MRI-negative PET-positive temporal lobe epilepsy (TLE) and mesial TLE differ with quantitative MRI and PET: a case control study

Background: 'MRI negative PET positive temporal lobe epilepsy' represents a substantial minority of temporal lobe epilepsy (TLE). Clinicopathological and qualitative imaging differences from mesial temporal lobe epilepsy are reported. We aimed to compare TLE with hippocampal sclerosis (HS+ve) and non lesional TLE without HS (HS-ve) on MRI, with respect to quantitative FDG-PET and MRI measures.

Methods: 30 consecutive HS-ve patients with well-lateralised EEG were compared with 30 age- and sex-matched HS+ve patients with well-lateralised EEG. Cerebral, cortical lobar and hippocampal volumetric and co-registered FDG-PET metabolic analyses were performed.

Results: There was no difference in whole brain, cerebral or cerebral cortical volumes. Both groups showed marginally smaller cerebral volumes ipsilateral to epileptogenic side (HS-ve 0.99, p = 0.02, HS+ve 0.98, p < 0.001). In HS+ve, the ratio of epileptogenic cerebrum to whole brain volume was less (p = 0.02); the ratio of epileptogenic cerebral cortex to whole brain in the HS+ve group approached significance (p = 0.06). Relative volume deficits were seen in HS+ve in insular and temporal lobes. Both groups showed marked ipsilateral hypometabolism (p < 0.001), most marked in temporal cortex. Mean hypointensity was more marked in epileptogenic-to-contralateral hippocampus in HS+ve (ratio: 0.86 vs 0.95, p < 0.001). The mean FDG-PET ratio of ipsilateral to contralateral cerebral cortex however was low in both groups (ratio: HS-ve 0.97, p < 0.0001; HS+ve 0.98, p = 0.003), and more marked in HS-ve across all lobes except insula.

Conclusion: Overall, HS+ve patients showed more hippocampal, but also marginally more ipsilateral cerebral and cerebrocortical atrophy, greater ipsilateral hippocampal hypometabolism but similar ipsilateral cerebral cortical hypometabolism, confirming structural and functional differences between these groups.

Hippocampal deformation mapping in MRI-negative PET-positive temporal lobe epilepsy

Objectives: To compare hippocampal surface structure, using large deformation high dimensional mapping (HDM-LD), in subjects with temporal lobe epilepsy (TLE) with (HS+ve) and without (HS−ve) hippocampal sclerosis.

Methods: The study included 30 HS−ve subjects matched with 30 HS+ve subjects from the previously reported epilepsy patient cohort. To control for normal right–left asymmetries of hippocampal surface structure, subjects were regrouped based on laterality of onset of epileptic seizures and presence of HS. Gender ratio, age, duration of epilepsy and seizure frequency were calculated for each of the four groups. Final HDM-LD surface maps of the right and left TLE groups were compared to define differences in subregional hippocampal involvement within the groups.

Results: There were no significant differences in comparisons of the left TLE (left HS−ve compared with HS+ve) or right TLE (right HS−ve compared with HS+ve) groups with respect to age, duration of epilepsy or seizure severity scores. HDM-LD maps showed accentuated surface changes over the lateral hippocampal surface, in the region of the Sommer sector, in the hippocampi affected by HS. However, HS−ve hippocampi showed maximal surface changes in a different pattern, and did not involve the region of Sommer sector.

Conclusion: We conclude that differences in segmental volume loss between the HS−ve and HS+ve groups are suggestive that the underlying pathophysiology of hippocampal changes in the two groups is different, and not related to chronic seizure duration or severity.

Cerebral cortex : an MRI-based study of volume and variance with age and sex

The aim of the present study was to examine quantitative differences in lobar cerebral cortical volumes in a healthy adult population. Quantitative volumetric MRI of whole brain, cerebral and cerebellar volumes was performed in a cross-sectional analysis of 97 normal volunteers, with segmented frontal, temporal, parietal and occipital cortical volumes measured in a subgroup of 60 subjects, 30 male and 30 female, matched for age and sex. The right cerebral hemisphere was larger than the left across the study group with a small (<1%) but significant difference in symmetry (P < 0.001). No difference was found between volumes of right and left cerebellar hemispheres. Rightward cerebral cortical asymmetry (right larger than left) was found to be significant across all lobes except parietal. Males had greater cerebral, cerebellar and cerebral cortical lobar volumes than females. Larger male cerebral cortical volumes were seen in all lobes except for left parietal. Females had greater left parietal to left cerebral hemisphere and smaller left temporal to left cerebral hemisphere ratios. There was a mild reduction in cerebral volumes with age, more marked in males. This study confirms and augments past work indicating underlying structural asymmetries in the human brain, and provides further evidence that brain structures in humans are differentially sensitive to the effects of both age and sex.

Maximum-likelihood based estimation for Q-ball imaging using diffusion-weighted MRI data

Q-ball imaging has been presented to reconstruct diffusion orientation distribution function using diffusion weighted MRI. In this thesiis, we present a novel and robust approach to satisfy the smoothness constraint required in Q-ball imaging. Moreover, we developed an improved estimator based on the actual distribution of the MR data.

Brain MRI thresholding using incomparability and overlap functions

In this work we present a new image thresholding algorithm for the segmentation of MRI brain images into two classes: gray matter and white matter. The proposed algorithm is based on the concept of incomparability proposed by Fodor and Roubens for fuzzy preference relations. We test our algorithm for local and global segmentation of brain images. We proof that global segmentation performs better results than local segmentation and improves the results obtained by other thresholding algorithm.

Emerging engineered magnetic nanoparticulate probes for molecular MRI of atherosclerosis : how far have we come?

Atherosclerosis is a chronic, progressive, immunoinflammatory disease of the large and medium-sized arteries, and a major cause of cardiovascular diseases. Atherosclerosis often progresses silently for decades until the occurrence of a major catastrophic clinical event such as myocardial infarction, cardiac arrest and stroke. The main challenge in the diagnosis and management of atherosclerosis is to develop a safe, noninvasive technique that is accurate and reproducible, which can detect the biologically active high-risk vulnerable plaques (with ongoing active inflammation, angiogenesis and apoptosis) before the occurrence of an acute clinical event. This Journal Article reviews the events involved in the pathogenesis of atherosclerosis in light of recently advanced understanding of the molecular pathogenesis of the disease. Next, we elaborate on the interesting developments in molecular MRI, by describing the recently engineered magnetic nanoparticulate probes targeting clinically promising molecular and cellular players/processes, involved in early atherosclerotic lesion formation to plaque rupture and erosion.

Non-uniform sparsity in rapid compressive sensing MRI

Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This process is time-consuming and can take several minutes to acquire one image. The aim of this research is to reduce the imaging process time of MRI. This issue is addressed by reducing the number of acquired measurements using theory of Compressive Sensing (CS). Compressive Sensing exploits sparsity in MR images. Randomly under sampled k-space generates incoherent noise which can be handled using a nonlinear image reconstruction method. In this paper, a new framework is presented based on the idea to exploit non-uniform nature of sparsity in MR images, where local sparsity constrains were used instead of traditional global constraint, to further reduce the sample set. Experimental results and comparison with CS using global constraint are demonstrated.

Defining sub-regions in locally sparsified compressive sensing MRI

Magnetic Resonance Imaging (MRI) is an important imaging technique. However, it is a time consuming process. The aim of this study is to make the imaging process ef?cient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsi?ed Compressed Sensing is a specialized case of CS which sub-divides the image and sparsi?es each region separately; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for de?ning the size and locality of sub-regions in image. Experiments were done on the regions de?ned by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper.

Investigating machine learning techniques for detection of depression using structural MRI volumetric features

Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.