Multimodal MRI analysis selecting key brain features for machine learning based classification of diabetic neuropathic pain and phenotypes

Cerebral alterations are associated with diabetic peripheral neuropathy (DPN) and neuropathic pain, including reductions in brain volumes, cortical thickness, sulcus depth, and alterations in metabolites and functional connectivity. This study combined multimodal magnetic resonance imaging (MRI) data to differentiate clinical phenotypes and uncover distinct associated brain features using machine-learning based classification. Seventy-six participants were recruited: 20 healthy and 56 with type 1 diabetes mellitus: 18 without DPN, 19 with painless DPN, and 19 with painful DPN. Three machine learning classifiers were evaluated, including accompanying feature importance from the highest performing classifier. Class membership probabilities (predicted probability of belonging to a group) were compared across classes and correlated with clinical measures of pain and nerve function for the class concerned. Accuracies were ≥ 0.75 for all classes except painless DPN, though separated by painful membership probability (p ≤ 0.02). Overall classification accuracy was 0.71. The most informative features were functional connectivity, followed by N-acetylaspartate/creatine and sulcal depth. The painful DPN group was separated from the remaining groups by painful membership probability (p ≤ 0.01), which correlated with pain measures. Diabetes without DPN membership probability correlated with sural nerve conduction (p ≤ 0.001, r_s≥0.49) and warm detection thresholds (p ≤ 0.001, r_s= - 0.51). This exploratory study suggests that different MRI modalities provide complementary information describing phenotypes of diabetes, DPN and DPN related pain, with functional connectivity being most essential. Thus, implying a multifactorial cerebral manifestation of neuropathy and pain in diabetes and aiding the development of grading and prognostic tools for personalised treatments. Studies of larger cohorts should validate these findings.