No abstract

Low-frequency peripheral electrical stimulation using a matrix electrode (PEMS) modulates spinal nociceptive pathways. However, the effects of this intervention on cortical oscillatory activity have not been assessed yet. The aim of this study was to investigate the effects of low-frequency PEMS (4 Hz) on cortical oscillatory activity in different brain states in healthy pain-free participants. In experiment 1, PEMS was compared to sham stimulation. In experiment 2, motor imagery (MI) was used to modulate the sensorimotor brain state. PEMS was applied either during MI-induced oscillatory desynchronization (concurrent PEMS) or after MI (delayed PEMS) in a cross-over design. For both experiments, PEMS was applied on the left forearm and resting-state electroencephalography (EEG) was recording before and after each stimulation condition. Experiment 1 showed a significant decrease of global resting-state beta power after PEMS compared to sham (p = 0.016), with a median change from baseline of −16% for PEMS and −0.54% for sham. A cluster-based permutation test showed a significant difference in resting-state beta power comparing pre- and post-PEMS (p = 0.018) that was most pronounced over bilateral central and left frontal sensors. Experiment 2 did not identify a significant difference in the change from baseline of global EEG power for concurrent PEMS compared to delayed PEMS. Two cluster-based permutation tests suggested that frontal beta power may be increased following both concurrent and delayed PEMS. This study provides novel evidence for supraspinal effects of low-frequency PEMS and an initial indication that the presence of a cognitive task such as MI may influence the effects of PEMS on beta activity. Chronic pain has been associated with changes in beta activity, in particular an increase of beta power in frontal regions. Thus, brain state-dependent PEMS may offer a novel approach to the treatment of chronic pain. However, further studies are warranted to investigate optimal stimulation conditions to achieve a reduction of pain.

No abstract

To develop and validate an automated morphometric analysis framework for the quantitative analysis of geometric hip joint parameters in MR images from the German National Cohort (GNC) study. Materials and Methods:A secondary analysis on 40 participants (mean age, 51 years; age range, 30-67 years; 25 women) from the prospective GNC MRI study (2015-2016) was performed. Based on a proton density-weighted three-dimensional fast spin-echo sequence, a morphometric analysis approach was developed, including deep learning−based landmark localization, bone segmentation of the femora and pelvis, and a shape model for annotation transfer. The centrum-collum-diaphyseal, center-edge (CE), three alpha angles, head-neck offset (HNO), and HNO ratio along with the acetabular depth, inclination, and anteversion were derived. Quantitative validation was provided by comparison with average manual assessments of radiologists in a cross-validation format. Paired-sample t tests with a Bonferroni-corrected significance level of .005 were employed alongside mean differences and 10th/90th percentiles, median absolute deviations (MADs), and intraclass correlation coefficients (ICCs).Results: High agreement in mean Dice similarity coefficients was achieved (average of 97.52% 6 0.46 [standard deviation]). The subsequent morphometric analysis produced results with low mean MAD values, with the highest values of 3.34° (alpha 03:00 o'clock position) and 0.87 mm (HNO) and ICC values ranging between 0.288 (HNO ratio) and 0.858 (CE) compared with manual assessments. These values were in line with interreader agreements, which at most had MAD values of 4.02° (alpha 12:00 o'clock position) and 1.07 mm (HNO) and ICC values ranging between 0.218 (HNO ratio) and 0.777 (CE). Conclusion:Automatic extraction of geometric hip parameters from MRI is feasible using a morphometric analysis approach with deep learning.

Myasthenia gravis (MG) is often accompanied with muscle weakness; however, little is known about mechanical adaptions of the affected muscles. As the latter can be assessed using ultrasound shear wave elastography (SWE), this study characterizes the biceps brachii muscle of 11 patients with MG and compares them with that of 14 healthy volunteers. Simultaneous SWE, elbow torque and surface electromyography measurements were performed during rest, maximal voluntary contraction (MVC) and submaximal isometric contractions (up to 25%, 50% and 75% MVC) at different elbow angles from flexion to extension. We found that, with increasing elbow angle, maximum elbow torque decreased (p < 0.001), whereas muscle stiffness increased during rest (p = 0.001), MVC (p = 0.004) and submaximal contractions (p < 0.001). Muscle stiffness increased with increasing contraction intensities during submaximal contractions (p < 0.001). In comparison to the healthy cohort, muscle stiffness of MG patients was 2.1 times higher at rest (p < 0.001) but 8.93% lower in active state (75% MVC, p = 0.044). We conclude that (i) increased muscle stiffness shown by SWE during rest might be an indicator of MG, (ii) SWE reflects muscle weakness and (iii) SWE can be used to characterize MG muscle.