TY - JOUR
T1 - Development and evaluation of a multimodal marker of major depressive disorder
AU - Yang, Jie
AU - Zhang, Mengru
AU - Ahn, Hongshik
AU - Zhang, Qing
AU - Jin, Tony B.
AU - Li, Ien
AU - Nemesure, Matthew
AU - Joshi, Nandita
AU - Jiang, Haoran
AU - Miller, Jeffrey M.
AU - Ogden, Robert Todd
AU - Petkova, Eva
AU - Milak, Matthew S.
AU - Sublette, Mary Elizabeth
AU - Sullivan, Gregory M.
AU - Trivedi, Madhukar H.
AU - Weissman, Myrna
AU - McGrath, Patrick J.
AU - Fava, Maurizio
AU - Kurian, Benji T.
AU - Pizzagalli, Diego A.
AU - Cooper, Crystal M.
AU - McInnis, Melvin
AU - Oquendo, Maria A.
AU - Mann, Joseph John
AU - Parsey, Ramin V.
AU - DeLorenzo, Christine
N1 - Publisher Copyright:
© 2018 Wiley Periodicals, Inc.
PY - 2018/11
Y1 - 2018/11
N2 - This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques—penalized logistic regression, random forest, and support vector machine (SVM)—were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r =.36 correlation coefficient (p <.001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses—two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.
AB - This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques—penalized logistic regression, random forest, and support vector machine (SVM)—were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r =.36 correlation coefficient (p <.001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses—two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.
KW - diffusion MRI
KW - magnetic resonance imaging
KW - major depressive disorder
KW - structural MRI
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85052534441&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052534441&partnerID=8YFLogxK
U2 - 10.1002/hbm.24282
DO - 10.1002/hbm.24282
M3 - Article
C2 - 30113112
AN - SCOPUS:85052534441
SN - 1065-9471
VL - 39
SP - 4420
EP - 4439
JO - Human Brain Mapping
JF - Human Brain Mapping
IS - 11
ER -