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Journal of Clinical and
Basic Psychosomatics Brain MRI alterations in MDD

46. Williams LM, Korgaonkar MS, Song YC, et al., 2015, 206: 116287.
Amygdala reactivity to emotional faces in the prediction https://doi.org/10.1016/j.neuroimage.2019.116287
of general and medication-specific responses to
antidepressant treatment in the randomized iSPOT-D trial. 56. Yin JB, Liang SH, Li F, et al., 2020, dmPFC-vlPAG projection
Neuropsychopharmacology, 40: 2398–2408. neurons contribute to pain threshold maintenance and
antianxiety behaviors. J Clin Invest, 130: 6555–6570.
https://doi.org/10.1038/npp.2015.89
https://doi.org/10.1172/JCI127607
47. Godlewska BR, Browning M, Norbury R, et al., 2016, Early
changes in emotional processing as a marker of clinical 57. Köhler CA, Carvalho AF, Alves GS, et al., 2015,
response to SSRI treatment in depression. Transl Psychiatry, Autobiographical memory disturbances in depression:
6: e957. A novel therapeutic target? Neural Plast, 2015: 759139.
https://doi.org/10.1038/tp.2016.130 https://doi.org/10.1155/2015/759139
48. Keedwell PA, Drapier D, Surguladze S, et al., 2010, 58. Zhang Z, Chen Y, Wei W, et al., 2021, Changes in regional
Subgenual cingulate and visual cortex responses to sad faces homogeneity of medication-free major depressive disorder
predict clinical outcome during antidepressant treatment patients with different onset ages. Front Psychiatry,
for depression. J Affect Disord, 120: 120–125. 12: 713614.
https://doi.org/10.1016/j.jad.2009.04.031 https://doi.org/10.3389/fpsyt.2021.713614
49. Godlewska BR, Browning M, Norbury R, et al., 2018, 59. Liu P, Tu H, Zhang A, et al., 2021, Brain functional alterations
Predicting treatment response in depression: The role of in MDD patients with somatic symptoms: A resting-state
anterior cingulate cortex. Int J Neuropsychopharmacol, fMRI study. J Affect Disord, 295: 788–796.
21: 988–996. https://doi.org/10.1016/j.jad.2021.08.143
https://doi.org/10.1093/ijnp/pyy069 60. Piguet C, Karahanoğlu FI, Saccaro LF, et al., 2021, Mood
50. Preuss A, Bolliger B, Schicho W, et al., 2020, SSRI treatment disorders disrupt the functional dynamics, not spatial
response prediction in depression based on brain activation organization of brain resting state networks. Neuroimage
by emotional stimuli. Front Psychiatry, 11: 538393. Clin, 32: 102833.
https://doi.org/10.3389/fpsyt.2020.538393 https://doi.org/10.1016/j.nicl.2021.102833
51. Miller JM, Schneck N, Siegle GJ, et al., 2013, fMRI response 61. Ma X, Liu J, Liu T, et al., 2019, Altered resting-state
to negative words and SSRI treatment outcome in major functional activity in medication-naive patients with first-
depressive disorder: A preliminary study. Psychiatry Res, episode major depression disorder vs. healthy control:
214: 296–305. A quantitative meta-analysis. Front Behav Neurosci, 13: 89.
https://doi.org/10.1016/j.pscychresns.2013.08.001 https://doi.org/10.3389/fnbeh.2019.00089
52. Williams RJ, Brown EC, Clark DL, et al., 2021, Early post- 62. Mulders PC, van Eijndhoven PF, Schene AH, et al., 2015,
treatment blood oxygenation level-dependent responses Resting-state functional connectivity in major depressive
to emotion processing associated with clinical response to disorder: A review. Neurosci Biobehav Rev, 56: 330–344.
pharmacological treatment in major depressive disorder. https://doi.org/10.1016/j.neubiorev.2015.07.014
Brain Behav, 11: e2287.
63. Fonseka TM, MacQueen GM, Kennedy SH, 2018,
https://doi.org/10.1002/brb3.2287 Neuroimaging biomarkers as predictors of treatment
53. Frodl T, Scheuerecker J, Schoepf V, et al., 2011, Different outcome in Major Depressive Disorder. J Affect Disord,
effects of mirtazapine and venlafaxine on brain activation: 233: 21–35.
An open randomized controlled fMRI study. J Clin https://doi.org/10.1016/j.jad.2017.10.049
Psychiatry, 72: 448–457.
64. Arbabshirani MR, Plis S, Sui J, et al., 2017, Single subject
https://doi.org/10.4088/jcp.09m05393blu prediction of brain disorders in neuroimaging: Promises
and pitfalls. Neuroimage, 145: 137–165.
54. Seminowicz DA, Mayberg HS, McIntosh AR, et al., 2004,
Limbic-frontal circuitry in major depression: A path https://doi.org/10.1016/j.neuroimage.2016.02.079
modeling metanalysis. Neuroimage, 22: 409–418.
65. Song Y, Talarico F, Greenshaw A, et al., 2020, Variability may
https://doi.org/10.1016/j.neuroimage.2004.01.015 limit the translation of neuroimaging findings comment on
“Variability in the analysis of a single neuroimaging dataset
55. Zhou HX, Chen X, Shen YQ, et al., 2020, Rumination and
the default mode network: Meta-analysis of brain imaging by many teams”. J Affect Disord, 277: 997–998.
studies and implications for depression. Neuroimage, https://doi.org/10.1016/j.jad.2020.09.048

Volume 1 Issue 1 (2023) 9 https://doi.org/10.36922/jcbp.0896