Identifying neuropathic pain using (18)F-FDG micro-PET: a multivariate pattern analysis

Area of Science:

• Neuroscience
• Medical Imaging
• Pain Research

Background:

• Pathological pain induces neuroplastic changes in various brain regions.
• Altered resting-state brain activity patterns may indicate neuropathic pain.

Purpose of the Study:

• To investigate if neuropathic pain alters distributed resting-state brain activity patterns in animal models.
• To determine if these altered patterns can identify neuropathic pain at an individual level.

Main Methods:

• FDG micro-PET imaging was used in rats with and without spinal nerve ligation (SNL).
• Multivariate pattern analysis (MVPA) with linear support vector machine (SVM) was applied to identify pain-related brain activity patterns.

Main Results:

• MVPA successfully identified neuropathic pain with high accuracy (91.30%).
• Increased metabolism was observed in prefrontal-limbic-brainstem areas; decreased metabolism in cortical and cerebellar regions.
• Univariate analysis showed lower prediction performance compared to MVPA.

Conclusions:

• Resting-state brain activity patterns are altered by neuropathic pain.
• Neuroimaging-based diagnostic tools for pathological pain are feasible by analyzing these patterns.