背景:精神疾病传统上属于诊断类别,但是这种方法有局限性。研究领域标准(RDoC)构成了基于跨越这些精神病学诊断的领域内的维度的精神疾病研究分类系统。RDoC的总体目标是更好地了解基础神经生物学和行为系统功能障碍方面的精神疾病,导致更好的诊断,预防和治疗。
方法:独特的脑电图(EEG)特征,称为纺锤过度β(SEB),已经研究了与冲动控制和睡眠有关的问题,作为唤醒/调节系统RDoC域的一部分。这里,我们研究了脑电图额叶β活性作为一种潜在的诊断生物标志物,能够诊断和预测冲动控制和睡眠问题.
结果:我们在第一个数据集(n=3279)中显示具有SEB的概率,由深度学习算法分类,与不良的睡眠维持和低的白天冲动控制有关。此外,另外两个,独立数据集(iSPOT-A,n=336;iSPOT-D,n=1008),我们揭示了传统的额中央β功率和/或SEB概率,被称为Brainmarker-III,与注意力缺陷多动障碍(ADHD)的诊断有关,以性别特异性方式缓解多动症儿童的哌醋甲酯,并以药物特异性方式缓解患有重度抑郁症的成年人的抗抑郁药物。
结论:我们的结果证明了RDoC方法在精神病学研究中发现具有诊断和治疗预测能力的生物标志物的价值。
BACKGROUND: Psychiatric disorders are traditionally classified within diagnostic categories, but this approach has limitations. Research Domain Criteria (
RDoC) constitute a research classification system for psychiatric disorders based on dimensions within domains that cut across these psychiatric diagnoses. The overall aim of
RDoC is to better understand mental illness in terms of dysfunction in fundamental neurobiological and behavioral systems, leading to better diagnosis, prevention and treatment.
METHODS: A unique electroencephalographic (EEG) feature, referred to as spindling excessive beta (SEB), has been studied in relation to impulse control and sleep, as part of the arousal/regulatory systems
RDoC domain. Here, we study EEG frontal beta activity as a potential transdiagnostic biomarker capable of diagnosing and predicting impulse control and sleep problems.
RESULTS: We show in the first dataset (n=3279) that the probability of having SEB, classified by a deep learning algorithm, is associated with poor sleep maintenance and low daytime impulse control. Furthermore, in two additional, independent datasets (iSPOT-A, n=336; iSPOT-D, n=1008), we revealed that conventional frontocentral beta power and/or SEB probability, referred to as Brainmarker-III, is associated with a diagnosis of attention deficit hyperactivity disorder (ADHD), with remission to methylphenidate in children with ADHD in a sex-specific manner, and with remission to antidepressant medication in adults with a major depressive disorder in a drug-specific manner.
CONCLUSIONS: Our results demonstrate the value of the
RDoC approach in psychiatry research for the discovery of biomarkers with diagnostic and treatment prediction capacities.