Huaxi MR Research Center, Department of Radiology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China.
Neuroimage. 2011 Apr 15;55(4):1497-503. doi: 10.1016/j.neuroimage.2010.11.079. Epub 2010 Dec 3.
Despite significant advances in the treatment of major depression, there is a high degree of variability in how patients respond to treatment. Approximately 70% of patients show some improvement following standard antidepressant treatment and are classified as having non-refractory depressive disorder (NDD), while the remaining 30% of patients do not respond to treatment and are classified as having refractory depressive disorder (RDD). At present, there are no objective, neurological markers which can be used to identify individuals with depression and predict clinical outcome. We therefore examined the diagnostic and prognostic potential of pre-treatment structural neuroanatomy using support vector machine (SVM). Sixty-one drug-naïve adults suffering from depression and 42 healthy volunteers were scanned using structural magnetic resonance imaging (sMRI). Patients then received standard antidepressant medication (either tricyclic, typical serotonin-norepinephrine reuptake inhibitor or typical selective serotonin reuptake inhibitor). Based on clinical outcome, we selected two groups of RDD (n=23) and NDD (n=23) patients matched for age, sex and pre-treatment severity of depression. Diagnostic accuracy of gray matter was 67.39% for RDD (p=0.01) and 76.09% for NDD (p<0.001), while diagnostic accuracy of white matter was 58.70% for RDD (p=0.13) and 84.65% for NDD (p<0.001). SVM applied to gray matter correctly distinguished between RDD and NDD patients with an accuracy of 69.57% (p=0.006); in contrast, SVM applied to white matter predicted clinical outcome with an accuracy of 65.22% (p=0.02). These results indicate that both gray and white matter have diagnostic and prognostic potential in major depression and may provide an initial step towards the use of biological markers to inform clinical treatment. Future studies will benefit from the integration of structural neuroimaging with other imaging modalities as well as genetic, clinical and cognitive information.
尽管在治疗重度抑郁症方面取得了重大进展，但患者对治疗的反应存在很大差异。大约 70%的患者在接受标准抗抑郁治疗后会有所改善，被归类为非难治性抑郁障碍（NDD），而其余 30%的患者对治疗无反应，被归类为难治性抑郁障碍（RDD）。目前，没有客观的神经生物学标志物可用于识别抑郁症患者并预测临床结局。因此，我们使用支持向量机（SVM）检查了预处理结构神经影像学的诊断和预后潜力。61 名未经药物治疗的成年抑郁症患者和 42 名健康志愿者接受了结构磁共振成像（sMRI）扫描。然后，患者接受了标准的抗抑郁药物治疗（三环类、典型的去甲肾上腺素和 5-羟色胺再摄取抑制剂或典型的选择性 5-羟色胺再摄取抑制剂）。根据临床结局，我们选择了两组 RDD（n=23）和 NDD（n=23）患者，这些患者在年龄、性别和治疗前抑郁严重程度方面相匹配。对于 RDD，灰质的诊断准确性为 67.39%（p=0.01），对于 NDD，诊断准确性为 76.09%（p<0.001），而对于 RDD，白质的诊断准确性为 58.70%（p=0.13），对于 NDD，诊断准确性为 84.65%（p<0.001）。SVM 应用于灰质可以正确区分 RDD 和 NDD 患者，准确率为 69.57%（p=0.006）；相比之下，SVM 应用于白质预测临床结局的准确率为 65.22%（p=0.02）。这些结果表明，灰质和白质在重度抑郁症中都具有诊断和预后潜力，可能为使用生物标志物为临床治疗提供依据。未来的研究将受益于结构神经影像学与其他影像学模式以及遗传、临床和认知信息的整合。
