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Purpose: Depression is common in temporal lobe epilepsy (TLE) and after temporal lobectomy, and its etiology is obscure. In nonepileptic depression (including depression associated with other neurologic disorders), a consistent PET imaging finding is frontal lobe hypometabolism. Many TLE patients have hypometabolism involving frontal regions. Thus in data available from routine clinical assessments in an epilepsy surgery unit, we tested the hypothesis that the pattern of hypometabolism, particularly in the frontal lobe, may be associated with the depression seen in patients with TLE and TLE surgery., Methods: We studied 23 medically refractory TLE patients who underwent anterior temporal lobectomy and who had preoperative FDG-PET scanning. All patients had pre- and postoperative psychiatric assessment. By using statistical parametric mapping (SPM-99), patterns of hypometabolism were compared between patients who had a preoperative history of depression (n=9) versus those who did not (n=14) and between those in whom postoperative depression developed (n=13) versus those in whom it did not (n=10). A significant region of hypometabolism was set at p<0.001 for a cluster of >or=20 contiguous voxels., Results: Patients with a history of depression at any time preoperatively showed focal hypometabolism in ipsilateral orbitofrontal cortex compared with those who did not (t=4.64; p<0.001). Patients in whom depression developed postoperatively also showed hypometabolism in the ipsilateral orbitofrontal region (t=5.10; p<0.001)., Conclusions: Although this study is methodologically limited, and other explanations merit consideration, orbitofrontal cortex dysfunction, already implicated in the pathophysiology of nonepileptic depression, may also be relevant to the depression of TLE and temporal lobectomy.

Features of epilepsy from human extracranial EEG recordings were obtained using the wavelet artificial neural network (WANN). The WANN is also a robust signal processing tool for the estimation of nonlinear time-frequency relation and it had previously been shown to be able to classify and predict state transitions in the in-vitro hippocampal slice model exhibiting spontaneous epilepsy. The variations in the power-frequency spectrum were analyzed. The accuracy of state classification was improved when more training data was used. The corresponding changes in synaptic weights between artificial neural units associated with more training data was studied to determine the correlations between learning in WANN and frequency information in human epilepsy.