Paschali Magdalini, Zhao Qingyu, Adeli Ehsan, Pohl Kilian M
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Center for Health Sciences, SRI International, Menlo Park, CA, USA.
Predict Intell Med. 2022 Sep;13564:13-23. doi: 10.1007/978-3-031-16919-9_2. Epub 2022 Sep 16.
A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an outcome (e.g., depression). In recent years, deep learning has become a potential alternative approach for conducting such analyses by predicting an outcome from a collection of factors and identifying the most "informative" ones driving the prediction. However, this approach has had limited impact as its findings are not linked to statistical significance of factors supporting hypotheses. In this article, we proposed a flexible and scalable approach based on the concept of permutation testing that integrates hypothesis testing into the data-driven deep learning analysis. We apply our approach to the yearly self-reported assessments of 621 adolescent participants of the National Consortium of Alcohol and Neurodevelopment in Adolescence (NCANDA) to predict negative valence, a symptom of major depressive disorder according to the NIMH Research Domain Criteria (RDoC). Our method successfully identifies categories of risk factors that further explain the symptom.
神经科学研究的一种基本方法是基于神经心理学和行为测量来检验假设，即某些因素（如与生活事件相关的因素）是否与某种结果（如抑郁症）相关。近年来，深度学习已成为进行此类分析的一种潜在替代方法，通过从一系列因素中预测结果并识别驱动预测的最“信息丰富”因素。然而，这种方法的影响有限，因为其结果与支持假设的因素的统计显著性没有关联。在本文中，我们基于排列检验的概念提出了一种灵活且可扩展的方法，该方法将假设检验集成到数据驱动的深度学习分析中。我们将我们的方法应用于青少年酒精与神经发育国家联盟（NCANDA）的621名青少年参与者的年度自我报告评估中，以根据美国国立精神卫生研究所研究领域标准（RDoC）预测负性情绪，这是重度抑郁症的一种症状。我们的方法成功识别出了进一步解释该症状的风险因素类别。
