Connectomic Profiling Predicts Cognitive Trajectories After Epilepsy Surgery in Children
Abstract number :
2.033
Submission category :
3. Neurophysiology / 3D. MEG
Year :
2022
Submission ID :
2204252
Source :
www.aesnet.org
Presentation date :
12/4/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:24 AM
Authors :
Olivia Arski, MSc – Hospital for Sick Children; Daniel Martire, MSc – Hospital for Sick Children; Julia Young, PhD – Hospital for Sick Children; Simeon Wong, MHSc – Hospital for Sick Children; Hrishikesh Suresh, MD – Hospital for Sick Children; Elizabeth Kerr, PhD – Hospital for Sick Children; Ayako Ochi, MD, PhD – Hospital for Sick Children; Hiroshi Otsubo, MD, PhD – Hospital for Sick Children; Roy Sharma, RET, REPT – Hospital for Sick Children; Elysa Widjaja, MD, MPH – Hospital for Sick Children; O. Carter Snead, MD – Hospital for Sick Children; Puneet Jain, MD, DM – Hospital for Sick Children; Elizabeth Donner, MD – Hospital for Sick Children; Mary Lou Smith, PhD – Hospital for Sick Children; George Ibrahim, MD, PhD – Hospital for Sick Children
Rationale: Neurocognitive outcomes following surgery for temporal lobe epilepsy in childhood are variable. Postoperative changes are not directly predicted by seizure-freedom and associations between epilepsy, neuropsychological function, and developing neural networks are poorly understood. Here, we leveraged whole-brain connectomic profiling in magnetoencephalography (MEG) to retrospectively study associations between brain connectivity and neuropsychological function in children with temporal lobe epilepsy undergoing resective surgery._x000D_
_x000D_
Methods: Clinical and MEG data were retrospectively analyzed for children who underwent temporal lobe epilepsy surgery at the Hospital for Sick Children from 2000 to 2021. Resting-state connectomes were constructed from neuromagnetic oscillations via the weighted phase lag index. Using a partial least-squares (PLS) approach, multidimensional associations between patient connectomes, neuropsychological scores, and clinical covariates were assessed. Bootstrap resampling statistics were performed to assess statistical significance._x000D_
_x000D_
Results: A total of 133 medical records were reviewed, and 5 PLS analyses were performed. Each PLS analysis probed a particular neuropsychological domain and the associations between its baseline and post-operative scores and the connectomic data. In each PLS analysis, a significant latent variable was identified, representing a specific percentage of the variance in the data, and relating neural networks to clinical covariates, which included changes in rote verbal memory (N=41, p = 0.01, σ² = 0.38), narrative/verbal memory (N=57, p = 0.00, σ² = 0.52), visual memory (N=51, p = 0.00, σ² = 0.43), working memory (N=44, p = 0.00, σ² = 0.52), and overall intellectual function (N=59, p = 0.00, σ² = 0.55). Children with more diffuse, bilateral intrinsic connectivity across several frequency bands showed lower scores on all neuropsychological assessments but demonstrated a greater propensity for gains following resective surgery._x000D_
_x000D_
Conclusions: Here, we report that connectomes characterized by diffuse connectivity, reminiscent of developmentally immature networks, are associated with lower pre-operative cognition and post-operative cognitive improvement. These findings provide a potential means to understand neurocognitive function in children with temporal lobe epilepsy and expected changes post-operatively._x000D_
_x000D_
Funding: Not applicable
Neurophysiology