholarPubMed
Bludau, S., Bzdok, D., Gruber, O., Kohn, N., Riedl, V., Sorg, C., & Eickhoff, S. B. (2016). Medial prefrontal aberrations in major depressive disorder revealed by cytoarchitectonically informed voxel-based morphometry. The American Journal of Psychiatry, 173(3), 291298. doi: 10.1176/appi.ajp.2015.15030349CrossRefGoogle ScholarPubMed
Bora, E., Fornito, A., Pantelis, C., & Yücel, M. (2012). Gray matter abnormalities in major depressive disorder: A meta-analysis of voxel based morphometry studies. Journal of Affective Disorders, 138(1–2), 918. doi: 10.1016/j.jad.2011.03.049CrossRefGoogle ScholarPubMed
Brambilla, P., Nicoletti, M. A., Harenski, K., Sassi, R. B., Mallinger, A. G., Frank, E., & Soares, J. C. (2002). Anatomical MRI study of subgenual prefrontal cortex in bipolar and unipolar subjects. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 27(5), 792799. doi: 10.1016/s0893-133x(02)00352-4CrossRefGoogle ScholarPubMed
Bremner, J. D., Narayan, M., Anderson, E. R., Staib, L. H., Miller, H. L., & Charney, D. S. (2000). Hippocampal volume reduction in major depression. The American Journal of Psychiatry, 157(1), 115118. doi: 10.1176/ajp.157.1.115CrossRefGoogle ScholarPubMed
Brown, E. C., Clark, D. L., Hassel, S., MacQueen, G., & Ramasubbu, R. (2019). Intrinsic thalamocortical connectivity varies in the age of onset subtypes in major depressive disorder. Neuropsychiatric Disease and Treatment, 15, 7582. doi: 10.2147/ndt.s184425CrossRefGoogle ScholarPubMed
Caetano, S. C., Hatch, J. P., Brambilla, P., Sassi, R. B., Nicoletti, M., Mallinger, A. G., & Soares, J. C. (2004). Anatomical MRI study of hippocampus and amygdala in patients with current and remitted major depression. Psychiatry Research, 132(2), 141147. doi: 10.1016/j.pscychresns.2004.08.002CrossRefGoogle ScholarPubMed
Campbell, S., Marriott, M., Nahmias, C., & MacQueen, G. M. (2004). Lower hippocampal volume in patients suffering from depression: A meta-analysis. The American Journal of Psychiatry, 161(4), 598607. doi: 10.1176/appi.ajp.161.4.598CrossRefGoogle ScholarPubMed
Chen, V. C., Shen, C. Y., Liang, S. H., Li, Z. H., Tyan, Y. S., Liao, Y. T., & Weng, J. C. (2016a). Assessment of abnormal brain structures and networks in major depressive disorder using morphometric and connectome analyses. Journal of Affective Disorders, 205, 103111. doi: 10.1016/j.jad.2016.06.066CrossRefGoogle Scholar
Chen, Y., Cui, Q., Fan, Y. S., Guo, X., Tang, Q., Sheng, W., & Chen, H. (2020). Progressive brain structural alterations assessed via causal analysis in patients with generalized anxiety disorder. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 45(10), 16891697. doi: 10.1038/s41386-020-0704-1CrossRefGoogle ScholarPubMed
Chen, Z., Peng, W., Sun, H., Kuang, W., Li, W., Jia, Z., & Gong, Q. (2016b). High-field magnetic resonance imaging of structural alterations in first-episode, drug-naive patients with major depressive disorder. Translational Psychiatry, 6(11), e942. doi: 10.1038/tp.2016.209CrossRefGoogle Scholar
Cheng, W., Rolls, E. T., Qiu, J., Yang, D., Ruan, H., Wei, D., & Feng, J. (2018). Functional connectivity of the precuneus in unmedicated patients with depression. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 3(12), 10401049. doi: 10.1016/j.bpsc.2018.07.008CrossRefGoogle ScholarPubMed
Christakou, A., Robbins, T. W., & Everitt, B. J. (2004). Prefrontal cortical-ventral striatal interactions involved in affective modulation of attentional performance: Implications for corticostriatal circuit function. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 24(4), 773780. doi: 10.1523/jneurosci.0949-03.2004CrossRefGoogle ScholarPubMed
Christoffel, D. J., Golden, S. A., Walsh, J. J., Guise, K. G., Mitra, H., Friedman, A. K., & Madeline, P. (2015). Excitatory transmission at thalamo–striatal synapses mediates susceptibility to social stress. Nature Neuroscience, 18(7), 962964.CrossRefGoogle ScholarPubMed
Coryell, W., Nopoulos, P., Drevets, W., Wilson, T., & Andreasen, N. C. (2005). Subgenual prefrontal cortex volumes in major depressive disorder and schizophrenia: Diagnostic specificity and prognostic implications. The American Journal of Psychiatry, 162(9), 17061712. doi: 10.1176/appi.ajp.162.9.1706CrossRefGoogle ScholarPubMed
Eippert, F., Veit, R., Weiskopf, N., Erb, M., Birbaumer, N., & Anders, S. (2007). Regulation of emotional responses elicited by threat-related stimuli. Human Brain Mapping, 28(5), 409423. doi: 10.1002/hbm.20291CrossRefGoogle ScholarPubMed
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), 1935. doi: 10.1080/15622970902737998CrossRefGoogle ScholarPubMed
Ende, G. (2015). Proton magnetic resonance spectroscopy: Relevance of glutamate and GABA to neuropsychology. Neuropsychology Review, 25(3), 315325. doi: 10.1007/s11065-015-9295-8CrossRefGoogle ScholarPubMed
Friedman, D. P., Aggleton, J. P., & Saunders, R. C. (2002). Comparison of hippocampal, amygdala, and perirhinal projections to the nucleus accumbens: Combined anterograde and retrograde tracing study in the Macaque brain. The Journal of Comparative Neurology, 450(4), 345365. doi: 10.1002/cne.10336CrossRefGoogle ScholarPubMed
Frodl, T., Meisenzahl, E. M., Zetzsche, T., Born, C., Jäger, M., Groll, C., & Möller, H. J. (2003). Larger amygdala volumes in first depressive episode as compared to recurrent major depression and healthy control subjects. Biological Psychiatry, 53(4), 338344. doi: 10.1016/s0006-3223(02)01474-9CrossRefGoogle ScholarPubMed
Frodl, T. S., Koutsouleris, N., Bottlender, R., Born, C., Jäger, M., Scupin, I., & Meisenzahl, E. M. (2008). Depression-related variation in brain morphology over 3 years: Effects of stress? Archives of General Psychiatry, 65(10), 11561165. doi: 10.1001/archpsyc.65.10.1156CrossRefGoogle ScholarPubMed
Gorwood, P., Corruble, E., Falissard, B., & Goodwin, G. M. (2008). Toxic effects of depression on brain function: Impairment of delayed recall and the cumulative length of depressive disorder in a large sample of depressed outpatients. The American Journal of Psychiatry, 165(6), 731739. doi: 10.1176/appi.ajp.2008.07040574CrossRefGoogle Scholar
Gradin, V. B., Kumar, P., Waiter, G., Ahearn, T., Stickle, C., Milders, M., & Steele, J. D. (2011). Expected value and prediction error abnormalities in depression and schizophrenia. Brain, 134, 17511764. doi: 10.1093/brain/awr059CrossRefGoogle Scholar
Han, S., Cui, Q., Wang, X., Chen, Y., Li, D., Li, L., & Chen, H. (2020a). The anhedonia is differently modulated by structural covariance network of NAc in bipolar disorder and major depressive disorder. Progress in Neuro-psychopharmacology & Biological Psychiatry, 99, 109865. doi: 10.1016/j.pnpbp.2020.109865CrossRefGoogle Scholar
Han, S., Cui, Q., Wang, X., Li, L., Li, D., He, Z., … Guo, X. (2020b). Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder. Human brain mapping 41(12), 32953304. doi: 10.1002/hbm.25017CrossRefGoogle Scholar
Hatton, S. N., Franz, C. E., Elman, J. A., Panizzon, M. S., Hagler, D. J. Jr., Fennema-Notestine, C., & Kremen, W. S. (2018). Negative fateful life events in midlife and advanced predicted brain aging. Neurobiology of Aging, 67, 19. doi: 10.1016/j.neurobiolaging.2018.03.004CrossRefGoogle 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), 638647. doi: 10.1016/j.biopsych.2017.10.030CrossRefGoogle ScholarPubMed
Hoover, W. B., & Vertes, R. P. (2007). Anatomical analysis of afferent projections to the medial prefrontal cortex in the rat. Brain Structure & Function, 212(2), 149179. doi: 10.1007/s00429-007-0150-4CrossRefGoogle ScholarPubMed
Jiang, Y., Luo, C., Li, X., Duan, M., He, H., Chen, X., & Yao, D. (2018). Progressive reduction in gray matter in patients with schizophrenia assessed with MR imaging by using causal network analysis. Radiology, 287(2), 633642. doi: 10.1148/radiol.2017171832CrossRefGoogle ScholarPubMed
Joormann, J., & Stanton, C. H. (2016). Examining emotion regulation in depression: A review and future directions. Behaviour Research and Therapy, 86, 3549. doi: 10.1016/j.brat.2016.07.007CrossRefGoogle ScholarPubMed
Kandilarova, S., Stoyanov, D., Sirakov, N., & Maes, M. (2019). Reduced grey matter volume in frontal and temporal areas in depression: Contributions from voxel-based morphometry study. ACTA Neuropsychiatr 31(5), 252257. doi: 10.1017/neu.2019.20CrossRefGoogle ScholarPubMed
Kempton, M. J., Salvador, Z., Munafò, M. R., Geddes, J. R., Simmons, A., Frangou, S., & Williams, S. C. (2011). Structural neuroimaging studies in major depressive disorder. Meta-analysis and comparison with bipolar disorder. Archives of General Psychiatry, 68(7), 675690. doi: 10.1001/archgenpsychiatry.2011.60CrossRefGoogle ScholarPubMed
Kendler, K. S., Thornton, L. M., & Gardner, C. O. (2001). Genetic risk, number of previous depressive episodes, and stressful life events in predicting onset of major depression. The American Journal of Psychiatry, 158(4), 582586. doi: 10.1176/appi.ajp.158.4.582CrossRefGoogle ScholarPubMed
Kessing, L. V., & Andersen, P. K. (2004). Does the risk of developing dementia increase with the number of episodes in patients with depressive disorder and in patients with bipolar disorder? Journal of Neurology, Neurosurgery, and Psychiatry, 75(12), 16621666. doi: 10.1136/jnnp.2003.031773CrossRefGoogle ScholarPubMed
Klauser, P., Fornito, A., Lorenzetti, V., Davey, C. G., Dwyer, D. B., Allen, N. B., & Yücel, M. (2015). Cortico-limbic network abnormalities in individuals with current and past major depressive disorder. Journal of Affective Disorders, 173, 4552. doi: 10.1016/j.jad.2014.10.041CrossRefGoogle ScholarPubMed
Kuhn, M., Höger, N., Feige, B., Blechert, J., Normann, C., & Nissen, C. (2014). Fear extinction as a model for synaptic plasticity in major depressive disorder. PloS One, 9(12), e115280. doi: 10.1371/journal.pone.0115280CrossRefGoogle ScholarPubMed
LeGates, T. A., Kvarta, M. D., Tooley, J. R., Francis, T. C., Lobo, M. K., Creed, M. C., & Thompson, S. M. (2018). Reward behaviour is regulated by the strength of hippocampus-nucleus accumbens synapses. Nature, 564(7735), 258262. doi: 10.1038/s41586-018-0740-8CrossRefGoogle ScholarPubMed
Lévesque, J., Eugène, F., Joanette, Y., Paquette, V., Mensour, B., Beaudoin, G., & Beauregard, M. (2003). Neural circuitry underlying voluntary suppression of sadness. Biological Psychiatry, 53(6), 502510. doi: 10.1016/s0006-3223(02)01817-6CrossRefGoogle ScholarPubMed
Likhtik, E., Pelletier, J. G., Paz, R., & Paré, D. (2005). Prefrontal control of the amygdala. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 25(32), 74297437. doi: 10.1523/jneurosci.2314-05.2005CrossRefGoogle ScholarPubMed
Lorenzetti, V., Allen, N. B., Fornito, A., & Yücel, M. (2009). Structural brain abnormalities in major depressive disorder: A selective review of recent MRI studies. Journal of Affective Disorders, 117(1–2), 117. doi: 10.1016/j.jad.2008.11.021CrossRefGoogle ScholarPubMed
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), 337343. doi: 10.1503/jpn.100002CrossRefGoogle ScholarPubMed
McKinnon, M. C., Yucel, K., Nazarov, A., & MacQueen, G. M. (2009). A meta-analysis examining clinical predictors of hippocampal volume in patients with major depressive disorder. Journal of Psychiatry & Neuroscience : JPN, 34(1), 4154.Google ScholarPubMed
Mervaala, E., Föhr, J., Könönen, M., Valkonen-Korhonen, M., Vainio, P., Partanen, K., & Lehtonen, J. (2000). Quantitative MRI of the hippocampus and amygdala in severe depression. Psychological Medicine, 30(1), 117125. doi: 10.1017/s0033291799001567CrossRefGoogle ScholarPubMed
Morris, G., Puri, B. K., Walker, A. J., Maes, M., Carvalho, A. F., Bortolasci, C. C., & Berk, M. (2019). Shared pathways for neuroprogression and somatoprogression in neuropsychiatric disorders. Neuroscience and Biobehavioral Reviews, 107, 862882. doi: 10.1016/j.neubiorev.2019.09.025CrossRefGoogle ScholarPubMed
Moulton, E. A., Elman, I., Pendse, G., Schmahmann, J., Becerra, L., & Borsook, D. (2011). Aversion-related circuitry in the cerebellum: Responses to noxious heat and unpleasant images. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 31(10), 37953804. doi: 10.1523/jneurosci.6709-10.2011CrossRefGoogle ScholarPubMed
Moylan, S., Maes, M., Wray, N. R., & Berk, M. (2013). The neuroprogressive nature of major depressive disorder: Pathways to disease evolution and resistance, and therapeutic implications. Molecular Psychiatry, 18(5), 595606. doi: 10.1038/mp.2012.33CrossRefGoogle Scholar
Murray, C. J., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., & Memish, Z. A. (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet (London, England), 380(9859), 21972223. doi: 10.1016/s0140-6736(12)61689-4CrossRefGoogle ScholarPubMed
Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. (2002). Rethinking feelings: An FMRI study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience, 14(8), 12151229. doi: 10.1162/089892902760807212CrossRefGoogle ScholarPubMed
Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D., & Gross, J. J. (2004). For better or for worse: Neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage, 23(2), 483499. doi: 10.1016/j.neuroimage.2004.06.030CrossRefGoogle ScholarPubMed
O'Reilly, J. X., Beckmann, C. F., Tomassini, V., Ramnani, N., & Johansen-Berg, H. (2010). Distinct and overlapping functional zones in the cerebellum defined by resting state functional connectivity. Cerebral Cortex (New York, N.Y. : 1991), 20(4), 953965. doi: 10.1093/cercor/bhp157CrossRefGoogle ScholarPubMed
Penttilä, J., Cachia, A., Martinot, J. L., Ringuenet, D., Wessa, M., Houenou, J., & Paillère-Martinot, M. L. (2009). Cortical folding difference between patients with early-onset and patients with intermediate-onset bipolar disorder. Bipolar Disorders, 11(4), 361370. doi: 10.1111/j.1399-5618.2009.00683.xCrossRefGoogle ScholarPubMed
Phan, K. L., Fitzgerald, D. A., Nathan, P. J., Moore, G. J., Uhde, T. W., & Tancer, M. E. (2005). Neural substrates for voluntary suppression of negative affect: A functional magnetic resonance imaging study. Biological Psychiatry, 57(3), 210219. doi: 10.1016/j.biopsych.2004.10.030CrossRefGoogle ScholarPubMed
Phillipson, O. T., & Griffiths, A. C. (1985). The topographic order of inputs to nucleus accumbens in the rat. Neuroscience, 16(2), 275296. doi: 10.1016/0306-4522(85)90002-8CrossRefGoogle ScholarPubMed
Quirk, G. J., Likhtik, E., Pelletier, J. G., & Paré, D. (2003). Stimulation of medial prefrontal cortex decreases the responsiveness of central amygdala output neurons. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 23(25), 88008807. doi: 10.1523/jneurosci.23-25-08800.2003CrossRefGoogle ScholarPubMed
Raichle, M. E. (2015). The brain's default mode network. Annual Review of Neuroscience, 38, 433447. doi: 10.1146/annurev-neuro-071013-014030CrossRefGoogle ScholarPubMed
Richard, J. M., & Berridge, K. C. (2013). Prefrontal cortex modulates desire and dread generated by nucleus accumbens glutamate disruption. Biological Psychiatry, 73(4), 360370. doi: 10.1016/j.biopsych.2012.08.009CrossRefGoogle ScholarPubMed
Rosenkranz, J. A., Moore, H., & Grace, A. A. (2003). The prefrontal cortex regulates lateral amygdala neuronal plasticity and responses to previously conditioned stimuli. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 23(35), 1105411064. doi: 10.1523/jneurosci.23-35-11054.2003CrossRefGoogle ScholarPubMed
Ruiz, N. A. L., Del Ángel, D. S., Olguín, H. J., & Silva, M. L. (2018). Neuroprogression: The hidden mechanism of depression. Neuropsychiatric Disease and Treatment, 14, 28372845. doi: 10.2147/ndt.s177973CrossRefGoogle 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), 900909. doi: 10.1038/mp.2016.60CrossRefGoogle 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), 806812. doi: 10.1038/mp.2015.69CrossRefGoogle ScholarPubMed
Serra-Blasco, M., Portella, M. J., Gómez-Ansón, B., de Diego-Adeliño, J., Vives-Gilabert, Y., Puigdemont, D., & Pérez, V. (2013). Effects of illness duration and treatment resistance on grey matter abnormalities in major depression. The British Journal of Psychiatry : The Journal of Mental Science, 202, 434440. doi: 10.1192/bjp.bp.112.116228CrossRefGoogle ScholarPubMed
Sheline, Y. I., Gado, M. H., & Kraemer, H. C. (2003). Untreated depression and hippocampal volume loss. The American Journal of Psychiatry, 160(8), 15161518. doi: 10.1176/appi.ajp.160.8.1516CrossRefGoogle ScholarPubMed
Sheline, Y. I., Gado, M. H., & Price, J. L. (1998). Amygdala core nuclei volumes are decreased in recurrent major depression. Neuroreport, 9(9), 20232028. doi: 10.1097/00001756-199806220-00021CrossRefGoogle ScholarPubMed
Sigurdsson, T., & Duvarci, S. (2015). Hippocampal-prefrontal interactions in cognition, behavior and psychiatric disease. Frontiers in Systems Neuroscience, 9, 190. doi: 10.3389/fnsys.2015.00190Google ScholarPubMed
Steinke, J., Gaser, C., Langbein, K., Dietzek, M., Gussew, A., Reichenbach, J. R., & Nenadić, I. (2017). Hippocampal metabolism and prefrontal brain structure: A combined 1H-MR spectroscopy, neuropsychological, and voxel-based morphometry (VBM) study. Brain Research, 1677, 1419. doi: 10.1016/j.brainres.2017.09.004CrossRefGoogle ScholarPubMed
Straub, J., Brown, R., Malejko, K., Bonenberger, M., Grön, G., Plener, P. L., & Abler, B. (2019). Adolescent depression and brain development: Evidence from voxel-based morphometry. Journal of Psychiatry & Neuroscience : JPN, 44(4), 237245. doi: 10.1503/jpn.170233CrossRefGoogle ScholarPubMed
Stuber, G. D., Sparta, D. R., Stamatakis, A. M., van Leeuwen, W. A., Hardjoprajitno, J. E., Cho, S., & Bonci, A. (2011). Excitatory transmission from the amygdala to nucleus accumbens facilitates reward seeking. Nature, 475(7356), 377–U129. doi: 10.1038/nature10194CrossRefGoogle 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), 285294. doi: 10.1016/j.biopsych.2014.06.018CrossRefGoogle ScholarPubMed
Truong, W., Minuzzi, L., Soares, C. N., Frey, B. N., Evans, A. C., MacQueen, G. M., & Hall, G. B. (2013). Changes in cortical thickness across the lifespan in major depressive disorder. Psychiatry Research, 214(3), 204211. doi: 10.1016/j.pscychresns.2013.09.003CrossRefGoogle ScholarPubMed
Turner, B. M., Paradiso, S., Marvel, C. L., Pierson, R., Boles Ponto, L. L., Hichwa, R. D., & Robinson, R. G. (2007). The cerebellum and emotional experience. Neuropsychologia, 45(6), 13311341. doi: 10.1016/j.neuropsychologia.2006.09.023CrossRefGoogle ScholarPubMed
van Eijndhoven, P., van Wingen, G., Katzenbauer, M., Groen, W., Tepest, R., Fernández, G., & Tendolkar, I. (2013). Paralimbic cortical thickness in first-episode depression: Evidence for trait-related differences in mood regulation. The American Journal of Psychiatry, 170(12), 14771486. doi: 10.1176/appi.ajp.2013.12121504CrossRefGoogle ScholarPubMed
van Eijndhoven, P., van Wingen, G., van Oijen, K., Rijpkema, M., Goraj, B., Jan Verkes, R., & Tendolkar, I. (2009). Amygdala volume marks the acute state in the early course of depression. Biological Psychiatry, 65(9), 812818. doi: 10.1016/j.biopsych.2008.10.027CrossRefGoogle Scholar
Vasic, N., Walter, H., Höse, A., & Wolf, R. C. (2008). Gray matter reduction associated with psychopathology and cognitive dysfunction in unipolar depression: A voxel-based morphometry study. Journal of Affective Disorders, 109(1–2), 107116. doi: 10.1016/j.jad.2007.11.011CrossRefGoogle ScholarPubMed
Verduijn, J., Milaneschi, Y., Schoevers, R. A., van Hemert, A. M., Beekman, A. T., & Penninx, B. W. (2015). Pathophysiology of major depressive disorder: Mechanisms involved in etiology are not associated with clinical progression. Translational Psychiatry, 5(9), e649. doi: 10.1038/tp.2015.137CrossRefGoogle Scholar
Wise, T., Radua, J., Via, E., Cardoner, N., & Abe, O. (2017). Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: Evidence from voxel-based meta-analysis. Molecular Psychiatry 22(10), 14551463. doi: 10.1038/mp.2016.72CrossRefGoogle ScholarPubMed
Yucel, K., McKinnon, M., Chahal, R., Taylor, V., Macdonald, K., Joffe, R., & Macqueen, G. (2009). Increased subgenual prefrontal cortex size in remitted patients with major depressive disorder. Psychiatry Research, 173(1), 7176. doi: 10.1016/j.pscychresns.2008.07.013CrossRefGoogle ScholarPubMed
Yucel, K., McKinnon, M. C., Chahal, R., Taylor, V. H., Macdonald, K., Joffe, R., & MacQueen, G. M. (2008). Anterior cingulate volumes in never-treated patients with major depressive disorder. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 33(13), 31573163. doi: 10.1038/npp.2008.40CrossRefGoogle ScholarPubMed
Zhang, Z., Liao, W., Xu, Q., Wei, W., Zhou, H. J., Sun, K., & Lu, G. (2017). Hippocampus-associated causal network of structural covariance measuring structural damage progression in temporal lobe epilepsy. Human Brain Mapping, 38(2), 753766. doi: 10.1002/hbm.23415CrossRefGoogle ScholarPubMed
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Progressive brain structural abnormality in depression assessed with MR imaging by using causal network analysis
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