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Meta-Analysis
. 2017 Sep 1;82(5):330-338.
doi: 10.1016/j.biopsych.2016.10.028. Epub 2016 Nov 9.

Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies

Joseph Kambeitz  1 , Carlos Cabral  2 , Matthew D Sacchet  3 , Ian H Gotlib  3 , Roland Zahn  4 , Mauricio H Serpa  5 , Martin Walter  6 , Peter Falkai  2 , Nikolaos Koutsouleris  2
Affiliations
Meta-Analysis

Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies

Joseph Kambeitz et al. Biol Psychiatry. .

Abstract

Background: Multiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies.

Methods: We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs.

Results: Thirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the meta-analysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity).

Conclusions: Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome.

Keywords: Affective disorder; Classification; Diagnosis; Prediction; Sensitivity; Specificity.

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Conflict of interest statement

DISCLOSURES

All authors report no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1.
Figure 1.
Forest plot of sensitivities and specificities. Summary estimates for sensitivity are computed using the approach described by Reitsma et al. (43). CI, confidence interval; DTI, diffusion tensor imaging; fMRI, functional magnetic resonance imaging; RE, random effects; rsfMRI, resting-state functional magnetic resonance imaging; sMRI, structural magnetic resonance imaging.
Figure 2.
Figure 2.
Summary receiver operating characteristic curve of the Reitsma model with the summary sensitivity and false positive rate indicated in black as well as color-coded sensitivity and false positive rates of the individual studies of different imaging modalities. DTI, diffusion tensor imaging; fMRI, functional magnetic resonance imaging; rsfMRI, restingstate functional magnetic resonance imaging; sMRI, structural magnetic resonance imaging.
Figure 3.
Figure 3.
Results from the moderator analysis: (A) effect of age, (B) differences in sensitivity and specificity between imaging modalities, and (C) clinical symptoms as measured by HAMD. DTI, diffusion tensor imaging; fMRI, functional magnetic resonance imaging; HAMD, Hamilton Depression Rating Scale; rsfMRI, resting-state functional magnetic resonance imaging; sMRI, structural magnetic resonance imaging. *p < .05; **p < .01.
See this image and copyright information in PMC

Comment in

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