Computational Modeling of the n-Back Task in the ABCD Study: Associations of Drift Diffusion Model Parameters to Polygenic Scores of Mental Disorders and Cardiometabolic Diseases

Mads L Pedersen; Dag Alnæs; Dennis van der Meer; Sara Fernandez-Cabello; Pierre Berthet; Andreas Dahl; Rikka Kjelkenes; Emanuel Schwarz; Wesley K Thompson; Deanna M Barch; Ole A Andreassen; Lars T Westlye

doi:10.1016/j.bpsc.2022.03.012

Computational Modeling of the n-Back Task in the ABCD Study: Associations of Drift Diffusion Model Parameters to Polygenic Scores of Mental Disorders and Cardiometabolic Diseases

Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Mar;8(3):290-299. doi: 10.1016/j.bpsc.2022.03.012. Epub 2022 Apr 12.

Authors

Affiliations

¹ Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway. Electronic address: m.l.pedersen@psykologi.uio.no.
² NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Bjørknes College, Institute of Psychology, Oslo, Norway.
³ NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
⁴ NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
⁵ Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
⁶ Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
⁷ Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Laboratory, University of California San Diego, La Jolla, California.
⁸ Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University, St. Louis, Missouri.
⁹ KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
¹⁰ Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

PMID: 35427796
DOI: 10.1016/j.bpsc.2022.03.012

Abstract

Background: Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with polygenic risk scores (PRSs) for mental disorders and comorbid cardiometabolic diseases.

Methods: We used the drift diffusion model to infer latent computational processes underlying decision making and working memory during the n-back task in 3707 children ages 9 to 10 years from the Adolescent Brain Cognitive Development (ABCD) Study. Single nucleotide polymorphism-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated n-back task performance, and neurocognitive assessments. PRSs were calculated for Alzheimer's disease, bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia, and type 2 diabetes.

Results: Heritability estimates of cognitive phenotypes ranged from 12% to 38%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRSs for CAD and schizophrenia. Longer nondecision time was associated with higher PRSs for Alzheimer's disease and lower PRSs for CAD. Narrower decision threshold was associated with higher PRSs for CAD. Load-dependent effects on nondecision time and decision threshold were associated with PRSs for Alzheimer's disease and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRSs for any of the mental or cardiometabolic phenotypes.

Conclusions: We identified distinct associations between computational cognitive processes and genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

Keywords: Cardiometabolic disease; Childhood; Cognitive computational modeling; Decision making; Mental disorder; Polygenic risk scores.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Alzheimer Disease* / genetics
Bayes Theorem
Cardiovascular Diseases*
Computer Simulation
Depressive Disorder, Major*
Diabetes Mellitus, Type 2* / genetics
Genetic Predisposition to Disease
Humans
Mental Disorders* / genetics

Abstract

Publication types

MeSH terms

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