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The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Open accessAbstract
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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In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Metadata
- 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.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 23
- Issue
- 7
- Pages
- 584-601
- ISSN
- 1364-6613 (ISSN-L)
- 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
- FWCI
- 5.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- 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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