Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

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

doi:10.3389/fneur.2023.1165603

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

Front Neurol. 2023 Jun 19:14:1165603. doi: 10.3389/fneur.2023.1165603. eCollection 2023.

Authors

Pan Xiao¹, Li Tao¹, Xiaoyu Zhang¹, Qin Li¹, Honge Gui¹, Bintao Xu¹, Xueyan Zhang¹, Wanlin He¹, Huiyue Chen¹, Hansheng Wang¹, Fajin Lv¹, Tianyou Luo¹, Oumei Cheng², Jin Luo², Yun Man², Zheng Xiao², Weidong Fang¹

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
² Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Abstract

Background: Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients.

Methods: The histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics.

Results: Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity.

Conclusion: Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.

Keywords: Radiomics; amplitude of low-frequency fluctuation; essential tremor; machine learning; resting-state fMRI.