Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies.
Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies.
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使用规范模型了解临床人群中的异质性：超越病例对照研究。
DOI：
10.1016/j.biopsych.2015.12.023
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发表时间：
2016-10-01
影响因子：
10.6
通讯作者：
Beckmann CF
中科院分区：
文献类型：
--
作者：
Marquand AF;Rezek I;Buitelaar J;Beckmann CF
尽管取得了许多成功，但病例对照方法在生物医学科学中存在问题。它引入了一种人为的对称性，由此所有临床组（例如，患者和对照受试者）被认为是明确的，而在生物学上它们通常是高度异质的。根据定义，它还排除了对诊断标签有效性的推断。作为回应，国家精神卫生研究所研究领域标准建议绘制症状维度与广泛的行为和生物领域之间的关系，跨越诊断类别。然而，到目前为止，研究领域标准已经促使很少的方法来有意义地分层临床队列。我们引入规范模型解析异质性的临床队列，同时允许在个体受试者水平的预测。这种方法的目的是映射队列内的变化，是不同的，并补充，现有的方法，解决异质性，采用聚类技术，以fractionalcohort。为了证明这种方法，我们在一个大型健康队列（N = 491）中绘制了特质冲动性和奖励相关脑活动之间的关系。我们确定参与者谁是这个分布中的离群值，并表明偏差的程度（离群值大小）与特定的注意力缺陷/多动障碍症状（多动，但不是注意力不集中）的基础上的个性化模式的异常。规范模型提供了一个自然的框架，在个人参与者的水平上研究疾病，而不二分法的队列。相反，疾病可以被认为是正常范围的极端或可能是特异质偏离正常功能。它还可以推断行为变量（包括诊断标签）映射到生物学上的程度。
Despite many successes, the case-control approach is problematic in biomedical science. It introduces an artificial symmetry whereby all clinical groups (e.g., patients and control subjects) are assumed to be well defined, when biologically they are often highly heterogeneous. By definition, it also precludes inference over the validity of the diagnostic labels. In response, the National Institute of Mental Health Research Domain Criteria proposes to map relationships between symptom dimensions and broad behavioral and biological domains, cutting across diagnostic categories. However, to date, Research Domain Criteria have prompted few methods to meaningfully stratify clinical cohorts. We introduce normative modeling for parsing heterogeneity in clinical cohorts, while allowing predictions at an individual subject level. This approach aims to map variation within the cohort and is distinct from, and complementary to, existing approaches that address heterogeneity by employing clustering techniques to fractionate cohorts. To demonstrate this approach, we mapped the relationship between trait impulsivity and reward-related brain activity in a large healthy cohort (N = 491). We identify participants who are outliers within this distribution and show that the degree of deviation (outlier magnitude) relates to specific attention-deficit/hyperactivity disorder symptoms (hyperactivity, but not inattention) on the basis of individualized patterns of abnormality. Normative modeling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort. Instead, disease can be considered as an extreme of the normal range or as—possibly idiosyncratic—deviation from normal functioning. It also enables inferences over the degree to which behavioral variables, including diagnostic labels, map onto biology.
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影响因子：
4.7
作者：
Dias, Taciana G. Costa;Iyer, Swathi P.;Carpenter, Samuel D.;Cary, Robert P.;Wilson, Vanessa B.;Mitchell, Suzanne H.;Nigg, Joel T.;Fair, Damien A.
通讯作者：
Fair, Damien A.
影响因子：
5.7
作者：
Franke, Katja;Ziegler, Gabriel;Gaser, Christian
通讯作者：
Gaser, Christian
DOI：
10.1073/pnas.1115365109
发表时间：
2012-04-24
影响因子：
11.1
作者：
Fair, Damien A.;Bathula, Deepti;Nigg, Joel T.
通讯作者：
Nigg, Joel T.
影响因子：
3.5
作者：
Kirby, Kris N.
通讯作者：
Kirby, Kris N.
影响因子：
5.7
作者：
Hyun JW;Li Y;Gilmore JH;Lu Z;Styner M;Zhu H
通讯作者：
Zhu H