Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm

Glucose Metabolism of Hippocampal Subfields in Medial Temporal Lobe Epilepsy

PURPOSE

Reduced glucose metabolism in the hippocampus is commonly observed in cases of medial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (HS). Glucose metabolism among the various hippocampal subfields has not been thoroughly investigated.

PATIENTS AND METHODS

This study examined 29 patients (18 females; 15-58 years) diagnosed with HS who underwent surgery for drug-resistant epilepsy. FreeSurfer 7.1.1 was used in the processing of MRI data and 18 F-FDG PET scans to derive volumetric data and the FDG SUVr in the whole hippocampus and hippocampal subfields, including the CA1, CA2-4, granule cell and molecular layer of the dentate gyrus (GC-ML-DG), and subiculum. Asymmetries in the volume and SUVr between the 2 sides from the subfields of the hippocampus were defined in terms of an asymmetry index. Comparisons of the asymmetry index among these regions were performed. The correlations between asymmetry index values and postoperative outcomes and presurgical neuropsychological test results were also evaluated.

RESULT

The CA1, CA2-4, subiculum, GC-ML-DG, and whole hippocampus presented reductions in volume and hypometabolism ipsilateral to MTLE. Asymmetries in volume and SUVr were significantly less pronounced in the CA1 and subiculum than in the CA2-4 or GC-ML-DG. Postoperative seizure outcomes were not correlated with the asymmetry index for volume or SUVr in any hippocampal subfield. In cases of left MTLE, scores of immediate logical memory and delayed logical memory were positively correlated with the asymmetry index for SUVr in the following subfields: CA1 ( R = 0.829, P = 0.021; R = 0.770, P = 0.043), CA2-4 ( R = 0.825, P = 0.022; R = 0.894, P = 0.007), subiculum ( R = 0.882, P = 0.009; R = 0.853, P = 0.015), GC-ML-DG ( R = 0.850, P = 0.015; R = 0.796, P = 0.032), and whole hippocampus ( R = 0.841, P = 0.018; R = 0.822, P = 0.023). In cases of right MTLE, the scores for delayed face memory were positively correlated with the asymmetry index for SUVr in the subiculum ( R = 0.935, P = 0.006).

CONCLUSIONS

In cases of HS, changes in glucose metabolism levels varied among the hippocampal subfields. Asymmetries in glucose metabolism among the CA-1, CA2-4, subiculum, and GC-ML-DG subregions were correlated with scores for verbal memory among patients with left MTLE. Asymmetric glucose metabolism in the subiculum was also correlated with visual memory scores among patients with right MTLE.

Clinical Evaluation of a Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis

OBJECTIVE

To evaluate the influence of quantitative reports (QReports) on the radiological assessment of hippocampal sclerosis (HS) from MRI of patients with epilepsy in a setting mimicking clinical reality.

METHODS

The study included 40 patients with epilepsy, among them 20 with structural abnormalities in the mesial temporal lobe (13 with HS). Six raters blinded to the diagnosis assessed the 3T MRI in two rounds, first using MRI only and later with both MRI and the QReport. Results were evaluated using inter-rater agreement (Fleiss' kappa [Formula: see text]) and comparison with a consensus of two radiological experts derived from clinical and imaging data, including 7T MRI.

RESULTS

For the primary outcome, diagnosis of HS, the mean accuracy of the raters improved from 77.5% with MRI only to 86.3% with the additional QReport (effect size [Formula: see text]). Inter-rater agreement increased from [Formula: see text] to [Formula: see text]. Five of the six raters reached higher accuracies, and all reported higher confidence when using the QReports.

CONCLUSION

In this pre-use clinical evaluation study, we demonstrated clinical feasibility and usefulness as well as the potential impact of a previously suggested imaging biomarker for radiological assessment of HS.

Deep learning and radiomics based automatic diagnosis of hippocampal sclerosis

Accurate and rapid segmentation of the hippocampus can help doctors perform intractable temporal lobe epilepsy (TLE) preoperative evaluations to identify good surgical candidates. This study aims to establish a radiomics system for the automatic diagnosis of hippocampal sclerosis with the help of machine learning. A total of 240 cases were analysed to develop a diagnostic model. First, an automatic hippocampal segmentation process was established that exploits a priori knowledge of the relatively fixed location of the hippocampus in brain partitions, as well as a deep-learning segmentation network based on an Attention U-net. Then, we extracted 527 radiomics features from each side of the segmented hippocampus. The iterative sparse representation based on feature selection and a support vector machine classifier were finally used to establish the diagnostic model of hippocampal sclerosis. The diagnostic model consists of two consecutive steps: distinguish hippocampal sclerosis (HS) from normal control (NC) and detect whether the HS is located on the left or right side. When the automatic diagnosis model identified HS and NC, the sensitivity and specificity reached 0.941 and 0.917 in the 10-fold cross-validation set and 0.920 and 0.909 in the independent testing set. When the diagnostic model detected HS lateralization, the sensitivity and specificity reached 0.923 and 0.920 in cross-validation and 0.909 and 0.929 in independent testing. Our results show that the developed radiomics model can help detect TLE patients with hippocampal sclerosis and has the potential to simplify preoperative evaluations and select surgical candidates.

Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation

In the last decade, the exclusive role of the hippocampus in human declarative learning has been challenged. Recently, we have shown that gains in performance observed in motor sequence learning (MSL) during the quiet rest periods interleaved with practice are associated with increased hippocampal activity, suggesting a role of this structure in motor memory reactivation. Yet, skill also develops offline as memory stabilizes after training and overnight. To examine whether the hippocampus contributes to motor sequence memory consolidation, here we used a network neuroscience strategy to track its functional connectivity offline 30 min and 24 h post learning using resting-state functional magnetic resonance imaging. Using a graph-analytical approach we found that MSL transiently increased network modularity, reflected in an increment in local information processing at 30 min that returned to baseline at 24 h. Within the same time window, MSL decreased the connectivity of a hippocampal-sensorimotor network, and increased the connectivity of a striatal-premotor network in an antagonistic manner. Finally, a supervised classification identified a low-dimensional pattern of hippocampal connectivity that discriminated between control and MSL data with high accuracy. The fact that changes in hippocampal connectivity were detected shortly after training supports a relevant role of the hippocampus in early stages of motor memory consolidation.

Incomplete hippocampal inversion in patients with mutations in genes involved in sonic hedgehog signaling

Sonic hedgehog (Shh) signaling pathways are known to play an important role in the morphological development of the hippocampus in vivo, but their actual roles in humans have not been clarified. Hypothalamic hamartoma (HH) is known to be associated with germline or somatic gene mutations of Shh signaling. We hypothesized that patients with HH and mutations of Shh-related genes also show hippocampal maldevelopment and an abnormal hippocampal infolding angle (HIA). We analyzed 45 patients (age: 1-37 years) with HH who underwent stereotactic radiofrequency thermocoagulation and found Shh-related gene mutations in 20 patients. In addition, 44 pediatric patients without HH (age: 2-25 years) who underwent magnetic resonance imaging (MRI) examinations under the same conditions during the same period were included in this study as a control group. HIA evaluated on MRI was compared between patients with gene mutations and the control group. The median HIA at the cerebral peduncle slice in patients with the gene mutation was 74.36° on the left and 76.11° on the right, and these values were significantly smaller than the corresponding values in the control group (80.46° and 80.56°, respectively, p < 0.01). Thus, mutations of Shh-related genes were correlated to incomplete hippocampal inversion. The HIA, particularly at the cerebral peduncle slice, is a potential indicator of abnormalities of the Shh-signaling pathway.

Mapping hippocampal glutamate in mesial temporal lobe epilepsy with glutamate weighted CEST (GluCEST) imaging

Temporal lobe epilepsy (TLE) is one of the most common subtypes of focal epilepsy, with mesial temporal sclerosis (MTS) being a common radiological and histopathological finding. Accurate identification of MTS during presurgical evaluation confers an increased chance of good surgical outcome. Here we propose the use of glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) at 7 Tesla for mapping hippocampal glutamate distribution in epilepsy, allowing to differentiate lesional from non-lesional mesial TLE. We demonstrate that a directional asymmetry index, which quantifies the relative difference between GluCEST contrast in hippocampi ipsilateral and contralateral to the seizure onset zone, can differentiate between sclerotic and non-sclerotic hippocampi, even in instances where traditional presurgical MRI assessments did not provide evidence of sclerosis. Overall, our results suggest that hippocampal glutamate mapping through GluCEST imaging is a valuable addition to the presurgical epilepsy evaluation toolbox.

Epilepsy in Pediatric Patients—Evaluation of Brain Structures’ Volume Using VolBrain Software

Epilepsy is one of the most frequent serious brain disorders. Approximately 30,000 of the 150,000 children and adolescents who experience unprovoked seizures are diagnosed with epilepsy each year. Magnetic resonance imaging is the method of choice in diagnosing and monitoring patients with this condition. However, one very effective tool using MR images is volBrain software, which automatically generates information about the volume of brain structures. A total of 57 consecutive patients (study group) suffering from epilepsy and 34 healthy patients (control group) who underwent MR examination qualified for the study. Images were then evaluated by volBrain. Results showed atrophy of the brain and particular structures—GM, cerebrum, cerebellum, brainstem, putamen, thalamus, hippocampus and nucleus accumbens volume. Moreover, the statistically significant difference in the volume between the study and the control group was found for brain, lateral ventricle and putamen. A volumetric analysis of the CNS in children with epilepsy confirms a decrease in the volume of brain tissue. A volumetric assessment of brain structures based on MR data has the potential to be a useful diagnostic tool in children with epilepsy and can be implemented in clinical work; however, further studies are necessary to enhance the effectiveness of this software.