Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth

Amelia Versace, Vinod Sharma, Michele A. Bertocci, Genna Bebko, Satish Iyengar, Amanda Dwojak, Lisa Bonar, Susan B. Perlman, Claudiu Schirda, Michael Travis, Mary Kay Gill, Vaibhav A. Diwadkar, Jeffrey L. Sunshine, Scott K. Holland, Robert A. Kowatch, Boris Birmaher, David Axelson, Thomas W. Frazier, L. Eugene Arnold, Mary A. FristadEric A. Youngstrom, Sarah M. Horwitz, Robert L. Findling, Mary L. Phillips

Research output: Contribution to journalArticle

Abstract

Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy regulation in youth. Recent studies indicate that machine learning techniques can help elucidate the role of neuroimaging measures in classifying individual subjects by specific symptom trajectory. Cortical thickness measures were extracted in sixty-eight anatomical regions covering the entire brain in 115 participants from the Longitudinal Assessment of Manic Symptoms (LAMS) study and 31 healthy comparison youth (12.5 y/o;-Male/Female = 15/16;-IQ = 104;-Right/Left handedness = 24/5). Using a combination of trajectories analyses, surface reconstruction, and machine learning techniques, the present study aims to identify the extent to which measures of cortical thickness can accurately distinguish youth with higher (n = 18) from those with lower (n = 34) trajectories of manic-like behaviors in a large sample of LAMS youth (n = 115; 13.6 y/o; M/F = 68/47, IQ = 100.1, R/L = 108/7). Machine learning analyses revealed that widespread cortical thickening in portions of the left dorsolateral prefrontal cortex, right inferior and middle temporal gyrus, bilateral precuneus, and bilateral paracentral gyri and cortical thinning in portions of the right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, and right parahippocampal gyrus accurately differentiate (Area Under Curve = 0.89;p = 0.03) youth with different (higher vs lower) trajectories of positive mood and energy dysregulation over a period up to 5years, as measured by the Parent General Behavior Inventory-10 Item Mania Scale. Our findings suggest that specific patterns of cortical thickness may reflect transdiagnostic neural mechanisms associated with different temporal trajectories of positive mood and energy dysregulation in youth. This approach has potential to identify patterns of neural markers of future clinical course.

Original languageEnglish
Article numbere018022
JournalPloS one
Volume12
Issue number7
DOIs
StatePublished - Jul 2017
Externally publishedYes

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    Versace, A., Sharma, V., Bertocci, M. A., Bebko, G., Iyengar, S., Dwojak, A., Bonar, L., Perlman, S. B., Schirda, C., Travis, M., Gill, M. K., Diwadkar, V. A., Sunshine, J. L., Holland, S. K., Kowatch, R. A., Birmaher, B., Axelson, D., Frazier, T. W., Arnold, L. E., ... Phillips, M. L. (2017). Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth. PloS one, 12(7), [e018022]. https://doi.org/10.1371/journal.pone.0180221