Frontiers in Neuroscience (Nov 2022)

Combined brain network topological metrics with machine learning algorithms to identify essential tremor

  • Qin Li,
  • Li Tao,
  • Pan Xiao,
  • Honge Gui,
  • Bintao Xu,
  • Xueyan Zhang,
  • Xiaoyu Zhang,
  • Huiyue Chen,
  • Hansheng Wang,
  • Wanlin He,
  • Fajin Lv,
  • Oumei Cheng,
  • Jing Luo,
  • Yun Man,
  • Zheng Xiao,
  • Weidong Fang

DOI
https://doi.org/10.3389/fnins.2022.1035153
Journal volume & issue
Vol. 16

Abstract

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Background and objectiveEssential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET.MethodsResting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics.ResultsAll classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity.ConclusionThese results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

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