TY - JOUR
T1 - Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth
AU - Versace, Amelia
AU - Sharma, Vinod
AU - Bertocci, Michele A.
AU - Bebko, Genna
AU - Iyengar, Satish
AU - Dwojak, Amanda
AU - Bonar, Lisa
AU - Perlman, Susan B.
AU - Schirda, Claudiu
AU - Travis, Michael
AU - Gill, Mary Kay
AU - Diwadkar, Vaibhav A.
AU - Sunshine, Jeffrey L.
AU - Holland, Scott K.
AU - Kowatch, Robert A.
AU - Birmaher, Boris
AU - Axelson, David
AU - Frazier, Thomas W.
AU - Arnold, L. Eugene
AU - Fristad, Mary A.
AU - Youngstrom, Eric A.
AU - Horwitz, Sarah M.
AU - Findling, Robert L.
AU - Phillips, Mary L.
N1 - Funding Information:
I have read the journal's policy and the authors of this manuscript have the following competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Dr. Findling receives or has received research support, acted as a consultant and/or served on a speaker's bureau for Alcobra, American Academy of Child & Adolescent Psychiatry, American Physician Institute, American Psychiatric Press, Bracket, CogCubed, Cognition Group, Coronado Biosciences, Dana Foundation, Elsevier, Forest, Guilford Press, Ironshore, Johns Hopkins University Press, Jubilant Clinsys, KemPharm, Lundbeck, Medgenics, Merck, NIH, Neurim, Novartis, Otsuka, Oxford University Press, Pfizer, Physicians Postgraduate Press, Purdue, Rhodes Pharmaceuticals, Roche, Sage, Shire, Sunovion, Supernus Pharmaceuticals, Teva, Transcept Pharmaceuticals, Tris, Validus, and WebMD. Dr. Arnold has received research funding from Curemark, Forest, Lilly, Neuropharm, Novartis, Noven, Shire, Supernus, and YoungLiving (as well as NIH and Autism Speaks) and has consulted with or been on advisory boards for Arbor, Gowlings, Ironshore, Neuropharm, Novartis, Noven, Organon, Otsuka, Pfizer, Roche, Seaside Therapeutics, Sigma Tau, Shire, Tris Pharma, and Waypoint and received travel support from Noven. Dr. Frazier has received federal funding or research support from, acted as a consultant to, received travel support from, and/or received a speaker’s honorarium from the Cole Family Research Fund, Simons Foundation, Ingalls Foundation, Forest Laboratories, Ecoeos, IntegraGen, Kugona LLC, Shire Development, Bristol-Myers Squibb, National Institutes of Health, and the Brain and Behavior Research Foundation. Dr. Youngstrom has consulted with Lundbeck, Janssen, Pearson, and Western Psychological Services about psychological assessment. Dr. Fristad receives royalties from Guilford Press, Inc., APPI, CFPSI and research funding from Janssen. Dr. Birmaher has or will receive royalties from for publications from Random House, Inc (New hope for children and teens with bipolar disorder) and Lippincott Williams & Wilkins (Treating Child and Adolescent Depression). He is employed by the University of Pittsburgh and the University of Pittsburgh Medical Center and receives research funding from NIMH. Dr. Kowatch is a consultant for Forest Pharmaceutical, Astra-Zeneca and the REACH Foundation. He receives research support from NIMH. He is employed by Ohio State University and an editor for Current Psychiatry. Dr. Sunshine receives research support from Siemens Healthcare. Drs. Versace, Sharma, Bertocci, Bebko, Iyengar, Perlman, Schirda, Travis, Diwadkar, Axelson, Holland, Horwitz and Phillips have declared that no competing interests exist. Mrs. Dwojak, Bonar and Gill reported no biomedical financial interests or potential conflicts of interest.
Funding Information:
This research was supported by the National Institute of Mental Health grant 2R01 MH73953-06A1 (Birmaher and Phillips, University of Pittsburgh) and by the Pittsburgh Foundation (Phillips, University of Pittsburgh Emmerling Endowed Chair in Psychotic Disorders).
Publisher Copyright:
© 2017 Versace et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/7
Y1 - 2017/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85021924963&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0180221
DO - 10.1371/journal.pone.0180221
M3 - Article
C2 - 28683115
AN - SCOPUS:85021924963
VL - 12
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 7
M1 - e018022
ER -