title: Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies.
abstract: 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.
authors.affiliation: Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, United Kingdom. Electronic address: a.f.marquand@fcdonders.ru.nl.
authors.forename: Andre F
authors.initials: AF
authors.lastname: Marquand
authors.affiliation: Schlumberger Gould Research Center, Cambridge, United Kingdom.
authors.forename: Iead
authors.initials: I
authors.lastname: Rezek
authors.affiliation: Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands; Karakter Child and Adolescent Psychiatric University Centre, Nijmegen, The Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: Buitelaar
authors.affiliation: Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands; Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, OxfordUnited Kingdom.
authors.forename: Christian F
authors.initials: CF
authors.lastname: Beckmann
mesh_terms: D000328:Adult; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis*; D001921:Brain / Q000502:physiology; D016022:Case-Control Studies; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D065786:Delay Discounting / Q000502:physiology; D005260:Female; D059907:Functional Neuroimaging; D006801:Humans; D007175:Impulsive Behavior / Q000502:physiology; D008279:Magnetic Resonance Imaging; D008297:Male; D015233:Models, Statistical; D012201:Reward; D055815:Young Adult
keywords: Gaussian process; Heterogeneity; Normative model; Outlier detection; Patient stratification; Research Domain Criteria