EPILEPTIC DISORDERS, vol.20, no.6, pp.517-524, 2018 (Peer-Reviewed Journal)
Aim. The somatic marker hypothesis is an influential model of human decision-making postulating that somatic feedback to the brain enhances decision-making in ambiguous circumstances, i.e. when the probabilities of various outcomes are unknown. The somatic feedback can be measured as autonomic responses, which are regulated by the amygdala. The failure to evoke this somatic feedback, which occurs in patients with amygdala lesions, impairs decision-making. The purpose of this study was to investigate the decision-making behaviour of mesial temporal lobe epilepsy patients with pre- and post-epilepsy surgery to ascertain whether the decision-making abilities of groups can be explained by means of the generation of somatic feedback responses.Methods. The preoperative group comprised 32 patients with mesial temporal lobe epilepsy due to hippocampal sclerosis, while the postoperative group comprised 23 patients who had undergone anterior temporal lobectomy. The age and gender-matched control group consisted of 30 healthy participants. Decision-making performances were assessed and skin resistance responses were measured simultaneously.Results. The findings of this study reveal that the decision-making performance of preoperative patients with unilateral mesial temporal lobe epilepsy was impaired under conditions of ambiguity, i.e. they did not generate somatic feedback responses before making decisions around ambiguous outcomes, and produced significantly poor scores overall based on a decision-making task. In addition, the resection of epileptogenic limbic structures positively affected the generation of somatic feedback responses, as demonstrated by the significant difference between the magnitudes of autonomic responses of the pre- and post-operative groups.Conclusions. The findings of the study validate the contribution of mesial temporal lobe structures to decision-making behaviour, and also point to the importance of examining the connectivity patterns between the neural structures involved in the decision-making network.