Learning to see the invisible: A data‐driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy

Abstract

Objective: To find the covert patterns of abnormality in patients with unilateral temporal lobe epilepsy (TLE) and visually normal brain magnetic resonance images (MRI-negative), comparing them to those with visible abnormalities (MRI-positive). Methods: We used multimodal brain MRI from patients with unilateral TLE and employed contemporary machine learning methods to predict the known laterality of seizure onset in 104 subjects (82 MRI-positive, 22 MRI-negative). A visualization approach entitled "Importance Maps" was developed to highlight image features predictive of seizure laterality in both the MRI-positive and MRI-negative cases. Results: Seizure laterality could be predicted with an area under the receiver operating characteristic curve of 0.981 (95% confidence interval [CI] =0.974-0.989) in MRI-positive and 0.842 (95% CI = 0.736-0.949) in MRI-negative cases. The known image features arising from the hippocampus were the leading predictors of seizure laterality in the MRI-positive cases, whereas widespread temporal lobe abnormalitieswere revealed in the MRI-negative cases. Significance: Covert abnormalities not discerned on visual reading were detected in MRI-negative TLE, with a spatial pattern involving the whole temporal lobe, rather than just the hippocampus. This suggests that MRI-negative TLE may be associated with subtle but widespread temporal lobe abnormalities. These abnormalities merit close inspection and postacquisition processing if there is no overt lesion. K E Y W O R D Sabnormality, data-driven, epilepsy, machine learning, MRI-negative

FLAIR-Wise Machine-Learning Classification and Lateralization of MRI-Negative 18F-FDG PET-Positive Temporal Lobe Epilepsy

“…One explanation might be that the hippocampal FLAIR signal is not informative in MRI-negative TLE because the focus is more likely to be located in cortical regions. As for the hippocampus region, our machine-learning based results are in agreement with findings from (18).…”

“…In the field of epilepsy neuroimaging, some studies have documented an important link between the hippocampus and epilepsy, particularly in TLE patients with hippocampal sclerosis (17). However, a recent study has reported that features in the hippocampus appear to be less important than those in the temporal lobe in MRI-negative TLE populations (18). To elucidate on the underlying mechanism hippocampus on the classification performances, we added hippocampus data along with other selected ROIs in our prediction models.…”

“…To validate the performance of FLAIR data for the diagnosis and lateralization of MRI-negative 18 F-FDG PET-positive TLE, we built three binary classification models (i.e., HC/RTLE, HC/LTLE, and RTLE/LTLE) as well as a multiclass classification model (i.e., HC/RTLE/LTLE). The performance of each classification model was computed using a LOOCV strategy such that, each time, one sample was used for the test set and the remainder for training.…”

“…Functional imaging techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are powerful modalities for identifying and monitoring patients with TLE when MRI is visually negative (3,4). Indeed, interictal 18 F-FDG PET achieves 85-90% sensitivity in TLE lateralization (5). Additionally, when 18 F-FDG PET provides positive findings in MRI-negative TLE, surgical treatment is associated with favorable prognosis in patients with hippocampal sclerosis (6)(7)(8).…”

“…Indeed, interictal 18 F-FDG PET achieves 85-90% sensitivity in TLE lateralization (5). Additionally, when 18 F-FDG PET provides positive findings in MRI-negative TLE, surgical treatment is associated with favorable prognosis in patients with hippocampal sclerosis (6)(7)(8). Thus, MRI-negative PET-positive TLE is also considered an important group with favorable surgical outcomes.…”