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Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach

Published online by Cambridge University Press:  11 October 2018

Andrew A. Nicholson ,
Maria Densmore ,
Margaret C. McKinnon ,
Richard W.J. Neufeld ,
Paul A. Frewen ,
Jean Théberge ,
Rakesh Jetly ,
J. Donald Richardson  and
Ruth A. Lanius
Andrew A. Nicholson*
Affiliation:
Department of Neuroscience, Western University, London, ON, Canada Department of Psychiatry, Western University, London, ON, Canada Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON, Canada Homewood Research Institute, Guelph, ON, Canada Imaging, Lawson Health Research Institute, London, ON, Canada
Maria Densmore
Affiliation:
Department of Psychiatry, Western University, London, ON, Canada Imaging, Lawson Health Research Institute, London, ON, Canada
Margaret C. McKinnon
Affiliation:
Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON, Canada Homewood Research Institute, Guelph, ON, Canada Department of Mood Disorders Program, St. Joseph's Healthcare, Hamilton, ON, Canada
Richard W.J. Neufeld
Affiliation:
Department of Neuroscience, Western University, London, ON, Canada Department of Psychiatry, Western University, London, ON, Canada Department of Psychology, Western University, London, ON, Canada
Paul A. Frewen
Affiliation:
Department of Neuroscience, Western University, London, ON, Canada Department of Psychology, Western University, London, ON, Canada
Jean Théberge
Affiliation:
Department of Psychiatry, Western University, London, ON, Canada Imaging, Lawson Health Research Institute, London, ON, Canada Department of Medical Imaging, Western University, London, ON, Canada Department of Medial Biophysics, Western University, London, ON, Canada Department of Diagnostic Imaging, St. Joseph's Healthcare, London, ON, Canada
Rakesh Jetly
Affiliation:
Canadian Forces, Health Services, Ottawa, Ontario, Canada
J. Donald Richardson
Affiliation:
Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON, Canada Homewood Research Institute, Guelph, ON, Canada Department of Mood Disorders Program, St. Joseph's Healthcare, Hamilton, ON, Canada
Ruth A. Lanius
Affiliation:
Department of Neuroscience, Western University, London, ON, Canada Department of Psychiatry, Western University, London, ON, Canada Imaging, Lawson Health Research Institute, London, ON, Canada
*
Author for correspondence: Andrew A. Nicholson, E-mail: anicho58@gmail.com
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Abstract

Background

The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition methods have been applied recently to predict many psychiatric disorders, these techniques have not been utilized to predict subtypes of posttraumatic stress disorder (PTSD), including the dissociative subtype of PTSD (PTSD + DS).

Methods

Using Multiclass Gaussian Process Classification within PRoNTo, we examined the classification accuracy of: (i) the mean amplitude of low-frequency fluctuations (mALFF; reflecting spontaneous neural activity during rest); and (ii) seed-based amygdala complex functional connectivity within 181 participants [PTSD (n = 81); PTSD + DS (n = 49); and age-matched healthy trauma-unexposed controls (n = 51)]. We also computed mass-univariate analyses in order to observe regional group differences [false-discovery-rate (FDR)-cluster corrected p < 0.05, k = 20].

Results

We found that extracted features could predict accurately the classification of PTSD, PTSD + DS, and healthy controls, using both resting-state mALFF (91.63% balanced accuracy, p < 0.001) and amygdala complex connectivity maps (85.00% balanced accuracy, p < 0.001). These results were replicated using independent machine learning algorithms/cross-validation procedures. Moreover, areas weighted as being most important for group classification also displayed significant group differences at the univariate level. Here, whereas the PTSD + DS group displayed increased activation within emotion regulation regions, the PTSD group showed increased activation within the amygdala, globus pallidus, and motor/somatosensory regions.

Conclusion

The current study has significant implications for advancing machine learning applications within the field of psychiatry, as well as for developing objective biomarkers indicative of diagnostic heterogeneity.

Keywords

Amygdaladissociative subtypefMRImachine learningPTSDresting-state

Information

Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 12 , September 2019 , pp. 2049 - 2059
Copyright
Copyright © Cambridge University Press 2018 

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