Fear Conditioning as an Intermediate Phenotype: An RDoC Inspired Methodological Analysis

Due to difficulties in elucidating neurobiological aspects of psychological disorders, the National Institute of Mental Health (NIMH) created the Research Domain Criteria (RDoC), which encourages novel conceptualizations of the relationship between neurobiological circuitry and clinical difficulties. This approach is markedly different from the Diagnostic and Statistical Manual of Mental Disorders (DSM) based approach that has dominated clinical research to date. Thus, RDoC necessitates exploration of novel experimental and statistical approaches. Fear learning paradigms represent a promising methodology for elucidating connections between acute threat (“fear”) circuitry and fear-related clinical difficulties. However, traditional analytical approaches rely on central tendency statistics, which are tethered to a priori categories and assume homogeneity within groups. Growth Mixture Modeling (GMM) methods such as Latent Class Growth Analysis (LCGA) may be uniquely suited for examining fear learning phenotypes. However, just three extant studies have applied GMM to fear learning and only one did so in a human population. Thus, the degree to which classes identified in known studies represent characteristics of the general population and to which GMM methodology is applicable across populations and paradigms is unclear. This preliminary study applied LCGA to a fear learning lab study in an attempt to identify heterogeneity in fear learning patterns based on a posteriori classification. The findings of this investigation may inform efforts to move toward a trans-diagnostic conceptualization of fear learning. Consistent with the goals laid out in RDoC, explication of fear learning phenotypes may eventually provide critical information needed to spur innovation in psychotherapeutic and psychopharmacological treatment.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Clincal Psychology

Department dc:contributor.department

Psychology

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2018

Author dc:creator

• Lewis, Michael

Chairs dc:contributor.committeechair

• Jones, Russell T.
• Friedman, Bruce H.

Committee member dc:contributor.committeemember

• Winett, Richard A.

• Fear Learning
• Threat Conditioning
• Latent Growth Mixture Modeling
• Latent Class Growth Analysis

Statement dc:rights

• Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-nd/3.0/us/

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10919/83837

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/83837