Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Sophia Frangou*, Danai Dima, Jigar Jogia

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.

Original languageEnglish
Pages (from-to)230-237
Number of pages8
JournalNeuroimage
Volume145
Issue numberPart B
Early online date10 Sep 2016
DOIs
Publication statusPublished - 15 Jan 2017

Fingerprint

Bipolar Disorder
Neuroimaging
Major Depressive Disorder
Magnetic Resonance Imaging
Psychopathology
Short-Term Memory
Sensitivity and Specificity
Predictive Value of Tests
Uncertainty
Observation
Brain
Population

Bibliographical note

© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Cite this

Frangou, Sophia ; Dima, Danai ; Jogia, Jigar. / Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder. In: Neuroimage. 2017 ; Vol. 145, No. Part B. pp. 230-237.
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Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder. / Frangou, Sophia; Dima, Danai; Jogia, Jigar.

In: Neuroimage, Vol. 145, No. Part B, 15.01.2017, p. 230-237.

Research output: Contribution to journalArticle

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