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Prediction of Subjective Affective State From Brain Activations

¹Oxford Centre for Computational Neuroscience, Oxford, United Kingdom; ²University of Oxford, Department of Experimental Psychology, Oxford, United Kingdom; and ³Department of Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain

Submitted 19 September 2008; accepted in final form 18 December 2008

Abstract

Decoding and information theoretic techniques were used to analyze the predictions that can be made from functional magnetic resonance neuroimaging data on individual trials. The subjective pleasantness produced by warm and cold applied to the hand could be predicted on single trials with typically in the range 60–80% correct from the activations of groups of voxels in the orbitofrontal and medial prefrontal cortex and pregenual cingulate cortex, and the information available was typically in the range 0.1–0.2 (with a maximum of 0.6) bits. The prediction was typically a little better with multiple voxels than with one voxel, and the information increased sublinearly with the number of voxels up to typically seven voxels. Thus the information from different voxels was not independent, and there was considerable redundancy across voxels. This redundancy was present even when the voxels were from different brain areas. The pairwise stimulus-dependent correlations between voxels, reflecting higher-order interactions, did not encode significant information. For comparison, the activity of a single neuron in the orbitofrontal cortex can predict with 90% correct and encode 0.5 bits of information about whether an affectively positive or negative visual stimulus has been shown, and the information encoded by small numbers of neurons is typically independent. In contrast, the activation of a 3 x 3 x 3-mm voxel reflects the activity of 0.8 million neurons or their synaptic inputs and is not part of the information encoding used by the brain, thus providing a relatively poor readout of information compared with that available from small populations of neurons.