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@ARTICLE{Habes:151771,
      author       = {Habes, I. and Krall, S. C. and Johnston, S. J. and Yuen, K.
                      S. L. and Healy, D. and Goebel, R. and Sorger, B. and
                      Linden, D. E. J.},
      title        = {{P}attern classification of valence in depression},
      journal      = {NeuroImage: Clinical},
      volume       = {2},
      issn         = {2213-1582},
      address      = {[Amsterdam u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2014-01654},
      pages        = {675 - 683},
      year         = {2013},
      abstract     = {Neuroimaging biomarkers of depression have potential to aid
                      diagnosis, identify individuals at risk and predict
                      treatment response or course of illness. Nevertheless none
                      have been identified so far, potentially because no single
                      brain parameter captures the complexity of the
                      pathophysiology of depression. Multi-voxel pattern analysis
                      (MVPA) may overcome this issue as it can identify patterns
                      of voxels that are spatially distributed across the brain.
                      Here we present the results of an MVPA to investigate the
                      neuronal patterns underlying passive viewing of positive,
                      negative and neutral pictures in depressed patients. A
                      linear support vector machine (SVM) was trained to
                      discriminate different valence conditions based on the
                      functional magnetic resonance imaging (fMRI) data of nine
                      unipolar depressed patients. A similar dataset obtained in
                      nine healthy individuals was included to conduct a group
                      classification analysis via linear discriminant analysis
                      (LDA). Accuracy scores of $86\%$ or higher were obtained for
                      each valence contrast via patterns that included limbic
                      areas such as the amygdala and frontal areas such as the
                      ventrolateral prefrontal cortex. The LDA identified two
                      areas (the dorsomedial prefrontal cortex and caudate
                      nucleus) that allowed group classification with $72.2\%$
                      accuracy. Our preliminary findings suggest that MVPA can
                      identify stable valence patterns, with more sensitivity than
                      univariate analysis, in depressed participants and that it
                      may be possible to discriminate between healthy and
                      depressed individuals based on differences in the brain's
                      response to emotional cues},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {333 - Pathophysiological Mechanisms of Neurological and
                      Psychiatric Diseases (POF2-333) / 89572 - (Dys-)function and
                      Plasticity (POF2-89572)},
      pid          = {G:(DE-HGF)POF2-333 / G:(DE-HGF)POF2-89572},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000209276800075},
      pubmed       = {pmid:24179819},
      doi          = {10.1016/j.nicl.2013.05.001},
      url          = {https://juser.fz-juelich.de/record/151771},
}