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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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Development and application of a diagnostic algorithm for posttraumatic stress disorder.

Lisa M James1, Ilana Belitskaya-Lévy2, Ying Lu3

  • 1Brain Sciences Center, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA; Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA; Center for Cognitive Sciences, University of Minnesota, Minneapolis, MN, USA.

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|December 1, 2014
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Summary

Synchronous neural interactions (SNI) anomalies detected via magnetoencephalography show promise as a biomarker for post-traumatic stress disorder (PTSD). This study validates SNI as a diagnostic tool for PTSD, even with comorbid conditions.

Keywords:
BiomarkerMagnetoencephalographyPosttraumatic stress disorderROC curveRegression analysisVeterans

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Biomarkers

Background:

  • Intact cognitive functions depend on neural synchrony; disruptions are linked to psychopathology.
  • Previous research identified anomalies in synchronous neural interactions (SNI) as a potential PTSD biomarker using magnetoencephalography.

Purpose of the Study:

  • To develop and validate a regression-based diagnostic algorithm using SNI for diagnosing post-traumatic stress disorder (PTSD).
  • To assess the efficacy of SNI as a biomarker in a large cohort of veterans, including those with comorbid conditions.

Main Methods:

  • Utilized magnetoencephalography (MEG) to measure SNI in 432 veterans (138 PTSD, 59 PTSD with comorbidities, 235 controls).
  • Applied multidimensional scaling (MDS) for data representation and least absolute shrinkage and selection operator (LASSO) regression to build a diagnostic algorithm.
  • Evaluated algorithm performance using receiver operating characteristic (ROC) curves, sensitivity, and specificity on multiple datasets.

Main Results:

  • MDS demonstrated that individuals with PTSD are neurologically distinct from controls, irrespective of comorbidities.
  • The LASSO-derived prediction model achieved high classification accuracy: AUCs ≥ 0.93 for men and 0.82 for women.
  • SNI analysis proved effective in distinguishing PTSD patients from controls.

Conclusions:

  • Neural functioning patterns, specifically SNI, can serve as a diagnostic marker for PTSD.
  • This study further validates SNI as a reliable biomarker for diagnosing PTSD, even in the presence of comorbid psychiatric disorders.