A novel geometry-based analysis of hippocampal morphometry in mesial temporal lobe epilepsy.

Hippocampal volumetry is an essential tool in researching and diagnosing mesial temporal lobe epilepsy (mTLE). However, it has a limited ability to detect subtle alterations in hippocampal morphometry. Here, we establish and apply a novel geometry-based tool that enables point-wise morphometric analysis based on an intrinsic coordinate system of the hippocampus. We hypothesized that this point-wise analysis uncovers structural alterations not measurable by volumetry, but associated with histological underpinnings and the neuropsychological profile of mTLE. We conducted a retrospective study in 204 individuals with mTLE and 57 age- and gender-matched healthy subjects. FreeSurfer-based segmentations of hippocampal subfields in 3T-MRI were subjected to a geometry-based analysis that resulted in a coordinate system of the hippocampal mid-surface and allowed for point-wise measurements of hippocampal thickness and other features. Using point-wise analysis, we found significantly lower thickness and higher FLAIR signal intensity in the entire affected hippocampus of individuals with hippocampal sclerosis (HS-mTLE). In the contralateral hippocampus of HS-mTLE and the affected hippocampus of MRI-negative mTLE, we observed significantly lower thickness in the presubiculum. Impaired verbal memory was associated with lower thickness in the left presubiculum. In HS-mTLE histological subtype 3, we observed higher curvature than in subtypes 1 and 2 (all p < .05). These findings could not be observed using conventional volumetry (Bonferroni-corrected p < .05). We show that point-wise measures of hippocampal morphometry can uncover structural alterations not measurable by volumetry while also reflecting histological underpinnings and verbal memory. This substantiates the prospect of their clinical application.

Shape description and volumetry of hippocampus and amygdala in temporal lobe epilepsy - A beneficial combination with a clinical perspective.

Shape-based markers have entered the field of morphometric neuroimaging analysis as a second mainstay alongside conventional volumetric approaches. We aimed to assess the added value of shape description for the analysis of lesional and autoimmune temporal lobe epilepsy (TLE) focusing on hippocampus and amygdala. We retrospectively investigated MRI and clinical data from 65 patients with lesional TLE (hippocampal sclerosis (HS) and astrogliosis) and from 62 patients with limbic encephalitis (LE) with serologically proven autoantibodies. Surface reconstruction and volumetric segmentation were performed with FreeSurfer. For the shape analysis, we used BrainPrint, a tool that utilizes eigenvalues of the Laplace-Beltrami operator on triangular meshes to calculate intra-subject asymmetry. Psychometric tests of memory performance were ascertained, to evaluate clinical relevance of the shape descriptor. The potential benefit of shape in addition to volumetric information for classification was assessed by five-fold repeated cross validation and logistic regression. For the LE group, the best performing classification model consisted of a combination of volume and shape asymmetry (mean AUC = 0.728), the logistic regression model was significantly improved considering both modalities instead of just volume asymmetry. For lesional TLE, the best model only considered volumetric information (mean AUC = 0.867). Shape asymmetry of the hippocampus was largely associated with verbal memory performance only in LE patients (OR = 1.07, p = 0.02). For lesional TLE, shape description is robust, but redundant when compared to volumetric approaches. For LE, in contrast, shape asymmetry as a complementary modality significantly improves the detection of subtle morphometric changes and is further associated with memory performance, which underscores the clinical relevance of shape asymmetry as a novel imaging biomarker.

An open presurgery MRI dataset of people with epilepsy and focal cortical dysplasia type II.

Automated detection of lesions using artificial intelligence creates new standards in medical imaging. For people with epilepsy, automated detection of focal cortical dysplasias (FCDs) is widely used because subtle FCDs often escape conventional neuroradiological diagnosis. Accurate recognition of FCDs, however, is of outstanding importance for affected people, as surgical resection of the dysplastic cortex is associated with a high chance of postsurgical seizure freedom. Here, we make publicly available a dataset of 85 people affected by epilepsy due to FCD type II and 85 healthy control persons. We publish 3D-T1 and 3D-FLAIR, manually labeled regions of interest, and carefully selected clinical features. The open presurgery MRI dataset may be used to validate existing automated algorithms of FCD detection as well as to create new approaches. Most importantly, it will enable comparability of already existing approaches and support a more widespread use of automated lesion detection tools.