Structural brain network abnormalities and the probability of seizure recurrence after epilepsy surgery: supplementary material

Objective: We assessed pre-operative structural brain networks and
clinical characteristics of patients with drug resistant temporal lobe
epilepsy (TLE) to identify correlates of post-surgical seizure
recurrences. Methods: We examined data from 51 TLE patients who underwent
anterior temporal lobe resection (ATLR) and 29 healthy controls. For each
patient, using the preoperative structural, diffusion, and post-operative
structural MRI, we generated two networks: ‘pre-surgery’ network and
‘surgically-spared’ network. Standardising these networks with respect to
controls, we determined the number of abnormal nodes before surgery and
expected to be spared by surgery. We incorporated these 2 abnormality
measures and 13 commonly acquired clinical data from each patient in a
robust machine learning framework to estimate patient-specific chances of
seizures persisting after surgery.    Results:
Patients with more abnormal nodes had lower chance of seizure freedom at 1
year and even if seizure-free at 1 year, were more likely to relapse
within five years. In the surgically-spared networks of poor outcome
patients, the number of abnormal nodes was greater and their locations
more widespread than in good outcome patients. We achieved 0.84±0.06 AUC
and 0.89±0.09 specificity in predicting unsuccessful seizure outcomes as
opposed to complete seizure freedom at 1-year. Moreover, the
model-predicted likelihood of seizure relapse was significantly correlated
with the grade of surgical outcome at year-one and associated with
relapses up-to five years post-surgery. Conclusion: Node abnormality
offers a personalised non-invasive marker, that can be combined with
clinical data, to better estimate the chances of seizure freedom at 1
year, and subsequent relapse up to 5 years after ATLR.