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

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 disor...

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Bibliographic Details
Published in:Psychological medicine 2019-09, Vol.49 (12), p.2049-2059
Main Authors: Nicholson, Andrew A., Densmore, Maria, McKinnon, Margaret C., Neufeld, Richard W.J., Frewen, Paul A., Théberge, Jean, Jetly, Rakesh, Richardson, J. Donald, Lanius, Ruth A.
Format: Article
Language:English
Subjects:
Amygdala
Artificial intelligence
Biological markers
Biomarkers
Brain mapping
Brain research
Classification
Discovery
Dissociative disorders
Emotional regulation
Emotions
Functional connectivity
Globus pallidus
Illnesses
Learning algorithms
Machine learning
Medical imaging
Medical research
Mental disorders
Multivariate analysis
Neural networks
Neuroimaging
Original Articles
Pattern analysis
Pattern recognition
Post traumatic stress disorder
Psychiatry
Regional variations
Resting
Subtypes
Trauma
Validity
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Summary: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). 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]. 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. 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.
ISSN:0033-2917
1469-8978
DOI:10.1017/S0033291718002866