FDG-PET Profiles of Extratemporal Metabolism as a Predictor of Surgical Failure in Temporal Lobe Epilepsy

Abstract

Objective: Metabolic abnormality in the extratemporal area on fluorine-18-fluorodeoxyglucose positron emission tomography (FDG-PET) is not an uncommon finding in drug-resistant temporal lobe epilepsy (TLE), however the correlation between extratemporal metabolic abnormalities and surgical long-term prognosis has not been fully elucidated. We aim to investigate FDG-PET extratemporal metabolic profiles predictive of failure in surgery for TLE patients.Methods: Eighty-two patients with unilateral TLE (48 female, 34 male; 25.6 ± 10.6 years old; 37 left TLE, 45 right TLE) and 30 healthy age-matched controls were enrolled. Patients were classified either as experiencing seizure-recurrence (SZR, Engel class II through IV) or seizure-free (SZF, Engel class I) at least 1 year after surgery. Regional cerebral metabolism was evaluated by FDG-PET with statistical parametric mapping (SPM12). Abnormal metabolic profiles and patterns on FDG-PET in SZR group were evaluated and compared with those of healthy control and SZF subjects on SPM12. Volume and intensity as well as special brain areas of abnormal metabolism in temporal and extratemporal regions were quantified and visualized.Results: With a median follow-up of 1.5 years, 60% of patients achieved Engel class I (SZF). SZR was associated with left TLE and widespread hypometabolism in FDG-PET visual assessment (both p < 0.05). All patients had hypometabolism in the ipsilateral temporal lobe but SZR was not correlated with volume or intensity of temporal hypometabolism (median, 1,456 vs. 1,040 mm3; p > 0.05). SZR was correlated with extratemporal metabolic abnormalities that differed according to lateralization: in right TLE, SZR exhibited larger volume in extratemporal areas compared to SZF (median, 11,060 vs. 2,112 mm3; p < 0.05). Surgical failure was characterized by Cingulum_Ant_R/L, Frontal_Inf_Orb_R abnormal metabolism in extratemporal regions. In left TLE, SZR presented a larger involvement of extratemporal areas similar to right TLE but with no significant (median, 5,873 vs. 3,464 mm3; p > 0.05), Cingulum_Ant_ R/L, Parietal_Inf_L, Postcentral_L, and Precuneus_R involved metabolic abnormalities were correlated with SZR.Conclusions: Extratemporal metabolic profiles detected by FDG-PET may indicate a prominent cause of TLE surgery failure and should be considered in predictive models for epilepsy surgery. Seizure control after surgery might be improved by investigating extratemporal areas as candidates for resection or neuromodulation.

Individual Brain Metabolic Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation Predicts Seizure Outcomes of Temporal Lobe Epilepsy

“…Furthermore, we tested for a consensus of significant metabolic connections between SZR and SZF TLE patients, as summarized in Figure 3 . The results accord with group-level studies showing analogous network changes in surgical failure ( Barba et al, 2016 ; Bernhardt et al, 2016 ; Chassoux et al, 2017 ; Gleichgerrcht et al, 2018 ; Tang et al, 2020 ).…”

“…Our approach was not meant to replace clinical judgment, but rather to enrich it by providing an objective and quantifiable estimate for a single key decision-driving factor (postoperative seizure outcome). In our previous study, in addition to discovering the number of metabolic abnormalities in the extratemporal area associated with surgical failure, we also described special patterns associated with the failure of TLE surgery (Tang et al, 2020), which spanned from the frontal and parietal regions to the occipital and contralateral regions, and some of which are consistent with electroclinical patterns corresponding to anterior and posterior spread (Chassoux et al, 2016). Our present brain connectome approach can measure local network properties and the entire network, and combined with MK-SVM, can provide powerful identification of the salient properties predictive of the surgical outcomes in SZF and SZR patients (accuracy of 83.59%).…”

“…A magnetic resonance imaging (MRI) study which used deep learning applied to the whole-brain connectome to determine seizure control after epilepsy surgery attained a positive predictive value (PPV; seizure-freedom) of 88% ( Gleichgerrcht et al, 2018 ). Taking advantage of newer in vivo neuroimaging techniques capable of revealing whole-brain metabolic abnormalities by means of positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG), our group demonstrated that extratemporal metabolic profiles could explain seizure failure after surgery for TLE patients ( Tang et al, 2020 ). Some FDG-PET studies detected multi-scale community structure with different normalization techniques for exhibiting inter-subject FDG-PET brain networks ( Sperry et al, 2018 ).…”

Cortical abnormalities of synaptic vesicle protein 2A in focal cortical dysplasia type II identified in vivo with 18F-SynVesT-1 positron emission tomography imaging

“…PET/computed tomography (CT) images were acquired by a Discovery Elite PET/CT scanner (GE Healthcare, Waukesha, USA). The scanning protocol was the same as described previously [24].…”

Machine Learning-Derived Multimodal Neuroimaging of Presurgical Target Area to Predict Individual's Seizure Outcomes After Epilepsy Surgery

“…PET images were acquired in three dimensions; the full width at half maximum of the scan was 5.4 mm. All images were reconstructed into a 256 × 256 trans-axial matrix (FOV of 350 mm) using the 3D VUE point ordered-subset expectationmaximization algorithm with six iterations and six subsets (Tang et al, 2018;Tang et al, 2020).…”