Summary:Purpose: Anterior temporal lobectomy (ATL) is an important option for treatment of medically refractory seizures. Patient selection is not always clear-cut, and there is inherent morbidity and mortality associated with the invasive and expensive surgical protocols. To determine whether patient selection might be facilitated by application of artifical intelligence, we developed a model that predicted seizure outcome after ATL, using a simulated neural network (SNN).Methods: Predictions of the model were compared with predictions derived from conventional discriminant function analysis. Neural networks and discriminant functions were devised that would predict the occurrence of both Class 1 outcomes (totally seizure-free), and Class 1 or Class 2 outcomes (nearly or totally seizure-free), using data from 87 patients from three surgical centers. The SNNs and discriminant functions were developed using data from a randomly selected subsample of 65 patients, and both models were cross-validated, using the remaining 22 patients.Results: The discriminant functions showed overall predictive accuracy of 78.5% and 72.7%, while the neural networks demonstrated overall accuracy of 81 3% and 95.4%.Conclusions: Simulated neural networks show promise as adjuncts to decision-making in the selection of epilepsy surgery patients.
The purpose of this paper is to lay the groundwork for the development of a scientific theory of complex human functioning. We first discuss the assumptions on which our thinking is based, then advance the argument that behavior, and human activity in general, may be more fully understood in light of current data on the structural organization of the central nervous system. The brain is organized as a modular, distributed, self-organizing system, which is in constant transaction with the environment. Because of its plasticity, structural and functional change occurs in the brain as a result of experience throughout life. It is our thesis that complex human behavior is organized in a similar manner - that is, human personality and behavior manifest themselves as modular systems. The insights provided by an understanding of the relationship of brain and behavior may enhance the capacity to explain both normal and pathological personality functioning.
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