Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.
Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.
Neuroimage. 2022 Sep;258:119348. doi: 10.1016/j.neuroimage.2022.119348. Epub 2022 Jun 2.
Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.
精神障碍是一生中最常见和最具致残性的疾病之一，通常始于儿童和青少年时期，这强调了研究发育中大脑的重要性。大多数先前的儿科神经影像学研究都采用了传统的单变量统计方法，且样本量相对较小。多变量机器学习方法具有克服这些方法局限性的巨大潜力。另一方面，绝大多数现有的多变量机器学习研究都集中在区分患有孤立性精神障碍的儿童和典型发育的儿童。然而，这种研究思路并不能反映现实情况，因为大多数患有临床诊断的儿童都有多发性精神障碍（多种疾病共存），因此，临床医生的任务是在不同的诊断和/或多种诊断的组合之间做出选择。因此，本基准的目标是预测儿童和青少年的精神多种疾病共存。为此，我们实施了两种机器学习基准挑战：第一个挑战针对可用数据集的七种最常见的 DSM-V 精神诊断的预测，其中每个人都可以同时表现出多种诊断（即多任务多标签分类）。基于行为和认知测量，第二个挑战侧重于在维度级别上预测精神症状的严重程度（即多元回归任务）。对于本基准挑战，我们将利用 Healthy Brain Network（HBN）倡议的生物库中的现有和未来数据，该倡议提供了一个独特的大样本数据集（N=2042），其中包含广泛的不同精神发育障碍和真实隐藏数据集。由于 MRI 测量在实际应用中的局限性和经济可行性有限，本挑战仅允许使用静息状态 EEG 数据和人口统计信息来得出预测模型。我们相信，使用先进的机器学习算法从这些数据中得出预测标志物的社区驱动努力可以帮助改善精神发育障碍的诊断。
