title: Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor.
abstract: We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups were subsequently formed: (i) subclinical (mild) mood disturbance (n = 17) and (ii) no mood disturbance (n = 17). Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE) positive subscale. The functional magnetic resonance imaging (fMRI) paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002), within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006). Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.
authors: Department of Psychosis Studies, Institute of Psychiatry, King's College London , London , United Kingdom.
authors: Gemma
authors: G
authors: Modinos
authors: Andrea
authors: A
authors: Mechelli
authors: William
authors: W
authors: Pettersson-Yeo
authors: Paul
authors: P
authors: Allen
authors: Philip
authors: P
authors: McGuire
authors: Andre
authors: A
authors: Aleman
keywords: Emotion; Machine learning; Neuroimaging; Psychosis proneness; Subclinical depression; Support vector machine; fMRI
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