The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

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

doi:10.1016/j.tics.2019.03.009

Cited by 443Mental Health Research TopicsMental Health and PsychiatryFunctional Brain Connectivity Studies

The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

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Feczko, Eric, Miranda-Dominguez, Oscar, Marr, Mollie, Graham, Alice M., Nigg, Joel T., Fair, Damien A.·2019·Trends in Cognitive Sciences

DOI 10.1016/j.tics.2019.03.009PMC PMC6821457

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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.

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One aspect of heterogeneity in ADHD symptoms and cognitive deficits is its subtypes.
“One aspect of heterogeneity in ADHD symptoms and cognitive deficits is its subtypes [8, 9] which includes a predominantly hyperactive (ADHD-H), inattentive (ADHD-I), and combined (ADHD-C) subtype.”
From ADHD subtype-specific cognitive correlates and association with self-esteem: a quantitative difference (2020) · cites this paper as [9]
Multiple routes likely contribute to the expression of EF and hyperactive/inattentive symptoms across disorders.
“but that multiple routes likely contribute to the expression of EF and hyperactive/inattentive symptoms across disorders (Chan et al., 2008; Feczko et al., 2019; Molitor et al., 2018; Volkow et al., 2011).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Feczko et al., 2019
A growing body of evidence suggests multiple pathways lead to shared symptoms among a given disorder.
“A growing body of evidence suggests multiple pathways lead to shared symptoms among a given disorder (Castellanos et al., 2006; Feczko et al., 2019).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Feczko et al., 2019
It's important for any machine learning model, in particular those using cross-validation, to further validate identified subgroups.
“It's important for any machine learning model, in particular those using cross-validation, to further validate identified subgroups (Feczko et al., 2019).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Feczko et al., 2019
This supports the transdiagnostic approach and is consistent with previous work indicating that the underlying mechanisms leading to observed symptoms are likely shared across ASD and ADHD, even though there may be more than one.
“This supports the transdiagnostic approach and is consistent with previous work indicating that the underlying mechanisms leading to observed symptoms are likely shared across ASD and ADHD, even though there may be more than one (Chan et al., 2008; Feczko et al., 2019; Leitner, 2014; Molitor et al., 2018).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Feczko et al., 2019

Several previous studies found linear age-related FCS decreases in medial prefrontal cortex, precuneus, insula, and calcarine and linear FCS increases in sensorimotor areas based on general linear model.
“Similar to our findings, several previous studies found linear-age-related FCS decreases in the medial prefrontal cortex, precuneus, insula, and calcarine and linear FCS increases in sensorimotor areas based on the general linear model [36-38].”
From Mapping neurophysiological subtypes of major depressive disorder using normative models of the functional connectome (2023) · cites this paper as [38]
From the homogeneity assumption, the comorbidity and heterogeneity problems emerge and limit psychiatric research.
“From the homo- geneity assumption, the comorbidity (17) and heterogeneity (18,19) problems emerge and limit psychiatric research (20–26).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
The appropriate subtypes are likely to differ based on whether one is interested in diagnosis, prognosis, treatment response, mechanism, etc.
“The appropriate subtypes are likely going to differ based on whether one is interested in diagnosis, prognosis, treatment response, mechanism, etc. (18).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
Most subtyping methods cannot discover unknown subtypes tied to a question.
“Critically, most subtyping methods cannot discover unknown subtypes tied to a question (18).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
The heterogeneity problem comprises two tenets: different underlying mechanisms may drive the same disorder, and factors important for mental health outcomes may be unrelated to such mechanisms depending on the outcome of interest.
“The heterogeneity problem comprises two tenets (18). First, different underlying mechanisms may drive the same disorder. Second, factors important for mental health outcomes may be unrelated to such mechanisms, depending on the outcome of interest.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
Newer hybrid models such as normative modeling and the functional random forest may overcome the limitations of the bifactor framework and allow new inferences in psychopathology research.
“Newer hybrid models, such as normative modeling (19,59–61) or the functional random forest (FRF) (18,62), may overcome these limitations and enable new inferences from psychopathology studies.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
The FRF is able to find trajectory subtypes in longitudinal data, for example p subtypes linked to ADHD outcomes.
“The FRF can identify trajectory subtypes from longitudinal data, such as p subtypes united or tied to ADHD outcomes.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
The FRF consists of several approaches that have been detailed elsewhere.
“The FRF comprises several ap- proaches, which have been explicitly detailed elsewhere (18).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
The bifactor framework has limits: because it is a supervised approach, it requires researchers to specify specific factors, so the researcher cannot discover new ones.
“Because it is a supervised approach, the bifactor framework requires researchers to specify specific factors, such as internalizing and externalizing. Therefore, the researcher cannot discover new ones (18).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]
The authors previously proposed the functional random forest to identify subtypes tied to an outcome.
“More recently, we proposed the FRF to identify subtypes tied to an outcome (18,62).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [18]

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Authors: Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A.
Affiliations: Oregon Health & Science University (US)
Corresponding author: Feczko, Eric
Fair, Damien A.
ORCID: Feczko, Eric
Marr, Mollie
Graham, Alice M.
Nigg, Joel T.
Fair, Damien A.
Venue: Trends in Cognitive Sciences
Publisher: Elsevier BV
Published: 2019-07
Volume: 23
Issue: 7
Pages: 584-601
ISSN: 1364-6613 (ISSN-L)
DOI: 10.1016/j.tics.2019.03.009
PMC: PMC6821457
OpenAlex: W2947752575
Cited by: 443
References: 126
Citations by year: 2026: 51 · 2025: 69 · 2024: 83 · 2023: 88 · 2022: 59 · 2021: 43 · 2020: 40 · 2019: 9 · 2013: 1
FWCI: 5.63
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Trends in Cognitive Sciences (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: DeStefano Family Foundation
National Library of Medicine (T15 LM007088)
National Institute of Mental Health (R01 MH096773, R00 MH091238, R01 MH096773-03S1, R01 MH 096773–05, R01 MH086654, R01 MH59107)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: psychiatric classification, dimensional models, curse of dimensionality, supervised learning, unsupervised learning, precision psychiatry, mental illness, causal mechanisms
MeSH terms: Mental Disorders, Brain, Cognition, Humans, Individuality, Models, Statistical

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The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

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