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Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: an exploratory machine‐learning study

Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: an exploratory machine‐learning study
Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: an exploratory machine‐learning study
Objective: features of posttraumatic stress disorder (PTSD) typically include sleep
disturbances, impaired declarative memory, and hyperarousal. This study evaluated whether these combined features may accurately delineate pathophysiological hanges associated with PTSD.

Method: we recruited a cohort of PTSD‐diagnosed individuals (N = 20), trauma
survivors without PTSD (TE; N = 20), and healthy controls (HC; N = 20). Analyses of between‐group differences and support vector machine (SVM)‐learning were applied to participant features.

Results: analyses of between‐group differences replicated previous findings, indicating that PTSD‐diagnosed individuals self‐reported poorer sleep quality, objectively demonstrated less sleep depth, and evidenced declarative memory deficits in comparison to HC. Integrative SVM‐learning distinguished HC from trauma participants with 80% accuracy using a combination of five features, including subjective and subjective sleep, neutral declarative memory, and metabolite variables. PTSD and TE participants could be distinguished with 70% accuracy using a combination of subjective and objective sleep variables but not by metabolite or declarative memory variables.

Conclusion: from among a broad range of sleep, cognitive, and biochemical variables, sleep characteristics were the primary features that could differentiate those with PTSD from those without. Our exploratory SVM‐learning analysis establishes a framework for future sleep‐ and memory‐based PTSD investigations that could drive improvements in diagnostic accuracy and treatment.
Diagnosis, machine learning, memory, metabolites, PTSD, Sleep
0885-6222
Baldwin, David
1beaa192-0ef1-4914-897a-3a49fc2ed15e
Breen, Michael
45e6487d-32c8-407e-8011-9ffaca49e812
Thomas, Kevin
d294a7c1-ddea-4551-b386-e3aebbdc691d
Lipinska, Gosia
22de076c-99cb-4e6b-9872-e53668b8aae3
Baldwin, David
1beaa192-0ef1-4914-897a-3a49fc2ed15e
Breen, Michael
45e6487d-32c8-407e-8011-9ffaca49e812
Thomas, Kevin
d294a7c1-ddea-4551-b386-e3aebbdc691d
Lipinska, Gosia
22de076c-99cb-4e6b-9872-e53668b8aae3

Baldwin, David, Breen, Michael, Thomas, Kevin and Lipinska, Gosia (2019) Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: an exploratory machine‐learning study. Human Psychopharmacology: Clinical and Experimental, 34 (2). (doi:10.1002/hup.2691).

Record type: Article

Abstract

Objective: features of posttraumatic stress disorder (PTSD) typically include sleep
disturbances, impaired declarative memory, and hyperarousal. This study evaluated whether these combined features may accurately delineate pathophysiological hanges associated with PTSD.

Method: we recruited a cohort of PTSD‐diagnosed individuals (N = 20), trauma
survivors without PTSD (TE; N = 20), and healthy controls (HC; N = 20). Analyses of between‐group differences and support vector machine (SVM)‐learning were applied to participant features.

Results: analyses of between‐group differences replicated previous findings, indicating that PTSD‐diagnosed individuals self‐reported poorer sleep quality, objectively demonstrated less sleep depth, and evidenced declarative memory deficits in comparison to HC. Integrative SVM‐learning distinguished HC from trauma participants with 80% accuracy using a combination of five features, including subjective and subjective sleep, neutral declarative memory, and metabolite variables. PTSD and TE participants could be distinguished with 70% accuracy using a combination of subjective and objective sleep variables but not by metabolite or declarative memory variables.

Conclusion: from among a broad range of sleep, cognitive, and biochemical variables, sleep characteristics were the primary features that could differentiate those with PTSD from those without. Our exploratory SVM‐learning analysis establishes a framework for future sleep‐ and memory‐based PTSD investigations that could drive improvements in diagnostic accuracy and treatment.

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HUP--proof-revised 2-resubmit - Accepted Manuscript
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Accepted/In Press date: 20 December 2018
e-pub ahead of print date: 22 February 2019
Published date: March 2019
Keywords: Diagnosis, machine learning, memory, metabolites, PTSD, Sleep

Identifiers

Local EPrints ID: 429486
URI: https://eprints.soton.ac.uk/id/eprint/429486
ISSN: 0885-6222
PURE UUID: 1c5f699e-7c33-492b-ab27-b84e9f86364a

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Date deposited: 27 Mar 2019 17:30
Last modified: 29 Apr 2019 16:30

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