Journal
JOURNAL OF AFFECTIVE DISORDERS
Volume 234, Issue -, Pages 14-19Publisher
ELSEVIER
DOI: 10.1016/j.jad.2018.02.076
Keywords
Bipolar disorder; Emotional processing; fMRI; Factor analysis; Modularity; Sustained attention
Categories
Funding
- National Institutes of Health [R01MH071931]
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Background: The neurophysiological substrates of cognition and emotion, as seen with fMRI, are generally explained using modular structures. The present study was designed to probe the modular structure of cognitive-emotional processing in bipolar and healthy individuals using factor analysis and compare the results with current conceptions of the neurophysiology of bipolar disorder. Methods: Exploratory factor analysis was used to assess patterns of covariation among brain regions-of-interest activated during the Continuous Performance Task with Emotional and Neutral Distractors in healthy and bipolar individuals without a priori constraints on the number or composition of latent factors. Results: Results indicated a common cognitive-emotional network consisting of prefrontal, medial temporal, limbic, parietal, anterior cingulate and posterior cingulate modules. However, reduced brain activation to emotional stimuli in the frontal, medial temporal and limbic modules was apparent in the bipolar relative to the healthy group, potentially accounting for emotional dysregulation in bipolar disorder. Limitations: This study is limited by a relatively small sample size recruited at a single site. The results have yet to be validated on a larger independent sample. Conclusions: Although the modular structure of cognitive-emotional processing is similar in bipolar and healthy individuals, activation in response to emotional/neutral cues varies. These findings are not only consistent with recent conceptions of mood regulation in bipolar disorder, but also suggest that regional activation can be considered within tighter modular structures without compromising data interpretation. This demonstration may serve as a template for data reduction in future region-of-interest analyses to increase statistical power.
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