Abstracts

Individual, Atypical Resting-state Networks Contribute to Cognitive Reorganization in Temporal Lobe Epilepsy

Abstract number : 2.229
Submission category : 5. Neuro Imaging / 5B. Functional Imaging
Year : 2023
Submission ID : 674
Source : www.aesnet.org
Presentation date : 12/3/2023 12:00:00 AM
Published date :

Authors :
Presenting Author: Joseph Tracy, PhD – Thomas Jefferson University

Qirui Zhang, MD – Farber Institute for Neuroscience, Department of Neurology – Thomas Jefferson University; Aaron Struck, MD – Department of Neurology – University of Wisconsin (Madison); Ankeeta A., PhD – Farber Institute for Neuroscience, Department of Neurology – Thomas Jefferson University; Sam Javidi, PhD – Farber Institute for Neuroscience, Department of Neurology – Thomas Jefferson University; Yolanda Kry, BA – Farber Institute for Neuroscience, Department of Neurology – Thomas Jefferson University; Michael Sperling, MD – Farber Institute for Neuroscience, Department of Neurology – Thomas Jefferson University; Bruce Herman, MD – Department of Neurology – Thomas Jefferson University

Rationale: Cognitive deficits are common in temporal lobe epilepsy (TLE) because processing hubs for key functions such as language and memory reside in the temporal lobe. Despite TLE pathology, a significant subgroup of patients are able to maintain normative cognitive functioning. This adaptive brain response is usually attributed to cognitive reorganization, involving a change in the brain representation of cognitive functions. However, the mechanisms implementing adaptive cognitive reorganization are unknown. Extant models emphasize the recruitment of unaffected cognitive systems with normative locations in the brain to compensate for the loss of functionality. The role played by atypical or highly individual cognitive networks are rarely modeled in efforts to explain the functional or structural basis of compensatory cognitive reorganization. Here, we identify TLE patients with intact versus impaired cognitive profiles, and interrogate for the presence of both normative and highly individual intrinsic connectivity networks utilizing resting state fMRI.

Methods:

Sample was comprised of 88 TLE patients (right=52; left=36) and matched healthy controls (HC, n= 91) with fMRI resting state and neuropsychological (Npsych) performance data available on TLE. FMRI data was decomposed using FSL MELODIC independent component analysis yielding 30 substantive components. Based upon spatial correlation we quantified the degree of spatial match to 20 canonical, normative intrinsic connectivity networks (n-ICNs) and classified components for overall strong (r ≥ .2), moderate (r ≥ .1), or poor (r <

Neuro Imaging