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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
Published on: December 18, 2016
The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy
Leonardo Bonilha1, Jens H Jensen2, Nathaniel Baker2
1From the Departments of Neurology (L.B., J.H.J., J.B., T.N.), Radiology and Radiological Science (J.H.J.), and Public Health Sciences (N.B.), Medical University of South Carolina, Charleston; the Department of Neurology (J.J.L.), University of California Irvine; the Departments of Neurology and Pediatrics (D.L.D.) and Radiology (A.M.S.), Emory University, Atlanta, GA; the Department of Neurology (J.R.B.), Medical College of Wisconsin, Milwaukee; and the Comprehensive Epilepsy Center (R.I.K.), New York University, New York. bonilha@musc.edu.
Objective:
We examined whether individual neuronal architecture obtained from the brain connectome can be used to estimate the surgical success of anterior temporal lobectomy (ATL) in patients with temporal lobe epilepsy (TLE).
Methods:
We retrospectively studied 35 consecutive patients with TLE who underwent ATL. The structural brain connectome was reconstructed from all patients using presurgical diffusion MRI. Network links in patients were standardized as Z scores based on connectomes reconstructed from healthy controls. The topography of abnormalities in linkwise elements of the connectome was assessed on subnetworks linking ipsilateral temporal with extratemporal regions. Predictive models were constructed based on the individual prevalence of linkwise Z scores >2 and based on presurgical clinical data.
Results:
Patients were more likely to achieve postsurgical seizure freedom if they exhibited fewer abnormalities within a subnetwork composed of the ipsilateral hippocampus, amygdala, thalamus, superior frontal region, lateral temporal gyri, insula, orbitofrontal cortex, cingulate, and lateral occipital gyrus. Seizure-free surgical outcome was predicted by neural architecture alone with 90% specificity (83% accuracy), and by neural architecture combined with clinical data with 94% specificity (88% accuracy).
Conclusions:
Individual variations in connectome topography, combined with presurgical clinical data, may be used as biomarkers to better estimate surgical outcomes in patients with TLE.
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