A neuroimaging model based on MRI, DTI, and spect findings for lateralization of temporal lobe epilepsy

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Conference Proceeding

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Purpose: Temporal lobe epilepsy (TLE) is the most widespread type of epilepsy with the most successful resection outcome. Interhemispheric variations detected in the images of T1-weighted and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI), and ictal and interictal single photon emission computed tomography (SPECT), and in the indices of mean diffusivity (MD) and fractional anisotropy (FA) of diffusion tensor imaging (DTI), are within the established markers ofTLE laterality. However, current non-quantitative imaging evaluations may not optimally incorporate the imaging information into the decision-making process prior to resection of mesial temporalstructures. We hypothesize that quantitative TLE lateralization response models of MRI, DTI, and SPECTneuroimaging attributes will optimize the selection ofsurgical candidates and reduce, in some cases, the need for extraoperative electrocorticography (eECoG). Method: Neuroimaging features of 138 retrospective TLE patients with Engel class l surgical outcomes were extracted, including the hippocampal volumes, normalized ictal-interictal SPECT and FLAIR intensities, and mean diffusivity, along with the cingulate and forniceal fractional anisotropy (FA). Using logistic function regression, univariate and multivariate models were developed. Results: The model incorporating all multivariate attributes for138 TLE cases that had at least one imaging attribute and imputing the missing attributes with the mean values of the corresponding attributes measured oncontrol cohort reached the probability of detection and false alarm of 0.83 and 0.17 for all cases, and 0.90 and 0.10 for the patients who underwent eECoG. Conclusion: Increased reliability in lateralizing TLE cases using the proposed response model involving the incorporation of the multivariate attributes reinforces the notion that eECoG in a number ofcases may be circumvented. The proposed response model can be further generalized by integrating attributes of additional neuroclinical, neurophysiological, neuropsychological, and neuroimaging attributes into the presurgical decision making process.



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