Structural Brain Alterations in Key Somatosensory and Nociceptive Regions in Diabetic Peripheral Neuropathy.

OBJECTIVE: Despite increasing evidence demonstrating structural and functional alterations within the central nervous system in diabetic peripheral neuropathy (DPN), the neuroanatomical correlates of painful and painless DPN have yet to be identified. Focusing on structural MRI, the aims of this study were to 1) define the brain morphological alterations in painful and painless DPN and 2) explore the relationships between brain morphology and clinical/neurophysiological assessments. RESEARCH DESIGN AND METHODS: A total of 277 participants with type 1 and 2 diabetes (no DPN [n = 57], painless DPN [n = 77], painful DPN [n = 77]) and 66 healthy volunteers (HVs) were enrolled. All underwent detailed clinical/neurophysiological assessment and brain 3T MRI. Participants with painful DPN were subdivided into the irritable (IR) nociceptor and nonirritable (NIR) nociceptor phenotypes using the German Research Network on Neuropathic Pain protocol. Cortical reconstruction and volumetric segmentation were performed with FreeSurfer software and voxel-based morphometry implemented in FSL. RESULTS: Both participants with painful and painless DPN showed a significant reduction in primary somatosensory and motor cortical thickness compared with HVs (P = 0.02; F[3,275] = 3.36) and participants with no DPN (P = 0.01; F[3,275] = 3.80). Somatomotor cortical thickness correlated with neurophysiological measures of DPN severity. There was also a reduction in ventrobasal thalamic nuclei volume in both painless and painful DPN. Participants with painful DPN with the NIR nociceptor phenotype had reduced primary somatosensory cortical, posterior cingulate cortical, and thalamic volume compared with the IR nociceptor phenotype. CONCLUSIONS: In this largest neuroimaging study in DPN to date, we demonstrated significant structural alterations in key somatomotor/nociceptive brain regions specific to painless DPN and painful DPN, including the IR and NIR nociceptor phenotypes.

Somatosensory network functional connectivity differentiates clinical pain phenotypes in diabetic neuropathy.

AIMS/HYPOTHESIS: The aim of this work was to investigate whether different clinical pain phenotypes of diabetic polyneuropathy (DPN) are distinguished by functional connectivity at rest. METHODS: This was an observational, cohort study of 43 individuals with painful DPN, divided into irritable (IR, n = 10) and non-irritable (NIR, n = 33) nociceptor phenotypes using the German Research Network of Neuropathic Pain quantitative sensory testing protocol. In-situ brain MRI included 3D T1-weighted anatomical and 6 min resting-state functional MRI scans. Subgroup differences in resting-state functional connectivity in brain regions involved with somatic (thalamus, primary somatosensory cortex, motor cortex) and non-somatic (insular and anterior cingulate cortices) pain processing were examined. Multidimensional reduction of MRI datasets was performed using a machine-learning approach to classify individuals into each clinical pain phenotype. RESULTS: Individuals with the IR nociceptor phenotype had significantly greater thalamic-insular cortex (p false discovery rate [FDR] = 0.03) and reduced thalamus-somatosensory cortex functional connectivity (p-FDR = 0.03). We observed a double dissociation such that self-reported neuropathic pain score was more associated with greater thalamus-insular cortex functional connectivity (r = 0.41; p = 0.01) whereas more severe nerve function deficits were more related to lower thalamus-somatosensory cortex functional connectivity (r = -0.35; p = 0.03). Machine-learning group classification performance to identify individuals with the NIR nociceptor phenotype achieved an accuracy of 0.92 (95% CI 0.08) and sensitivity of 90%. CONCLUSIONS/INTERPRETATION: This study demonstrates differences in functional connectivity in nociceptive processing brain regions between IR and NIR phenotypes in painful DPN. We also establish proof of concept for the utility of multimodal MRI as a biomarker for painful DPN by using a machine-learning approach to classify individuals into sensory phenotypes.