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

Janaina Mourão-Miranda, Jorge R.C. Almeida, Stefanie Hassel, Leticia de Oliveira, Amelia Versace, Andre F. Marquand, Joao R. Sato, Michael Brammer, Mary L. Phillips

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)451-460
Number of pages10
JournalBipolar Disorders
Volume14
Issue number4
DOIs
Publication statusPublished - Jun 2012

Bibliographical note

Open Access

Keywords

  • magnetic resonance imaging
  • functional neuroimaging
  • bipolar disorder
  • humans
  • linear models
  • brain
  • predictive value of tests
  • depressive disorder
  • facial expression
  • adult
  • case-control studies
  • middle aged
  • automated pattern recognition
  • adolescent
  • female
  • male
  • depression
  • fMRI
  • Gaussian process
  • patient classification
  • pattern recognition

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