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.

项目摘要 儿童精神病学中起病最早、最常见且代价高昂的两种神经发育障碍 注意力缺陷多动障碍(ADHD)和自闭症谱系障碍(ASD)。临床部 异质性和它们的病因学区别的不精确本质从根本上代表了 混杂因素限制了对其病因、预防和治疗的更好了解。简而言之，简单 关于典型和非典型总体中“样本同质性”的设计假设可以解释 在精神病理学研究中经常是非常小的影响规模。在临床上，这些相同的假设可能 解释了为什么治疗通常有微弱或不可预测的影响。 计算科学的最新发展使模型的实施成为可能 足够复杂，足以解决上述关于亚群的情况；然而，很少有人与 调查员提出的具体结果或问题的结果。在家长的资助下，我们 开发并发布了一种新的混合监督/非监督机器学习方法来表征 ADHD和/或ASD-功能随机林(FRF)中与生物相关的异质性。混合动力车 FRF结合了机器学习和图论分析，以识别相关的种群亚型 对于临床上最重要的结果(在本建议的情况下，负价症状)， 诊断(ASD、ADHD、TD)。 尽管开发了功能磁共振成像(FRF)，但使用功能磁共振成像(FMRI)信号的亚型结果仍然落后 行为特征的亚型。此外，它们还没有变得足够敏感和具体，因为 快速转化为临床实践。幸运的是，功能神经成像的平行进展允许 精确的个体功能图谱，并可与FRF协同结合，大大提升 我们从fMRI数据中对单个患者进行亚型和特征划分的能力。在这里，我们将FRF与 精确的图谱，揭示全球大脑组织中的共同变异和个体特异性。这个 拟议的特定于个人的精度映射超越了群体平均方法，这些方法是 掩盖与潜在的不同病理生理学相关的重要个体间差异 跨诊断(ADHD、ASD、TD)的价态。 因此，当前的提案旨在将FRF算法应用于跨诊断(TD、ASD、ADHD) 行为和精确的功能映射RSFC数据，以识别ASD中不同的亚群， ADHD和TD与负性效价症状维度相关。