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. 2013 Sep;203(3):310-1.
doi: 10.1192/bjp.bp.112.122838. Epub 2013 Aug 22.

Pattern recognition analysis of anterior cingulate cortex blood flow to classify depression polarity

J R C Almeida  1 , J Mourao-Miranda, H J Aizenstein, A Versace, F A Kozel, H Lu, A Marquand, E J LaBarbara, M Brammer, M Trivedi, D J Kupfer, M L Phillips
Affiliations

Pattern recognition analysis of anterior cingulate cortex blood flow to classify depression polarity

J R C Almeida et al. Br J Psychiatry. 2013 Sep.

Abstract

Differentiating bipolar from recurrent unipolar depression is a major clinical challenge. In 18 healthy females and 36 females in a depressive episode--18 with bipolar disorder type I, 18 with recurrent unipolar depression--we applied pattern recognition analysis using subdivisions of anterior cingulate cortex (ACC) blood flow at rest, measured with arterial spin labelling. Subgenual ACC blood flow classified unipolar v. bipolar depression with 81% accuracy (83% sensitivity, 78% specificity).

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Conflict of interest statement

Declaration of interest

H. J. A. has received support from Novartis for an unrelated study. F. A. K. holds patents through the Medical University of South Carolina and has patents pending as an inventor of imaging and neuromodulation technologies. M. T. has received unrelated research support and consulting and speaker fees from several pharmaceutical and medical technology companies with interests in the treatment and detection of depression and bipolar illness.

Figures

Fig. 1
Fig. 1
Distance from the hyperplane (or test margin) for each participant based on the spatial pattern of resting blood flow within the subgenual anterior cingulate cortex (ACC, BA25) classifier. Participants were classified with unipolar depression (UD, triangle, 14/18 accurately classified) or bipolar depression (BD, circle, 15/18 accurately classified) with 80.6% accuracy (P = 0.001). Top right: schematic representation of the brain with the subgenual (sg) ACC highlighted by a circle.
See this image and copyright information in PMC

Comment in

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