Dataset Information

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression.

ABSTRACT:

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.

SUBMITTER: Mourao-Miranda J

PROVIDER: S-EPMC3510302 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Publications

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression.

Mourão-Miranda Janaina J Almeida Jorge R C JR Hassel Stefanie S de Oliveira Leticia L Versace Amelia A Marquand Andre F AF Sato Joao R JR Brammer Michael M Phillips Mary L ML

Bipolar disorders 20120601 4

<h4>Objectives</h4>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.<h4>Methods</h4>We used a novel analytic framework to examine the extent to which unipolar and bipolar d ...[more]

PMID: 22631624

Similar Datasets

Project description:ImportanceThe structural abnormalities in the brain that accurately differentiate unipolar depression (UD) and bipolar depression (BD) remain unidentified.ObjectivesFirst, to investigate and compare morphometric changes in UD and BD, and to replicate the findings at 2 independent neuroimaging sites; second, to differentiate UD and BD using multivariate pattern classification techniques.Design, setting, and participantsIn a 2-center cross-sectional study, structural gray matter data were obtained at 2 independent sites (Pittsburgh, Pennsylvania, and Münster, Germany) using 3-T magnetic resonance imaging. Voxel-based morphometry was used to compare local gray and white matter volumes, and a novel pattern classification approach was used to discriminate between UD and BD, while training the classifier at one imaging site and testing in an independent sample at the other site. The Pittsburgh sample of participants was recruited from the Western Psychiatric Institute and Clinic at the University of Pittsburgh from 2008 to 2012. The Münster sample was recruited from the Department of Psychiatry at the University of Münster from 2010 to 2012. Equally divided between the 2 sites were 58 currently depressed patients with bipolar I disorder, 58 age- and sex-matched unipolar depressed patients, and 58 matched healthy controls.Main outcomes and measuresMagnetic resonance imaging was used to detect structural differences between groups. Morphometric analyses were applied using voxel-based morphometry. Pattern classification techniques were used for a multivariate approach.ResultsAt both sites, individuals with BD showed reduced gray matter volumes in the hippocampal formation and the amygdala relative to individuals with UD (Montreal Neurological Institute coordinates x = -22, y = -1, z = 20; k = 1938 voxels; t = 4.75), whereas individuals with UD showed reduced gray matter volumes in the anterior cingulate gyrus compared with individuals with BD (Montreal Neurological Institute coordinates x = -8, y = 32, z = 3; k = 979 voxels; t = 6.37; all corrected P < .05). Reductions in white matter volume within the cerebellum and hippocampus were found in individuals with BD. Pattern classification yielded up to 79.3% accuracy (P < .001) by differentiating the 2 depressed groups, training and testing the classifier at one site, and up to 69.0% accuracy (P < .001), training the classifier at one imaging site (Pittsburgh) and testing it at the other independent sample (Münster). Medication load did not alter the pattern of results.Conclusions and relevanceIndividuals with UD and those with BD are differentiated by structural abnormalities in neural regions supporting emotion processing. Neuroimaging and multivariate pattern classification techniques are promising tools to differentiate UD from BD and show promise as future diagnostic aids.

Dataset Information

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression.

Objectives

Methods

Results

Conclusions

Publications

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression.

Similar Datasets

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