Resting State Functional Connectivity in Bilateral Temporal Lobe Epilepsy
Abstract number :
2.198
Submission category :
5. Neuro Imaging / 5B. Functional Imaging
Year :
2022
Submission ID :
2204761
Source :
www.aesnet.org
Presentation date :
12/4/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:26 AM
Authors :
Alfredo Lucas, BS, MS – University of Pennsylvania, Perelman School of Medicine; Eli Cornblath, MD, PhD – Neurology – University of Pennsylvania, Perelman School of Medicine; Nishant Sinha, PhD – University of Pennsylvania, Perelman School of Medicine; Peter Hadar, MD, MSTR – Neurology – University of Pennsylvania, Perelman School of Medicine; Lorenzo Caciagli, MD, PhD – Bioengineering – University of Pennsylvania; Leonardo Bonilha, MD, PhD – Emory University; Simon Keller, PhD – University of Liverpool; Joel Stein, MD, PhD – Radiology – University of Pennsylvania, Perelman School of Medicine; Sandhitsu Das, PhD – Neurology – University of Pennsylvania, Perelman School of Medicine; Ezequiel Gleichgerrcht, MD, PhD – Neurology – The Medical University of South Carolina; Kathryn Davis, MD – Neurology – University of Pennsylvania, Perelman School of Medicine
This abstract has been invited to present during the Broadening Representation Inclusion and Diversity by Growing Equity (BRIDGE) poster session
Rationale: Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy, characterized by seizures originating from the temporal lobe and adjacent structures. An increasingly identified subset of TLE patients show bilateral temporal lobe involvement during seizures. Bilateral TLE (BiTLE) remains understudied, likely due to its complex underlying pathophysiology and heterogeneous clinical presentation. Non-invasive biomarkers that distinguish BiTLE from unilateral TLE (UTLE) can aid in better management and clinical stratification of BiTLE during presurgical evaluation.
Methods: We measured whole brain, interictal functional networks of 19 BiTLE patients and compared them to those of 75 UTLE patients, using resting state functional MRI (rs-fMRI). We quantified whole brain topological properties in resting state brain networks using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity (Figure 1). For each network metric, we computed an average across all nodes (brain regions), and iterated this process across network densities ranging from 0.10-0.50. Curves of network density versus each network metric were compared between groups. Finally, by combining whole brain average clustering coefficient and global efficiency curves and applying dimensionality reduction through principal component analysis (PCA), we derived a combined metric which we term the “integration-segregation axis.” In this axis, values greater than zero correspond to higher network segregation, whereas values less than zero indicate higher network integration.
Results: Compared to UTLE, BiTLE had decreased global efficiency (Figure 2A) and decreased whole brain average participation coefficient across a range of network densities (p < 0.05, corrected). BiTLE was also found to have decreased clustering coefficient (p < 0.05, corrected) in regions belonging to the default mode network, particularly in the posterior cingulate cortex (PCC). We also identified a larger number of communities in BiTLE than in UTLE after applying modularity maximization (p < 0.05, corrected). Finally, we demonstrate that the differences in network properties separate BiTLE and UTLE along the integration-segregation axis (Figure 2C
Neuro Imaging