Abstract
Refractory epilepsy is a complex case of epileptic disease. The quantitative analysis of fluorodeoxyglucose positron emission tomography (FDG-PET) images complements visual assessment and helps localize the epileptogenic zone (EZ) for better curative treatment. Statistical parametric mapping (SPM) and its computational anatomy toolbox (SPM-CAT) are two commonly applied tools in neuroimaging analysis. This study compares SPM and SPM-CAT with different parameters to find the optimal approach for localizing EZ in refractory epilepsy. The current study enrolled 45 subjects, including 25 refractory epilepsy patients and 20 healthy controls. All of the 25 patients underwent surgical operations. Pathological results and the postoperative outcome evaluation by the Engel scale were likewise presented. SPM and SPM-CAT were used to assess FDG-PET images with three different uncorrected p-values and the corresponding cluster sizes (k), as in voxels in the cluster, namely p < 0.0002, k > 25; p < 0.001, k > 100; p < 0.005, and k > 200. When combining three settings, SPM and SPM-CAT yielded overall positive finding scores of 96.0% (24/25) and 100.0% (25/25) respectively. However, for the individual setting, SPM-CAT achieved the diverse positive finding scores of 96.0% (24/25), 96.0% (24/25), and 88.0% (22/24), which are higher than those of SPM [88.0% (22/25), 76.0% (19/25), and 72.0% (18/25)]. SPM and SPM-CAT localized EZ correctly with 28.0% (7/25) and 64.0% (16/25), respectively. SPM-CAT with parameter settings p < 0.0002 and k > 25 yielded a correct localization at 56.0% (14/25), which is slightly higher than that for the other two settings (48.0 and 20.0%). Moderate concordance was found between the confirmed and pre-surgical EZs, identified by SPM-CAT (kappa value = 0.5). Hence, SPM-CAT is more efficient than SPM in localizing EZ for refractory epilepsy by quantitative analysis of FDG-PET images. SPM-CAT with the setting of p < 0.0002 and k > 25 might perform as an objective complementary tool to the visual assessment for EZ localization.
Highlights
Epilepsy is among the most common neurological disorders affecting people of all ages
For the data processing of Statistical parametric mapping (SPM), the brain tissue was first segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF)
After selecting the canonical template, the diffeomorphic anatomic registration through an exponentiated lie (DARTEL) algebra algorithm was used to normalize the segmented scans into a standard Montreal Neurological Institute (MNI) space [17]
Summary
Epilepsy is among the most common neurological disorders affecting people of all ages. It is characterized by unpredictable seizures and can give rise to other health problems. Recent statistics indicate that epilepsy affects more than 50 million people worldwide [1]. Refractory epilepsy is a drug-resistant epilepsy; patients are considered to suffer from refractory epilepsy if disabling seizures continue despite treatment trials with two anti-seizure drugs, either alone or in combination [2]. Diagnosing refractory epilepsy remains a tedious task. While several researchers investigated refractory epilepsy to diagnose and reveal possible causes [3, 4], the main cause remains unknown, and doctors are yet to determine why some patients are receptive to medicine and others not
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