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

Adult Amygdala / diagnostic imaging Brain Mapping / methods Case-Control Studies Dissociative Disorders / diagnostic imaging Emotions Female Humans Machine Learning Magnetic Resonance Imaging Male Middle Aged Multivariate Analysis Neural Pathways / diagnostic imaging Rest Stress Disorders, Post-Traumatic / diagnostic imaging Young Adult
Amygdala PTSD dissociative subtype fMRI machine learning resting-state

Journal

Psychological medicine
ISSN: 1469-8978
Titre abrégé: Psychol Med
Pays: England
ID NLM: 1254142

Informations de publication

Date de publication:
09 2019
Historique:
pubmed: 12 10 2018
medline: 17 7 2020
entrez: 12 10 2018
Statut: ppublish

Résumé

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.

Sections du résumé

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.

Identifiants

DOI: 10.1017/S0033291718002866 PMID: 30306886
pubmed: 30306886
pii: S0033291718002866
doi: 10.1017/S0033291718002866
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2049-2059

Subventions

Organisme : CIHR
Pays : Canada

Auteurs

Andrew A Nicholson (AA)

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 (M)

Department of Psychiatry, Western University, London, ON, Canada.
Imaging, Lawson Health Research Institute, London, ON, Canada.

Margaret C McKinnon (MC)

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 (RWJ)

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 (PA)

Department of Neuroscience, Western University, London, ON, Canada.
Department of Psychology, Western University, London, ON, Canada.

Jean Théberge (J)

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 (R)

Canadian Forces, Health Services, Ottawa, Ontario, Canada.

J Donald Richardson (JD)

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 (RA)

Department of Neuroscience, Western University, London, ON, Canada.
Department of Psychiatry, Western University, London, ON, Canada.
Imaging, Lawson Health Research Institute, London, ON, Canada.

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Classifications MeSH

questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Prosencéphale › Télencéphale › Cerveau › Noyaux gris centraux › Amygdale (système limbique) › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Techniques d'investigation › Neuroimagerie › Neuroimagerie fonctionnelle › Cartographie cérébrale › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études cas-témoins › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Troubles mentaux › Troubles dissociatifs › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Comportement et mécanismes comportementaux › Émotions › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Adulte d'âge moyen › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Analyse de variance › Analyse multifactorielle › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Système nerveux › Voies nerveuses › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Activités humaines › Activités de loisirs › Relaxation › Repos › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Troubles mentaux › Troubles liés aux traumatismes et au stress › Troubles de stress traumatique › Troubles de stress post-traumatique › Machine learning multivariate pattern analysis...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Jeune adulte › Machine learning multivariate pattern analysis...