尽管取得了许多成功,在生物医学科学中,病例对照方法是有问题的。它引入了一种人工对称,由此所有临床组(例如,患者和对照受试者)被认为是明确的,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,美国国家精神卫生研究所领域标准建议绘制症状维度与广泛的行为和生物学领域之间的关系图,跨越诊断类别。然而,到目前为止,研究领域标准已提示有意义的分层临床队列的方法很少。
我们在临床队列中引入了解析异质性的规范建模,同时允许在个体学科水平上进行预测。这种方法旨在绘制队列内的变异图,并补充,通过采用聚类技术分割队列来解决异质性的现有方法。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大脑活动之间的关系。
我们确定了在这种分布中是异常值的参与者,并表明偏差程度(异常值)与特定的注意力缺陷/多动障碍症状(多动,但不是不注意)基于个性化的异常模式。
规范模型提供了一个自然的框架,可以在个体参与者水平上研究疾病,而无需对队列进行二分法。相反,疾病可以被认为是正常范围的极端或可能与正常功能的特殊偏离。它还可以推断行为变量的程度,包括诊断标签,映射到生物学。
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.