Williams Leanne M, Yesavage Jerome
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA.
Mental Illness Research, Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA 94304 USA.
Pers Med Psychiatry. 2024 Jul-Aug;45-46. doi: 10.1016/j.pmip.2024.100126. Epub 2024 Apr 30.
We previously identified a cognitive biotype of depression characterized by dysfunction of the brain's cognitive control circuit, comprising the dorsolateral prefrontal cortex (dLPFC) and dorsal anterior cingulate cortex (dACC), derived from functional magnetic resonance imaging (fMRI). We evaluate these circuit metrics as personalized predictors of antidepressant remission.
We undertook a secondary analysis of data from the international Study to Predict Optimized Treatment in Depression (iSPOT-D) for 159 patients who completed fMRI during a GoNoGo task, 8 weeks treatment with one of three study antidepressants and who were assessed for remission status (Hamilton Depression Rating Scale score of ≤ 7). Circuit predictors of remission were dLPFC and dACC activity and connectivity quantified in standard deviations. Using established software implementing receiver operating analysis (ROC) we calculated the sensitivity and specificity of these predictors at every cut-point for every circuit measure. We calculated number needed to treat (NNT) metrics for the ROC model identifying optimal cut-point values.
ROC models identified maximum separation of remitters (62.5%) from non-remitters (21.2%) at an initial cut-point of -0.75 standard deviations for dLPFC activity and averaged circuit metrics at secondary cutpoints. The NNT was 3.72, implying that if 4 patients (rounding of 3.72) were randomly selected, one would be likely to remit, but if circuit metrics informed treatment, two would be likely to remit.
Our findings contribute to identifying clinically actionable circuit measures for clinical trials and clinical practice. Future studies are needed to replicate these findings and expand the assessment of longer-term outcomes.
我们之前通过功能磁共振成像（fMRI）确定了一种抑郁症的认知生物型，其特征为大脑认知控制回路功能失调，该回路包括背外侧前额叶皮质（dLPFC）和背侧前扣带回皮质（dACC）。我们将这些回路指标评估为抗抑郁药缓解的个性化预测指标。
我们对国际抑郁症优化治疗预测研究（iSPOT-D）的数据进行了二次分析，该研究中有159名患者在执行GoNoGo任务期间完成了fMRI，接受了三种研究抗抑郁药之一为期8周的治疗，并接受了缓解状态评估（汉密尔顿抑郁量表评分≤7）。缓解的回路预测指标是dLPFC和dACC的活动以及以标准差量化的连通性。使用实施接受者操作分析（ROC）的既定软件，我们计算了每个回路测量在每个切点处这些预测指标的敏感性和特异性。我们计算了识别最佳切点值的ROC模型的治疗所需人数（NNT）指标。
ROC模型在dLPFC活动的初始切点为-0.75标准差以及在二级切点处的平均回路指标时，确定缓解者（62.5%）与未缓解者（21.2%）的最大分离度。NNT为3.72，这意味着如果随机选择4名患者（3.72四舍五入），其中一名可能会缓解，但如果回路指标为治疗提供信息，则两名可能会缓解。
我们的研究结果有助于确定临床试验和临床实践中具有临床可操作性的回路测量方法。需要进一步的研究来重复这些发现并扩大对长期结果的评估。
