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

Cardiac magnetic resonance imaging parameters as surrogate endpoints in clinical trials of acute myocardial infarction

Cardiac magnetic resonance (CMR) offers a variety of parameters potentially suited as surrogate endpoints in clinical trials of acute myocardial infarction such as infarct size, myocardial salvage, microvascular obstruction or left ventricular volumes and ejection fraction. The present article reviews each of these parameters with regard to the pathophysiological basis, practical aspects, validity, reliability and its relative value (strengths and limitations) as compared to competitive modalities. Randomized controlled trials of acute myocardial infarction which have used CMR parameters as a primary endpoint are presented

Agreement of left ventricular mass in steady state free precession and delayed enhancement MR images: implications for quantification of fibrosis in congenital and ischemic heart disease

<p>Abstract</p> <p>Background</p> <p>Left ventricular mass (LVM) is used when expressing infarct or fibrosis as a percentage of the left ventricle (LV). Quantification of LVM is interchangeably carried out in cine steady state free precession (SSFP) and delayed enhancement (DE) magnetic resonance imaging (MRI). However, these techniques may yield different LVM. Therefore, the aim of the study was to compare LVM determined by SSFP and DE MRI in patients and determine the agreement with these sequences with ex vivo data in an experimental animal model.</p> <p>Methods</p> <p>Ethics committees approved human and animal studies. Informed written consent was obtained from all patients. SSFP and DE images were acquired in 60 patients (20 with infarction, 20 without infarction and 20 pediatric patients). Ex vivo MRI was used as reference method for LVM in 19 pigs and compared to in vivo SSFP and DE.</p> <p>Results</p> <p>LVM was greater in SSFP than in DE (p < 0.001) with a bias of 5.0 ± 6.7% in humans (r²= 0.98), and a bias of 7.3 ± 6.7% (p < 0.001) in pigs (r²= 0.83). Bias for SSFP and DE images compared to ex vivo LVM was -0.2 ± 9.0% and -7.7 ± 8.5% respectively.</p> <p>Conclusions</p> <p>LVM was higher when measured with SSFP compared to DE. Thus, the percentage infarction of the LV will differ if SSFP or DE is used to determine LVM. There was no significant difference between SSFP and ex vivo LVM suggesting that SSFP is more accurate for LVM quantification. To avoid intrinsic error due to the differences between the sequences, we suggest using DE when expressing infarct as a percentage of LVM.</p