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

基本信息

  • 批准号:
    10600093
  • 负责人:
  • 金额:
    $ 114.56万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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 儿童精神病学中两种最早发病、最常见和代价高昂的神经发育障碍 注意力缺陷多动障碍(ADHD)和自闭症谱系障碍(ASD)。临床 异质性和疾病分类学差异的不精确性从根本上代表了 混杂因素限制了对其病因、预防和治疗的更好理解。简而言之, 关于典型和非典型群体中“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效应量。在临床上,这些假设可能 解释了为什么治疗往往效果微弱或不可预测。 计算科学的最新发展使模型的实现成为可能。 足够复杂,以解决上述有关亚群的情况;然而,很少有联系 特定结果的输出或研究者提出的问题。在父母补助金下,我们 开发并发布了一种新的混合监督/无监督机器学习方法, ADHD和/或ASD的生物学相关异质性-功能随机森林(FRF)。混合 FRF结合了机器学习和图论分析,以识别与人类相关的人群亚型。 临床上最重要的结果(在这种情况下,本建议,负效价症状)反式- 诊断(ASD,ADHD,TD)。 尽管开发了FRF,但使用功能性MRI(fMRI)信号的分型结果已经落后 行为特征的分型此外,它们尚未变得足够敏感和具体, 快速转化为临床实践。幸运的是,功能性神经影像学的平行发展, 精确的个人功能映射,并可以与FRF协同组合,大大提高 我们能够从功能磁共振成像数据中对个体患者进行分型和特征化。在这里,我们将FRF与联合收割机 精确映射,以揭示全球大脑组织中的常见变异和个体特异性。的 建议的个人特定精度映射超越了群体平均方法, 掩盖了与阴性基础的不同病理生理学相关的重要个体间差异, 诊断之间的效价(ADHD,ASD,TD)。 因此,目前的建议旨在将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ASD中的不同亚群, ADHD和TD与负效价症状维度相关。

项目成果

期刊论文数量(21)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks.
  • DOI:
    10.1371/journal.pone.0091322
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Gates KM;Molenaar PC;Iyer SP;Nigg JT;Fair DA
  • 通讯作者:
    Fair DA
Prenatal domoic acid exposure disrupts mouse pro-social behavior and functional connectivity MRI.
  • DOI:
    10.1016/j.bbr.2016.03.039
  • 发表时间:
    2016-07-15
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    Mills BD;Pearce HL;Khan O;Jarrett BR;Fair DA;Lahvis GP
  • 通讯作者:
    Lahvis GP
Implications of newborn amygdala connectivity for fear and cognitive development at 6-months-of-age.
  • DOI:
    10.1016/j.dcn.2015.09.006
  • 发表时间:
    2016-04
  • 期刊:
  • 影响因子:
    4.7
  • 作者:
    Graham AM;Buss C;Rasmussen JM;Rudolph MD;Demeter DV;Gilmore JH;Styner M;Entringer S;Wadhwa PD;Fair DA
  • 通讯作者:
    Fair DA
Structural and functional rich club organization of the brain in children and adults.
  • DOI:
    10.1371/journal.pone.0088297
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Grayson DS;Ray S;Carpenter S;Iyer S;Dias TG;Stevens C;Nigg JT;Fair DA
  • 通讯作者:
    Fair DA
MR connectomics: a conceptual framework for studying the developing brain.
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Nico Dosenbach其他文献

Nico Dosenbach的其他文献

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{{ truncateString('Nico Dosenbach', 18)}}的其他基金

Functional Connectivity, Brain Development, and Outcomes in Chiari Type I Malformation
Chiari I 型畸形的功能连接、大脑发育和结果
  • 批准号:
    10629122
  • 财政年份:
    2023
  • 资助金额:
    $ 114.56万
  • 项目类别:
PEDIATRIC BRAIN INJURY RECOVERY VIA USE-DRIVEN FUNCTIONAL NETWORK REORGANIZATION
通过使用驱动的功能网络重组实现小儿脑损伤康复
  • 批准号:
    9244075
  • 财政年份:
    2015
  • 资助金额:
    $ 114.56万
  • 项目类别:
PEDIATRIC BRAIN INJURY RECOVERY VIA USE-DRIVEN FUNCTIONAL NETWORK REORGANIZATION
通过使用驱动的功能网络重组实现小儿脑损伤康复
  • 批准号:
    8996726
  • 财政年份:
    2015
  • 资助金额:
    $ 114.56万
  • 项目类别:
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
  • 财政年份:
    2012
  • 资助金额:
    $ 114.56万
  • 项目类别:
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
  • 财政年份:
    2012
  • 资助金额:
    $ 114.56万
  • 项目类别:

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