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: 10181076
• 负责人: Nico Dosenbach
• 金额: $ 115.71万
• 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/10181076
• 关键词: Address; Adolescent; Adult; Affect; Algorithms; Amygdaloid structure; Anger; Anxiety; Attention deficit hyperactivity disorder; Behavior; Behavioral; Biological; 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.

项目概要 儿童精神病学中最早发病、最常见且代价高昂的两种神经发育障碍 临床上有注意力缺陷多动障碍（ADHD）和自闭症谱系障碍（ASD）。 异质性及其分类学区别的不精确本质代表了一种根本性的 混杂因素限制了对其病因、预防和治疗的更好理解。 关于典型和非典型人群中“样本同质性”的设计假设可以解释 在临床上，这些相同的假设可能会产生非常小的影响。 解释为什么治疗通常效果较弱或不可预测。 计算科学的最新发展使得模型的实现成为可能 然而，很少有领带 根据家长资助，我们将输出具体结果或提出的问题。 开发并发布了一种新颖的混合监督/无监督机器学习方法来表征 ADHD 和/或 ASD 中的生物学相关异质性——功能随机森林 (FRF)。 FRF 结合了机器学习和图论分析，以识别相关的人群亚型 到临床上最重要的结果（在本提议的情况下，负价症状）反式 诊断（ASD、ADHD、TD）。 尽管开发了 FRF，但使用功能 MRI (fMRI) 信号的子类型结果仍然落后 此外，它们还没有变得足够敏感和具体。 幸运的是，功能神经影像学的并行进展使得这一技术得以快速转化为临床实践。 精准的个体功能映射，并可与FRF协同结合，大幅提升 我们能够根据功能磁共振成像数据对个体患者进行分类和表征。在这里，我们将 FRF 与 FRF 结合起来。 精确绘图以揭示全球大脑组织中的常见变异和个体特异性。 所提出的个体特定的精确映射超越了组平均方法，这些方法是 模糊了与负面影响背后的不同病理生理学相关的重要个体差异 跨诊断的效价（ADHD、ASD、TD）。 因此，当前的提案旨在将 FRF 算法应用于跨诊断（TD、ASD、ADHD） 行为和精确功能映射 RSFC 数据，以识别跨 ASD 的不同亚群， ADHD 和 TD 与负价症状维度相关。