The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy

Abstract

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.

Figure 1. Distribution of linkwise Z scores in seizure-free patients vs patients who were not seizure-free

(A) Cumulative distribution function (CDF) of Z scores across the entire connectome for patients (separated by surgical outcome). The shaded areas demonstrate the range of CDFs per group with 50% confidence interval (CI). (B) Distribution of linkwise median Z scores (across all links, per patient) is different between outcome groups.

Figure 2. Results from the model constructed from individual connectome data

(A) The range of areas under the curve (AUCs) (y-axis) obtained from the model constructed from a subnetwork involving an ascending number of links (x-axis). The shaded area demonstrates the 95% confidence interval (CI) when resampling the linkwise data. (B) Corresponding predictive values. (C) The probability of surgical success as a function of the number of links above the critical Z threshold (Z = 2), when a subnetwork composed of 30 links is assessed. (D) The AUC obtained from the networks model (also including 30 links), in comparison with the AUCs from clinical variables. The error bars represent the AUCs obtained from bootstrapping. NPV = negative predictive value; PPV = positive predictive value.

Figure 3. Connectome links were more commonly associated with surgical outcome, taking into account individual topographical variability

Connectome links that were repeatedly chosen by the cross-validation model (in green), in relationship with cortical regions of interest (ROIs) (represented by spheres located in the ROI center of mass). Spheres in yellow represent the 8 ROIs defined as pertaining to the temporal region. The links in this figure correspond to the links outlined in table 1. In general, patients who exhibited a cumulative number of weights Z >2 among these links were less likely to become seizure-free after surgery.