Authors :
Presenting Author: Nishant Sinha, PhD – University of Pennsylvania
Alfredo Lucas, BS – Univesity of Pennsylvania; Ryan Gallagher, BS – University of Pennsylvania; Akash Pattnaik, BS – University of Pennsylvania; Joshua LaRoque, MD, PhD – University of Pennsylvania; John Bernabei, MD, PhD – University of Pennsylvania; James Gugger, MD – University of Pennsylvania; Sandhitsu Das, PhD – University of Pennsylvania; Joel stein, MD, PhD – University of Pennsylvania; Brian Litt, MD – University of Pennsylvania; Kathryn Davis, MD – University Of Pennsylvania
Rationale:
Of approximately 70 million patients with epilepsy, 30–40% are medication-resistant and suffer from chronic uncontrolled seizures. Surgical removal of seizure-causing brain tissue is often the best treatment to make many of these patients completely seizure-free. Unfortunately, only 40-60% of patients who undergo surgery achieve seizure freedom. Clinicians use multiple non-invasive brain imaging modalities to guide invasive evaluations, like implanting electrodes in the brain (iEEG) and epilepsy surgery. Our objective was to quantitively map abnormal brain areas from structural brain imaging to augment clinical decisions when identifying areas to implant iEEG electrodes.
Methods:
We retrospectively analyzed forty four patients who underwent presurgical evaluation for epilepsy at the Hospital of the University of Pennsylvania and thirty five demographically matched controls. We analyzed the T1-weighted and diffusion-weighted MRI in all patients and controls to extract metrics quantifying cortical morphology and structural brain connectivity. We inferred a normative baseline from the control subjects and followed our previous work [1,2] to estimate a normal range of variations in these metrics. We measured the deviation in each of these metrics in individual epilepsy patients and incorporated them as features in a machine-learning regression model to quantify abnormality in individual brain areas. By mapping the brain areas implanted by iEEG, we analyzed how many abnormal brain areas on whole-brain structural imaging were implanted by iEEG.
Results:
Patients with distributed iEEG networks have more abnormal connections compared to patients with focal iEEG networks (p < 0.01). Patients who were not seizure-free after surgery had more abnormal brain areas as defined by whole brain noninvasive structural imaging than patients who underwent resection or ablation therapy with a seizure-free surgical outcome (d = 0.78, p = 0.02).
Conclusions:
Quantitively mapping the abnormalities in whole-brain structural imaging can assist in identifying brain areas that should be implanted by iEEG. Appropriately targeting abnormalities by iEEG is significant because it can enable the identification of the location and extent of the epileptic network in individual epilepsy patients.
Funding:
AL and KAD received support from NINDS (R01NS116504). NS received support from American Epilepsy Society (953257) and NINDS (R01NS116504). The authors would also like to thank the Thornton Foundation for their generous support. Brian Litt acknowledges funding from the Pennsylvania Tobacco Fund, NINDS R56099348, NIH DP1NS122038, the Mirowski Family Foundation, Jonathan Rothberg, and Neil and Barbara Smit.