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

使用多模式神经影像和多任务学习预测精神病理学轨迹

• 批准号: 10825010
• 负责人: Robert J Jirsaraie
• 金额: $ 4.13万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2025-09-19
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10825010
• 关键词: Adolescence; Adolescent; Age; Aptitude; Area; Biological Markers; Brain; Brain Diseases; Brain Pathology; Childhood; Clinical; Cognitive; Data; Data Set; Development; Dimensions; Early Diagnosis; Ensure; Exhibits; Fostering; Funding; Genetic Predisposition to Disease; Goals; Human; Impairment; Individual; Individual Differences; Knowledge; Longevity; Maps; Mental disorders; Methods; Modality; Modeling; Performance; Practice Guidelines; Principal Investigator; Property; Psychopathology; Psychoses; Psychosocial Factor; Quality of life; Reproducibility; Research; Research Personnel; Risk; Risk Factors; Sampling; Severities; Societies; Stress; Substance abuse problem; Symptoms; Time; Training; United States National Institutes of Health; Work; age related; aging brain; career; catalyst; connectome; cost; course development; deep learning; deep learning model; experience; functional outcomes; improved; innovation; insight; longitudinal analysis; machine learning method; multi-task learning; multimodal neuroimaging; multimodality; multitask; neural; neural network; neurodevelopment; neuroimaging; novel; novel marker; novel strategies; personalized intervention; prediction algorithm; risk prediction; skills; specific biomarkers; success

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.

项目总结/摘要： 大多数形式的精神病理学越来越被认为是大脑疾病，出现在早期， 发展，并在整个生命周期中持续存在。考虑到精神疾病的巨大成本， 制定方法，确定哪些青少年是最易受伤害的，这可能导致更准确和 个性化的干预。在这里，我们建议使用一种新的预测框架，可以更好地捕捉 神经发育起源的精神病理学，从而产生更准确的预测精神病理学。 具体来说，我们计划开发多任务神经网络，这些神经网络在三个空间地图上进行训练。 神经成像模式，并产生对个体年龄（“脑年龄”）的同时预测， 精神病理学（“大脑病理学”）。通过整合大脑年龄和大脑病理学的预测， 新的多任务框架，我们可以得到具有改进的预测能力的模型，这也将是有用的 来揭示精神病理学各个方面的特定生物标志物。调查这些 研究问题并复制我们的发现，我们将使用两个多模态神经发育数据， 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 在发展（HCD）和青少年大脑认知发展（ABCD）样本。与使用 单任务模型，我们假设，多任务预测的大脑年龄和大脑病理将是 能够更好地检测在任何给定时间点的个体差异（目标1），并且这样的预测将最好 映射到整个青春期的主体内变化（目标2）。此外，我们将使用多种功能 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）。这个F31的建议可能被证明是有用的，在确定青少年谁是最 易受精神病理学（“个性化”）的影响，在发展过程中更早地接触风险 （“精确度”）。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来 产生样本外预测，这可能对神经成像研究人员有广泛的应用， 儿科临床医生通过追求这些研究目标，申请人将获得必要的培训 在以下领域：1）深度/机器学习方法，2）多模式神经成像，3）高级 精神病理学，4）进行严格和可重复的研究，5）专业发展， 申请人的职业发展是独立的，NIH资助的学术研究人员。组装的 培训队伍在每一个学科领域都有丰富的专业知识。在他们的支持下，申请人将 发展理论，分析和专业能力，以促进他的研究和职业抱负。 总之，这个F31提案将是催化剂，以帮助申请人在他的目标，成为一个独立的 首席研究员，使用多模型方法来描述最重要的决定因素， 心理病理学和预测风险的个人基础上。