Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Department of Psychology, Vanderbilt University, Nashville, TN, 37235, USA.
Neuropsychopharmacology. 2021 Mar;46(4):783-790. doi: 10.1038/s41386-020-00871-w. Epub 2020 Oct 2.
Depression is a common psychiatric illness that often begins in youth, and is sometimes associated with cognitive deficits. However, there is significant variability in cognitive dysfunction, likely reflecting biological heterogeneity. We sought to identify neurocognitive subtypes and their neurofunctional signatures in a large cross-sectional sample of depressed youth. Participants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (MDD n = 368, TD n = 200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A recently developed semi-supervised machine learning algorithm was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task. We identified three neurocognitive subtypes in the depressed group. Subtype 1 was high-performing (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), and Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition. Taken together, these data suggest disparate mechanisms of cognitive vulnerability and resilience in depressed youth, which may inform the identification of biomarkers for prognosis and treatment response.
抑郁是一种常见的精神疾病,通常始于青年时期,有时与认知缺陷有关。然而,认知功能障碍存在显著的变异性,可能反映了生物学的异质性。我们试图在一个大的抑郁青年横断面样本中识别神经认知亚型及其神经功能特征。参与者来自费城神经发育队列,包括 712 名有过一生中重度抑郁发作史的青少年和 712 名年龄和性别匹配的典型发育(TD)青少年。一小部分(MDD n=368,TD n=200)也完成了神经影像学检查。认知是用宾夕法尼亚计算机神经认知电池评估的。最近开发的半监督机器学习算法用于描绘神经认知亚型。在 n 回工作记忆 fMRI 任务中,对两种临床病理和大脑激活进行了评估。我们在抑郁组中确定了三种神经认知亚型。第 1 型表现良好(准确率高,速度适中),第 2 型认知受损(准确率低,速度慢),第 3 型冲动(准确率低,速度快)。虽然亚型在临床病理方面没有差异,但它们在执行功能关键区域的激活模式上存在差异,这反映了认知差异。总之,这些数据表明抑郁青少年的认知脆弱性和弹性存在不同的机制,这可能为预后和治疗反应的生物标志物的识别提供信息。