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Subtypes of inhibitory and reward activation associated with substance use variation in adolescence: A latent profile analysis of brain imaging data.
Martz, Meghan E; Cope, Lora M; Hardee, Jillian E; Brislin, Sarah J; Weigard, Alexander; Zucker, Robert A; Heitzeg, Mary M.
Afiliación
  • Martz ME; Department of Psychiatry and Addiction Center, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA. mmartz@med.umich.edu.
  • Cope LM; Department of Psychiatry and Addiction Center, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
  • Hardee JE; Department of Psychiatry and Addiction Center, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
  • Brislin SJ; Department of Psychiatry and Addiction Center, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
  • Weigard A; Department of Psychiatry and Addiction Center, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
  • Zucker RA; Department of Psychiatry and Addiction Center, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
  • Heitzeg MM; Department of Psychiatry and Addiction Center, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
Cogn Affect Behav Neurosci ; 21(5): 1101-1114, 2021 10.
Article en En | MEDLINE | ID: mdl-33973159
ABSTRACT
The present study identified subgroups based on inhibitory and reward activation, two key neural functions involved in risk-taking behavior, and then tested the extent to which subgroup differences varied by age, sex, behavioral and familial risk, and substance use. Participants were 145 young adults (18-21 years old; 40.0% female) from the Michigan Longitudinal Study. Latent profile analysis (LPA) was used to establish subgroups using task-based brain activations. Demographic and substance use differences between subgroups were then examined in logistic regression analyses. Whole-brain task activations during a functional magnetic resonance imaging go/no-go task and monetary incentive delay task were used to identify beta weights as input for LPA modeling. A four-class model showed the best fit with the data. Subgroups were categorized as (1) low inhibitory activation/moderate reward activation (39.7%), (2) moderate inhibitory activation/low reward activation (22.7%), (3) moderate inhibitory activation/high reward activation (25.2%), and (4) high inhibitory activation/high reward activation (12.4%). Compared with the other subgroups, Class 2 was older, less likely to have parental alcohol use disorder, and had less alcohol use. Class 4 was the youngest and had greater marijuana use. Classes 1 and 3 did not differ significantly from the other subgroups. These findings demonstrate that LPA applied to brain activations can be used to identify distinct neural profiles that may explain heterogeneity in substance use outcomes and may inform more targeted substance use prevention and intervention efforts.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Recompensa / Trastornos Relacionados con Sustancias Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Female / Humans / Male Idioma: En Revista: Cogn Affect Behav Neurosci Asunto de la revista: CIENCIAS DO COMPORTAMENTO / NEUROLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Recompensa / Trastornos Relacionados con Sustancias Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Female / Humans / Male Idioma: En Revista: Cogn Affect Behav Neurosci Asunto de la revista: CIENCIAS DO COMPORTAMENTO / NEUROLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos