Abstract
Introduction:
Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. On MRI, around 70% of patients show hippocampal atrophy lateralized to the side of the seizure focus. Hippocampal atrophy is associated with favorable seizure outcome after surgery, with a predictive accuracy surpassing most electro-clinical markers. The ease to evaluate the hippocampus and its clinical utility have influenced presurgical decision-making for the past decades. This is reflected in a widely accepted dichotomization of TLE patients based on presence/absence of hippocampal atrophy. Increasing
evidence, however, challenges the classification of TLE into two distinct groups. Notably, marked structural changes are present in adjacent mesiotemporal structures, such as amygdala and entorhinal cortex, with significant individual variations in degree and regional distribution of anomalies. Such patterns of mesiotemporal damage may hamper individual prediction of post-surgical outcome solely based on hippocampal assessment. Here, we applied pattern learning to surface-based MRI data of the mesiotemporal lobe to classify TLE patients and to evaluate the diagnostic validity of our method.
Methods:
We studied 134 consecutive patients referred to our hospital for drug-resistant TLE (63 males; 16 to 57 years). We manually segmented the hippocampus, amygdala, and entorhinal cortex on high-resolution T1-weighted MRI (Bernasconi et al., 2003) and applied a surface-based method to measure local volume changes with submillimetric precision (Kim et al., 2008). We subdivided mesiotemporal surfaces into 12 parcels according to histological atlases. For each parcel, we calculated the average z-score, resulting in a subject-specific profile of 12 normalized measures per hemisphere. We applied k-means clustering, an unsupervised algorithm, and aggregated individual patients into distinct classes.
Patterns of structural abnormalities in each data-driven TLE class were compared to a group of 46 age-and sex-matched controls through t-tests at each mesiotemporal surface point and corrected at a false discovery rate of 0.05 (Benjamini and Hochberg, 1995). We assessed the ability of mesiotemporal surface data and class information to lateralize the seizure focus in the entire patient cohort and to predict surgical outcome in a subset of 92 operated patients who had long-term follow-up (4.4±3.1 years), using linear discriminant analysis (LDA) with leave-one-out cross-validation.
Results:
Data-driven clustering segregated patients into four classes (Fig. 1). While showing comparable degrees of entorhinal cortex atrophy, each class presented with a unique signature of hippocampal and amygdala damage (i.e., TLE I: marked bilateral atrophy, II: marked ipsilateral atrophy, III: subtle atrophy, IV: hypertrophy; FDR<0.05; Fig. 2). MRI surface features of the three mesiotemporal structures and class information lateralized the focus in 93% of patients and predicted seizure-free outcome in 95%, outperforming conventional hippocampal volumetry, which accurately lateralized 71% of patients and predicted outcome in 52% (Fig. 3).
Conclusions:
Surface-based MRI profiling provides a nuanced, highly accurate characterization of the TLE imaging spectrum, serving as an effective biomarker for presurgical diagnostics.
