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

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 abnormalities were 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.