Neuro magnetic resonance spectroscopy using wavelet decomposition and statistical testing identifies biochemical...

Spinal cord injury (SCI) can be accompanied by chronic pain, the mechanisms for which are poorly understood. Here we report that magnetic resonance spectroscopy measurements from the brain, collected at 3T, and processed using wavelet-based feature extraction and classification algorithms, can identify biochemical changes that distinguish control subjects from subjects with SCI as well as subdividing the SCI group into those with and without chronic pain. The results from control subjects (n=10) were compared to those with SCI (n=10). The SCI cohort was made up of subjects with chronic neuropathic pain (n=5) and those without chronic pain (n=5). The wavelet-based decomposition of frequency domain MRS signals employs statistical significance testing to identify features best suited to discriminate different classes. Moreover, the features benefit from careful attention to the post-processing of the spectroscopy data prior to the comparison of the three cohorts. The spectroscopy data, from the thalamus, best distinguished control subjects without SCI from those with SCI with a sensitivity and specificity of 0.9 (Percentage of Correct Classification). The spectroscopy data obtained from the prefrontal cortex and anterior cingulate cortex both distinguished between SCI subjects with chronic neuropathic pain and those without pain with a sensitivity and specificity of 1.0. In this study, where two underlying mechanisms co-exist (i.e. SCI and pain), the thalamic changes appear to be linked more strongly to SCI, while the anterior cingulate cortex and prefrontal cortex changes appear to be specifically linked to the presence of pain.