- Location: Wilson Hall • 111 21St Ave S • Nashville, TN 37240
- Room: 316
- Contact: Angel Gaither
- Email: email@example.com
- Phone: 615-322-0080
- Website: https://www.vanderbilt.edu/psychological_sciences/events/index.php
- Audience: Free and Open to the Public
Department of Psychology (Zald Lab)
Higher-order psychopathology associations with fMRI reward processing: a brain-wide, voxel-wise SEM proof of concept study
Recent work suggests the high comorbidity across psychiatric disorders can be modeled using continuous transdiagnostic dimensions, including a general psychopathology factor that reflects non-specific vulnerability to all symptom dimensions, and specific factors for externalizing and internalizing syndromes. These higher-order psychopathology factors offer an advantage over traditional case-controlled approaches by allowing us to identify neural correlates associated with broader vulnerabilities to psychopathology while accounting for the full structure of psychopathology, which is not possible in case-controlled designs. However, the flip side of this promise is that it is difficult to test for associations between latent variables and brain measures in a voxel-wise fashion, especially in larger samples with issues such as non-independence.
In this talk, I will present a proof of concept study in which I sought to identify whether higher-order psychopathology factors were associated with activation in the reward anticipation and reward attainment stages of the Monetary Incentive Delay task using a novel voxel-wise SEM approach. I will briefly introduce and discuss my voxel-wise SEM approach, which leveraged tools such as Neuropointillist and Mplus to test for associations between activation in voxels across the brain and higher-order psychopathology, while addressing non-independence issues introduced by twinness in our sample (Tennessee Twin Study Wave 2, N = 326). I will then discuss our findings and possible future directions.
This talk is a follow-up to my talk from last year; it is the same dataset, although being analyzed using a different approach and more in depth. This talk will also serve as an open defense for my Master’s thesis.