Mapping The Structural Connectome of Temporal Lobe Epilepsy Variants to Improve Surgical Outcomes
Poster No:
1793
Submission Type:
Abstract Submission
Authors:
Emna Guibene1,2, Émile Lemoine1,2,3, Maxime Descoteaux4, François Rheault5, Arash Sarshoghi1,2, Dang Nguyen1,2,3, Guillaume Theaud1, Sami Obaid1,3,6
Institutions:
1University of Montreal Hospital Center Research Center (CRCHUM), Montreal, Canada, 2Department of Neurosciences, Faculty of Medicine, University of Montreal, Montreal, Canada, 3Department of Neurology, University of Montreal Hospital Center (CHUM), Montreal, Canada, 4Sherbrooke Connectivity Imaging Lab (SCIL), University of Sherbrooke, Sherbrooke, Canada, 5Medical Imaging and Neuroimaging (MINi) Lab, University of Sherbrooke, Sherbrooke, Canda, 6Department of Surgery, Faculty of Medicine, University of Montreal, Montreal, Canada
First Author:
Emna Guibene
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurosciences, Faculty of Medicine, University of Montreal
Montreal, Canada|Montreal, Canada
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurosciences, Faculty of Medicine, University of Montreal
Montreal, Canada|Montreal, Canada
Co-Author(s):
Émile Lemoine
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurosciences, Faculty of Medicine, University of Montreal|Department of Neurology, University of Montreal Hospital Center (CHUM)
Montreal, Canada|Montreal, Canada|Montreal, Canada
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurosciences, Faculty of Medicine, University of Montreal|Department of Neurology, University of Montreal Hospital Center (CHUM)
Montreal, Canada|Montreal, Canada|Montreal, Canada
Maxime Descoteaux
Sherbrooke Connectivity Imaging Lab (SCIL), University of Sherbrooke
Sherbrooke, Canada
Sherbrooke Connectivity Imaging Lab (SCIL), University of Sherbrooke
Sherbrooke, Canada
François Rheault
Medical Imaging and Neuroimaging (MINi) Lab, University of Sherbrooke
Sherbrooke, Canda
Medical Imaging and Neuroimaging (MINi) Lab, University of Sherbrooke
Sherbrooke, Canda
Arash Sarshoghi
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurosciences, Faculty of Medicine, University of Montreal
Montreal, Canada|Montreal, Canada
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurosciences, Faculty of Medicine, University of Montreal
Montreal, Canada|Montreal, Canada
Dang Nguyen
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurosciences, Faculty of Medicine, University of Montreal|Department of Neurology, University of Montreal Hospital Center (CHUM)
Montreal, Canada|Montreal, Canada|Montreal, Canada
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurosciences, Faculty of Medicine, University of Montreal|Department of Neurology, University of Montreal Hospital Center (CHUM)
Montreal, Canada|Montreal, Canada|Montreal, Canada
Sami Obaid
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurology, University of Montreal Hospital Center (CHUM)|Department of Surgery, Faculty of Medicine, University of Montreal
Montreal, Canada|Montreal, Canada|Montreal, Canada
University of Montreal Hospital Center Research Center (CRCHUM)|Department of Neurology, University of Montreal Hospital Center (CHUM)|Department of Surgery, Faculty of Medicine, University of Montreal
Montreal, Canada|Montreal, Canada|Montreal, Canada
Introduction:
Epilepsy is a neurological disorder affecting ~70M people globally. Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy, with one-third of patients remaining resistant to anti-seizure medications (Harroud, 2012). For these patients, surgery is a viable option, but it fails to control seizures in ~30% of cases (Harroud, 2012). This failure may be due to the presence of an epileptogenic network that extends beyond the temporal lobe (TL), encompassing the contralateral TL (bitemporal epilepsy – BTE) or extratemporal regions (temporal plus epilepsy – TPE) (Bernhardt, 2015). Identifying and delineating these complex networks, with the goal of better selecting surgical candidates (i.e. those with epileptic networks restricted to one TL, namely unilateral TLE), remains a major challenge using conventional diagnostic tests. However, with diffusion MRI tractography, we can now map non-invasively epileptic networks. This approach could provide structural insights to accurately identify epileptogenic zones and potentially help streamline patients for surgery (Obaid, 2021).
Methods:
This study included 27 TLE, 12 BTE, 15 TPE patients and 49 healthy controls (HC) who underwent a standardized 3T MRI protocol, including T1-weighted and diffusion-weighted imaging sequences. Raw images were processed using TractoFlow (Theaud, 2020) to generate tractograms (Figure 1). Surface-Enhanced Tractography (SET) was used to optimize cortical coverage (St-Onge, 2018), and SET-derived tractograms were processed with Connectoflow (Rheault, 2021) to create connectivity matrices weighted by Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT), a measure of microstructural connectivity strength (Daducci, 2015) (Figure 1). To ensure uniformity in the analysis, all connectivity matrices from TLE and TPE patients with left-sided epileptogenic zones were side-flipped to the right hemisphere. Group comparisons of COMMIT-weighted connectivity matrices were conducted using two-sample t-tests with a false discovery rate-corrected threshold of p < 0.05. Local graph theory measures – including nodal strength (NS), betweenness centrality (BC), clustering coefficient (CC), path length, and local efficiency (LE) (Rubinov & Sporns, 2010) – were then computed and analyzed using the same test and statistical thresholds to identify altered network features.
Results:
Comparisons revealed significant connectivity differences for all epilepsy groups relative to HC, with TPE and BTE showing more extensive alterations than TLE. BTE exhibited stronger overall connectivity than TLE. Restricting BTE vs. TLE comparisons to the TL revealed two significantly increased connections – one on the left TL and one interhemispheric – reflecting a more widespread temporal network in BTE involving both TLs. While whole-brain comparisons between TPE and TLE showed no differences in network connectivity, analyses of the right hemisphere identified one significant connection in TPE, namely the connection adjoining the medial amygdala and rostral thalamus (Figure 2).
Graph theoretical analyses revealed significant alterations in BTE compared to TLE, characterized by increased BC and NS notably in the left TL and bilateral limbic regions, as well as higher CC and LE in the left TL. In contrast, TPE vs. TLE comparisons showed changes in bilateral subcortical-limbic regions and association cortices, reflected by an increase in BC, NS, CC and LE, indicating distinct network reorganization outside the TL. These findings underscore the more localized disturbances in TLE compared to the more widespread connectivity changes observed in BTE and TPE.
Graph theoretical analyses revealed significant alterations in BTE compared to TLE, characterized by increased BC and NS notably in the left TL and bilateral limbic regions, as well as higher CC and LE in the left TL. In contrast, TPE vs. TLE comparisons showed changes in bilateral subcortical-limbic regions and association cortices, reflected by an increase in BC, NS, CC and LE, indicating distinct network reorganization outside the TL. These findings underscore the more localized disturbances in TLE compared to the more widespread connectivity changes observed in BTE and TPE.
Conclusions:
This work highlights potential structural signatures of TLE, BTE and TPE that could assist in non-invasively differentiating them, aiming to improve the selection of surgical candidates and postoperative seizure outcomes.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Neuroinformatics and Data Sharing:
Workflows 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Computing
Data analysis
DISORDERS
Epilepsy
STRUCTURAL MRI
Tractography
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Diagnostic method
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Daducci, A. (2015). COMMIT: Convex optimization modeling for microstructure informed tractography. IEEE Transactions on Medical Imaging, 34(1), 246–257. https://doi.org/10.1109/TMI.2014.2352414
Harroud, A. (2012). Temporal lobe epilepsy surgery failures: A review. Epilepsy Research and Treatment, 2012, 201651. https://doi.org/10.1155/2012/201651
Obaid, S. (2021). Structural connectivity alterations in operculo-insular epilepsy. Brain Sciences, 11(8), 1041. http://dx.doi.org/10.3390/brainsci11081041
Rheault, F. (2021). Connectoflow: A cutting-edge Nextflow pipeline for structural connectomics. ISMRM & SMRT Annual Meeting & Exhibition. https://archive.ismrm.org/2021/4301.html
Rubinov, M. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Stafstrom, C.E. (2015). Seizures and epilepsy: An overview for neuroscientists. Cold Spring Harbor Perspectives in Medicine, 5(6), a022426. https://doi.org/10.1101/cshperspect.a022426
St-Onge, E. (2018). Surface-enhanced tractography (SET). NeuroImage, 169, 524–539. https://doi.org/10.1016/j.neuroimage.2017.12.036
Theaud, G. (2020). TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow and Singularity. NeuroImage, 218, 116889. https://doi.org/10.1016/j.neuroimage.2020.116889