The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Neurosurgical Applications of Magnetic Resonance Diffusion Tensor Imaging

Magnetic Resonance (MR) Diffusion Tensor Imaging (DTI) is a rapidly evolving technology that enables the visualization of neural fiber bundles, or white matter (WM) tracts. There are numerous neurosurgical applications for MR DTI including: (1) Tumor grading and staging; (2) Pre-surgical planning (determination of resectability, determination of surgical approach, identification of WM tracts at risk); (3) Intraoperative navigation (tumor resection that spares WM damage, epilepsy resection that spares WM damage, accurate location of deep brain stimulation structures); (4) Post-operative assessment and monitoring (identification of WM damage, identification of tumor recurrence). Limitations of MR DTI include difficulty tracking small and crossing WM tracts, lack of standardized data acquisition and post-processing techniques, and practical equipment, software, and timing considerations. Overall, MR DTI is a useful tool for planning, performing, and following neurosurgical procedures, and has the potential to significantly improve patient care. Technological improvements and increased familiarity with DTI among clinicians are next steps

The role of preoperative diffusion tensor imaging in predicting and improving functional outcome in pediatric patients undergoing epilepsy surgery: a systematic review

Objective: Diffusion tensor imaging (DTI) is a useful neuroimaging technique for surgical planning in adult patients. However, no systematic review has been conducted to determine its utility for pre-operative analysis and planning of Pediatric Epilepsy surgery. We sought to determine the benefit of pre-operative DTI in predicting and improving neurological functional outcome after epilepsy surgery in children with intractable epilepsy. Methods: A systematic review of articles in English using PubMed, EMBASE and Scopus databases, from inception to January 10, 2020 was conducted. All studies that used DTI as either predictor or direct influencer of functional neurological outcome (motor, sensory, language and/or visual) in pediatric epilepsy surgical candidates were included. Data extraction was performed by two blinded reviewers. Risk of bias of each study was determined using the QUADAS 2 Scoring System. Results: 13 studies were included (6 case reports/series, 5 retrospective cohorts, and 2 prospective cohorts) with a total of 229 patients. Seven studies reported motor outcome; three reported motor outcome prediction with a sensitivity and specificity ranging from 80 to 85.7 and 69.6 to 100%, respectively; four studies reported visual outcome. In general, the use of DTI was associated with a high degree of favorable neurological outcomes after epilepsy surgery. Conclusion: Multiple studies show that DTI helps to create a tailored plan that results in improved functional outcome. However, more studies are required in order to fully assess its utility in pediatric patients. This is a desirable field of study because DTI offers a non-invasive technique more suitable for children. Advances in knowledge: This systematic review analyses, exclusively, studies of pediatric patients with drug-resistant epilepsy and provides an update of the evidence regarding the role of DTI, as part of the pre-operative armamentarium, in improving post-surgical neurological sequels and its potential for outcome prediction

Optic radiation structure and anatomy in the normally developing brain determined using diffusion MRI and tractography

The optic radiation (OR) is a component of the visual system known to be myelin mature very early in life. Diffusion tensor imaging (DTI) and its unique ability to reconstruct the OR in vivo were used to study structural maturation through analysis of DTI metrics in a cohort of 90 children aged 5–18 years. As the OR is at risk of damage during epilepsy surgery, we measured its position relative to characteristic anatomical landmarks. Anatomical distances, DTI metrics and volume of the OR were investigated for age, gender and hemisphere effects. We observed changes in DTI metrics with age comparable to known trajectories in other white matter tracts. Left lateralization of DTI metrics was observed that showed a gender effect in lateralization. Sexual dimorphism of DTI metrics in the right hemisphere was also found. With respect to OR dimensions, volume was shown to be right lateralised and sexual dimorphism demonstrated for the extent of the left OR. The anatomical results presented for the OR have potentially important applications for neurosurgical planning

Optic radiation tractography and vision in anterior temporal lobe resection.

Anterior temporal lobe resection (ATLR) is an effective treatment for refractory temporal lobe epilepsy but may result in a contralateral superior visual field deficit (VFD) that precludes driving in the seizure-free patient. Diffusion tensor imaging (DTI) tractography can delineate the optic radiation preoperatively and stratify risk. It would be advantageous to incorporate display of tracts into interventional magnetic resonance imaging (MRI) to guide surgery

Meyer's loop tractography for image-guided surgery depends on imaging protocol and hardware

Introduction Surgical resection is an effective treatment for temporal lobe epilepsy but can result in visual field defects. This could be minimized if surgeons knew the exact location of the anterior part of the optic radiation (OR), the Meyer's loop. To this end, there is increasing prevalence of image-guided surgery using diffusion MRI tractography. Despite considerable effort in developing analysis methods, a wide discrepancy in Meyer's loop reconstructions is observed in the literature. Moreover, the impact of differences in image acquisition on Meyer's loop tractography remains unclear. Here, while employing the same state-of-the-art analysis protocol, we explored the extent to which variance in data acquisition leads to variance in OR reconstruction. Methods Diffusion MRI data were acquired for the same thirteen healthy subjects using standard and state-of-the-art protocols on three scanners with different maximum gradient amplitudes (MGA): Siemens Connectom (MGA = 300 mT/m); Siemens Prisma (MGA = 80 mT/m) and GE Excite-HD (MGA = 40 mT/m). Meyer's loop was reconstructed on all subjects and its distance to the temporal pole (ML-TP) was compared across protocols. Results A significant effect of data acquisition on the ML-TP distance was observed between protocols (p < .01 to 0.0001). The biggest inter-acquisition discrepancy for the same subject across different protocols was 16.5 mm (mean: 9.4 mm, range: 3.7–16.5 mm). Conclusion We showed that variance in data acquisition leads to substantive variance in OR tractography. This has direct implications for neurosurgical planning, where part of the OR is at risk due to an under-estimation of its location using conventional acquisition protocols

Imaging language pathways predicts postoperative naming deficits

Naming difficulties are a well recognised, but difficult to predict, complication of anterior temporal lobe resection (ATLR) for refractory epilepsy. We used MR tractography preoperatively to demonstrate the structural connectivity of language areas in patients undergoing dominant hemisphere ATLR. Greater lateralisation of tracts to the dominant hemisphere was associated with greater decline in naming function. We suggest that this method has the potential to predict language deficits in patients undergoing ATLR

Advanced neuroimaging techniques in epilepsy

PURPOSE OF REVIEW: We review significant advances in epilepsy imaging in recent years. RECENT FINDINGS: Structural MRI at 7T with optimization of acquisition and postacquisition image processing increases the diagnostic yield but artefactual findings remain a challenge. MRI analysis from multiple sites indicates different atrophy patterns and white matter diffusion abnormalities in temporal lobe and generalized epilepsies, with greater abnormalities close to the presumed seizure source. Structural and functional connectivity relate to seizure spread and generalization; longitudinal studies are needed to clarify the causal relationship of these associations. Diffusion MRI may help predict surgical outcome and network abnormalities extending beyond the epileptogenic zone. Three-dimensional multimodal imaging can increase the precision of epilepsy surgery, improve seizure outcome and reduce complications. Language and memory fMRI are useful predictors of postoperative deficits, and lead to risk minimization. FDG PET is useful for clinical studies and specific ligands probe the pathophysiology of neurochemical fluxes and receptor abnormalities. SUMMARY: Improved structural MRI increases detection of abnormalities that may underlie epilepsy. Diffusion, structural and functional MRI indicate the widespread associations of epilepsy syndromes. These can assist stratification of surgical outcome and minimize risk. PET has continued utility clinically and for research into the pathophysiology of epilepsies

Imaging Structural Connections of the Brain in Epilepsy

Introduction Temporal lobe epilepsy (TLE) is the most common cause of medically intractable partial epilepsy in adults. For many patients, anterior temporal lobe resection (ATLR) is an effective means of treatment, but can cause a significant decline in language or memory function, and visual field deficits. Diffusion tensor imaging and tractography is an MRI technique that can be used to probe white matter structure, and delineate the white matter tracts relevant to vision, language, and memory function. Aims We aimed to use diffusion MRI to increase understanding of the causes and consequences of TLE, and identify patients who are at risk of language, and visual impairment after surgery. Methods and Analysis Techniques Healthy controls, and patients with TLE were scanned pre- and post operatively using 3T MRI. All patients in the study underwent a comprehensive pre- and post-surgical evaluation including clinical, MRI, video-EEG, and neuropsychological assessment. Whole brain analysis of both pre-, and post-operative diffusion MRI was carried out. Tractography was used to assess white matter relevant to memory, language and vision. Correlation analysis of white matter data, and neuropsychological and clinical variables was carried out using the statistical software package, SPSS. Results and Discussion This thesis demonstrates the widespread changes in white matter microstructure present in patients with TLE, and the relationship between medial temporal lobe connections and memory function. It demonstrates how white matter microstructure changes after anterior temporal lobe resection, and how this information can be used to aid prediction of post-operative language deficits in patients. It concludes by showing that tractography can be used to predict postoperative visual field deficits. Conclusion Diffusion Mill can be used to increase our understanding of the causes and consequences of TLE, and to improve pre-surgical planning