The role of the temporal pole in temporal lobe epilepsy: A diffusion kurtosis imaging study

This study aimed to evaluate the use of diffusion kurtosis imaging (DKI) to detect microstructural abnormalities within the temporal pole (TP) and its temporopolar cortex in temporal lobe epilepsy (TLE) patients. DKI quantitative maps were obtained from fourteen lesional TLE and ten non-lesional TLE patients, along with twenty-three healthy controls. Data collected included mean (MK); radial (RK) and axial kurtosis (AK); mean diffusivity (MD) and axonal water fraction (AWF). Automated fiber quantification (AFQ) was used to quantify DKI measurements along the inferior longitudinal (ILF) and uncinate fasciculus (Unc). ILF and Unc tract profiles were compared between groups and tested for correlation with disease duration. To characterize temporopolar cortex microstructure, DKI maps were sampled at varying depths from superficial white matter (WM) towards the pial surface. Patients were separated according to the temporal lobe ipsilateral to seizure onset and their AFQ results were used as input for statistical analyses. Significant differences were observed between lesional TLE and controls, towards the most temporopolar segment of ILF and Unc proximal to the TP within the ipsilateral temporal lobe in left TLE patients for MK, RK, AWF and MD. No significant changes were observed with DKI maps in the non-lesional TLE group. DKI measurements correlated with disease duration, mostly towards the temporopolar segments of the WM bundles. Stronger differences in MK, RK and AWF within the temporopolar cortex were observed in the lesional TLE and noticeable differences (except for MD) in non-lesional TLE groups compared to controls. This study demonstrates that DKI has potential to detect subtle microstructural alterations within the temporopolar segments of the ILF and Unc and the connected temporopolar cortex in TLE patients including non-lesional TLE subjects. This could aid our understanding of the extrahippocampal areas, more specifically the temporal pole role in seizure generation in TLE and might inform surgical planning, leading to better seizure outcomes

Diffusion Kurtosis Imaging in Temporal Lobe Epilepsy

Epilepsy constitutes one of the most common neurological clinicopathological entities affecting approximately 1% of the general population. Temporal lobe epilepsy (TLE) represents by far the most common form of medically intractable focal epilepsy in adults. Surgical resection is the common form of treatment when lesions are clearly delineated, either from patient’s magnetic resonance imaging (MRI) structural scans or by invasive seizure monitoring techniques (e.g., intracranial EEG) for patients with non-lesional MRI scans. Increasing numbers of studies have suggested that TLE is more of a network disorder, therefore full delineation of pathological tissue is difficult resulting in incomplete resection, possibly contributing to long-term recurrence of seizures after surgery. Diffusion MRI, an advanced MRI technique that is sensitive to the tissue at the microstructural level, has been studied, hoping to detect subtle microstructural changes related to TLE. In this thesis, we investigated the ability of a diffusion MRI model, called diffusion kurtosis imaging, (DKI) to quantify TLE patients brain microstructure. Each chapter discusses the method developed to accomplish this, beginning with Chapter 1 giving the general background and the motivation behind this thesis. Chapter 2 develops a method of assessing the reproducibility in whole-brain high-resolution DKI at varying b-values. A shorter protocol was identified with comparable precision as the protocol with three b-values, supporting DKI for aiding clinical tools to assess brain tissue microstructure. Chapter 3 focuses on identifying microstructural abnormalities in the white matter (WM) and grey matter (GM) of the temporal pole, a region underappreciated in TLE patients. The method developed combining DKI measurements and tract-specific analysis uncovered temporal pole microstructural abnormalities in TLE patients (includes non-lesional TLE patients) compared to healthy controls. The work described in Chapter 4 explores a machine learning approach to laterialize TLE patients, demonstrating that DKI-based classifiers obtained slight increase in their general accuracy for GM region. Finally, Chapter 5 discusses the contributions of the thesis and provide suggestions for future research