|
|
Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies |
Ziqi Chen1, Xiaoqi Huang1,2,3(), Qiyong Gong1,2,3(), Bharat B. Biswal4,5 |
1. Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China 2. Research Unit of Psychoradiology. Chinese Academy of Medical Sciences, Chengdu 610041, China 3. Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu 610041, China 4. Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA 5. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China |
|
|
Abstract Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.
|
Keywords
psychoradiology
major depressive disorder
MRI
biomarker
|
Corresponding Author(s):
Xiaoqi Huang,Qiyong Gong
|
Just Accepted Date: 29 October 2020
Online First Date: 03 February 2021
Issue Date: 23 September 2021
|
|
1 |
Z Jia, X Huang, Q Wu, T Zhang, S Lui, J Zhang, N Amatya, W Kuang, RC Chan, GJ Kemp, A Mechelli, Q Gong. High-field magnetic resonance imaging of suicidality in patients with major depressive disorder. Am J Psychiatry 2010; 167(11): 1381–1390
https://doi.org/10.1176/appi.ajp.2010.09101513
pmid: 20843871
|
2 |
G Sözeri-Varma. Depression in the elderly: clinical features and risk factors. Aging Dis 2012; 3(6): 465–471
pmid: 23251852
|
3 |
EJ Nestler, M Barrot, RJ DiLeone, AJ Eisch, SJ Gold, LM Monteggia. Neurobiology of depression. Neuron 2002; 34(1): 13–25
https://doi.org/10.1016/S0896-6273(02)00653-0
pmid: 11931738
|
4 |
KJ Ressler, HS Mayberg. Targeting abnormal neural circuits in mood and anxiety disorders: from the laboratory to the clinic. Nat Neurosci 2007; 10(9): 1116–1124
https://doi.org/10.1038/nn1944
pmid: 17726478
|
5 |
EI Martin, KJ Ressler, E Binder, CB Nemeroff. The neurobiology of anxiety disorders: brain imaging, genetics, and psychoneuroendocrinology. Psychiatr Clin North Am 2009; 32(3): 549–575
https://doi.org/10.1016/j.psc.2009.05.004
pmid: 19716990
|
6 |
XS Suo, DL Lei, LL Li, WL Li, JD Dai, SW Wang, MH He, HZ Zhu, GJK Kemp, QG Gong. Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders. J Psychiatry Neurosci 2018; 43(6): 416–427
https://doi.org/10.1503/jpn.170214
pmid: 30375837
|
7 |
AT Drysdale, L Grosenick, J Downar, K Dunlop, F Mansouri, Y Meng, RN Fetcho, B Zebley, DJ Oathes, A Etkin, AF Schatzberg, K Sudheimer, J Keller, HS Mayberg, FM Gunning, GS Alexopoulos, MD Fox, A Pascual-Leone, HU Voss, BJ Casey, MJ Dubin, C Liston. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017; 23(1): 28–38
https://doi.org/10.1038/nm.4246
pmid: 27918562
|
8 |
M Sinyor, A Schaffer, A Levitt. The sequenced treatment alternatives to relieve depression (STAR*D) trial: a review. Can J Psychiatry 2010; 55(3): 126–135
https://doi.org/10.1177/070674371005500303
pmid: 20370962
|
9 |
KR Connolly, ME Thase. If at first you don’t succeed: a review of the evidence for antidepressant augmentation, combination and switching strategies. Drugs 2011; 71(1): 43–64
https://doi.org/10.2165/11587620-000000000-00000
pmid: 21175239
|
10 |
N Stimpson, N Agrawal, G Lewis. Randomised controlled trials investigating pharmacological and psychological interventions for treatment-refractory depression: systematic review. Br J Psychiatry 2002; 181(4): 284–294
https://doi.org/10.1192/bjp.181.4.284
pmid: 12356654
|
11 |
AF Leuchter, IA Cook, SP Hamilton, KL Narr, A Toga, AM Hunter, K Faull, J Whitelegge, AM Andrews, J Loo, B Way, SF Nelson, S Horvath, BD Lebowitz. Biomarkers to predict antidepressant response. Curr Psychiatry Rep 2010; 12(6): 553–562
https://doi.org/10.1007/s11920-010-0160-4
pmid: 20963521
|
12 |
S Lui, XJ Zhou, JA Sweeney, Q Gong. Psychoradiology: the frontier of neuroimaging in psychiatry. Radiology 2016; 281(2): 357–372
https://doi.org/10.1148/radiol.2016152149
pmid: 27755933
|
13 |
EJR van Beek, C Kuhl, Y Anzai, P Desmond, RL Ehman, Q Gong, G Gold, V Gulani, M Hall-Craggs, T Leiner, CCT Lim, JG Pipe, S Reeder, C Reinhold, M Smits, DK Sodickson, C Tempany, HA Vargas, M Wang. Value of MRI in medicine: more than just another test? J Magn Reson Imaging 2019; 49(7): e14–e25
https://doi.org/10.1002/jmri.26211
pmid: 30145852
|
14 |
YJ Zhao, MY Du, XQ Huang, S Lui, ZQ Chen, J Liu, Y Luo, XL Wang, GJ Kemp, QY Gong. Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis. Psychol Med 2014; 44(14): 2927–2937
https://doi.org/10.1017/S0033291714000518
pmid: 25065859
|
15 |
QY Gong. Psychoradiology. Neuroimaging Clin N Am 2020; 30(1): 1–124
https://doi.org/10.1016/j.neubiorev.2013.07.018
pmid: 23928089
|
16 |
ZQ Chen, MY Du, YJ Zhao, XQ Huang, J Li, S Lui, JM Hu, HQ Sun, J Liu, GJ Kemp, QY Gong. Voxel-wise meta-analyses of brain blood flow and local synchrony abnormalities in medication-free patients with major depressive disorder. J Psychiatry Neurosci 2015; 40(6): 401–411
https://doi.org/10.1503/jpn.140119
pmid: 25853283
|
17 |
Z Chen, H Zhang, Z Jia, J Zhong, X Huang, M Du, L Chen, W Kuang, JA Sweeney, Q Gong. Magnetization transfer imaging of suicidal patients with major depressive disorder. Sci Rep 2015; 5(1): 9670
https://doi.org/10.1038/srep09670
pmid: 25853872
|
18 |
GR Ridgway, SM Henley, JD Rohrer, RI Scahill, JD Warren, NC Fox. Ten simple rules for reporting voxel-based morphometry studies. Neuroimage 2008; 40(4): 1429–1435
https://doi.org/10.1016/j.neuroimage.2008.01.003
pmid: 18314353
|
19 |
J Ashburner, KJ Friston. Voxel-based morphometry—the methods. Neuroimage 2000; 11(6): 805–821
https://doi.org/10.1006/nimg.2000.0582
pmid: 10860804
|
20 |
W Peng, Z Chen, L Yin, Z Jia, Q Gong. Essential brain structural alterations in major depressive disorder: a voxel-wise meta-analysis on first episode, medication-naive patients. J Affect Disord 2016; 199: 114–123
https://doi.org/10.1016/j.jad.2016.04.001
pmid: 27100056
|
21 |
W Wang, Y Zhao, X Hu, X Huang, W Kuang, S Lui, GJ Kemp, Q Gong. Conjoint and dissociated structural and functional abnormalities in first-episode drug-naive patients with major depressive disorder: a multimodal meta-analysis. Sci Rep 2017; 7(1): 10401
https://doi.org/10.1038/s41598-017-08944-5
pmid: 28871117
|
22 |
Y Zhao, L Chen, W Zhang, Y Xiao, C Shah, H Zhu, M Yuan, H Sun, Q Yue, Z Jia, W Zhang, W Kuang, Q Gong, S Lui. Gray matter abnormalities in non-comorbid medication-naive patients with major depressive disorder or social anxiety disorder. EBioMedicine 2017; 21: 228–235
https://doi.org/10.1016/j.ebiom.2017.06.013
pmid: 28633986
|
23 |
JS Suh, MA Schneider, L Minuzzi, GM MacQueen, SC Strother, SH Kennedy, BN Frey. Cortical thickness in major depressive disorder: a systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry 2019; 88: 287–302
https://doi.org/10.1016/j.pnpbp.2018.08.008
pmid: 30118825
|
24 |
DK Jones, A Leemans. Diffusion tensor imaging. Methods Mol Biol 2011; 711: 127–144
https://doi.org/10.1007/978-1-61737-992-5_6
pmid: 21279600
|
25 |
B Stieltjes, WE Kaufmann, PC van Zijl, K Fredericksen, GD Pearlson, M Solaiyappan, S Mori. Diffusion tensor imaging and axonal tracking in the human brainstem. Neuroimage 2001; 14(3): 723–735
https://doi.org/10.1006/nimg.2001.0861
pmid: 11506544
|
26 |
Y Liao, X Huang, Q Wu, C Yang, W Kuang, M Du, S Lui, Q Yue, RC Chan, GJ Kemp, Q Gong. Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. J Psychiatry Neurosci 2013; 38(1): 49–56
https://doi.org/10.1503/jpn.110180
pmid: 22691300
|
27 |
ME Tipping. Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 2001; 1: 211–244
|
28 |
G Orrù, W Pettersson-Yeo, AF Marquand, G Sartori, A Mechelli. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 2012; 36(4): 1140–1152
https://doi.org/10.1016/j.neubiorev.2012.01.004
pmid: 22305994
|
29 |
SG Costafreda, CH Fu, M Picchioni, T Toulopoulou, C McDonald, E Kravariti, M Walshe, D Prata, RM Murray, PK McGuire. Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry 2011; 11(1): 18
https://doi.org/10.1186/1471-244X-11-18
pmid: 21276242
|
30 |
F Li, X Huang, W Tang, Y Yang, B Li, GJ Kemp, A Mechelli, Q Gong. Multivariate pattern analysis of DTI reveals differential white matter in individuals with obsessive-compulsive disorder. Hum Brain Mapp 2014; 35(6): 2643–2651
https://doi.org/10.1002/hbm.22357
pmid: 24048702
|
31 |
X Hu, Q Liu, B Li, W Tang, H Sun, F Li, Y Yang, Q Gong, X Huang. Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy. Eur Neuropsychopharmacol 2016; 26(2): 246–254
https://doi.org/10.1016/j.euroneuro.2015.12.014
pmid: 26708318
|
32 |
B Mwangi, KP Ebmeier, K Matthews, JD Steele. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 2012; 135(5): 1508–1521
https://doi.org/10.1093/brain/aws084
pmid: 22544901
|
33 |
L Qiu, X Huang, J Zhang, Y Wang, W Kuang, J Li, X Wang, L Wang, X Yang, S Lui, A Mechelli, Q Gong. Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images. J Psychiatry Neurosci 2014; 39(2): 78–86
pmid: 24083459
|
34 |
J Yang, M Zhang, H Ahn, Q Zhang, TB Jin, I Li, M Nemesure, N Joshi, H Jiang, JM Miller, RT Ogden, E Petkova, MS Milak, ME Sublette, GM Sullivan, MH Trivedi, M Weissman, PJ McGrath, M Fava, BT Kurian, DA Pizzagalli, CM Cooper, M McInnis, MA Oquendo, JJ Mann, RV Parsey, C DeLorenzo. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp 2018; 39(11): 4420–4439
https://doi.org/10.1002/hbm.24282
pmid: 30113112
|
35 |
ME Culang-Reinlieb, LC Johnert, AM Brickman, DC Steffens, E Garcon, JR Sneed. MRI-defined vascular depression: a review of the construct. Int J Geriatr Psychiatry 2011; 26(11): 1101–1108
pmid: 21192018
|
36 |
S Simpson, RC Baldwin, A Jackson, A Burns, P Thomas. Is the clinical expression of late-life depression influenced by brain changes? MRI subcortical neuroanatomical correlates of depressive symptoms. Int Psychogeriatr 2000; 12(4): 425–434
https://doi.org/10.1017/S1041610200006542
pmid: 11263709
|
37 |
DC Steffens. Establishing diagnostic criteria for vascular depression. J Neurol Sci 2004; 226(1-2): 59–62
https://doi.org/10.1016/j.jns.2004.09.013
pmid: 15537521
|
38 |
JC Soares, JJ Mann. The anatomy of mood disorders—review of structural neuroimaging studies. Biol Psychiatry 1997; 41(1): 86–106
https://doi.org/10.1016/S0006-3223(96)00006-6
pmid: 8988799
|
39 |
KI Salo, J Scharfen, ID Wilden, RI Schubotz, H Holling. Confining the concept of vascular depression to late-onset depression: a meta-analysis of MRI-defined hyperintensity burden in major depressive disorder and bipolar disorder. Front Psychol 2019; 10: 1241
https://doi.org/10.3389/fpsyg.2019.01241
pmid: 31214072
|
40 |
LL Herrmann, M Le Masurier, KP Ebmeier. White matter hyperintensities in late life depression: a systematic review. J Neurol Neurosurg Psychiatry 2008; 79(6): 619–624
https://doi.org/10.1136/jnnp.2007.124651
pmid: 17717021
|
41 |
K Takahashi, A Oshima, I Ida, H Kumano, N Yuuki, M Fukuda, M Amanuma, K Endo, M Mikuni. Relationship between age at onset and magnetic resonance image-defined hyperintensities in mood disorders. J Psychiatr Res 2008; 42(6): 443–450
https://doi.org/10.1016/j.jpsychires.2007.05.003
pmid: 17588605
|
42 |
JH Park, SB Lee, JJ Lee, JC Yoon, JW Han, TH Kim, HG Jeong, PA Newhouse, WD Taylor, JH Kim, JI Woo, KW Kim. Epidemiology of MRI-defined vascular depression: a longitudinal, community-based study in Korean elders. J Affect Disord 2015; 180: 200–206
https://doi.org/10.1016/j.jad.2015.04.008
pmid: 25913805
|
43 |
I Yanai, T Fujikawa, J Horiguchi, S Yamawaki, Y Touhouda. The 3-year course and outcome of patients with major depression and silent cerebral infarction. J Affect Disord 1998; 47(1-3): 25–30
https://doi.org/10.1016/S0165-0327(97)00148-1
pmid: 9476740
|
44 |
YI Sheline, CF Pieper, DM Barch, K Welsh-Bohmer, RC McKinstry, JR MacFall, G D’Angelo, KS Garcia, K Gersing, C Wilkins, W Taylor, DC Steffens, RR Krishnan, PM Doraiswamy. Support for the vascular depression hypothesis in late-life depression: results of a 2-site, prospective, antidepressant treatment trial. Arch Gen Psychiatry 2010; 67(3): 277–285
https://doi.org/10.1001/archgenpsychiatry.2009.204
pmid: 20194828
|
45 |
S Simpson, RC Baldwin, A Jackson, AS Burns. Is subcortical disease associated with a poor response to antidepressants? Neurological, neuropsychological and neuroradiological findings in late-life depression. Psychol Med 1998; 28(5): 1015–1026
https://doi.org/10.1017/S003329179800693X
pmid: 9794009
|
46 |
JR Sneed, ME Culang-Reinlieb. The vascular depression hypothesis: an update. Am J Geriatr Psychiatry 2011; 19(2): 99–103
https://doi.org/10.1097/JGP.0b013e318202fc8a
pmid: 21328801
|
47 |
HJ Aizenstein, A Khalaf, SE Walker, C Andreescu. Magnetic resonance imaging predictors of treatment response in late-life depression. J Geriatr Psychiatry Neurol 2014; 27(1): 24–32
https://doi.org/10.1177/0891988713516541
pmid: 24381231
|
48 |
HJ Aizenstein, A Baskys, M Boldrini, MA Butters, BS Diniz, MK Jaiswal, KA Jellinger, LS Kruglov, IA Meshandin, MD Mijajlovic, G Niklewski, S Pospos, K Raju, K Richter, DC Steffens, WD Taylor, O Tene. Vascular depression consensus report—a critical update. BMC Med 2016; 14(1): 161
https://doi.org/10.1186/s12916-016-0720-5
pmid: 27806704
|
49 |
LC Foland-Ross, MD Sacchet, G Prasad, B Gilbert, PM Thompson, IH Gotlib. Cortical thickness predicts the first onset of major depression in adolescence. Int J Dev Neurosci 2015; 46(1): 125–131
https://doi.org/10.1016/j.ijdevneu.2015.07.007
pmid: 26315399
|
50 |
TS Frodl, N Koutsouleris, R Bottlender, C Born, M Jäger, I Scupin, M Reiser, HJ Möller, EM Meisenzahl. Depression-related variation in brain morphology over 3 years: effects of stress? Arch Gen Psychiatry 2008; 65(10): 1156–1165
https://doi.org/10.1001/archpsyc.65.10.1156
pmid: 18838632
|
51 |
T Kanai, H Takeuchi, TA Furukawa, R Yoshimura, T Imaizumi, T Kitamura, K Takahashi. Time to recurrence after recovery from major depressive episodes and its predictors. Psychol Med 2003; 33(5): 839–845
https://doi.org/10.1017/S0033291703007827
pmid: 12877398
|
52 |
C Soriano-Mas, R Hernández-Ribas, J Pujol, M Urretavizcaya, J Deus, BJ Harrison, H Ortiz, M López-Solà, JM Menchón, N Cardoner. Cross-sectional and longitudinal assessment of structural brain alterations in melancholic depression. Biol Psychiatry 2011; 69(4): 318–325
https://doi.org/10.1016/j.biopsych.2010.07.029
pmid: 20875637
|
53 |
D Zaremba, K Dohm, R Redlich, D Grotegerd, R Strojny, S Meinert, C Bürger, V Enneking, K Förster, J Repple, N Opel, BT Baune, P Zwitserlood, W Heindel, V Arolt, H Kugel, U Dannlowski. Association of brain cortical changes with relapse in patients with major depressive disorder. JAMA Psychiatry 2018; 75(5): 484–492
https://doi.org/10.1001/jamapsychiatry.2018.0123
pmid: 29590315
|
54 |
K Sawyer, E Corsentino, N Sachs-Ericsson, DC Steffens. Depression, hippocampal volume changes, and cognitive decline in a clinical sample of older depressed outpatients and non-depressed controls. Aging Ment Health 2012; 16(6): 753–762
https://doi.org/10.1080/13607863.2012.678478
pmid: 22548411
|
55 |
NV Malykhin, R Carter, P Seres, NJ Coupland. Structural changes in the hippocampus in major depressive disorder: contributions of disease and treatment. J Psychiatry Neurosci 2010; 35(5): 337–343
https://doi.org/10.1503/jpn.100002
pmid: 20731966
|
56 |
V Lorenzetti, NB Allen, A Fornito, M Yücel. Structural brain abnormalities in major depressive disorder: a selective review of recent MRI studies. J Affect Disord 2009; 117(1-2): 1–17
https://doi.org/10.1016/j.jad.2008.11.021
pmid: 19237202
|
57 |
L Schmaal, DJ Veltman, TG van Erp, PG Sämann, T Frodl, N Jahanshad, E Loehrer, H Tiemeier, A Hofman, WJ Niessen, MW Vernooij, MA Ikram, K Wittfeld, HJ Grabe, A Block, K Hegenscheid, H Völzke, D Hoehn, M Czisch, J Lagopoulos, SN Hatton, IB Hickie, R Goya-Maldonado, B Krämer, O Gruber, B Couvy-Duchesne, ME Rentería, LT Strike, NT Mills, GI de Zubicaray, KL McMahon, SE Medland, NG Martin, NA Gillespie, MJ Wright, GB Hall, GM MacQueen, EM Frey, A Carballedo, LS van Velzen, MJ van Tol, NJ van der Wee, IM Veer, H Walter, K Schnell, E Schramm, C Normann, D Schoepf, C Konrad, B Zurowski, T Nickson, AM McIntosh, M Papmeyer, HC Whalley, JE Sussmann, BR Godlewska, PJ Cowen, FH Fischer, M Rose, BW Penninx, PM Thompson, DP Hibar. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Mol Psychiatry 2016; 21(6): 806–812
https://doi.org/10.1038/mp.2015.69
pmid: 26122586
|
58 |
JJ Maller, K Broadhouse, AJ Rush, E Gordon, S Koslow, SM Grieve. Increased hippocampal tail volume predicts depression status and remission to anti-depressant medications in major depression. Mol Psychiatry 2018; 23(8): 1737–1744
https://doi.org/10.1038/mp.2017.224
pmid: 29133948
|
59 |
T Frodl, M Jäger, I Smajstrlova, C Born, R Bottlender, T Palladino, M Reiser, HJ Möller, EM Meisenzahl. Effect of hippocampal and amygdala volumes on clinical outcomes in major depression: a 3-year prospective magnetic resonance imaging study. J Psychiatry Neurosci 2008; 33(5): 423–430
pmid: 18787661
|
60 |
KT Kronmüller, J Pantel, S Köhler, D Victor, F Giesel, VA Magnotta, C Mundt, M Essig, J Schröder. Hippocampal volume and 2-year outcome in depression. Br J Psychiatry 2008; 192(6): 472–473
https://doi.org/10.1192/bjp.bp.107.040378
pmid: 18515903
|
61 |
R Colle, I Dupong, O Colliot, E Deflesselle, P Hardy, B Falissard, D Ducreux, M Chupin, E Corruble. Smaller hippocampal volumes predict lower antidepressant response/remission rates in depressed patients: a meta-analysis. World J Biol Psychiatry 2018; 19(5): 360–367
https://doi.org/10.1080/15622975.2016.1208840
pmid: 27376473
|
62 |
X Hu, L Zhang, X Hu, L Lu, S Tang, H Li, X Bu, Q Gong, X Huang. Abnormal hippocampal subfields may be potential predictors of worse early response to antidepressant treatment in drug-naïve patients with major depressive disorder. J Magn Reson Imaging 2019; 49(6): 1760–1768
https://doi.org/10.1002/jmri.26520
pmid: 30295348
|
63 |
I Nouretdinov, SG Costafreda, A Gammerman, A Chervonenkis, V Vovk, V Vapnik, CH Fu. Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. Neuroimage 2011; 56(2): 809–813
https://doi.org/10.1016/j.neuroimage.2010.05.023
pmid: 20483379
|
64 |
H Ito, K Inoue, R Goto, S Kinomura, Y Taki, K Okada, K Sato, T Sato, I Kanno, H Fukuda. Database of normal human cerebral blood flow measured by SPECT: I. Comparison between I-123-IMP, Tc-99m-HMPAO, and Tc-99m-ECD as referred with O-15 labeled water PET and voxel-based morphometry. Ann Nucl Med 2006; 20(2): 131–138
https://doi.org/10.1007/BF02985625
pmid: 16615422
|
65 |
K Lameka, MD Farwell, M Ichise. Positron emission tomography. Handb Clin Neurol 2016; 135: 209–227
https://doi.org/10.1016/B978-0-444-53485-9.00011-8
pmid: 27432667
|
66 |
S Lui, LM Parkes, X Huang, K Zou, RC Chan, H Yang, L Zou, D Li, H Tang, T Zhang, X Li, Y Wei, L Chen, X Sun, GJ Kemp, QY Gong. Depressive disorders: focally altered cerebral perfusion measured with arterial spin-labeling MR imaging. Radiology 2009; 251(2): 476–484
https://doi.org/10.1148/radiol.2512081548
pmid: 19401575
|
67 |
L Su, Y Cai, Y Xu, A Dutt, S Shi, E Bramon. Cerebral metabolism in major depressive disorder: a voxel-based meta-analysis of positron emission tomography studies. BMC Psychiatry 2014; 14(1): 321
https://doi.org/10.1186/s12888-014-0321-9
pmid: 25407081
|
68 |
DF Smith, S Jakobsen. Molecular neurobiology of depression: PET findings on the elusive correlation with symptom severity. Front Psychiatry 2013; 4: 8
https://doi.org/10.3389/fpsyt.2013.00008
pmid: 23459670
|
69 |
M Filippi, F Agosta. Diffusion tensor imaging and functional MRI. Handb Clin Neurol 2016; 136: 1065–1087
https://doi.org/10.1016/B978-0-444-53486-6.00056-9
pmid: 27430459
|
70 |
AK Azeez, BB Biswal. A review of resting-state analysis methods. Neuroimaging Clin N Am 2017; 27(4): 581–592
https://doi.org/10.1016/j.nic.2017.06.001
pmid: 28985930
|
71 |
MD Fox, ME Raichle. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007; 8(9): 700–711
https://doi.org/10.1038/nrn2201
pmid: 17704812
|
72 |
RC Craddock, PE Holtzheimer 3rd, XP Hu, HS Mayberg. Disease state prediction from resting state functional connectivity. Magn Reson Med 2009; 62(6): 1619–1628
https://doi.org/10.1002/mrm.22159
pmid: 19859933
|
73 |
B Sundermann, S Feder, H Wersching, A Teuber, W Schwindt, H Kugel, W Heindel, V Arolt, K Berger, B Pfleiderer. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm (Vienna) 2017; 124(5): 589–605
https://doi.org/10.1007/s00702-016-1673-8
pmid: 28040847
|
74 |
X Zhong, H Shi, Q Ming, D Dong, X Zhang, LL Zeng, S Yao. Whole-brain resting-state functional connectivity identified major depressive disorder: a multivariate pattern analysis in two independent samples. J Affect Disord 2017; 218: 346–352
https://doi.org/10.1016/j.jad.2017.04.040
pmid: 28499208
|
75 |
R Bhaumik, LM Jenkins, JR Gowins, RH Jacobs, A Barba, DK Bhaumik, SA Langenecker. Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity. Neuroimage Clin 2017; 16: 390–398
https://doi.org/10.1016/j.nicl.2016.02.018
pmid: 28861340
|
76 |
LL Zeng, H Shen, L Liu, D Hu. Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp 2014; 35(4): 1630–1641
https://doi.org/10.1002/hbm.22278
pmid: 23616377
|
77 |
B Jing, Z Long, H Liu, H Yan, J Dong, X Mo, D Li, C Liu, H Li. Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases. Oncotarget 2017; 8(52): 90452–90464
https://doi.org/10.18632/oncotarget.19860
pmid: 29163844
|
78 |
K Yoshida, Y Shimizu, J Yoshimoto, M Takamura, G Okada, Y Okamoto, S Yamawaki, K Doya. Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression. PLoS One 2017; 12(7): e0179638
https://doi.org/10.1371/journal.pone.0179638
pmid: 28700672
|
79 |
X Wang, Y Ren, W Zhang. Depression disorder classification of fMRI data using sparse low-rank functional brain network and graph-based features. Comput Math Methods Med 2017; 2017: 3609821
https://doi.org/10.1155/2017/3609821
pmid: 28487746
|
80 |
R Ramasubbu, MR Brown, F Cortese, I Gaxiola, B Goodyear, AJ Greenshaw, SM Dursun, R Greiner. Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage Clin 2016; 12: 320–331
https://doi.org/10.1016/j.nicl.2016.07.012
pmid: 27551669
|
81 |
M Wei, J Qin, R Yan, H Li, Z Yao, Q Lu. Identifying major depressive disorder using Hurst exponent of resting-state brain networks. Psychiatry Res 2013; 214(3): 306–312
https://doi.org/10.1016/j.pscychresns.2013.09.008
pmid: 24113289
|
82 |
L Cao, S Guo, Z Xue, Y Hu, H Liu, TE Mwansisya, W Pu, B Yang, C Liu, J Feng, EY Chen, Z Liu. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci 2014; 68(2): 110–119
https://doi.org/10.1111/pcn.12106
pmid: 24552631
|
83 |
LL Zeng, H Shen, L Liu, L Wang, B Li, P Fang, Z Zhou, Y Li, D Hu. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 2012; 135(5): 1498–1507
https://doi.org/10.1093/brain/aws059
pmid: 22418737
|
84 |
B Sundermann, S Feder, H Wersching, A Teuber, W Schwindt, H Kugel, W Heindel, V Arolt, K Berger, B Pfleiderer. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm (Vienna) 2017; 124(5): 589–605
https://doi.org/10.1007/s00702-016-1673-8
pmid: 28040847
|
85 |
A Lord, D Horn, M Breakspear, M Walter. Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One 2012; 7(8): e41282
https://doi.org/10.1371/journal.pone.0041282
pmid: 22916105
|
86 |
R Cáceda, K Bush, GA James, ZN Stowe, CD Kilts. Modes of resting functional brain organization differentiate suicidal thoughts and actions: a preliminary study. J Clin Psychiatry 2018; 79(4): 17m11901
https://doi.org/10.4088/JCP.17m11901
pmid: 29995357
|
87 |
R Dinga, L Schmaal, BWJH Penninx, MJ van Tol, DJ Veltman, L van Velzen, M Mennes, NJA van der Wee, AF Marquand. Evaluating the evidence for biotypes of depression: methodological replication and extension of Drysdale et al. (2017). Neuroimage Clin 2019; 22: 101796
https://doi.org/10.1016/j.nicl.2019.101796
pmid: 30935858
|
88 |
PM Pan, JR Sato, GA Salum, LA Rohde, A Gadelha, A Zugman, J Mari, A Jackowski, F Picon, EC Miguel, DS Pine, E Leibenluft, RA Bressan, A Stringaris. Ventral striatum functional connectivity as a predictor of adolescent depressive disorder in a longitudinal community-based sample. Am J Psychiatry 2017; 174(11): 1112–1119
https://doi.org/10.1176/appi.ajp.2017.17040430
pmid: 28946760
|
89 |
BG Shapero, XJ Chai, M Vangel, J Biederman, CS Hoover, S Whitfield-Gabrieli, JDE Gabrieli, DR Hirshfeld-Becker. Neural markers of depression risk predict the onset of depression. Psychiatry Res Neuroimaging 2019; 285: 31–39
https://doi.org/10.1016/j.pscychresns.2019.01.006
pmid: 30716688
|
90 |
DR Hirshfeld-Becker, JDE Gabrieli, BG Shapero, J Biederman, S Whitfield-Gabrieli, XJ Chai. Intrinsic functional brain connectivity predicts onset of major depression disorder in adolescence: a pilot study. Brain Connect 2019; 9(5): 388–398
https://doi.org/10.1089/brain.2018.0646
pmid: 30848160
|
91 |
SA Langenecker, LM Jenkins, JP Stange, YS Chang, SR DelDonno, KL Bessette, AM Passarotti, R Bhaumik, O Ajilore, RH Jacobs. Cognitive control neuroimaging measures differentiate between those with and without future recurrence of depression. Neuroimage Clin 2018; 20: 1001–1009
https://doi.org/10.1016/j.nicl.2018.10.004
pmid: 30321791
|
92 |
NA Farb, AK Anderson, RT Bloch, ZV Segal. Mood-linked responses in medial prefrontal cortex predict relapse in patients with recurrent unipolar depression. Biol Psychiatry 2011; 70(4): 366–372
https://doi.org/10.1016/j.biopsych.2011.03.009
pmid: 21531382
|
93 |
S Lui, Q Wu, L Qiu, X Yang, W Kuang, RC Chan, X Huang, GJ Kemp, A Mechelli, Q Gong. Resting-state functional connectivity in treatment-resistant depression. Am J Psychiatry 2011; 168(6): 642–648
https://doi.org/10.1176/appi.ajp.2010.10101419
pmid: 21362744
|
94 |
G Grimaldi, GP Argyropoulos, A Boehringer, P Celnik, MJ Edwards, R Ferrucci, JM Galea, SJ Groiss, K Hiraoka, P Kassavetis, E Lesage, M Manto, RC Miall, A Priori, A Sadnicka, Y Ugawa, U Ziemann. Non-invasive cerebellar stimulation—a consensus paper. Cerebellum 2014; 13(1): 121–138
https://doi.org/10.1007/s12311-013-0514-7
pmid: 23943521
|
95 |
T Paus, J Barrett. Transcranial magnetic stimulation (TMS) of the human frontal cortex: implications for repetitive TMS treatment of depression. J Psychiatry Neurosci 2004; 29(4): 268–279
pmid: 15309043
|
96 |
S Rossi, M Hallett, PM Rossini, A, Safety of TMS Consensus Group Pascual-Leone. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol 2009; 120(12): 2008–2039
https://doi.org/10.1016/j.clinph.2009.08.016
pmid: 19833552
|
97 |
P Turriziani, D Smirni, G Zappalà, GR Mangano, M Oliveri, L Cipolotti. Enhancing memory performance with rTMS in healthy subjects and individuals with mild cognitive impairment: the role of the right dorsolateral prefrontal cortex. Front Hum Neurosci 2012; 6: 62
https://doi.org/10.3389/fnhum.2012.00062
pmid: 22514525
|
98 |
LL Herrmann, KP Ebmeier. Factors modifying the efficacy of transcranial magnetic stimulation in the treatment of depression: a review. J Clin Psychiatry 2006; 67(12): 1870–1876
https://doi.org/10.4088/JCP.v67n1206
pmid: 17194264
|
99 |
TV Salomons, K Dunlop, SH Kennedy, A Flint, J Geraci, P Giacobbe, J Downar. Resting-state cortico-thalamic-striatal connectivity predicts response to dorsomedial prefrontal rTMS in major depressive disorder. Neuropsychopharmacology 2014; 39(2): 488–498
https://doi.org/10.1038/npp.2013.222
pmid: 24150516
|
100 |
C Liston, AC Chen, BD Zebley, AT Drysdale, R Gordon, B Leuchter, HU Voss, BJ Casey, A Etkin, MJ Dubin. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry 2014; 76(7): 517–526
https://doi.org/10.1016/j.biopsych.2014.01.023
pmid: 24629537
|
101 |
C Moret. Combination/augmentation strategies for improving the treatment of depression. Neuropsychiatr Dis Treat 2005; 1(4): 301–309
pmid: 18568111
|
102 |
BN Gaynes, SB Dusetzina, AR Ellis, RA Hansen, JF Farley, WC Miller, T Stürmer. Treating depression after initial treatment failure: directly comparing switch and augmenting strategies in STAR*D. J Clin Psychopharmacol 2012; 32(1): 114–119
https://doi.org/10.1097/JCP.0b013e31823f705d
pmid: 22198447
|
103 |
WE Craighead, BW Dunlop. Combination psychotherapy and antidepressant medication treatment for depression: for whom, when, and how. Annu Rev Psychol 2014; 65(1): 267–300
https://doi.org/10.1146/annurev.psych.121208.131653
pmid: 24405361
|
104 |
CL McGrath, ME Kelley, BW Dunlop, PE Holtzheimer 3rd, WE Craighead, HS Mayberg. Pretreatment brain states identify likely nonresponse to standard treatments for depression. Biol Psychiatry 2014; 76(7): 527–535
https://doi.org/10.1016/j.biopsych.2013.12.005
pmid: 24462230
|
105 |
JZ Konarski, SH Kennedy, ZV Segal, MA Lau, PJ Bieling, RS McIntyre, HS Mayberg. Predictors of nonresponse to cognitive behavioural therapy or venlafaxine using glucose metabolism in major depressive disorder. J Psychiatry Neurosci 2009; 34(3): 175–180
pmid: 19448846
|
106 |
DD Dougherty, AP Weiss, GR Cosgrove, NM Alpert, EH Cassem, AA Nierenberg, BH Price, HS Mayberg, AJ Fischman, SL Rauch. Cerebral metabolic correlates as potential predictors of response to anterior cingulotomy for treatment of major depression. J Neurosurg 2003; 99(6): 1010–1017
https://doi.org/10.3171/jns.2003.99.6.1010
pmid: 14705729
|
107 |
HS Mayberg, AM Lozano, V Voon, HE McNeely, D Seminowicz, C Hamani, JM Schwalb, SH Kennedy. Deep brain stimulation for treatment-resistant depression. Neuron 2005; 45(5): 651–660
https://doi.org/10.1016/j.neuron.2005.02.014
pmid: 15748841
|
108 |
CR Conway, JT Chibnall, S Gangwani, MA Mintun, JL Price, T Hershey, LA Giuffra, RD Bucholz, JJ Christensen, YI Sheline. Pretreatment cerebral metabolic activity correlates with antidepressant efficacy of vagus nerve stimulation in treatment-resistant major depression: a potential marker for response? J Affect Disord 2012; 139(3): 283–290
https://doi.org/10.1016/j.jad.2012.02.007
pmid: 22397889
|
109 |
GJ Siegle, WK Thompson, A Collier, SR Berman, J Feldmiller, ME Thase, ES Friedman. Toward clinically useful neuroimaging in depression treatment: prognostic utility of subgenual cingulate activity for determining depression outcome in cognitive therapy across studies, scanners, and patient characteristics. Arch Gen Psychiatry 2012; 69(9): 913–924
https://doi.org/10.1001/archgenpsychiatry.2012.65
pmid: 22945620
|
110 |
RJ Davidson, W Irwin, MJ Anderle, NH Kalin. The neural substrates of affective processing in depressed patients treated with venlafaxine. Am J Psychiatry 2003; 160(1): 64–75
https://doi.org/10.1176/appi.ajp.160.1.64
pmid: 12505803
|
111 |
SH Kennedy, KR Evans, S Krüger, HS Mayberg, JH Meyer, S McCann, AI Arifuzzman, S Houle, FJ Vaccarino. Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. Am J Psychiatry 2001; 158(6): 899–905
https://doi.org/10.1176/appi.ajp.158.6.899
pmid: 11384897
|
112 |
SH Kennedy, JZ Konarski, ZV Segal, MA Lau, PJ Bieling, RS McIntyre, HS Mayberg. Differences in brain glucose metabolism between responders to CBT and venlafaxine in a 16-week randomized controlled trial. Am J Psychiatry 2007; 164(5): 778–788
https://doi.org/10.1176/ajp.2007.164.5.778
pmid: 17475737
|
113 |
BW Dunlop, JK Rajendra, WE Craighead, ME Kelley, CL McGrath, KS Choi, B Kinkead, CB Nemeroff, HS Mayberg. Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive-behavioral therapy or antidepressant medication for major depressive disorder. Am J Psychiatry 2017; 174(6): 533–545
https://doi.org/10.1176/appi.ajp.2016.16050518
pmid: 28335622
|
114 |
CL McGrath, ME Kelley, PE Holtzheimer, BW Dunlop, WE Craighead, AR Franco, RC Craddock, HS Mayberg. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 2013; 70(8): 821–829
https://doi.org/10.1001/jamapsychiatry.2013.143
pmid: 23760393
|
115 |
X Huang, Q Gong, JA Sweeney, BB Biswal. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92(1101): 20181000
https://doi.org/10.1259/bjr.20181000
pmid: 31170803
|
116 |
G Krueger, C Granziera, CR Jack Jr, JL Gunter, A Littmann, B Mortamet, S Kannengiesser, AG Sorensen, CP Ward, DA Reyes, PJ Britson, H Fischer, MA Bernstein. Effects of MRI scan acceleration on brain volume measurement consistency. J Magn Reson Imaging 2012; 36(5): 1234–1240
https://doi.org/10.1002/jmri.23694
pmid: 22570196
|
117 |
Z Caramanos, VS Fonov, SJ Francis, S Narayanan, GB Pike, DL Collins, DL Arnold. Gradient distortions in MRI: characterizing and correcting for their effects on SIENA-generated measures of brain volume change. Neuroimage 2010; 49(2): 1601–1611
https://doi.org/10.1016/j.neuroimage.2009.08.008
pmid: 19682586
|
118 |
GM Preboske, JL Gunter, CP Ward, CR Jack Jr. Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI. Neuroimage 2006; 30(4): 1196–1202
https://doi.org/10.1016/j.neuroimage.2005.10.049
pmid: 16380273
|
119 |
H Lee, K Nakamura, S Narayanan, RA Brown, DL Arnold , Alzheimer’s Disease Neuroimaging Initiative. Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements. Neuroimage 2019; 184: 555–565
https://doi.org/10.1016/j.neuroimage.2018.09.062
pmid: 30253207
|
120 |
MR Group of Chinese Society of Radiology, Chinese Medical Association. Chinese guidelines for the standardized application of MRI brain structure imaging technique in schizophrenia. Chin J Radiol (Zhonghua Fang She Xue Za Zhi) 2019; 53: 170–176 (in Chinese)
|
121 |
R Smieskova, P Allen, A Simon, J Aston, K Bendfeldt, J Drewe, K Gruber, U Gschwandtner, M Klarhoefer, C Lenz, K Scheffler, RD Stieglitz, EW Radue, P McGuire, A Riecher-Rössler, SJ Borgwardt. Different duration of at-risk mental state associated with neurofunctional abnormalities. A multimodal imaging study. Hum Brain Mapp 2012; 33(10): 2281–2294
https://doi.org/10.1002/hbm.21360
pmid: 21922599
|
122 |
P Fusar-Poli, OD Howes, P Allen, M Broome, I Valli, MC Asselin, PM Grasby, PK McGuire. Abnormal frontostriatal interactions in people with prodromal signs of psychosis: a multimodal imaging study. Arch Gen Psychiatry 2010; 67(7): 683–691
https://doi.org/10.1001/archgenpsychiatry.2010.77
pmid: 20603449
|
123 |
RC Kessler, M Gruber, JM Hettema, I Hwang, N Sampson, KA Yonkers. Co-morbid major depression and generalized anxiety disorders in the National Comorbidity Survey follow-up. Psychol Med 2008; 38(3): 365–374
https://doi.org/10.1017/S0033291707002012
pmid: 18047766
|
124 |
JF Coutinho, SV Fernandesl, JM Soares, L Maia, OF Gonçalves, A Sampaio. Default mode network dissociation in depressive and anxiety states. Brain Imaging Behav 2016; 10(1): 147–157
https://doi.org/10.1007/s11682-015-9375-7
pmid: 25804311
|
125 |
TM Fonseka, GM MacQueen, SH Kennedy. Neuroimaging biomarkers as predictors of treatment outcome in major depressive disorder. J Affect Disord 2018; 233: 21–35
https://doi.org/10.1016/j.jad.2017.10.049
pmid: 29150145
|
126 |
V Enneking, EJ Leehr, U Dannlowski, R Redlich. Brain structural effects of treatments for depression and biomarkers of response: a systematic review of neuroimaging studies. Psychol Med 2020; 50(2): 187–209
pmid: 31858931
|
127 |
A Etkin. A reckoning and research agenda for neuroimaging in psychiatry. Am J Psychiatry 2019; 176(7): 507–511
https://doi.org/10.1176/appi.ajp.2019.19050521
pmid: 31256624
|
128 |
MH Serpa, Y Ou, MS Schaufelberger, J Doshi, LK Ferreira, R Machado-Vieira, PR Menezes, M Scazufca, C Davatzikos, GF Busatto, MV Zanetti. Neuroanatomical classification in a population-based sample of psychotic major depression and bipolar I disorder with 1 year of diagnostic stability. BioMed Res Int 2014; 2014: 706157
https://doi.org/10.1155/2014/706157
pmid: 24575411
|
129 |
B Mwangi, KP Ebmeier, K Matthews, JD Steele. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 2012; 135(5): 1508–1521
https://doi.org/10.1093/brain/aws084
pmid: 22544901
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|