Hippocampal Shape Is Associated with Memory Deficits in Temporal Lobe Epilepsy.

OBJECTIVE: Cognitive problems, especially disturbances in episodic memory, and hippocampal sclerosis are common in temporal lobe epilepsy (TLE), but little is known about the relationship of hippocampal morphology with memory. We aimed to relate hippocampal surface-shape patterns to verbal and visual learning. METHODS: We analyzed hippocampal surface shapes on high-resolution magnetic resonance images and the Adult Memory and Information Processing Battery in 145 unilateral refractory TLE patients undergoing epilepsy surgery, a validation set of 55 unilateral refractory TLE patients, and 39 age- and sex-matched healthy volunteers. RESULTS: Both left TLE (LTLE) and right TLE (RTLE) patients had lower verbal (LTLE 44 ± 11; RTLE 45 ± 10) and visual learning (LTLE 34 ± 8, RTLE 30 ± 8) scores than healthy controls (verbal 58 ± 8, visual 39 ± 6; p < 0.001). Verbal learning was more impaired the greater the atrophy of the left superolateral hippocampal head. In contrast, visual memory was worse with greater bilateral inferomedial hippocampal atrophy. Postsurgical verbal memory decline was more common in LTLE than in RTLE (reliable change index in LTLE 27% vs RTLE 7%, p = 0.006), whereas there were no differences in postsurgical visual memory decline between those groups. Preoperative atrophy of the left hippocampal tail predicted postsurgical verbal memory decline. INTERPRETATION: Memory deficits in TLE are associated with specific morphological alterations of the hippocampus, which could help stratify TLE patients into those at high versus low risk of presurgical or postsurgical memory deficits. This knowledge could improve planning and prognosis of selective epilepsy surgery and neuropsychological counseling in TLE. ANN NEUROL 2020 ANN NEUROL 2020;88:170-182.

Automated T2 relaxometry of the hippocampus for temporal lobe epilepsy.

OBJECTIVE: 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. METHODS: Fifty patients with unilateral or bilateral HS and 50 healthy controls underwent T1 -weighted and dual-echo fast recovery fast spin echo scans. Hippocampi were automatically segmented using a multi-atlas-based segmentation algorithm (STEPS) and a template database. Voxelwise T2 maps were determined using a monoexponential fit. The hippocampal segmentations were registered to the T2 maps and eroded to reduce partial volume effect. Voxels with T2 >170 msec excluded to minimize cerebrospinal fluid (CSF) contamination. Manual determination of T2 values was performed twice in each subject. Twenty controls underwent repeat scans to assess interscan reproducibility. RESULTS: Hippocampal T2 values were reliably determined using the automated method. There was a significant ipsilateral increase in T2 values in HS (p < 0.001), and a smaller but significant contralateral increase. The combination of hippocampal volumes and T2 values separated the groups well. There was a strong correlation between automated and manual methods for hippocampal T2 measurement (0.917 left, 0.896 right, both p < 0.001). Interscan reproducibility was superior for automated compared to manual measurements. SIGNIFICANCE: Automated hippocampal segmentation can be reliably extended to the determination of hippocampal T2 values, and a combination of hippocampal volumes and T2 values can separate subjects with HS from healthy controls. There is good agreement with manual measurements, and the technique is more reproducible on repeat scans than manual measurement. This protocol can be readily introduced into a clinical workflow for the assessment of patients with focal epilepsy.

Automated hippocampal segmentation in patients with epilepsy: available free online.

PURPOSE: 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. METHODS: Manual hippocampal segmentation was performed on 876, 3T MRI scans and 202, 1.5T scans. A template database of 400 high-quality manual segmentations was used to perform automated segmentation of all scans with a multi-atlas-based segmentation propagation method adapted to perform label fusion based on local similarity to ensure accurate segmentation regardless of pathology. Agreement between manual and automated segmentations was assessed by degree of overlap (Dice coefficient) and comparison of hippocampal volumes. KEY FINDINGS: The automated segmentation algorithm provided robust delineation of the hippocampi on 3T scans with no more variability than that seen between different human raters (Dice coefficients: interrater 0.832, manual vs. automated 0.847). In addition, the algorithm provided excellent results with the 1.5T scans (Dice coefficient 0.827), and automated segmentation remained accurate even in small sclerotic hippocampi. There was a strong correlation between manual and automated hippocampal volumes (Pearson correlation coefficient 0.929 on the left and 0.941 on the right in 3T scans). SIGNIFICANCE: We demonstrate reliable identification of hippocampal atrophy in patients with hippocampal sclerosis, which is crucial for clinical management of epilepsy, particularly if surgical treatment is being contemplated. We provide a free online Web-based service to enable hippocampal volumetry to be available globally, with consequent greatly improved evaluation of those with epilepsy.

The value of repeat neuroimaging for epilepsy at a tertiary referral centre: 16 years of experience.

PURPOSE: Magnetic resonance imaging (MRI) is the investigation of choice for detecting structural lesions that underlie and may accompany epilepsy. Despite advances in imaging technology, 20-30% of patients with refractory focal epilepsy have normal MRI scans. We evaluated the role of repeated imaging with improved MRI technology - an increase in field strength from 1.5T to 3T and superior head coils - in detecting pathology not previously seen. METHODS: Retrospective review of a large cohort of patients attending a tertiary epilepsy referral centre who underwent MRI at 1.5T (1995-2004) and subsequently 3T (2004-2011) with improved head coils. Scan reports were reviewed for the diagnoses and medical notes for the epilepsy classification. RESULTS: 804 patients underwent imaging on both scanners, the majority with focal epilepsy (87%). On repeat scanning at 3T, 37% of scans were normal and 20% showed incidental findings. Positive findings included hippocampal sclerosis (13%), malformations of cortical development (8%), other abnormalities (4%) and previous surgery (18%). A total of 37 (5%) relevant new diagnoses were made on the 3T scans not previously seen at 1.5T. The most common new findings were hippocampal sclerosis, focal cortical dysplasia and dysembryoplastic neuroepithelial tumour. These findings affected patient management with several patients undergoing neurosurgery. CONCLUSIONS: The higher field strength and improved head coils were associated with a clinically relevant increased diagnostic yield from MRI. This highlights the importance of technological advances and suggests that rescanning patients with focal epilepsy and previously negative scans is clinically beneficial.

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. METHODS: Twenty-five patients with neuroradiologically reported FCD were included and compared to 25 healthy control subjects using Statistical Parametric Mapping (SPM5). T2 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. RESULTS: SPM correctly identified the FCD in 88% of cases (22/25) with only one false positive finding in the control group. In all but one of these cases the FCD was the most significant finding in the whole brain. All three missing cases could be detected at lower threshold levels but this would give rise to more false positive findings and thus reduce specificity. CONCLUSION: We present a novel technique that uses standard clinical T2 FLAIR scans to automatically detect FCDs. It can give supplementary information to the established T1-based automated techniques and could be useful for additional screening test, to complement the visual reading and clinical interpretation of MRI scans.