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J Neurophysiol (December 24, 2008). doi:10.1152/jn.91049.2008
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Submitted on September 19, 2008
Revised on November 27, 2008
Accepted on December 18, 2008

Prediction of Subjective Affective State from Brain Activations

Edmund T Rolls1*, Fabian Grabenhorst2, and Leonardo Franco3

1 Oxford Centre for Computational Neuroscience
2 Oxford University
3 University of Malaga, Spain

* To whom correspondence should be addressed. E-mail: edmund.rolls{at}oxcns.org.

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 7 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 3x3x3 mm voxel reflects the activity of approximately 0.8 million neurons or their synaptic inputs, and is not part of the information encoding used by the brain, so provides relatively poor readout of information compared to that available from small populations of neurons.







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