Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning. Issue 11 (9th September 2021)

• Record Type: Journal Article
• Authors: Wu, Dongyan; Yang, Liyuan; Gong, Gaolang; Zheng, Yumin; Jin, Chaoling; Qi, Lei; Li, Yanran; Wu, Di; Cui, Zaixu; He, Xiaosong; Ren, Liankun
• Is Part Of: Journal of neuroscience research. Volume 99:Issue 11(2021)
• Page Start: 3035
• Page End: 3046
• Publication Date: 2021-09-09
• Subjects: age at epilepsy onset -- machine learning -- mesial temporal lobe epilepsy -- metabolism; Neurobiology -- Periodicals; 612
• DOI: 10.1002/jnr.24951
• Languages: English
• ISSNs: 0360-4012

Abstract

Abstract: Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy, presenting both structural and metabolic abnormalities in the ipsilateral mesial temporal lobe. While it has been demonstrated that the metabolic abnormalities in MTLE actually extend beyond the epileptogenic zone, how such multidimensional information is associated with the diagnosis of MTLE remains to be tested. Here, we explore the whole‐brain metabolic patterns in 23 patients with MTLE and 24 healthy controls using [ 18 F]fluorodeoxyglucose PET imaging. Based on a multivariate machine learning approach, we demonstrate that the brain metabolic patterns can discriminate patients with MTLE from controls with a superior accuracy (>95%). Importantly, voxels showing the most extreme contributing weights to the classification (i.e., the most important regional predictors) distribute across both hemispheres, involving both ipsilateral negative weights over the anterior part of lateral and medial temporal lobe, posterior insula, and lateral orbital frontal gyrus, and contralateral positive weights over the anterior frontal lobe, temporal lobe, and lingual gyrus. Through region‐of‐interest analyses, we verify that in patients with MTLE, the negatively weighted regions are hypometabolic, and the positively weighted regions are hypermetabolic, compared to controls. Interestingly, despite that both hypo‐ and hypermetabolism have mutually contributed to our model, they may reflect different pathological and/or compensative responses. For instance, patients with earlier age at epilepsy onset present greater hypometabolism in the ipsilateral inferior temporal gyrus, while we find no evidence of such association with hypermetabolism. In summary, quantitative models utilizing multidimensional brain metabolic information may provide additional assistance to presurgical workups in TLE. Abstract : We applied multivariate linear support vector classification on voxel‐wise glucose uptake measured through PET imaging, and effectively classified mesial temporal lobe epilepsy (MTLE) patients from healthy controls (cross‐validated accuracy >95%). Both ipsilateral hypometabolism and contralateral hypermetabolism significantly contributed to the model, confirming the added values of metabolic abnormalities in assisting MTLE diagnosis through quantitative approaches.