Risk factors for depression have long been identified. However, it remains unclear what are the mechanisms whereby these risk factors lead to depression. Therefore the current research examined cognitive and neurophysiological functioning in a sample of high risk vs. low risk never-depressed young adults. Risk for depression was defined by high neuroticism (N) scores on the Eysenck Personality Questionnaire (EPQ). Results indicated that, compared to low N volunteers, high N volunteers show widespread negative biases across emotional processing tasks, including self-referent words categorization and memory, facial expression recognition, and emotion potentiated startle. The neural substrates of these negative biases were further illustrated by our brain-imaging experiments using fMRI. In these studies, high N is associated with increased neural signals for negative self-referent personality attributes and fearful facial expressions in a distributed network known to be involved in emotional processing, including the fusiform-amygdala circuitry, anterior cingulate, and the superior parietal cortex. By contrast, these neurocognitive biases did not seem to be accompanied by impairments in more global executive function or disturbances in biological response to stress measured by awakening salivary cortisol. Consistent with the idea that emotional processing biases represent key mechanisms underlying vulnerability to depression, our final longitudinal study showed that depression symptoms in high N volunteers were well predicted (up to 91%) at an 18 month follow up by a combination of these negative biases and stressful life events. Taken together, the current investigations therefore suggest that neurocognitive biases in emotional processing are trait vulnerability markers for depression prior to illness onset.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:491341 |
Date | January 2008 |
Creators | Chan, Stella Wing Yan |
Contributors | Harmer, Catherine |
Publisher | University of Oxford |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
Source | http://ora.ox.ac.uk/objects/uuid:4d472e39-81f4-4b2d-a230-977425dd01d0 |
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