Jane L. Burdett

Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation

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.

Voxel‐based analysis of whole brain FLAIR at 3T detects focal cortical dysplasia

Background: 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 voxel‐based technique using normalized FLAIR signal intensity (nFSI) at 3 Tesla.

Automated hippocampal segmentation in patients with epilepsy: Available free online

Hippocampal sclerosis, a common cause of refractory focal epilepsy, requires hippocampal volumetry for accurate diagnosis and surgical planning. Manual segmentation is time‐consuming and subject to interrater/intrarater variability. Automated algorithms perform poorly in patients with temporal lobe epilepsy. We validate and make freely available online a novel automated method.

Effect of topiramate and zonisamide on fMRI cognitive networks

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.

Increased sensitivity to pathological brain changes using co-registration of magnetic resonance imaging scans

Purpose: To compare automatic software-based co-registration of serial magnetic resonance imaging (MRI) scans with conventional visual comparison, by expert neuroradiologists. Material and Methods: Sixty-four patients who were referred to our epilepsy MRI unit for cerebral imaging were identified as having potentially, non-, or slow-growing lesions or cerebral atrophy and followed with sequential scans over a period of up to 8 years, resulting in a total of 92 pairs of scans. Scans were categorized as showing either lesions or atrophy. Each pair of scans was reviewed twice for the presence of change, with and without co-registration, performed using automated software. Results: Co-registration and visual reporting without co-registration were discordant in the lesions group in nine out of 69 datasets (13%), and in 16 out of 23 pairs of scans in the atrophy group (69%). The most common cause of discordance was visual reporting not detecting changes apparent by co-registration. In three cases, changes detected visually were not detected following co-registration. Conclusion: In the group of patients studied, co-registration was more sensitive for detecting changes than visual comparison, particularly with respect to atrophic changes of the brain. With the increasing availability of sophisticated independent consoles attached to MRI scanners that may be used for image co-registration, we propose that serial T1-weighted volumetric MRI brain co-registration should be considered for integration into routine clinical practice to assess patients with suspected progressive disease.

Automated T2 relaxometry of the hippocampus for temporal lobe epilepsy

Hippocampal sclerosis (HS), the most common cause of refractory temporal lobe epilepsy, is associated with hippocampal volume loss and increased T2 signal. These can be identified on quantitative imaging with hippocampal volumetry and T2 relaxometry. Although hippocampal segmentation for volumetry has been automated, T2 relaxometry currently involves subjective and time‐consuming manual delineation of regions of interest. In this work, we develop and validate an automated technique for hippocampal T2 relaxometry.

Effects of carbamazepine and lamotrigine on functional magnetic resonance imaging cognitive networks

To investigate the effects of sodium channel–blocking antiepileptic drugs (AEDs) on functional magnetic resonance imaging (fMRI) language network activations in patients with focal epilepsy.

The impact of brain-derived neurotrophic factor Val66Met polymorphism on cognition and functional brain networks in patients with intractable partial epilepsy

Medial temporal lobe epilepsy (mTLE) is the most common refractory focal epilepsy in adults. Around 30%‐40% of patients have prominent memory impairment and experience significant postoperative memory and language decline after surgical treatment. BDNF Val66Met polymorphism has also been associated with cognition and variability in structural and functional hippocampal indices in healthy controls and some patient groups.