The segmentation from MRI of macroscopically ill-defined and highly variable structures, such as the hippocampus (Hc) and the amygdala (Am), requires the use of specific constraints. Here, we describe and evaluate a fast fully automatic hybrid segmentation that uses knowledge derived from probabilistic atlases and anatomical landmarks, adapted from a semi-automatic method. The algorithm was designed at the outset for application on images from healthy subjects and patients with hippocampal sclerosis. Probabilistic atlases were built from 16 healthy subjects, registered using SPM5. Local mismatch in the atlas registration step was automatically detected and corrected. Quantitative evaluation with respect to manual segmentations was performed on the 16 young subjects, with a leave-one-out strategy, a mixed cohort of 8 controls and 15 patients with epilepsy with variable degrees of hippocampal sclerosis, and 8 healthy subjects acquired on a 3 T scanner. Seven performance indices were computed, among which error on volumes RV and Dice overlap K. The method proved to be fast, robust and accurate. For Hc, results with the new method were: 16 young subjects {RV = 5%, K = 87%}; mixed cohort {RV = 8%, K = 84%}; 3 T cohort {RV = 9%, K = 85%}. Results were better than with atlas-based (thresholded probability map) or semi-automatic segmentations. Atlas mismatch detection and correction proved efficient for the most sclerotic Hc. For Am, results were: 16 young controls {RV = 7%, K = 85%}; mixed cohort {RV = 19%, K = 78%}; 3 T cohort {RV = 10%, K = 77%}. Results were better than with the semi-automatic segmentation, and were also better than atlas-based segmentations for the 16 young subjects.

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Highlights20–30% of patients with refractory focal epilepsy have normal MRI scans.We evaluated the role of repeated MRI with better technology in detecting pathology.804 patients underwent MRI at 1.5T and subsequently at 3T with superior head coils.Relevant new diagnoses were made in 37 (5%) and affected patient management.Rescanning patients with focal epilepsy and previously normal MRI is beneficial.

Objective:To investigate the effects of topiramate (TPM), zonisamide (ZNS), and levetiracetam (LEV) on cognitive network activations in patients with focal epilepsy using an fMRI language task.Methods:In a retrospective, cross-sectional study, we identified patients from our clinical database of verbal fluency fMRI studies who were treated with either TPM (n = 32) or ZNS (n = 51). We matched 62 patients for clinical measures who took LEV but not TPM or ZNS. We entered antiepileptic comedications as nuisance variables and compared out-of-scanner psychometric measures for verbal fluency and working memory between groups.Results:Out-of-scanner psychometric data showed overall poorer performance for TPM compared to ZNS and LEV and poorer working memory performance in ZNS-treated patients compared to LEV-treated patients. We found common fMRI effects in patients taking ZNS and TPM, with decreased activations in cognitive frontal and parietal lobe networks compared to those taking LEV. Impaired deactivation was seen only with TPM.Conclusions:Our findings suggest that TPM and ZNS are associated with similar dysfunctions of frontal and parietal cognitive networks, which are associated with impaired performance. TPM is also associated with impaired attenuation of language-associated deactivation. These studies imply medication-specific effects on the functional neuroanatomy of language and working memory networks.Classification of evidence:This study provides Class III evidence that in patients with focal epilepsy, TPM and ZNS compared to LEV lead to disruption of language and working memory networks.

SUMMARYBackground: Focal Cortical Dysplasia (FCD) is an important cause for pharmacoresistant epilepsy that can be treated surgically. The identification of the abnormal cortex on standard MRI can be difficult and computational techniques have been developed to increase sensitivity. In this study we evaluate the potential of a novel whole-brain voxelbased technique using normalized FLAIR signal intensity (nFSI) at 3 Tesla. Methods: Twenty-five patients with neuroradiologically reported FCD were included and compared to 25 healthy control subjects using Statistical Parametric Mapping (SPM5). T 2 FLAIR scans were intensity normalized and each individual patient was compared against the control group. Each control subject was compared against the remaining control group.