Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression.
OBJECTIVE
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.
Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK. j.mourao-miranda@cs.ucl.ac.uk
Janaina
J
Mourão-Miranda
Jorge R C
JR
Almeida
Stefanie
S
Hassel
Leticia
L
de Oliveira
Amelia
A
Versace
Andre F
AF
Marquand
Joao R
JR
Sato
Michael
M
Brammer
Mary L
ML
Phillips
D000293:Adolescent; D000328:Adult; D001714:Bipolar Disorder / Q000175:diagnosis / Q000503:physiopathology* / Q000523:psychology; D001921:Brain / Q000503:physiopathology*; D016022:Case-Control Studies; D003866:Depressive Disorder / Q000175:diagnosis / Q000503:physiopathology* / Q000523:psychology; D005149:Facial Expression; D005260:Female; D059907:Functional Neuroimaging; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests