Inter-session Variability Research Articles

Frame-Based Phone Classification Using EMG Signals

This paper evaluates the impact of inter-speaker and inter-session variability on the development of a silent speech interface (SSI) based on electromyographic (EMG) signals from the facial muscles. The final goal of the SSI is to provide a communication tool for Spanish-speaking laryngectomees by generating audible speech from voiceless articulation. However, before moving on to such a complex task, a simpler phone classification task in different modalities regarding speaker and session dependency is performed for this study. These experiments consist of processing the recorded utterances into phone-labeled segments and predicting the phonetic labels using only features obtained from the EMG signals. We evaluate and compare the performance of each model considering the classification accuracy. Results show that the models are able to predict the phonetic label best when they are trained and tested using data from the same session. The accuracy drops drastically when the model is tested with data from a different session, although it improves when more data are added to the training data. Similarly, when the same model is tested on a session from a different speaker, the accuracy decreases. This suggests that using larger amounts of data could help to reduce the impact of inter-session variability, but more research is required to understand if this approach would suffice to account for inter-speaker variability as well.

Influence of Keratoconus Severity on Detecting True Progression with Scheimpflug Imaging and Anterior Segment Optical Coherence Tomography.

To determine consistent change over time in keratoconus disease, it is necessary to establish progression cut-off values based on intersession variability of the device used to monitor the cornea. The aim of this study was to analyze the accuracy of corneal parameters using Scheimpflug tomography and anterior segment optical coherence tomography in healthy and keratoconic eyes of varying severity to determine the cut-off values that indicate real progression. Three repeated measurements of each cornea of healthy (20 eyes) and keratoconic eyes (mild = 16, moderate = 25 and severe = 20) were recorded using Pentacam and Casia SS-1000 devices, which were repeated 2-3 weeks later. K1, K2, maximal anterior and posterior keratometry, and corneal thickness at the thinnest location (TCT) were collected. The accuracy was excellent with both devices; however, the Casia device presented better repeatability and reproducibility in all parameters in all groups compared to the Pentacam. The cut-off of the Pentacam and Casia in the mild stage were lower (K1 = 0.50 and 0.37 D; K2 = 0.51 and 0.37 D; Kmax-A = 1.24 and 0.65 D; Kmax-P = 0.38 and 0.17 D; TCT = 19.64 and 11.19 µm) than that of the severe stage (K1 = 1.09 and 0.88 D; K2 = 1.41 and 0.87 D; Kmax-A = 2.74 and 2.15 D; Kmax-P = 0.82 and 0.22 D; TCT = 28.68 and 14.83 µm). These results show that the greater the keratoconus severity, the greater the change that must occur for it to be considered real.

Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait

Twenty-one experienced runners completed three treadmill running sessions on different days. Each session consisted of three consecutive 2 min trials at self-selected speeds (RPE = 3, 5, and 7). An eight-camera marker-less motion capture system and instrumented pressure treadmill (TM) collected data over the final ~25 s at each speed. Lower extremity joint angles (ankle, knee, and hip) and segmental angles (pelvis and trunk) were computed for each trial with foot contact and toe off being kinematically determined. Spatiotemporal metrics (ground contact time, step length, and cadence) were measured via TM and compared to their kinematically derived counterparts. All spatiotemporal metrics demonstrated excellent agreement (ICCs > 0.98). Both intra-trial and inter-session variability, averaged across the entire running cycle, for all lower extremity joint angles in all planes were low (intra-trial: sagittal = 2.0°, frontal = 1.2°, and transverse = 1.9°; inter-session: sagittal = 1.4°, frontal = 0.8°, and transverse = 1.3°). Discrete measures of lower extremity joint and segmental angles were evaluated for inter-session reliability at foot contact, toe off, and peak value during the stance phase. On average, discrete measures demonstrated good reliability (ICCsagittal = 0.85, ICCfrontal = 0.83, and ICCtransverse = 0.77) with average standard error of measurement < 1°. Marker-less motion capture reliably measured treadmill running kinematics in a group of runners demonstrating heterogenous foot strike patterns (13 rearfoot strike and 8 non-rearfoot strike) across a range of speeds (2.67–4.44 m/s).

Intersession Variability of Knee Extension Kinetics Using a Strain Gauge Device With Differing Clinically Practical Physical Constraints.

Intrasession reliabilities of isometric knee extension kinetics via portable strain gauge have been reported across several knee joint angles and constraints. However, intersession variabilities, which are more valuable, have yet to be determined. Therefore, we aimed to quantify the intersession variability of knee extension kinetics over 3 testing sessions using an affordable and portable strain gauge. Participants performed maximum voluntary isometric contractions of the knee extensors over 3 sessions. Eleven (6 men and 5 women; 31 [6.4]y) volunteers performed maximum voluntary isometric contractions in constrained (isokinetic setup with thigh and chest straps) and unconstrained (treatment plinth) conditions. Peak force (PF), peak rate of force development, rate of force development (RFD), and impulse (IMP) from 20% to 80% of PF were assessed. Means, SDs, percentage changes, minimal detectable changes, coefficients of variation (CV), and intraclass correlation coefficients (ICC) were calculated and reported. PF had the lowest intersession variability regardless of condition (CV = 5.5%-13.8%, ICC = .67-.93). However, variability of peak rate of force development (CV [range] = 12.2%-24.7%, ICC = .50-.78), RFD (CV = 10.0%-26.8%, ICC = .48-.84), and IMP (CV = 15.2%-35.4%, ICC = .44-.88) was moderate at best. The constrained condition (CV [SD] = 14.1% [4.8%], ICC = .74 [.08]) had lower variability compared with the plinth (CV = 19.8% [7.9%], ICC = .68 [.15]). Variability improved from sessions 1 to 2 (CV = 20.4% [7.7%], ICC = .64 [.14]) and to sessions 2 to 3 (CV = 15.3% [6.4%], ICC = .76 [.10]). PF can be assessed regardless of setup. However, RFD and IMP changes across sessions should be approached with caution. Backrests and thigh straps improve RFD and IMP variability, and at least 1 familiarization session should be provided before relying on knee-extensor kinetics while utilizing a portable strain gauge.

A test-retest resting, and cognitive state EEG dataset during multiple subject-driven states

Here we present a test-retest dataset of electroencephalogram (EEG) acquired at two resting (eyes open and eyes closed) and three subject-driven cognitive states (memory, music, subtraction) with both short-term (within 90 mins) and long-term (one-month apart) designs. 60 participants were recorded during three EEG sessions. Each session includes EEG and behavioral data along with rich samples of behavioral assessments testing demographic, sleep, emotion, mental health and the content of self-generated thoughts (mind wandering). This data enables the investigation of both intra- and inter-session variability not only limited to electrophysiological changes, but also including alterations in resting and cognitive states, at high temporal resolution. Also, this dataset is expected to add contributions to the reliability and validity of EEG measurements with open resource.

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions.

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T2 relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T2 relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space () in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T2 spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92.

Clothing condition does not affect meaningful clinical interpretation in markerless motion capture

Markerless motion capture allows whole-body movements to be captured without the need for physical markers to be placed on the body. This enables motion capture analyses to be conducted in more ecologically valid environments. However, the influences of varied clothing on video-based markerless motion capture assessments remain largely unexplored. This study investigated two types of clothing conditions, “Sport” (gym shirt and shorts) and “Street” (unrestricted casual clothing), on gait parameters during overground walking by 29 participants at self-selected speeds using markerless motion capture. Segment lengths, gait spatiotemporal parameters, and lower-limb kinematics were compared between the two clothing conditions. Mean differences in segment length for the forearm, upper arm, thigh, and shank between clothing conditions ranged from 0.2 cm for the forearm to 0.9 cm for the thigh (p < 0.05 for thigh and shank) but below typical marker placement errors (1 – 2 cm). Seven out of 9 gait spatiotemporal parameters demonstrated statistically significant differences between clothing conditions (p < 0.05), however, these differences were approximately ten times smaller than minimal detectable changes in movement-related pathologies including multiple sclerosis and cerebral palsy. Hip, knee, and ankle joint angle root-mean-square deviation values averaged 2.6° and were comparable to previously reported average inter-session variability for this markerless system (2.8°). The results indicate that clothing, a potential limiting factor in markerless motion capture performance, would negligibly alter meaningful clinical interpretations under the conditions investigated.

A Domain Adaptation-Based Method for Classification of Motor Imagery EEG

Non-stationarity of EEG signals lead to high variability across sessions, which results in low classification accuracy. To reduce the inter-session variability, an unsupervised domain adaptation method is proposed. Arithmetic mean and covariance are exploited to represent the data distribution. First, overall mean alignment is conducted between the source and target data. Then, the data in the target domain is labeled by a classifier trained with the source data. The per-class mean and covariance of the target data are estimated based on the predicted labels. Next, an alignment from the source domain to the target domain is performed according to the covariance of each class in the target domain. Finally, per-class mean adaptation is required after covariance alignment to remove the shift of data distribution caused by covariance alignment. Two public BCI competition datasets, namely the BCI competition III dataset IVa and the BCI competition IV dataset IIa were used to evaluate the proposed method. On both datasets, the proposed method effectively improved classification accuracy.

Seismocardiographic Signal Variability During Regular Breathing And Breath Holding

<h3>Introduction</h3> Seismocardiography signals (SCG) are acoustic vibrations generated by heart activity and measured non-invasively at the chest surface. The potential utility of SCG for HF diagnosis and monitoring may be limited by signal variability. Respiration is a known, yet not well understood, source of variability. The objective of this pilot study was to quantify the SCG variability during breathing maneuvers. <h3>Methods</h3> SCG, electrocardiography (ECG) and airflow signals were acquired in two healthy subjects during normal breathing and breath holding (at end-inspiration and end-expiration while the glottis remained open). Data recording was repeated three times, while subjects resting on a 45 degree-elevated tilt table. The SCG events were detected and segmented utilizing the ECG R wave. Although the waveform varied, using unsupervised machine learning (k-medoid clustering) one could separate SCG waveforms during breathing into two "clusters" of similar events. The SCG variability was calculated using Dynamic Time Warping (DTW). The time interval between SCG1 and SCG 2 (which corresponds to the heart-sound ejection-period or HSEP) was also calculated. <h3>Results</h3> Table 1 shows the mean and standard deviation (SD) of the following SCG features: intra-cluster variability, inter-session variability and HSEP during regular breathing, breath hold at end inspiration and end expiration. SCG average intra-cluster and inter session variability was 27% and 24% lower, respectively, during breath holding for both end-inspiration and end-expiration compared to normal breathing. In addition, HSEP was 12% lower during breath holding compared to normal breathing. <h3>Conclusion</h3> Appropriate accounting for SCG variability introduced by breathing appears to account for a significant portion of the signal variability. Hence algorithms integrating respiratory changes may lead to more robust SCG analysis for HF diagnosis and monitoring. In addition, the observed respiration-dependent variability may prove to be a useful feature for HF management. Further studies are needed to confirm these findings in HF patients.

Inter-Person Differences in Isometric Coactivations of Triceps Surae and Tibialis Anterior Decrease in Young, but Not in Older Adults After 14 Days of Bed Rest

We examined activation patterns of the gastrocnemius medialis (GM), gastrocnemius lateralis (GL), soleus (SO), and tibialis anterior (TA) muscles in eight older (58.4 ± 3.3 years) and seven young (23.1 ± 2.9 years) participants, before and after 14 days of horizontal bed rest. Visual feedback on the exerted muscle torque was provided to the participants. The discharge patterns of individual motor units (MUs) were studied in three repetitions of isometric plantar flexion at 30 and 60% of Maximum Voluntary Contraction (MVC), before, and 1 day after the 14-day bed rest, respectively. In the GL and GM muscles, the older participants demonstrated higher MU discharge rates than the young, regardless of the contraction level, both before and after the bed rest. In the TA and SO muscles, the differences between the older and young participants were less consistent. Detailed analysis revealed person-specific changes in the MU discharge rates after the bed rest. To quantify the coactivation patterns we calculated the correlation coefficients between the cumulative spike trains of identified MUs from each muscle, and measured the root mean square difference of the correlation coefficients between the trials of the same session (intra-session variability) and between different sessions (inter-session variability) in each participant (intra-person comparison) and across participants (inter-person comparison). In the intra-person comparison, the inter-session variability was higher than the intra-session variability, either before or after the bed rest. At 60% MVC torque, the young demonstrated higher inter-person variability of coactivation than the older participants, but this variability decreased significantly after the bed rest. In older participants, inter-person variability was consistently lower at 60% than at 30% MVC torque. In young participants, inter-person variability became lower at 60% than at 30% MVC torque only after the bed rest. Precaution is required when analyzing the MU discharge and coactivation patterns, as individual persons demonstrate individual adaptations to aging or bed rest.

Statistical Evaluation of Factors Influencing Inter-Session and Inter-Subject Variability in EEG-Based Brain Computer Interface

A cognitive alteration in the form of diverse mental states has a significant impact on the performance of electroencephalography (EEG) based brain computer interface (BCI). Such alterations include change in concentration levels commonly recognized as being indicated by the alpha rhythm, drowsiness or mental fatigue which occurs during EEG signal acquisition. Change in mental state give rise to a challenge of variability in EEG characteristics across sessions and subjects. Consequently, this variability constitutes to low intention detection rate (IDR) that renders BCI performance unreliable. This study investigates the impact of multiple factors that lead to the poor performance of the EEG-BCI. Five factors (i) concentration level, (ii) selection of independent components(IC), (iii) inter-session variability, (iv) inter-subject variability, and (v) classification methods on the IDR in EEG based BCI. The alpha rhythm, as the indicator of concentration level, is validated, and the relationship between the alpha rhythm and the IDR is studied among sessions. In addition, ICs are examined to determine their effects on the IDR across sessions. The possibility of two sessions to contain similar EEG characteristics is also examined, where both sessions are acquired from the same subject in different days. Moreover, the possibility of two different subjects to containing similar EEG characteristics is examined. Furthermore, to conquer the challenge of variability in EEG dynamics a feature transfer learning (TL) approach is proposed in this study. Furthermore, three classification methods (TL, KNN and NB) are examined and compared to determine whether multi-source neural information can improve the classification accuracy of individual sessions or subjects. Three EEG datasets acquired using different paradigms are used for experiments. The datasets include steady state motion visual evoked potential (SSMVEP), motor imagery (MI) and BCI competition IV-a dataset. Experimental results have shown that selection of independent components has an effect on the IDR. In this case IC-2 and IC-11 achieved a lowest and highest accuracies of 51% and 100% for SSMVEP datasets, while IC-9 and the double-component (IC-2 and IC-13) achieved a lowest and highest accuracies of 40% and 69% for MI datasets respectively. The second experiment demonstrated that higher alpha rhythm, depicted by a lower IDR corresponds to a lower concentration level. While a lower alpha rhythm depicted by a higher IDR corresponds to a higher concentration level. Moreover, variability within sessions can significantly deteriorate intention detection rate across sessions. As such a decline in accuracy from 82% to 61%, and from 56% to 44% was observed across both SSMVEP and MI sessions during inter-session experiment respectively. Integration of samples from different sessions but same subject resulted in a highest accuracy of 65%, 59% and 40% for SSMVEP, MI and BCI competition dataset. Integration of samples from different subjects resulted in a highest accuracy of 65%, 44% and 48% for SSMVEP, BCI competition and MI datasets. When three classifiers are evaluated and compared to determine whether multi-source neural information can improve the classification accuracy of individual sessions and subjects or domains, both KNN and NB achieved highest accuracies of 59% and 52% respectively, while TL showed a significant increase with an accuracy of 98% achieved using SSMVEP sessions. In a similarly manner both KNN and NB achieved highest accuracies of 49% and 42% respectively using SSMVEP subjects, while TL showed a significant increase with an accuracy of 64% achieved. Furthermore, when 9 MI subject acquired from BCI competition dataset, both KNN and NB achieved highest accuracies of 68% and 65% respectively, while a significant increase in accuracy was observed when TL is used with accuracy of 99% achieved. In conclusion, the change of alpha rhythm magnitude among sessions significantly affect the IDR across sessions. While component selection across sessions has significant effects due to non-linear and non-stationary nature of EEG signals. Moreover, merging of ICs from different sessions, and inter-subject factor introduce challenges of overfitting resulting in low IDR. The classification methods are also found critical, because some advanced classification methods can improve the classification accuracy.

Training load responses to football game profile-based training (GPBT) formats: effects of locomotive demands manipulation.

The aim of this study was to compare internal and external load profiles of different game profile-based training (GPBT) formats among elite young football players. Twenty-one participants (age: 18.7 ± 0.6 years) performed three sessions of three GPBT formats, which were matched for training volume but structured with different high-speed running and sprint demands: i) performed along linear paths (GPBT-L); ii) performed as repetitive actions of short distance including many multi-directional changes of direction (GPBT-S) and, iii) a combination of the other two protocols, that is linear high-speed runs and sprint efforts with a single change of direction (GPBT-M). External load outputs were collected using GPS units, physiological and perceptual responses were monitored with heart rate (HR) monitors, and ratings of perceived exertion (RPE), respectively. While no differences were found between formats for HR and RPE, distinct external load profiles were observed for high-speed running (HSD) and sprint distances (SD), (GPBT-L > GPBT-M > GBPT-S, all p < 0.05), and high-intensity acceleration and deceleration efforts (HIE), (GPBT-S > GPBT-M > GPBT-L, all p < 0.05). Moreover, the GPBT-S format was characterized by greater intra-session variability for HSD, SD, and HIE (CV% = 24.2%, 16.5% and 20.4%, respectively) and inter-session variability for HSD and SD (CV% = 10% and 15.7%, respectively) compared to the other two formats. Considering their load profiles and the associated reliability scores, football practitioners can implement GPBT formats interchangeably to elicit necessary internal load responses and selectively to prioritize specific external load outputs.

Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications

Tactile sensing is a crucial perception mode for robots and human amputees in need of controlling a prosthetic device. Today, robotic and prosthetic systems are still missing the important feature of accurate tactile sensing. This lack is mainly due to the fact that the existing tactile technologies have limited spatial and temporal resolution and are either expensive or not scalable. In this article, we present the design and implementation of a hardware–software embedded system called SmartHand. It is specifically designed to enable the acquisition and real-time processing of high-resolution tactile information from a hand-shaped multisensor array for prosthetic and robotic applications. During data collection, our system can deliver a high throughput of 100 frames per second, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.7\times $ </tex-math></inline-formula> higher than previous related work. This has allowed the collection of a new tactile dataset consisting of 340 000 frames while interacting with 16 objects from everyday life during five different sessions. Together with the empty hand, the dataset presents a total of 17 classes. We propose a compact yet accurate convolutional neural network that requires one order of magnitude less memory and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15.6\times $ </tex-math></inline-formula> fewer computations compared with related work without degrading classification accuracy. The top-1 and top-3 cross-validation accuracies on the collected dataset are, respectively, 98.86% and 99.83%. We further analyze the intersession variability and obtain the best top-3 leave-one-out-validation accuracy of 77.84%. We deploy the trained model on a high-performance ARM Cortex-M7 microcontroller achieving an inference time of only 100 ms minimizing the response latency. The overall measured power consumption is 505 mW. Finally, we fabricate a new control sensor and perform additional experiments to provide analyses on sensor degradation and slip detection. This work is a step forward in giving robotic and prosthetic devices a sense of touch by demonstrating the practicality of a smart embedded system that uses a scalable tactile sensor with embedded tiny machine learning.

The Effects of Normalisations on Intra-Session and Inter-Session Variability of Surface and Fire-Wire EMG in Clinical Gait Analysis

The Effects of Normalisations on Intra-Session and Inter-Session Variability of Surface and Fire-Wire EMG in Clinical Gait Analysis

Improving reproducibility of motor evoked potentials in mice

BackgroundIn preclinical research involving murine models of neurological diseases, Motor Evoked Potentials (MEPs) can detect pathological alterations in nerve conduction throughout the cortico-spinal tract. In mice, MEPs elicited by electrical stimulation of the motor cortex can be performed with epicranial or subdermal electrodes such as implanted screws or removable needles, which are associated with invasive surgery and variability in placement of the stimulating electrodes, respectively. MethodsWe compared MEPs induced by epicranial or subcutaneous stimulation with a non-invasive surface cup electrode over five recording sessions, in healthy C57BL/6 mice. Additionally, using surface stimulation, we examined the recordings obtained with intramuscular needles or surface electrodes to understand if MEP reproducibility could be improved. ResultsResting motor threshold (RMT), MEP latency and amplitude were comparable among the different stimulation methods. Epicranial, subcutaneous and surface stimulation techniques presented good repeatability over time, with surface stimulation showing a significantly reduced inter-session variability. Compared with intramuscular needles, MEPs recorded with surface electrode showed reduced peak-to-peak amplitude at all timepoints. RMT and MEP latency were comparable with both recording methods. On the other hand, amplitudes recorded with the surface electrode presented a significantly lower inter-session variance, resulting in improved repeatability. ConclusionOverall, there is evidence for highly reproducible results using different stimulating methods, with indication for reduced inter-session variability for surface stimulation. Moreover, MEP recording with surface electrode provided a decrease in amplitude variability over time, indicating improved measurement stability when considering amplitude as functional outcome in longitudinal studies.

Concurrent Achilles tendon vibration and tibial nerve stimulation to estimate persistent inward current strength in motoneurons.

Vibratory (Tvib) and sustained (Tsust) torque responses to concurrent Achilles tendon vibration and neuromuscular electrical stimulation applied over the muscle belly (vib+stim) are used as indicators of motoneuron facilitation and, theoretically, persistent inward current strength. However, neuromuscular electrical stimulation (NMES) applied to the nerve trunk may potentiate motoneuronal excitability more than muscle belly NMES, yet it remains unclear whether NMES applied over the nerve evokes robust Tvib and Tsust responses when used during the vib+stim protocol. This study tested whether a nerve-targeted vib+stim protocol elicits Tvib and Tsust responses in the ankle plantar flexors with acceptable intra- and inter-session reliability. Fifteen men performed the vib+stim protocol with NMES applied over the tibial nerve three times across two sessions; twice in a single session (5-min apart) to test intrasession reliability and then again after 48 h to test intersession reliability. Intraclass correlation coefficients (ICC3,1), within-participant coefficients of variation (CV) and pairwise comparisons were used to verify relative and absolute reliability as well as systematic bias. Thirteen men presented Tvib and Tsust responses (response rate of 87%). Intrasession Tvib and Tsust ICCs were >0.73 but inter-session ICCs were <0.5. Although no systematic bias was detected (p>0.05), both intra- and inter-session CVs were large (>10%) for Tvib and Tsust. The Vib+stim protocol with NMES applied over the nerve evoked Tvib and Tsust in almost all participants, but presented a large intra- and inter-session variability. The method does not appear to be effective for assessing motoneuron facilitation in the plantar flexors.

Intrasession and Intersession Variabilities of Intraocular Pressure Measured by Noncontact Tonometer in Normal Volunteers.

PurposeIntraocular pressure (IOP) measured using a noncontact tonometer is evaluated by performing multiple measurements because IOP is affected by the ocular pulse. We investigated the relationship between value fluctuations in multiple measurements during noncontact tonometer measurements and cardiac rate.Materials and MethodsForty-two healthy subjects were included and IOP was measured using a noncontact tonometer. The measurement was performed three times each for the right eye and the left eye, for a total of six times. Blood pressure and cardiac rate were measured at the same time as the IOP measurement. Using repeated-measures analysis of variance, we examined whether the measured IOP and cardiac rate fluctuate throughout the day over the course of 4 days.ResultsThere was a fluctuation in the IOP in a sequence only on day 1 of the four measurement days (P < 0.001). The IOP on day 1 tended to be high for the first and second measurements (P = 0.0111–0.0015). Systolic blood pressure and diastolic blood pressure did not fluctuate over the 4 days (P = 0.6247 and 0.7132), but cardiac rate was high only on day 1 (P = 0.0276).ConclusionThe IOP on day 1 tended to be high in the first and second measurements. The IOP measured on days 2–4 did not fluctuate during the sequence of measurements. The cardiac rate measured at the same time as the IOP was high only on the first day.

Neural Dynamics of a Single Human with Long-Term, High Temporal Density Electroencephalography.

We undertake a longitudinal study with high temporal recording density, capturing daily electroencephalograms (EEG) of an individual in an in-situ setting for 370 consecutive days. Resting-state EEG retains a high level of stability over the course of the year, and inter-session variability remains unchanged, whether the sessions are one day, one week, or one month apart. On the other hand, EEG for certain cognitive tasks experience a steady decline in similarity over the same time period. Clustering analysis reveals that days with low similarity scores should not be considered as outliers, but instead are part of a cluster of days with a consistent alternate spectral signature. This has methodological and design implications for the selection of baseline references or templates in fields ranging from neurophysiology to brain-computer interfaces (BCI) and neurobiometrics.

Dynamic Alterations in Blood Flow in Glaucoma Measured with Laser Speckle Contrast Imaging

To assess the repeatability of blood flow velocity index (BFVi) metrics obtained with a recently Food and Drug Administration-cleared laser speckle contrast imaging device, the XyCAM RI (Vasoptic Medical, Inc), and to characterize differences in these metrics among control, glaucoma suspect, and glaucoma participants. Prospective, observational study. Forty-six participants: 20 control, 16 glaucoma suspect, and 10 glaucoma participants, 1 eye per participant. Key dynamic BFVi metrics-mean, peak, dip, volumetric rise index (VRI), volumetric fall index (VFI), time to rise (TtR), time to fall (TtF), blow-out time (BOT), skew, and acceleration time index-were measured in the optic disc, optic disc vessels, optic disc perfusion region, and macula in 4 imaging sessions on the same day. Intrasession and intersession variability were calculated using the coefficient of variation (CV) for each metric in each region of interest (ROI). Values for each dynamic BFVi variable were compared between glaucoma, glaucoma suspect, and control participants using bivariate and multivariate analysis. Pearson correlation coefficients were used to correlate each variable in each ROI with age, intraocular pressure, cup-to-disc ratio (CDR), mean deviation, pattern standard deviation, retinal nerve fiber layer thickness, and minimum rim width. Coefficient of variation for the intrasession and intersession variability for each dynamic BFVi metric in each ROI and differences in each metric in each ROI between each diagnostic group. Intersession CV for mean, peak, dip, VRI, VFI, TtR, and TtF ranged from 3.2 ± 2.5% to 11.0 ± 3.8%. Age, CDR, OCT metrics, and visual field metrics showed significant correlations with dynamic BFVi variables. Peak, mean, dip, VRI, and VFI were significantly lower in patients with glaucoma than in control participants in all ROIs except the fovea. These metrics also were significantly lower in glaucoma patients than glaucoma suspect patients in the disc vessels. Dynamic blood flow metrics measured with the XyCAM RI are reliable, are associated with structural and functional glaucoma metrics, and are significantly different among glaucoma, glaucoma suspect, and control participants. The XyCAM RI may serve as an important tool in glaucoma management in the future.

Monitoring for glaucoma progression with SAP, electroretinography (PERG and PhNR) and OCT

PurposeTo investigate the value of pattern electroretinography (PERG) and photopic negative response (PhNR) in monitoring glaucoma compared to standard clinical tests (standard automated perimetry (SAP) and clinical optic disc assessment) and structural measurements using spectral-domain OCT.MethodsA prospective study included 32 subjects (32 eyes) with ocular hypertension, suspect or early glaucoma monitored for progression with clinical examination, SAP, PERG, PhNR and OCT for at least 4 years. Progression was defined clinically by the documented change of the optic disc and/or significant visual field progression (EyeSuite™ trend analysis). One eye per patient was included in the analysis.ResultsDuring the follow-up, 13 eyes (40.6%) showed progression, whereas 19 remained stable. In the progressing group, all parameters showed significant worsening over time, except for the PhNR, whereas in the stable group only the OCT parameters showed a significant decrease at the last visit. The trend of change over time using linear regression was steepest for the OCT parameters. At baseline, only the ganglion cell complex (GCC) and peripapillary retinal nerve fibre (pRNFL) thicknesses significantly discriminated between the stable and progressing eyes with the area under the ROC curve of 0.72 and 0.71, respectively. The inter-session variability for the first two visits in the stable group was lower for OCT (% limits of agreement within ± 17.4% of the mean for pRNFL and ± 3.6% for the GCC thicknesses) than for ERG measures (within ± 35.9% of the mean for PERG N95 and ± 59.9% for PhNR). The coefficient of variation for repeated measurements in the stable group was 11.9% for PERG N95 and 23.6% for the PhNR, while it was considerably lower for all OCT measures (5.6% for pRNFL and 1.7% for GCC thicknesses).ConclusionsAlthough PERG and PhNR are sensitive for early detection of glaucomatous damage, they have limited usefulness in monitoring glaucoma progression in clinical practice, mainly due to high inter-session variability. On the contrary, OCT measures show low inter-session variability and might have a predicting value for early discrimination of progressing cases.