Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses Article Swipe
Xiao‐Ya Wei
,
Li‐Qiong Wang
,
Fang-Ting Yu
,
Chih‐Kai Lee
,
Ni Liu
,
Mengmeng Ren
,
Jian‐Feng Tu
,
Hang Zhou
,
Guang‐Xia Shi
,
Xu Wang
,
Cun‐Zhi Liu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2022.1036487
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2022.1036487
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2022.1036487
- https://www.frontiersin.org/articles/10.3389/fnins.2022.1036487/pdf
- OA Status
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- Cited By
- 6
- References
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- Related Works
- 10
- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4310474305Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fnins.2022.1036487Digital Object Identifier
- Title
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Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analysesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-30Full publication date if available
- Authors
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Xiao‐Ya Wei, Li‐Qiong Wang, Fang-Ting Yu, Chih‐Kai Lee, Ni Liu, Mengmeng Ren, Jian‐Feng Tu, Hang Zhou, Guang‐Xia Shi, Xu Wang, Cun‐Zhi LiuList of authors in order
- Landing page
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https://doi.org/10.3389/fnins.2022.1036487Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fnins.2022.1036487/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fnins.2022.1036487/pdfDirect OA link when available
- Concepts
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Sciatica, Neuroimaging, Artificial intelligence, Computer science, Physical medicine and rehabilitation, Machine learning, Neuroscience, Medicine, Psychology, Pattern recognition (psychology), Physical therapyTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 2, 2024: 2, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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