Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, and Emory University], Atlanta, GA, USA; School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, and Emory University], Atlanta, GA, USA.
Psychiatry Res Neuroimaging. 2023 Aug;333:111655. doi: 10.1016/j.pscychresns.2023.111655. Epub 2023 May 9.
Clinicians often face a dilemma in diagnosing bipolar disorder patients with complex symptoms who spend more time in a depressive state than a manic state. The current gold standard for such diagnosis, the Diagnostic and Statistical Manual (DSM), is not objectively grounded in pathophysiology. In such complex cases, relying solely on the DSM may result in misdiagnosis as major depressive disorder (MDD). A biologically-based classification algorithm that can accurately predict treatment response may help patients suffering from mood disorders. Here we used an algorithm to do so using neuroimaging data. We used the neuromark framework to learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework achieves up to 95.45% accuracy, 0.90 sensitivity, and 0.92 specificity in predicting antidepressant (AD) vs. mood stabilizer (MS) response in patients. We incorporated two additional datasets to evaluate the generalizability of our approach. The trained algorithm achieved up to 89% accuracy, 0.88 sensitivity, and 0.89 specificity in predicting the DSM-based diagnosis on these datasets. We also translated the model to distinguish responders to treatment from nonresponders with up to 70% accuracy. This approach reveals multiple salient biomarkers of medication-class of response within mood disorders.
临床医生在诊断复杂症状的双相情感障碍患者时常常面临两难境地，这些患者处于抑郁状态的时间比处于躁狂状态的时间长。目前，这种诊断的黄金标准，即《精神疾病诊断与统计手册》（DSM），在病理生理学上并没有客观依据。在这种复杂的情况下，仅仅依靠 DSM 可能会导致误诊为重度抑郁症（MDD）。一种基于生物学的分类算法，可以准确预测治疗反应，可能有助于患有情绪障碍的患者。在这里，我们使用神经影像学数据来使用算法做到这一点。我们使用神经标记框架在多个特征子空间上学习支持向量机（SVM）的核函数。神经标记框架在预测抗抑郁药（AD）与情绪稳定剂（MS）对患者的反应方面达到了高达 95.45%的准确率、0.90 的敏感性和 0.92 的特异性。我们还纳入了两个额外的数据集来评估我们方法的泛化能力。在这些数据集中，训练有素的算法在预测基于 DSM 的诊断方面达到了高达 89%的准确率、0.88 的敏感性和 0.89 的特异性。我们还将模型转换为能够以高达 70%的准确率区分治疗反应者和无反应者。这种方法揭示了情绪障碍中药物反应类别的多个显著生物标志物。
