Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASD

使用 ADHD 和 ASD 的深度表型分析和精确功能图谱识别基于结果的亚群

• 批准号: 10402304
• 负责人: Nico Dosenbach
• 金额: $ 115.08万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-06 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10402304
• 关键词: Address; Adolescent; Adult; Affect; Algorithms; Amygdaloid structure; Anger; Anxiety; Attention deficit hyperactivity disorder; Behavior; Behavioral; Biological Markers; Brain; Categories; Child; Child Psychiatry; Childhood; Clinical; Communities; Complement; Complex; Computational Science; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Dimensions; Disease; Etiology; Fright; Functional Magnetic Resonance Imaging; Functional disorder; Future; Genetic; Graph; Heterogeneity; Hybrids; Imaging Techniques; Individual; Individual Differences; Investigation; Knowledge; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Mental disorders; Methods; Modeling; Monitor; Morbidity - disease rate; Motion; Movement; Myelin; National Institute of Mental Health; Nature; Negative Valence; Neurobiology; Neurodevelopmental Disorder; Outcome; Output; Participant; Patients; Pediatrics; Phenotype; Physiology; Population; Prevention; Procedures; Psychiatry; Psychopathology; Publishing; Research; Research Domain Criteria; Research Personnel; Rest; Retinal blind spot; Sample Size; Sampling; Scanning; Signal Transduction; Site; Specificity; Supervision; Symptoms; System; Testing; Therapeutic; Time; Translating; Translations; Variant; Work; Youth; autism spectrum disorder; base; behavioral study; brain behavior; clinical care; clinical heterogeneity; clinical practice; cognitive control; cognitive development; cohort; computerized tools; cost; design; disorder subtype; gray matter; improved; individual patient; innovation; interest; machine learning method; mortality; network architecture; neuroimaging; next generation; novel; parent grant; pediatric patients; personalized diagnostics; random forest; symptomatology; treatment stratification; treatment trial; unsupervised learning

Project Summary Two of the earliest onset, most common, and costly neurodevelopmental disorders in child psychiatry are Attention Deficit Hyperactivity Disorder (ADHD) and Autism spectrum disorders (ASD). The clinical heterogeneity and the imprecise nature of their nosological distinctions represents a fundamentally confounding factor limiting a better understanding of their etiology, prevention, and treatment. In short, simple design assumptions regarding `homogeneity in samples' in typical and atypical populations may explain the frequently very small effect sizes in psychopathology research. Clinically, these same assumptions may account for why treatments often have weak or unpredictable effects. Recent developments in the computational sciences, have enabled the implementation of models sufficiently complex to address the aforementioned situation regarding subpopulations; however, very few tie the outputs to the specific outcome or questions being asked by the investigator. Under the parent grant, we developed and published a novel hybrid supervised/unsupervised machine learning method to characterize biologically relevant heterogeneity in ADHD and/or ASD – the Functional Random Forest (FRF). The hybrid FRF combines machine learning and graph theoretic analyses in order to identify population subtypes related to the clinically most important outcomes (in the case, of this proposal, negative valence symptoms) trans- diagnostically (ASD, ADHD, TD). Despite developing the FRF, subtyping results using functional MRI (fMRI) signals have lagged behind the subtyping of behavioral profiles. In addition, they have yet to become sufficiently sensitive and specific, for rapid translation into clinical practice. Fortunately, parallel advances in functional neuroimaging, allow for precision functional mapping of individuals, and can be synergistically combined with the FRF to greatly boost our ability to subtype and characterize individual patients from fMRI data. Here we combine the FRF with precision mapping to reveal common variants and individual specificity in global brain organization. The proposed individual-specific precision mapping moves beyond group averaging approaches, which are obscuring important inter-individual differences related to distinct pathophysiologies underlying negative valence across diagnoses (ADHD, ASD, TD). Thus, the current proposal aims to apply FRF algorithms to trans-diagnostic (TD, ASD, ADHD) behavioral and precision functional mapping RSFC data to identify distinct sub-populations across ASD, ADHD, and TD that relate to negative valence symptom dimensions.

项目摘要 儿童精神病学中最早的两种，最常见和昂贵的神经发育障碍 是注意力缺陷多动症（ADHD）和自闭症谱系障碍（ASD）。临床 异质性和其牙龈学区别的浸渍性质从根本上代表 混淆因素限制了对其病因，预防和治疗的更好理解。简而言之，简单 有关典型和非典型人群中“样本中的同质性”的设计假设可能解释 心理病理学研究中的效果通常很小。临床上，这些相同的假设可能 解释为什么治疗通常会产生弱或不可预测的影响。 计算科学的最新发展已实现模型 足够复杂，可以解决有关亚群的理由情况；但是，很少有领带 调查员提出的特定结果或问题的输出。根据父母的赠款，我们 开发并发表了一种新颖的混合监督/无监督的机器学习方法，以表征 ADHD和/或ASD的生物学相关异质性 - 功能随机森林（FRF）。杂种 FRF结合了机器学习和图理论分析，以识别人群亚型相关 对于临床上最重要的结果（在此提案中，负面造成症状）的转变 诊断（ASD，ADHD，TD）。 尽管开发了FRF，但使用功能性MRI（fMRI）信号却落后于功能性MRI（fMRI）的结果 行为概况的亚型。此外，它们尚未变得足够敏感和具体，因为 快速转化为临床实践。幸运的是，功能神经影像学的并行进步，允许 精确的个体功能映射，可以与FRF协同结合以极大的提升 我们从功能磁共振成像数据中亚型和表征个别患者的能力。在这里，我们将FRF与 精确映射以揭示全球大脑组织中常见变体和个人特异性。这 提议的个人特定精度映射的移动超出了组平均方法，这是 掩盖重要的个人间差异，与阴性的不同病理生理有关 跨诊断范围的价（ADHD，ASD，TD）。 这是当前的建议旨在将FRF算法应用于跨诊断（TD，ASD，ADHD） 行为和精确的功能映射RSFC数据，以识别ASD之间不同的子人群 与负相关症状维度有关的ADHD和TD。