Investigating the predictive value of functional MRI to appetitive and aversive stimuli: a pattern classification approachMcCabe, C. ORCID: https://orcid.org/0000-0001-8704-3473 and Rocha-Rego, V. (2016) Investigating the predictive value of functional MRI to appetitive and aversive stimuli: a pattern classification approach. PLoS ONE, 11 (11). e0165295. ISSN 1932-6203
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1371/journal.pone.0165295 Abstract/SummaryAbstract Background. Dysfunctional neural responses to appetitive and aversive stimuli have been investigated as possible biomarkers for psychiatric disorders. However it is not clear to what degree these are separate processes across the brain or in fact overlapping systems. To help clarify this issue we used Gaussian process classifier (GPC) analysis to examine appetitive and aversive processing in the brain. Method. 25 healthy controls underwent functional MRI whilst seeing pictures and receiving tastes of pleasant and unpleasant food. We applied GPCs to discriminate between the appetitive and aversive sights and tastes using functional activity patterns. Results. The diagnostic accuracy of the GPC for the accuracy to discriminate appetitive taste from neutral condition was 86.5% (specificity = 81%, sensitivity =92%, p=0.001). If a participant experienced neutral taste stimuli the probability of correct classification was 92. The accuracy to discriminate aversive from neutral taste stimuli was 82.5% (specificity = 73%, sensitivity =92%, p=0.001) and appetitive from aversive taste stimuli was 73% (specificity = 77%, sensitivity =69%, p=0.001). In the sight modality, the accuracy to discriminate appetitive from neutral condition was 88.5% (specificity = 85%, sensitivity =92%, p=0.001), to discriminate aversive from neutral sight stimuli was 92% (specificity = 92%, sensitivity =92%, p=0.001), and to discriminate aversive from appetitive sight stimuli was 63.5% (specificity = 73%, sensitivity =54%, p=0.009). Conclusions. Our results demonstrate the predictive value of neurofunctional data in discriminating emotional and neutral networks of activity in the healthy human brain. It would be of interest to use pattern recognition techniques and fMRI to examine network dysfunction in the processing of appetitive, aversive and neutral stimuli in psychiatric disorders. Especially where problems with reward and punishment processing have been implicated in the pathophysiology of the disorder.
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