Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters

Objectives Recent studies have indicated that pattern recognition techniques of functional magnetic resonance imaging (fMRI) data for individual classification may be valuable for distinguishing between major depressive disorder (MDD) and bipolar disorder (BD). Importantly, medication may have affec...

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Published in:Bipolar disorders 2016-11, Vol.18 (7), p.612-623
Main Authors: Rive, Maria M, Redlich, Ronny, Schmaal, Lianne, Marquand, André F, Dannlowski, Udo, Grotegerd, Dominik, Veltman, Dick J, Schene, Aart H, Ruhé, Henricus G
Format: Article
Language:English
Subjects:
Adult
bipolar disorder
Bipolar Disorder - diagnosis
Bipolar Disorder - physiopathology
Bipolar Disorder - psychology
Depressive Disorder, Major - diagnosis
Depressive Disorder, Major - physiopathology
Depressive Disorder, Major - psychology
diagnosis
Diagnosis, Differential
Female
fMRI
Gray Matter - pathology
Humans
machine learning
Magnetic Resonance Imaging - methods
major depressive disorder
Male
Middle Aged
mood state
Nerve Net - physiopathology
Organ Size
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Summary:Objectives Recent studies have indicated that pattern recognition techniques of functional magnetic resonance imaging (fMRI) data for individual classification may be valuable for distinguishing between major depressive disorder (MDD) and bipolar disorder (BD). Importantly, medication may have affected previous classification results as subjects with MDD and BD use different classes of medication. Furthermore, almost all studies have investigated only depressed subjects. Therefore, we focused on medication‐free subjects. We additionally investigated whether classification would be mood state independent by including depressed and remitted subjects alike. Methods We applied Gaussian process classifiers to investigate the discriminatory power of structural MRI (gray matter volumes of emotion regulation areas) and resting‐state fMRI (resting‐state networks implicated in mood disorders: default mode network [DMN], salience network [SN], and lateralized frontoparietal networks [FPNs]) in depressed (n=42) and remitted (n=49) medication‐free subjects with MDD and BD. Results Depressed subjects with MDD and BD could be classified based on the gray matter volumes of emotion regulation areas as well as DMN functional connectivity with 69.1% prediction accuracy. Prediction accuracy using the FPNs and SN did not exceed chance level. It was not possible to discriminate between remitted subjects with MDD and BD. Conclusions For the first time, we showed that medication‐free subjects with MDD and BD can be differentiated based on structural MRI as well as resting‐state functional connectivity. Importantly, the results indicated that research concerning diagnostic neuroimaging tools distinguishing between MDD and BD should consider mood state as only depressed subjects with MDD and BD could be correctly classified. Future studies, in larger samples are needed to investigate whether the results can be generalized to medication‐naïve or first‐episode subjects.
ISSN:1398-5647
1399-5618
DOI:10.1111/bdi.12446