Exploring ASL perfusion MRI as a substitutive modality for 18F-FDG PET in determining the laterality of mesial temporal lobe epilepsy.

OBJECTIVE: The aim of this investigation was to determine whether a correlation could be discerned between perfusion acquired through ASL MRI and metabolic data acquired via 18F-fluorodeoxyglucose (18F-FDG) PET in mesial temporal lobe epilepsy (mTLE). METHODS: ASL MRI and 18F-FDG PET data were gathered from 22 mTLE patients. Relative cerebral blood flow (rCBF) asymmetry index (AIs) were measured using ASL MRI, and standardized uptake value ratio (SUVr) maps were obtained from 18F-FDG PET, focusing on bilateral vascular territories and key bitemporal lobe structures (amygdala, hippocampus, and parahippocampus). Intra-group comparisons were carried out to detect hypoperfusion and hypometabolism between the left and right brain hemispheres for both rCBF and SUVr in right and left mTLE. Correlations between the two AIs computed for each modality were examined. RESULTS: Significant correlations were observed between rCBF and SUVr AIs in the middle temporal gyrus, superior temporal gyrus, and hippocampus. Significant correlations were also found in vascular territories of the distal posterior, intermediate anterior, intermediate middle, proximal anterior, and proximal middle cerebral arteries. Intra-group comparisons unveiled significant differences in rCBF and SUVr between the left and right brain hemispheres for right mTLE, while hypoperfusion and hypometabolism were infrequently observed in any intracranial region for left mTLE. CONCLUSION: The study's findings suggest promising concordance between hypometabolism estimated by 18F-FDG PET and hypoperfusion determined by ASL perfusion MRI. This raises the possibility that, with prospective technical enhancements, ASL perfusion MRI could be considered an alternative modality to 18F-FDG PET in the future.

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.

PURPOSE: Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE). METHODS: Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE. RESULTS: Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%. CONCLUSION: Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

Distinct patterns of hippocampal subfield volume loss in left and right mesial temporal lobe epilepsy.

OBJECTIVE: To investigate the pattern and severity of hippocampal subfield volume loss in patients with left and right mesial temporal lobe epilepsy (mTLE) using quantitative MRI volumetric analysis. METHODS: A total of 21 left and 14 right mTLE subjects, as well as 15 healthy controls, were enrolled in this cross-sectional study. A publically available magnetic resonance imaging (MRI) brain volumetry system (volBrain) was used for volumetric analysis of hippocampal subfields. The T1-weighted images were processed with a HIPS pipeline. RESULTS: A distinct pattern of hippocampal subfield atrophy was found between left and right mTLE patients when compared with controls. Patients with left mTLE exhibited ipsilateral hippocampal atrophy and segmental volume depletion of the Cornu Ammonis (CA) 2/CA3, CA4/dentate gyrus (DG), and strata radiatum-lacunosum-moleculare (SR-SL-SM). Those with right mTLE exhibited similar ipsilateral hippocampal atrophy but with additional segmental CA1 volume depletion. More extensive bilateral subfield volume loss was apparent with right mTLE patients. CONCLUSION: We demonstrate that left and right mTLE patients show a dissimilar pattern of hippocampal subfield atrophy, suggesting the pathophysiology of epileptogenesis in left and right mTLE to be different.

Dynamic causal modeling of reorganization of memory and language networks in temporal lobe epilepsy.

OBJECTIVE: To evaluate the alterations of language and memory functions using dynamic causal modeling, in order to identify the epileptogenic hemisphere in temporal lobe epilepsy (TLE). METHODS: Twenty-two patients with left TLE and 13 patients with right TLE underwent functional magnetic resonance imaging (fMRI) during four memory and four language mapping tasks. Dynamic causal modeling (DCM) was employed on fMRI data to examine effective directional connectivity in memory and language networks and the alterations in people with TLE compared to healthy individuals. RESULTS: DCM analysis suggested that TLE can influence the memory network more widely compared to the language network. For memory mapping, it demonstrated overall hyperconnectivity from the left hemisphere to the other cranial regions in the picture encoding, and from the right hemisphere to the other cranial regions in the word encoding tasks. On the contrary, overall hypoconnectivity was seen from the brain hemisphere contralateral to the seizure onset in the retrieval tasks. DCM analysis further manifested hypoconnectivity between the brain's hemispheres in the language network in patients with TLE compared to controls. The CANTAB® neuropsychological test revealed a negative correlation for the left TLE and a positive correlation for the right TLE cohorts for the connections extracted by DCM that were significantly different between the left and right TLE cohorts. INTERPRETATION: In this study, dynamic causal modeling evidenced the reorganization of language and memory networks in TLE that can be used for a better understanding of the effects of TLE on the brain's cognitive functions.

Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy.

OBJECTIVE: To investigate the application of graph theory with functional connectivity to distinguish left from right temporal lobe epilepsy (TLE). METHODS: Alterations in functional connectivity within several brain networks - default mode (DMN), attention (AN), limbic (LN), sensorimotor (SMN) and visual (VN) - were examined using resting-state functional MRI (rs-fMRI). The study accrued 21 left and 14 right TLE as well as 17 nonepileptic control subjects. The local nodal degree, a feature of graph theory, was calculated foreach of the brain networks. Multivariate logistic regression analysis was performed to determine the accuracy of identifying seizure laterality based on significant differences in local nodal degree in the selected networks. RESULTS: Left and right TLE patients showed dissimilar patterns of alteration in functional connectivity when compared to control subjects. Compared with right TLE, patients with left TLE exhibited greater nodal degree' (i.e. hyperconnectivity) with right superomedial frontal gyrus (in DMN), inferior frontal gyrus pars triangularis (in AN), right caudate and left superior temporal gyrus (in LN) and left paracentral lobule (in SMN), while showing lesser nodal degree (i.e. hypoconnectivity) with left temporal pole (in DMN), right insula (in LN), left supplementary motor area (in SMN), and left fusiform gyrus (in VN). The LN showed the highest accuracy of 82.9% among all considered networks in determining laterality of the TLE. By combinations of local degree attributes in the DMN, AN, LN, and VN, logistic regression analysis demonstrated an accuracy of 94.3% by comparison. CONCLUSION: Our study demonstrates the utility of graph theory application to brain network analysis as a potential biomarker to assist in the determination of TLE laterality and improve the confidence in presurgical decision-making in cases of TLE.

Evolution of Graph Theory in Dynamic Functional Connectivity for Lateralization of Temporal Lobe Epilepsy.

Resting-state functional magnetic resonance imaging (rsfMRI) has described the functional architecture of the human brain in the absence of any task or stimulus. Since the functional connectivity (FC), has non-stationary nature, it is evidenced to be varying over time. Using dynamic functional connectivity, six graph theoretical characteristics were measured and compared between left and right temporal lobe epilepsy (TLE). We also obtain a trend for each characteristic in the time course of experiments. The results demonstrated that the static connectivity analysis failed to fully separate the left and right TLE patients for some characteristics, whereby the dynamic analysis has been shown capable of identifying the laterality. Furthermore, the results suggest that the temporal trend of some graph theoretical characteristics can be exploited as a novel marker for TLE laterality.