TY - JOUR
T1 - Development and application of a diagnostic algorithm for posttraumatic stress disorder
AU - James, Lisa M
AU - Belitskaya-Lévy, Ilana
AU - Lu, Ying
AU - Wang, Hui
AU - Engdahl, Brian
AU - Leuthold, Arthur C
AU - Georgopoulos, Apostolos P
N1 - Publisher Copyright:
© 2014.
PY - 2015/1/30
Y1 - 2015/1/30
N2 - Intact cognitive functions rely on synchronous neural activity; conversely, alterations in synchrony are thought to underlie psychopathology. We recently demonstrated that anomalies in synchronous neural interactions (SNI) determined by magnetoencephalography represent a putative PTSD biomarker. Here we develop and apply a regression-based diagnostic algorithm to further validate SNI as a PTSD biomarker in 432 veterans (235 controls; 138 pure PTSD; 59 PTSD plus comorbid disorders). Correlation coefficients served as proximities in multidimensional scaling (MDS) to obtain a two-dimensional representation of the data. In addition, least absolute shrinkage and selection operator (LASSO) regression was used to derive a diagnostic algorithm for PTSD. Performance of this algorithm was assessed by the area under the receiver operating characteristic (ROC) curves, sensitivity, and specificity in 1000 randomly divided testing and validation datasets and in independent samples. MDS revealed that individuals with PTSD, regardless of comorbid psychiatric conditions, are highly distinct from controls. Similarly, application of the LASSO regression-derived prediction model demonstrated remarkable classification accuracy (AUCs≥0.93 for men, AUC=0.82 for women). Neural functioning in individuals with PTSD, regardless of comorbid psychiatric diagnoses, can be used as a diagnostic test to determine patient disease status, further validating SNI as a PTSD biomarker.
AB - Intact cognitive functions rely on synchronous neural activity; conversely, alterations in synchrony are thought to underlie psychopathology. We recently demonstrated that anomalies in synchronous neural interactions (SNI) determined by magnetoencephalography represent a putative PTSD biomarker. Here we develop and apply a regression-based diagnostic algorithm to further validate SNI as a PTSD biomarker in 432 veterans (235 controls; 138 pure PTSD; 59 PTSD plus comorbid disorders). Correlation coefficients served as proximities in multidimensional scaling (MDS) to obtain a two-dimensional representation of the data. In addition, least absolute shrinkage and selection operator (LASSO) regression was used to derive a diagnostic algorithm for PTSD. Performance of this algorithm was assessed by the area under the receiver operating characteristic (ROC) curves, sensitivity, and specificity in 1000 randomly divided testing and validation datasets and in independent samples. MDS revealed that individuals with PTSD, regardless of comorbid psychiatric conditions, are highly distinct from controls. Similarly, application of the LASSO regression-derived prediction model demonstrated remarkable classification accuracy (AUCs≥0.93 for men, AUC=0.82 for women). Neural functioning in individuals with PTSD, regardless of comorbid psychiatric diagnoses, can be used as a diagnostic test to determine patient disease status, further validating SNI as a PTSD biomarker.
KW - Biomarker
KW - Magnetoencephalography
KW - Posttraumatic stress disorder
KW - ROC curve
KW - Regression analysis
KW - Veterans
UR - http://www.scopus.com/inward/record.url?scp=84916636809&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84916636809&partnerID=8YFLogxK
U2 - 10.1016/j.pscychresns.2014.11.007
DO - 10.1016/j.pscychresns.2014.11.007
M3 - Article
C2 - 25433425
AN - SCOPUS:84916636809
SN - 0925-4927
VL - 231
SP - 1
EP - 7
JO - Psychiatry Research - Neuroimaging
JF - Psychiatry Research - Neuroimaging
IS - 1
ER -