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Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression
Article first published online: 25 MAY 2012
© 2012 John Wiley and Sons A/S
Volume 14, Issue 4, pages 451–460, June 2012
How to Cite
Mourão-Miranda, J., Almeida, J. R., Hassel, S., de Oliveira, L., Versace, A., Marquand, A. F., Sato, J. R., Brammer, M. and Phillips, M. L. (2012), Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression. Bipolar Disorders, 14: 451–460. doi: 10.1111/j.1399-5618.2012.01019.x
- Issue published online: 25 MAY 2012
- Article first published online: 25 MAY 2012
- Received 20 June 2011, revised and accepted for publication 13 March 2012
- bipolar disorder;
- Gaussian process;
- patient classification;
- pattern recognition
Mourão-Miranda J, Almeida JRC, Hassel S, de Oliveira L, Versace A, Marquand AF, Sato JR, Brammer M, Phillips ML. Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression. Bipolar Disord 2012: 14: 451–460. © 2012 The Authors. Journal compilation © 2012 John Wiley & Sons A/S.
Objectives: Recently, pattern recognition approaches have been used to classify patterns of brain activity elicited by sensory or cognitive processes. In the clinical context, these approaches have been mainly applied to classify groups of individuals based on structural magnetic resonance imaging (MRI) data. Only a few studies have applied similar methods to functional MRI (fMRI) data.
Methods: We used a novel analytic framework to examine the extent to which unipolar and bipolar depressed individuals differed on discrimination between patterns of neural activity for happy and neutral faces. We used data from 18 currently depressed individuals with bipolar I disorder (BD) and 18 currently depressed individuals with recurrent unipolar depression (UD), matched on depression severity, age, and illness duration, and 18 age- and gender ratio-matched healthy comparison subjects (HC). fMRI data were analyzed using a general linear model and Gaussian process classifiers.
Results: The accuracy for discriminating between patterns of neural activity for happy versus neutral faces overall was lower in both patient groups relative to HC. The predictive probabilities for intense and mild happy faces were higher in HC than in BD, and for mild happy faces were higher in HC than UD (all p < 0.001). Interestingly, the predictive probability for intense happy faces was significantly higher in UD than BD (p = 0.03).
Conclusions: These results indicate that patterns of whole-brain neural activity to intense happy faces were significantly less distinct from those for neutral faces in BD than in either HC or UD. These findings indicate that pattern recognition approaches can be used to identify abnormal brain activity patterns in patient populations and have promising clinical utility as techniques that can help to discriminate between patients with different psychiatric illnesses.