Predicting trajectories of psychopathology using multimodal neuroimaging and multi-task learning

项目摘要/摘要： 大多数形式的精神病理学越来越多地被认为是早期出现的大脑疾病 发展，并在整个生命周期中坚持。考虑到精神疾病的巨大代价，这是势在必行的 开发识别最脆弱青少年的方法，这可能导致更准确和 个性化干预。在这里，我们建议使用一种新的预测框架，该框架可以更好地捕捉 精神病理学的神经发育起源，从而产生对精神病理学更准确的预测。 具体地说，我们计划开发多任务神经网络，这些神经网络根据来自三个国家的空间地图进行训练 神经成像方法并同时预测个体的年龄(“大脑年龄”)和 精神病理学(“脑病理学”)。通过将大脑年龄和大脑病理的预测结合在一起 新的多任务框架，我们可能会得到具有改进的预测能力的模型，这也将是有用的 以揭示构成精神病理学各个维度的特定生物标记物。为了调查这些 研究问题并复制我们的发现，我们将使用来自两个 最大的神经成像数据集，也包含三个纵向时间点-即人类连接组 发育中(HCD)和青少年大脑认知发育(ABCD)样本。与使用 单任务模型中，我们假设脑年龄和脑病理的多任务预测将是 能够更好地发现任何给定时间点的个体差异(目标1)，这样的预测将是最好的 映射到整个青春期内受试者的变化(目标2)。此外，我们将使用多个功能 重要的方法，以确定哪些大脑区域和神经特性增加了最大的预测能力 我们最精确的模型(目标3)。这项F31建议可能会被证明在识别青少年中最 易受精神病态影响(“个人化”)和在发育过程中较早接触风险 (“精度”)。我们还将公开我们的深度学习模型，以便任何人都可以使用它们 产生样本外预测，这可能会在神经成像研究人员和 儿科临床医生。通过追求这些研究目标，申请者将接受必要的培训 在以下领域：1)深度/机器学习方法，2)多模式神经成像，3)先进 精神病理学，4)进行严谨和可重复的研究，5)专业发展，如 申请者将成为一名独立的、由NIH资助的学术研究人员。已组装好的 培训团队在每个主题领域都有丰富的专业知识。在他们的支持下，申请人将 培养理论、分析和专业能力，以培养他的研究和职业抱负。 总之，这份F31提案将成为催化剂，帮助申请者实现成为独立人士的目标 首席研究员，使用多模型方法来描述最重要的决定因素 精神病理学和预测风险的个人基础。

PROJECT SUMMARY / ABSTRACT: Most forms of psychopathology have been increasingly recognized as brain disorders that emerge early in development and persist throughout the lifespan. Given the considerable costs of mental illness, it is imperative to develop ways of identifying adolescents who are the most vulnerable, which may lead to more precise and personalized interventions. Here, we propose using a novel predictive framework that may better capture the neurodevelopmental origins of psychopathology, thereby yielding more accurate predictions of psychopathology. Specifically, we plan to develop multi-task neural networks that are trained on spatial maps from three neuroimaging modalities and yield simultaneous predictions of an individual’s age (“brain age”) and psychopathology (“brain pathology”). By integrating predictions of brain age and brain pathology through this novel multi-task framework, we may derive models with improved predictive power, which would also be useful for uncovering the specific biomarkers that underlie each dimension of psychopathology. To investigate these research questions and replicate our findings, we will use multimodal neurodevelopmental data from two of the largest neuroimaging datasets that also contain three longitudinal timepoints – namely the Human Connectome in Development (HCD) and the Adolescent Brain Cognitive Developmental (ABCD) samples. In contrast to using single-task models, we hypothesize that the multi-task predictions of brain age and brain pathology would be better able at detect individual differences at any given point in time (Aim 1) and such predictions would best map onto within-subject changes throughout adolescence (Aim 2). Further, we will use multiple feature importance methods to identify which brain areas and neural properties added the largest predictive power to our most accurate models (Aim 3). This F31 proposal may prove useful in identifying adolescents who are most vulnerable to psychopathology (“personalization”) and accessing risk earlier in the course of development (“precision”). We will also make our deep learning models publicly available so that anyone could use them to yield out-of-sample predictions, which may have wide-spread applications for neuroimaging researchers and pediatric clinicians. Through the pursuit of these research objectives, the applicant will receive essential training in the following areas: 1) deep/machine learning methods, 2) multimodal neuroimaging, 3) advanced psychopathology, 4) conducting rigorous and reproducible research, 5) professional development as the applicant progresses toward a career as an independent, NIH-funded academic researcher. The assembled training team has substantial expertise in each of these subject domains. With their support, the applicant will develop the theoretical, analytical, and professional aptitude needed to foster his research and career ambitions. Altogether, this F31 proposal will be catalyst to help the applicant in his goal of becoming an independent principal investigator that uses multi-model approaches to delineate the most important determents of psychopathology and predict risk on an individual-basis.