AccScience Publishing / JCBP / Online First / DOI: 10.36922/jcbp.0896
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Pharmacological treatment-associated brain structural and functional alterations in major depressive disorder: A narrative review

Lulu Zhang1,2 Jingping Zhao1 Wenbin Guo1*
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1 Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
2 Department of Psychiatry, Guangzhou First People’s Hospital, the Second Affiliated Hospital, South China University of Technology, Guangzhou, Guangdong, China
Submitted: 4 May 2023 | Accepted: 5 July 2023 | Published: 21 July 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Major depressive disorder (MDD) is a complicated mental disorder with an unclear etiology and a relatively limited response to treatment. Magnetic resonance imaging (MRI) can help identify the underlying neuropathophysiological mechanisms of MDD. A better understanding of structural and functional alterations in the brain before and after treatment is necessary to improve treatment targets at the brain level. This narrative review summarizes the literature on longitudinal MRI studies in MDD, which identify brain regions with changes in neural activity after antidepressant treatment, providing an objective imaging basis for early diagnosis and assessment of the efficacy of antidepressant therapy. It was found that the neural activity in the prefrontal cortex, cingulate cortex, precuneus, and hippocampus was reduced in patients with untreated MDD. In addition, functional connectivity of the default mode network (DMN), central executive, and salience networks was enhanced, gray matter volume was reduced, and white matter structure was damaged. After antidepressant treatment, neural activity in some brain regions increased and functional connectivity of brain networks and gray and white matter structures returned to normal states. Although the results have been inconsistent, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli are promising predictors of successful treatment, and hyperconnectivity within and between the DMN and executive control network exhibit similar results.

Keywords
Major depressive disorder
Magnetic resonance imaging
Neuroimaging
Antidepressants
Treatment
Funding
Science and Technology Program of Guangzhou
Basic and Applied Basic Research Fund of Guangdong
National Natural Science Foundation of China
Conflict of interest
The authors have no conflicts to disclose.
References
  1. Herrman H, Patel V, Kieling C, et al., 2022, Time for united action on depression: A Lancet-world Psychiatric Association Commission. Lancet, 399: 957–1022. https://doi.org/10.1016/S0140-6736(21)02141-3

 

  1. Kang SG, Cho SE, 2020, Neuroimaging biomarkers for predicting treatment response and recurrence of major depressive disorder. Int J Mol Sci, 21: 2148. https://doi.org/10.3390/ijms21062148

 

  1. Spellman T, Liston C, 2020, Toward circuit mechanisms of pathophysiology in depression. Am J Psychiatry, 177: 381–390. https://doi.org/10.1176/appi.ajp.2020.20030280

 

  1. Shao X, Zhu G, 2020, Associations among monoamine neurotransmitter pathways, personality traits, and major depressive disorder. Front Psychiatry, 11: 381. https://doi.org/10.3389/fpsyt.2020.00381

 

  1. Dwyer JB, Aftab A, Radhakrishnan R, et al., 2020, Hormonal treatments for major depressive disorder: State of the art. Am J Psychiatry, 177: 686–705. https://doi.org/10.1176/appi.ajp.2020.19080848

 

  1. Suda K, Matsuda K, 2022, How microbes affect depression: Underlying mechanisms via the gut-brain axis and the modulating role of probiotics. Int J Mol Sci, 23: 1172. https://doi.org/10.3390/ijms23031172

 

  1. Wiebenga JXM, Heering HD, Eikelenboom M, et al., 2022, Associations of three major physiological stress systems with suicidal ideation and suicide attempts in patients with a depressive and/or anxiety disorder. Brain Behav Immun, 102: 195–205. https://doi.org/10.1016/j.bbi.2022.02.021

 

  1. Chen X, Lu B, Li HX, et al., 2022, The DIRECT consortium and the REST-meta-MDD project: Towards neuroimaging biomarkers of major depressive disorder. Psychoradiology, 2: 32–42. https://doi.org/10.1093/psyrad/kkac005

 

  1. Arnone D, 2019, Functional MRI findings, pharmacological treatment in major depression and clinical response. Prog Neuropsychopharmacol Biol Psychiatry, 91: 28–37. https://doi.org/10.1016/j.pnpbp.2018.08.004

 

  1. Undurraga J, Baldessarini RJ, 2012, Randomized, placebo-controlled trials of antidepressants for acute major depression: Thirty-year meta-analytic review. Neuropsychopharmacology, 37: 851–864. https://doi.org/10.1038/npp.2011.306

 

  1. Van Velzen LS, Kelly S, Isaev D, et al., 2020, White matter disturbances in major depressive disorder: A coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. Mol Psychiatry, 25: 1511–1525. https://doi.org/10.1038/s41380-019-0477-2

 

  1. Jiang J, Zhao YJ, Hu XY, et al., 2017, Microstructural brain abnormalities in medication-free patients with major depressive disorder: A systematic review and meta-analysis of diffusion tensor imaging. J Psychiatry Neurosci, 42: 150–163.

 

  1. Rutland JW, Brown S, Verma G, et al., 2019, Hippocampal subfield-specific connectivity findings in major depressive disorder: A 7 Tesla diffusion MRI study. J Psychiatr Res, 111: 186–192. https://doi.org/10.1016/j.jpsychires.2019.02.008

 

  1. Sydnor VJ, Lyall AE, Cetin-Karayumak S, et al., 2020, Studying pre-treatment and ketamine-induced changes in white matter microstructure in the context of ketamine’s antidepressant effects. Transl Psychiatry, 10: 432. https://doi.org/10.1038/s41398-020-01122-8

 

  1. Melloni EMT, Poletti S, Dallaspezia S, et al., 2020, Changes of white matter microstructure after successful treatment of bipolar depression. J Affect Disord, 274: 1049–1056. https://doi.org/10.1016/j.jad.2020.05.146

 

  1. Voineskos AN, Mulsant BH, Dickie EW, et al., 2020, Effects of antipsychotic medication on brain structure in patients with major depressive disorder and psychotic features: Neuroimaging findings in the context of a randomized placebo-controlled clinical trial. JAMA Psychiatry, 77: 674–683. https://doi.org/10.1001/jamapsychiatry.2020.0036

 

  1. Wise T, Radua J, Via E, et al., 2017, Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: Evidence from voxel-based meta-analysis. Mol Psychiatry, 22: 1455–1463. https://doi.org/10.1038/mp.2016.72

 

  1. Yu M, Cullen N, Linn KA, et al., 2021, Structural brain measures linked to clinical phenotypes in major depression replicate across clinical centres. Mol Psychiatry, 26: 2764–2775. https://doi.org/10.1038/s41380-021-01039-8

 

  1. Zarate-Garza PP, Ortega-Balderas JA, de la Barquera JA, et al., 2021, Hippocampal volume as treatment predictor in antidepressant naïve patients with major depressive disorder. J Psychiatr Res, 140: 323–328. https://doi.org/10.1016/j.jpsychires.2021.06.008

 

  1. Bartlett EA, DeLorenzo C, Sharma P, et al., 2018, Pretreatment and early-treatment cortical thickness is associated with SSRI treatment response in major depressive disorder. Neuropsychopharmacology, 43: 2221–2230. https://doi.org/10.1038/s41386-018-0122-9

 

  1. Kraus C, Seiger R, Pfabigan DM, et al., 2019, Hippocampal subfields in acute and remitted depression-an ultra-high field magnetic resonance imaging study. Int J Neuropsychopharmacol, 22: 513–522. https://doi.org/10.1093/ijnp/pyz030

 

  1. Reed JL, Nugent AC, Furey ML, et al., 2019, Effects of ketamine on brain activity during emotional processing: Differential findings in depressed versus healthy control participants. Biol Psychiatry Cogn Neurosci Neuroimaging, 4: 610–618. https://doi.org/10.1016/j.bpsc.2019.01.005

 

  1. Narr KL, Bilder RM, Toga AW, et al., 2005, Mapping cortical thickness and gray matter concentration in first episode schizophrenia. Cereb Cortex, 15: 708–719. https://doi.org/10.1093/cercor/bhh172

 

  1. Cotter D, MacKay D, Chana G, et al., 2002, Reduced neuronal size and glial cell density in area 9 of the dorsolateral prefrontal cortex in subjects with major depressive disorder. Cereb Cortex, 12: 386–394. https://doi.org/10.1093/cercor/12.4.386

 

  1. Duman RS, Aghajanian GK, Sanacora G, et al., 2016, Synaptic plasticity and depression: New insights from stress and rapid-acting antidepressants. Nat Med, 22: 238–249. https://doi.org/10.1038/nm.4050

 

  1. Blier P, El Mansari M, 2013, Serotonin and beyond: Therapeutics for major depression. Philos Trans R Soc Lond B Biol Sci, 368: 20120536. https://doi.org/10.1098/rstb.2012.0536

 

  1. Zhuo C, Li G, Lin X, et al., 2019, The rise and fall of MRI studies in major depressive disorder. Transl Psychiatry, 9: 335. https://doi.org/10.1038/s41398-019-0680-6

 

  1. Wang L, Li K, Zhang Q, et al., 2014, Short-term effects of escitalopram on regional brain function in first-episode drug-naive patients with major depressive disorder assessed by resting-state functional magnetic resonance imaging. Psychol Med, 44: 1417–1426. https://doi.org/10.1017/S0033291713002031

 

  1. Jiang X, Fu S, Yin Z, et al., 2020, Common and distinct neural activities in frontoparietal network in first-episode bipolar disorder and major depressive disorder: Preliminary findings from a follow-up resting state fMRI study. J Affect Disord, 260: 653–659. https://doi.org/10.1016/j.jad.2019.09.063

 

  1. Zhang Q, Hong S, Cao J, et al., 2021, Hippocampal subfield volumes in major depressive disorder adolescents with a history of suicide attempt. Biomed Res Int, 2021: 5524846. https://doi.org/10.1155/2021/5524846

 

  1. Lai CH, Wu YT, 2012, Frontal regional homogeneity increased and temporal regional homogeneity decreased after remission of first-episode drug-naïve major depressive disorder with panic disorder patients under duloxetine therapy for 6 weeks. J Affect Disord, 136: 453–458. https://doi.org/10.1016/j.jad.2011.11.004

 

  1. Yang C, Zhang A, Jia A, et al., 2018, Identify abnormalities in resting-state brain function between first-episode, drug-naive major depressive disorder and remitted individuals: A 3-year retrospective study. Neuroreport, 29: 907–916. https://doi.org/10.1097/WNR.0000000000001054

 

  1. Cheng Y, Xu J, Arnone D, et al., 2017, Resting-state brain alteration after a single dose of SSRI administration predicts 8-week remission of patients with major depressive disorder. Psychol Med, 47: 438–450. https://doi.org/10.1017/S0033291716002440

 

  1. Wang M, Ju Y, Lu X, et al., 2020, Longitudinal changes of amplitude of low-frequency fluctuations in MDD patients: A 6-month follow-up resting-state functional magnetic resonance imaging study. J Affect Disord, 276: 411–417. https://doi.org/10.1016/j.jad.2020.07.067

 

  1. Li B, Liu L, Friston KJ, et al., 2013, A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry, 74: 48–54. https://doi.org/10.1016/j.biopsych.2012.11.007

 

  1. Sahib AK, Loureiro JR, Vasavada M, et al., 2020, Modulation of the functional connectome in major depressive disorder by ketamine therapy. Psychol Med, 52: 2596–2605. https://doi.org/10.1017/s0033291720004560

 

  1. Li Y, Dai X, Wu H, et al., 2021, Establishment of effective biomarkers for depression diagnosis with fusion of multiple resting-state connectivity measures. Front Neurosci, 15: 729958. https://doi.org/10.3389/fnins.2021.729958

 

  1. Wang L, Xia M, Li K, et al., 2015, The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp, 36: 768–778. https://doi.org/10.1002/hbm.22663

 

  1. Fatt CRC, Jha MK, Cooper CM, et al., 2020, Effect of intrinsic patterns of functional brain connectivity in moderating antidepressant treatment response in major depression. Am J Psychiatry, 177: 143–154. https://doi.org/10.1176/appi.ajp.2019.18070870

 

  1. Karim HT, Andreescu C, Tudorascu D, et al., 2017, Intrinsic functional connectivity in late-life depression: Trajectories over the course of pharmacotherapy in remitters and non-remitters. Mol Psychiatry, 22: 450–457. https://doi.org/10.1038/mp.2016.55

 

  1. Fu CH, Costafreda SG, Sankar A, et al., 2015, Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine. BMC Psychiatry, 15: 82. https://doi.org/10.1186/s12888-015-0457-2

 

  1. Sankar A, Adams TM, Costafreda SG, et al., 2017, Effects of antidepressant therapy on neural components of verbal working memory in depression. J Psychopharmacol, 31: 1176–1183. https://doi.org/10.1177/0269881117724594

 

  1. Fales CL, Barch DM, Rundle MM, et al., 2009, Antidepressant treatment normalizes hypoactivity in dorsolateral prefrontal cortex during emotional interference processing in major depression. J Affect Disord, 112: 206–211. https://doi.org/10.1016/j.jad.2008.04.027

 

  1. Wang Y, Xu C, Cao X, et al., 2012, Effects of an antidepressant on neural correlates of emotional processing in patients with major depression. Neurosci Lett, 527: 55–59. https://doi.org/10.1016/j.neulet.2012.08.034

 

  1. Heller AS, Johnstone T, Light SN, et al., 2013, Relationships between changes in sustained fronto-striatal connectivity and positive affect in major depression resulting from antidepressant treatment. Am J Psychiatry, 170: 197–206. https://doi.org/10.1176/appi.ajp.2012.12010014

 

  1. Williams LM, Korgaonkar MS, Song YC, et al., 2015, Amygdala reactivity to emotional faces in the prediction of general and medication-specific responses to antidepressant treatment in the randomized iSPOT-D trial. Neuropsychopharmacology, 40: 2398–2408. https://doi.org/10.1038/npp.2015.89

 

  1. Godlewska BR, Browning M, Norbury R, et al., 2016, Early changes in emotional processing as a marker of clinical response to SSRI treatment in depression. Transl Psychiatry, 6: e957. https://doi.org/10.1038/tp.2016.130

 

  1. Keedwell PA, Drapier D, Surguladze S, et al., 2010, Subgenual cingulate and visual cortex responses to sad faces predict clinical outcome during antidepressant treatment for depression. J Affect Disord, 120: 120–125. https://doi.org/10.1016/j.jad.2009.04.031

 

  1. Godlewska BR, Browning M, Norbury R, et al., 2018, Predicting treatment response in depression: The role of anterior cingulate cortex. Int J Neuropsychopharmacol, 21: 988–996. https://doi.org/10.1093/ijnp/pyy069

 

  1. Preuss A, Bolliger B, Schicho W, et al., 2020, SSRI treatment response prediction in depression based on brain activation by emotional stimuli. Front Psychiatry, 11: 538393. https://doi.org/10.3389/fpsyt.2020.538393

 

  1. Miller JM, Schneck N, Siegle GJ, et al., 2013, fMRI response to negative words and SSRI treatment outcome in major depressive disorder: A preliminary study. Psychiatry Res, 214: 296–305. https://doi.org/10.1016/j.pscychresns.2013.08.001

 

  1. Williams RJ, Brown EC, Clark DL, et al., 2021, Early post-treatment blood oxygenation level-dependent responses to emotion processing associated with clinical response to pharmacological treatment in major depressive disorder. Brain Behav, 11: e2287. https://doi.org/10.1002/brb3.2287

 

  1. Frodl T, Scheuerecker J, Schoepf V, et al., 2011, Different effects of mirtazapine and venlafaxine on brain activation: An open randomized controlled fMRI study. J Clin Psychiatry, 72: 448–457. https://doi.org/10.4088/jcp.09m05393blu

 

  1. Seminowicz DA, Mayberg HS, McIntosh AR, et al., 2004, Limbic-frontal circuitry in major depression: A path modeling metanalysis. Neuroimage, 22: 409–418. https://doi.org/10.1016/j.neuroimage.2004.01.015

 

  1. Zhou HX, Chen X, Shen YQ, et al., 2020, Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression. Neuroimage, 206: 116287. https://doi.org/10.1016/j.neuroimage.2019.116287

 

  1. Yin JB, Liang SH, Li F, et al., 2020, dmPFC-vlPAG projection neurons contribute to pain threshold maintenance and antianxiety behaviors. J Clin Invest, 130: 6555–6570. https://doi.org/10.1172/JCI127607

 

  1. Köhler CA, Carvalho AF, Alves GS, et al., 2015, Autobiographical memory disturbances in depression: A novel therapeutic target? Neural Plast, 2015: 759139. https://doi.org/10.1155/2015/759139

 

  1. Zhang Z, Chen Y, Wei W, et al., 2021, Changes in regional homogeneity of medication-free major depressive disorder patients with different onset ages. Front Psychiatry, 12: 713614. https://doi.org/10.3389/fpsyt.2021.713614

 

  1. Liu P, Tu H, Zhang A, et al., 2021, Brain functional alterations in MDD patients with somatic symptoms: A resting-state fMRI study. J Affect Disord, 295: 788–796. https://doi.org/10.1016/j.jad.2021.08.143

 

  1. Piguet C, Karahanoğlu FI, Saccaro LF, et al., 2021, Mood disorders disrupt the functional dynamics, not spatial organization of brain resting state networks. Neuroimage Clin, 32: 102833. https://doi.org/10.1016/j.nicl.2021.102833

 

  1. Ma X, Liu J, Liu T, et al., 2019, Altered resting-state functional activity in medication-naive patients with first-episode major depression disorder vs. healthy control: A quantitative meta-analysis. Front Behav Neurosci, 13: 89. https://doi.org/10.3389/fnbeh.2019.00089

 

  1. Mulders PC, van Eijndhoven PF, Schene AH, et al., 2015, Resting-state functional connectivity in major depressive disorder: A review. Neurosci Biobehav Rev, 56: 330–344. https://doi.org/10.1016/j.neubiorev.2015.07.014

 

  1. Fonseka TM, MacQueen GM, Kennedy SH, 2018, Neuroimaging biomarkers as predictors of treatment outcome in Major Depressive Disorder. J Affect Disord, 233: 21–35. https://doi.org/10.1016/j.jad.2017.10.049

 

  1. Arbabshirani MR, Plis S, Sui J, et al., 2017, Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage, 145: 137–165. https://doi.org/10.1016/j.neuroimage.2016.02.079

 

  1. Song Y, Talarico F, Greenshaw A, et al., 2020, Variability may limit the translation of neuroimaging findings comment on “Variability in the analysis of a single neuroimaging dataset by many teams”. J Affect Disord, 277: 997–998. https://doi.org/10.1016/j.jad.2020.09.048
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Journal of Clinical and Basic Psychosomatics, Electronic ISSN: 2972-4414 Print ISSN: 3060-8562, Published by AccScience Publishing