Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses

Xiaoya Wei; Liqiong Wang; Fangting Yu; Chihkai Lee; Ni Liu; Mengmeng Ren; Jianfeng Tu; Hang Zhou; Guangxia Shi; Xu Wang; Cun-Zhi Liu

doi:10.3389/fnins.2022.1036487

Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses

Front Neurosci. 2022 Nov 30:16:1036487. doi: 10.3389/fnins.2022.1036487. eCollection 2022.

Authors

Xiaoya Wei¹, Liqiong Wang¹, Fangting Yu¹, Chihkai Lee¹, Ni Liu², Mengmeng Ren², Jianfeng Tu¹, Hang Zhou¹, Guangxia Shi¹, Xu Wang³, Cun-Zhi Liu¹

Affiliations

¹ International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture- Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China.
² Department of Radiology, Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, Beijing, China.
³ School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.

Abstract

Introduction: Sciatica is a pain disorder often caused by the herniated disk compressing the lumbosacral nerve roots. Neuroimaging studies have identified functional abnormalities in patients with chronic sciatica (CS). However, few studies have investigated the neural marker of CS using brain structure and the classification value of multidimensional neuroimaging features in CS patients is unclear.

Methods: Here, structural and resting-state functional magnetic resonance imaging (fMRI) was acquired for 34 CS patients and 36 matched healthy controls (HCs). We analyzed cortical surface area, cortical thickness, amplitude of low-frequency fluctuation (ALFF), regional homogeneity (REHO), between-regions functional connectivity (FC), and assessed the correlation between neuroimaging measures and clinical scores. Finally, the multimodal neuroimaging features were used to differentiate the CS patients and HC individuals by support vector machine (SVM) algorithm.

Results: Compared to HC, CS patients had a larger cortical surface area in the right banks of the superior temporal sulcus and rostral anterior cingulate; higher ALFF value in the left inferior frontal gyrus; enhanced FCs between somatomotor and ventral attention network. Three FCs values were associated with clinical pain scores. Furthermore, the three multimodal neuroimaging features with significant differences between groups and the SVM algorithm could classify CS patients and HC with an accuracy of 90.00%.

Discussion: Together, our findings revealed extensive reorganization of local functional properties, surface area, and network metrics in CS patients. The success of patient identification highlights the potential of using artificial intelligence and multimodal neuroimaging markers in chronic pain research.

Keywords: ALFF; brain networks; chronic pain; chronic sciatica; cortical surface area; fMRI; support vector machines.