Development and evaluation of a multimodal marker of major depressive disorder.
Development and evaluation of a multimodal marker of major depressive disorder.
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DOI：
10.1002/hbm.24282
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发表时间：
2018-11
影响因子：
4.8
通讯作者：
DeLorenzo C
中科院分区：
文献类型：
--
作者：
Yang J;Zhang M;Ahn H;Zhang Q;Jin TB;Li I;Nemesure M;Joshi N;Jiang H;Miller JM;Ogden RT;Petkova E;Milak MS;Sublette ME;Sullivan GM;Trivedi MH;Weissman M;McGrath PJ;Fava M;Kurian BT;Pizzagalli DA;Cooper CM;McInnis M;Oquendo MA;Mann JJ;Parsey RV;DeLorenzo C
本研究旨在通过将神经影像衍生的测量与二元（MDD/对照）、有序（重度MDD/轻度MDD/对照）或连续（抑郁严重程度）结局相关联，确定重度抑郁障碍（MDD）的生物标志物。为了解决MDD异质性，因素（精神抑郁的严重程度，动机，焦虑，精神病和睡眠障碍）也被用作结果。使用了多中心、多模式成像（扩散MRI、dMRI和结构MRI、sMRI）队列（52名对照和147名MDD患者）和多种建模技术-惩罚逻辑回归（PLR）、随机森林（RF）和支持向量机（SVM）-。另外一个队列（25名对照和83名MDD患者）用于验证。最佳性能分类器（SVM）具有26.0%的误分类率（二进制）、52.2±1.69%的准确度（有序）和r =0.36的相关系数（p值<0.001，连续）。使用SVM，预测任何MDD因素的R2值<10%。外部数据集的二元分类导致87.95%的灵敏度和32.00%的特异性。虽然观察到的分类率对于临床实用性来说太低，但是四个基于图像的特征有助于所有模型和分析的准确性-两个基于dMRI的测量（右楔和左楔的平均分数各向异性）和两个基于sMRI的测量（三角部和小脑的体积不对称），并且可以作为未来分析的先验区域。这里发现的分类和预测结果的准确性差反映了目前模棱两可的发现，并揭示了使用这些模式进行MDD生物标志物鉴定的挑战。此外，本研究提出了一个范例（如多分类器评价与外部验证），为未来的研究，以避免不可推广的结果。
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 multi-site, multimodal imaging (diffusion MRI, dMRI, and structural MRI, sMRI) cohort (52 controls and 147 MDD patients) and several modeling techniques- penalized logistic regression (PLR), random forest (RF) 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 =0.36 correlation coefficient (p-value<0.001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external dataset 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 non-generalizable results.
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