Associations of nerve conduction parameters and OCT angiography results in adolescents with type 1 diabetes

Aim To evaluate dependence of abnormalities in peripheral nerves and retina in children with type 1 diabetes (T1D) using optical coherence tomography angiography (OCTA) and nerve conduction studies (NCS). Material and methods 50 adolescents with T1D without any signs and symptoms of diabetic retinopathy and neuropathy (mean age 16.92±1.6 years, diabetes duration 6.88 ±4.34years) were included. In OCTA capillary plexuses superficial (SCP) and deep (DCP) vessel density: whole, foveal and parafoveal, ganglion cell complex (GCC), loss volume focal (FLV) and global loss volume (GLV) were analyzed in relation to NCS parameters (motor nerves median and tibial potential amplitude (CMAP), velocity (CV), distal latency (DML) and F wave and sensory nerves median and sural potential amplitude (SNAP), CV and distal latency (DSL). Results We detected the correlations between median sensory SNAP and GCC (r = -0.3, p <0.04), motor nerves tibial DML and CV and FLV (respectively r = -0.53, p<0.001, and r = -0.34, p<0.05), and median DML and GLV (r = 0.47, p<0.001). Vessel densities were related to changes in motor nerves tibial velocity (whole SCP r = 0.43, p <0.01, parafoveal SCP r = 0.41, p <0.01), CMAP (parafoveal SCP r = -0.35, p<0.03), median DML (whole DC r = 0.36, p<0.03, foveal DCP r = 0.37, p<0.02) and in sensory median SNAP (whole SCP r = -0.31, p<0.05). Conclusions In adolescents with T1D without diabetic neuropathy and retinopathy we detected associations between NCS and OCT and OCTA parameters, regarding decreased GCC and density of superficial and deep vessel plexuses in relation to DML and CV and amplitudes of sensory and motor potential.

A Method to Obtain Parameters of One-Column Jansen–Rit Model Using Genetic Algorithm and Spectral Characteristics

In this paper, a method of obtaining parameters of one-column Jansen–Rit model was proposed. Methods present in literature are focused on obtaining parameters in an on-line manner, producing a set of parameters for every point in time. The method described in this paper can provide one set of parameters for a whole, arbitrarily long signal. The procedure consists of obtaining specific frequency features, then minimizing mean square error of those features between the measured signal and the modeled signal, using genetic algorithm. This method produces an 8-element vector, which can be treated as an EEG signal feature vector specific for a person. The parameters which were being obtained are maximum postsynaptic potential amplitude, maximum inhibitory potential amplitude, ratio of the number of connections between particular neuron populations, the shape of a nonlinear function transforming the average membrane potential into the firing rate and the input noise range. The method shows high reproducibility (intraclass correlation coefficient for particular parameters ranging from 0.676 to 0.978) and accuracy (ranging from 0.662 to 0.863). It was additionally verified using EEG signal obtained for a single participant. This signal was measured using Emotiv EPOC+ NeuroHeadset.

Lack of cortical or Ia-afferent spinal pathway involvement in muscle force loss after passive static stretching

This study is the first to specifically examine potential sites underlying the decreases in neural activation of muscle and force production after a bout of muscle stretching. However, no changes were found in either the H-reflex or motor-evoked potential amplitude during submaximal contractions.