Abnormal Degree Centrality as a Potential Imaging Biomarker for Right Temporal Lobe Epilepsy: A Resting-state Functional Magnetic Resonance Imaging Study and Support Vector Machine Analysis

Neuroscience. 2022 Apr 1;487:198-206. doi: 10.1016/j.neuroscience.2022.02.004. Epub 2022 Feb 11.

Abstract

Previous studies have reported altered neuroimaging features in right temporal lobe epilepsy (rTLE). However, the alterations in degree centrality (DC) as a diagnostic method for rTLE have not been reported. Therefore, we aimed to explore abnormalities in the DC of the rTLE and whether such alterations could be applied to the diagnosis of rTLE. Resting-state functional magnetic resonance imaging (fMRI) was used to scan 82 patients with rTLE and 69 healthy controls. The DC and support vector machine (SVM) methods were used for an analysis of the imaging data. Compared to the control group, the rTLE patients exhibited lower DC values in the right hippocampus, right superior temporal gyrus, and right caudate. Compared to the control group, the rTLE patients showed higher DC values in the right medial superior frontal gyrus (SFGmed), left dorsolateral superior frontal gyrus (SFGdor), right inferior parietal lobule (IPL), and the left postcentral. The highest diagnostic accuracy of 99.34% (150/151), based on SVM analysis, was demonstrated for the combination of abnormal DC in the right IPL and the left SFGdor, along with a sensitivity of 100% (82/82), and a specificity of 98.55% (68/69) for the differentiation of rTLE patients from healthy controls. The study demonstrated abnormal functional connectivity in rTLE patients. Thus, a distinctive DC pattern may serve as an imaging marker for the diagnosis of rTLE patients.

Keywords: degree centrality; imaging biomarker; resting-state functional magnetic resonance imaging; right temporal lobe epilepsy; support vector machine.

MeSH terms

  • Biomarkers
  • Brain / pathology
  • Brain Mapping / methods
  • Epilepsy, Temporal Lobe* / pathology
  • Humans
  • Magnetic Resonance Imaging / methods
  • Support Vector Machine*
  • Temporal Lobe / pathology

Substances

  • Biomarkers