Connectomics in Children with Focal Cortical Dysplasia Reveal Network Differences That Correspond to Epilepsy Surgical Outcome
Abstract number :
1.242
Submission category :
5. Neuro Imaging / 5A. Structural Imaging
Year :
2022
Submission ID :
2203992
Source :
www.aesnet.org
Presentation date :
12/3/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:22 AM
Authors :
Easton West, Student – Baylor College of Medicine; Anne Anderson, MD – Department of Neuroscience, Baylor College of Medicine, Houston, Texas, U.S.A; The Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital; David Chu, PhD – Edward B. Singleton Department of Radiology Pediatric neuroradiologist, Associate Professor of Radiology, Texas Children’s Hospital, Baylor college of Medicine; Chelsey Ortman, MD – Baylor College of Medicine, Texas Children's Hospital; Michael Quach, MD – Baylor College of Medicine, Texas Children's Hospital; Howard Weiner, MD – Division of Pediatric Neurosurgery, Department of Surgery, Texas Children's Hospital,; Avner Meoded, MD – Edward B. Singleton Department of Radiology Pediatric neuroradiologist, Associate Professor of Radiology, Texas Children’s Hospital, Baylor college of Medicine
Rationale: Focal cortical dysplasia (FCD) is a common cause of pediatric medically intractable epilepsy. Patients undergoing epilepsy surgery with resection of FCD have variable seizure outcomes. Therefore, predicting who is likely to benefit from surgery is crucial to tailor therapeutic approaches. Identifying noninvasive methods to further evaluate the seizure onset zone and networks prior to resection is a crucial area of research. Epilepsy is commonly considered a network disease.2 The connectome identified with neuroimaging has the potential to improve the diagnosis and treatment of epilepsy by moving from a focus detection paradigm to a global network approach.3 In these studies we used connectomics to determine differences in pre-surgical networks between children who are seizure-free and those with persistent seizures following surgery.
Methods: Retrospective analysis of pre-surgical MRI of children who underwent resection of right frontal FCD to treat medically intractable epilepsy was performed. Connectometry analysis was used to compare the pre-operative structural connectome in patients with good vs. worse surgical outcome using the Engel scale 1 year after surgery. An FDR threshold of 0.05 was used.
Results: Ten (10) patients were included, six (6) patients with seizure freedom (Engel scale 1A) after surgery, and four (4) patients with persistent seizures (Engel scale ≥ 1C). Connectometry analysis identified a subnetwork composed of the corpus callosum, corticothalamic pathway, and cortico-striatal pathways with increased connectivity in seizure-free patients compared to patients with persistent seizures following surgery.
Conclusions: Our findings revealed significant differences in the pre-operative neural network among children without seizures versus children with persistent seizures one year after surgery. These findings support the concept that the analysis of connectomics offers a unique opportunity to study the effect of FCD on brain networks and potentially help guide in surgical planning and predicting outcome. The putative lesion should be considered a key node in the patient-specific network. Surgical target definition and the prediction of treatment outcomes will likely benefit from incorporation of network data. In addition, the goals of surgery and risk-benefit analysis are patient-specific, hence further supporting personalized medicine.
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References:
1. Kwan P, Brodie MJ. Early identification of refractory epilepsy. The New England Journal of Medicine 2000;342:314-319.
2. Tavakol S, Royer J, Lowe AJ, et al. Neuroimaging and connectomics of drug-resistant epilepsy at multiple scales: From focal lesions to macroscale networks. Epilepsia 2019;60:593-604.
3. Meoded A, Huisman T, Casamassima MGS, et al. The structural connectome in children: basic concepts, how to build it, and synopsis of challenges for the developing pediatric brain. Neuroradiology 2017;59:445-460.
Funding: None
Neuro Imaging