The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

Eric Feczko, Oscar Miranda-Dominguez, Mollie Marr, Alice M. Graham, Joel T. Nigg, Damien A. Fair

Research output: Contribution to journalReview articlepeer-review

49 Scopus citations

Abstract

The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the ‘heterogeneity problem’: different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to ‘the curse of dimensionality’. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.

Original languageEnglish (US)
Pages (from-to)584-601
Number of pages18
JournalTrends in Cognitive Sciences
Volume23
Issue number7
DOIs
StatePublished - Jul 2019

Keywords

  • functional random forest
  • heterogeneity
  • machine learning
  • mental health
  • surrogate variable analysis

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

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