Intact Subjects Research Articles

Effects of attentional instructions on the behavioral and neural mechanisms of speech auditory feedback control

The present study investigated how instructions for paying attention to auditory feedback may affect speech error detection and sensorimotor control. Electroencephalography (EEG) and speech signals were recorded from 21 neurologically intact adult subjects while they produced the speech vowel sound /a/ and received randomized ±100 cents pitch-shift alterations in their real-time auditory feedback. Subjects were instructed to pay attention to their auditory feedback and press a button to indicate whether they detected a pitch-shift stimulus during trials. Data for this group was compared with 22 matched subjects who completed the same speech task under altered auditory feedback condition without attentional instructions. Results revealed a significantly smaller magnitude of speech compensations in the attentional-instruction vs. no-instruction group and a positive linear association between the magnitude of compensations and P2 event-related potential (ERP) amplitudes. In addition, we found that the amplitude of P2 ERP component was significantly larger in the attentional-instruction vs. no-instruction group. Source localization analysis showed that this effect was accounted for by significantly stronger neural activities in the right hemisphere insula, precentral gyrus, postcentral gyrus, transverse temporal gyrus, and superior temporal gyrus in the attentional-instruction group. These findings suggest that attentional instructions may enhance speech auditory feedback error detection, and subsequently improve sensorimotor control via generating more stable speech outputs (i.e., smaller compensations) in response to pitch-shift alterations. Our data are informative for advancing theoretical models and motivating targeted interventions with a focus on the role of attentional instructions for improving treatment outcomes in patients with motor speech disorders.

Removal of Interference from Electromyogram based on Empirical Mode Decomposition and Correlation Coefficient Thresholding

Introduction:: Electromyography (EMG) signals are contaminated by various noise components. These noises directly degrade the EMG processing performance, thereby affecting the classification accuracy of the EMG signals for implementing various hand movements of the prosthetic arm from the amputee’s residual muscle. Methods:: This study mainly aims to denoise the EMG signals using the empirical mode decomposition (EMD) and correlation coefficient thresholding (CCT) methods. The noisy EMG signal is obtained from NinaPro Database 2. Then, EMD is used to decompose it into intrinsic mode functions. Each hand movement noise is identified within specific modes and removed separately using correlation coefficient–dependent thresholding and wavelet denoising. The performance metrics signal-to-noise ratio (SNR) and root mean square error (RMSE) were used to evaluate the noise removal performance from the EMG signals of five intact subjects. The proposed method outperforms the wavelet denoising method in terms of noise interference removal. In this method, the SNR is obtained in the 17-22 dB range with a very low RMSE. Results:: The experimental results illustrate that the proposed method removes noise from six repetitions of six movements performed by five subjects. This study explores the special characteristics of EMD and demonstrates the possibility of using the EMD-based CCT filter for denoising EMG signals. Conclusion:: The proposed filter is more efficient than wavelet denoising in removing noise interference. It can also be used in any application that requires EMG signal filtering at the preprocessing stage, such as EMG pattern recognition.

Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%.

A surface electromyography based hand gesture recognition framework leveraging variational mode decomposition technique and deep learning classifier

Deciphering and classifying surface electromyography (sEMG) signals is highly essential in rehabilitation robotics, myoelectric prosthetic control, sign languages and human–computer-interface. Researchers have generally focused on utilizing multi-channel sEMG signals for gesture classification. However, in case of amputees, the residual muscles are limited and the gesture classification needs to be reframed with minimum sEMG channels. Moreover, another key problem in the gesture classification system is the nonlinear and non-stationary nature of sEMG signals, leading to poor generalization ability. Hence, to address the aforementioned problems, this study puts forward a novel deep learning classifier framework which leverages the potentials of variational mode decomposition (VMD) technique and a hybrid convolutional neural network–long short term memory (CNN–LSTM) classifier model to recognize the hand gestures from single channel sEMG signals. Collecting the sEMG signals from forearm muscles of 25 intact subjects for ten functional and grasping actions, this work implements a VMD technique to identify the prominent frequency modes in the sEMG signals. From the decomposed modes of sEMG signals, the prominent intrinsic mode functions (IMFs) are extracted through spectral analysis to minimize the computation burden on the hybrid classifier model. Furthermore, as the muscle contractions result in substantial temporal dependencies, this work exploits the potentials of CNN and LSTM networks and extracts the spatiotemporal features of the sEMG signals for various hand gestures. The experimental results corroborate that the proposed classifier framework can achieve an average classification accuracy of 98.04% and provide 3% improvement in the classification accuracy compared to conventional CNN classifier.

Plasma based assessment of extracellular vesicles biomarkers in Alzheimer’s Disease

AbstractBackgroundCurrently there are no widely accepted diagnostic tests for Alzheimer’s disease (AD). Although there is much excitement in the field of biomarker development, most proposed tests for AD are expensive, require highly specialized sample handling, invasive approaches or radiation exposure. The goal of this project was to develop non‐invasive AD biomarker assessment using nanoflow cytometry to examine proteins on circulating brain‐derived extracellular vesicles in plasma.MethodPlasma samples were collected from a population of individuals clinically diagnosed with MCI and mild, moderate or severe Alzheimer’s disease and cognitively intact control subjects. Plasma was incubated with fluorescently‐conjugated antibodies against candidate biomarkers including oligomeric amyloid, fibrillar amyloid, amyloidβ‐42, pTau‐T181, pTau‐T217, pTau‐S235, Ubiquitin, Neurofilament, α‐synuclein and synaptophysin. After incubation, labelled events were quantified using the Apogee Microplus nanoflow cytometer. Using Receiver‐Operator Curve (ROC) analysis, the ability of individual markers and various combinations to distinguish diagnostic groups was determined.ResultNanoflow cytometry quantification of pTau‐T181, pTau‐T217, pTau‐S235, pTau‐T231, Aβ‐42 (among others) successfully distinguished MCI and AD plasma from healthy controls. Using ROC analysis, our most sensitive antibodies (pTau‐ T231) separate controls from AD patients with an AUC of 0.96 (Sens: 0.95/Spec: 1.0). Other prominent antibodies include pTau 181 with an AUC of 0.93 (Sens: 0.92/Spec: 0.92). The most promising combination of antibodies combines beta‐amyloid42 and pTau‐181 to separate AD patients from controls with an AUC of 0.960 (Sens: 0.93, Spec: 0.92). Isolation of EVs from plasma and validation of marker labeling was performed with Western Blot, ELISA and transmission electron microscopy.ConclusionHere we demonstrate the use of nanoflow cytometry to directly quantitate proteins on circulating extracellular vesicles in plasma from MCI and AD patients. The assays are rapid, inexpensive, directly quantitative, and require mini sample volume and manipulation. Future studies are required involving a larger population of patients who have had ‘gold standard’ biomarkers (Amyloid and tau CSF measurements), longitudinal follow‐up and/or autopsy confirmed diagnosis of AD.

Spatial mapping of posture-dependent resistance to passive displacement of the hypertonic arm post-stroke

BackgroundMuscles in the post-stroke arm commonly demonstrate abnormal reflexes that result in increased position- and velocity-dependent resistance to movement. We sought to develop a reliable way to quantify mechanical consequences of abnormal neuromuscular mechanisms throughout the reachable workspace in the hemiparetic arm post-stroke.MethodsSurvivors of hemiparetic stroke (HS) and neurologically intact (NI) control subjects were instructed to relax as a robotic device repositioned the hand of their hemiparetic arm between several testing locations that sampled the arm's passive range of motion. During transitions, the robot induced motions at either the shoulder or elbow joint at three speeds: very slow (6°/s), medium (30°/s), and fast (90°/s). The robot held the hand at the testing location for at least 20 s after each transition. We recorded and analyzed hand force and electromyographic activations from selected muscles spanning the shoulder and elbow joints during and after transitions.ResultsHand forces and electromyographic activations were invariantly small at all speeds and all sample times in NI control subjects but varied systematically by transport speed during and shortly after movement in the HS subjects. Velocity-dependent resistance to stretch diminished within 2 s after movement ceased in the hemiparetic arms. Hand forces and EMGs changed very little from 2 s after the movement ended onward, exhibiting dependence on limb posture but no systematic dependence on movement speed or direction. Although each HS subject displayed a unique field of hand forces and EMG responses across the workspace after movement ceased, the magnitude of steady-state hand forces was generally greater near the outer boundaries of the workspace than in the center of the workspace for the HS group but not the NI group.ConclusionsIn the HS group, electromyographic activations exhibited abnormalities consistent with stroke-related decreases in the stretch reflex thresholds. These observations were consistent across repeated testing days. We expect that the approach described here will enable future studies to elucidate stroke's impact on the interaction between the neural mechanisms mediating control of upper extremity posture and movement during goal-directed actions such as reaching and pointing with the arm and hand.

Resilience to Alzheimer’s Disease: Unraveling the Molecular Mechanisms From Post‐Mortem Tissue

AbstractBackgroundApproximately one‐third of elderly individuals remain cognitively intact, despite substantial Alzheimer’s Disease (AD) neuropathological changes. The exact, molecular underpinnings towards resilience in AD remain to be elucidated. Here, we set out to investigate changes in gene expression in the prefrontal cortex of cognitively intact donors with AD pathology, referred to as resilient, and compare them to AD and control subjects.MethodPost‐mortem tissue of individuals with an intermediate or high AD neuropathological score according to the NIA‐AA, but with intact cognition, AD patients with high pathology and low cognition, and age‐matched controls (i.e. cognitively intact subjects without a neurological or psychological disorders) was obtained from the Netherlands Brain Bank. Bulk RNA sequencing was performed on RNA isolated from the prefrontal cortex. Several molecular mechanisms of interest were further immunohistochemically confirmed in post‐mortem tissue.ResultAnalysis revealed transcriptional changes between resilient and AD donors including mitochondrial functioning, metallothionein signaling and the Tyrobp network. Additionally, we found profound changes in the extracellular matrix, including changes in perineuronal nets (PNNs) between resilient, AD and control donors. Using immunohistochemistry, we demonstrated altered morphology and a reduction of PNNs in AD while PNNs were more preserved in resilient donors compared to control donors.ConclusionOur data indicates that changes in mitochondria, altered signaling in astrocytes and microglia via metallothionein and Tyrobp, respectively, and preservation of PNNs were associated with intact cognition despite the presence of AD pathology and could ultimately point to novel targets for therapeutic development.

Sex‐ or depression‐specific relationships with CSF sAPPα, sAPPβ and their associations with CSF sTREM2 in older adults

AbstractBackgroundFemale sex and depression are associated with increased risk for Alzheimer’s disease (AD) but the mechanisms for the increased risk remain poorly understood. Increased brain amyloid deposition and microglial activation have both been implicated; however, the role of sex‐ and depression‐related effects on microglial activation remain understudied.Both sAPPα and sAPPβ, cleavage products of non‐amyloidogenic and amyloidogenic APP cleavage, respectively, have been reported to induce microglial activation. These observations prompted us to examine the relationships between sex, depression, and levels of sAPPα and sAPPβ. CSF levels of soluble triggering receptor expressed on myeloid cells 2 (sTREM2), which is a possible proxy for phagocytic anti‐inflammatory TREM2‐dependent microglial activation, was also examined.MethodFifty‐one cognitively intact subjects (31 subjects with late‐life major depression (LLMD) and 20 controls) aged 60 years and older, completed a 3‐year longitudinal study. Independent‐samples Mann‐Whitney‐U tests were used to examine group differences by diagnosis, sex, and APOE genotype for sAPPα and sAPPβ.ResultThere were no significant differences between subjects with LLMD and Controls in CSF sAPPα or sAPPβ levels. Irrespective of diagnosis, females had significantly higher levels of both sAPPα (p = 0.016) and sAPPβ (p = 0.010) compared to males. sAPPα and sAPPβ were significantly correlated with each other (rho = 0.930, p<0.001). In the whole sample, sTREM2 correlated with both sAPPα (rho = 0.462, p = 0.001) and sAPPβ (rho = 0.504, p<0.001). When examining the relationships as a function of diagnosis, sAPPα was found to be associated with sTREM2 in Controls only (rho = 0.474, p = 0.035) while sAPPβ correlated with sTREM2 in LLMD only (rho = 0.370, p = 0.048). When examining the relationship as a function of sex, we found that sTREM2 significantly correlated with both sAPPα (rho = 0.629, p = 0.002) and sAPPβ (rho = 0.673, p = 0.001) in females but not in males (sAPPα: rho = 0.310, p = 0.109; sAPPβ: rho = 0.341, p = 0.076).ConclusionMean CSF sAPPα and sAPPβ levels between Controls and LLMD showed no group differences but were significantly increased in females compared to males. Females but not males showed significant positive associations between CSF sTREM2, sAPPα and sAPPβ. Future studies should determine if these results are associated with differences in microglial activation and to the increased risk for AD and depression in females.

Hand gesture classification framework leveraging the entropy features from sEMG signals and VMD augmented multi-class SVM

To improve the classification accuracy of hand movements from sEMG signals, this paper puts forward a unified hand gesture classification framework which exploits the potentials of variational mode decomposition (VMD) and multi-class support vector machine (SVM). Acquiring the sEMG signals from 25 intact subjects for ten functional activities in real-time, we implement a non-recursive adaptive decomposition technique to sEMG signals and perform power spectral analysis to identify the dominant narrow-band intrinsic mode functions (IMFs) that contain prominent biomarkers. Subsequently, to compute the optimal feature vectors from a set of entropy measures, this work investigates the performance of two techniques namely minimum redundancy and maximum relevance (MRMR) technique and kernel principal component analysis (kPCA). After extracting the optimal set of entropy features, the proposed approach implements a multi-class SVM based on one-vs-one (OVO) strategy to classify the hand gestures. The performance of the multi-class SVM compared with those of the K-nearest neighbor (KNN) and naïve bayes (NB) classifiers highlight that multi-class SVM offers superior performance with an average classification accuracy of 99.98%. Moreover, for statistical analysis of the experimental results, this work performs Friedman test to analyze the significance of the SVM, KNN and NB classifier performances. Finally, the performance comparison of the proposed approach with those of the state-of-the-art techniques highlights the superiority of the proposed framework to improve the hand gesture classification accuracy.

Sensitivity Analysis of CMAP Scan Step Index to Different Stimulation Parameters and Examination of Muscles Affected by Spinal Cord Injury.

Step index (STEPIX) is a recently developed compound muscle action potential (CMAP) scan method for evaluating motor unit loss and remodeling changes. This study investigates the influence of different stimulation parameters during CMAP scan on STEPIX and its examination of muscles affected by spinal cord injury (SCI). CMAP scan of the first dorsal interosseous (FDI) muscle was performed using different stimulus pulse widths (0.1 ms, 0.2 ms) and different numbers of stimuli (500, 1000) in 12 neurologically intact subjects. STEPIX was derived from each CMAP scan of all subjects. A significantly higher STEPIX was obtained using 1000 stimuli than 500 stimuli, while no significant difference in STEPIX was observed using 0.1 and 0.2 ms stimulus pulse widths. STEPIX was further applied to process CMAP scans of the FDI muscle from 13 tetraplegia and 13 healthy control subjects using the same stimulation parameter setting (0.1 ms, 500 stimuli), along with other methods including MScanFit motor unit number estimation (MUNE) and D50. STEPIX was significantly lower for the SCI subjects compared with the healthy control subjects. STEPIX was significantly correlated with MscanFit MUNE and D50, but had a smaller relative width of the overlapping zone (WOZ%) between tetraplegic and healthy control groups compared with MScanFit MUNE and D50. The findings of the study highlight the importance of maintaining a consistent stimulation parameter setting in CMAP scan studies and confirm the usefulness of STEPIX as a convenient CMAP scan parameter for examination of motor unit number changes.

Post Doc Competition (Knowledge Generation) ID 1986617

Background Transcutaneous spinal stimulation (TSS) is emerging as a valuable tool for electrophysiological and clinical assessments. Objective The objectives of this study were to investigate: (1) TSS above and below a spinal cord injury (SCI) lesion results in different recruitment pattern of upper limb (UL) motor pools compared to neurologically intact subjects (NIS), and (2) the relationship between recruitment pattern of UL motor pools and neurological and functional status in individuals with SCI. Methods: Eleven NIS and six participants with cervical SCI were recruited in this study. We compared spinally evoked motor potentials in UL muscles during TSS delivered to the cervical spinal cord between the spinous process of C3-C4 and C7-T1 vertebrae. Results As expected, TSS delivered over the cervical spinal cord in NIS can preferentially activate proximal and distal muscles along the rostrocaudal axis, as well as ipsilateral UL muscles along the mediolateral axis. However, in participants with SCI, the responses registered in UL muscles innervated by motor pools above and below the lesion demonstrated different recruitment compared to NIS, and were dependent on the level, extent, and side of SCI. Conclusions The spatial map obtained from assessing evoked potentials above and below the lesion contributes to the comprehensive understanding of the viability and function of specific UL motor pools, and can be utilized to monitor changes in recruitment patterns of the UL motor pools during neurorehabilitation interventions after SCI.

Actuated Thumb Exoskeleton with Variable Axes of Rotation For Stroke Rehabilitation

Chronic hand impairment is very common amongst stroke survivors. More specifically, movement of the thumb is highly restricted causing a major decrease in the functionality of the entire hand. The non-orthogonal, non-intersecting joint axes of the thumb and the many actuating muscles involved make it very difficult to understand the thumb, let alone provide techniques for rehabilitation. Thus, an exoskeleton that incorporatesthe 5 anatomical Degrees of Freedom (DOF) of the thumb, both for actuation and measuring, was created in order to better understand motor control of the thumb, both in neurologically intact individuals and in stroke survivors. A mounting bracket was developed to affix the exoskeleton to a forearm brace. The exoskeleton was then tested to determine if it restricted movement. The movement space of the thumb was tracked both withthe thumb connected to the exoskeleton and for the free thumb using a Digital Motion Analysis System (DMAS). Neurologically intact subjects were able to cover an average of 72% of their normal thumb working space while wearing the exoskeleton. Most of the restriction occurred in the upper region of the workspace. Thus, the device seems to allow considerable freedom of movement of the thumb, particularly in the region of the workspace used for everyday tasks. Subjects remarked that thumb movement felt quite natural. Further research using this exoskeleton can be done to better understand the impairments that occur in the thumb and thus provide better insight into rehabilitation methods. For example, the use of a system including motors and flexible shafts to drive the joints of the exoskeleton which in turn drives the joints of the thumb might provide a successful mode of rehabilitation.

Sagittal Spinal Alignment in People with Chronic Spinal Cord Injury and Normal Individual: A Comparison Study Using 3D Ultrasound Imaging.

The aim of this study was to compare the sagittal spinal alignment of people with chronic spinal cord injury (SCI) with normal individuals and to determine whether transcutaneous electrical spinal cord stimulation (TSCS) could cause a change in the thoracic kyphosis (TK) and lumbar lordosis (LL) to re-establish normal sagittal spinal alignment. A case series study was conducted, wherein twelve individuals with SCI and ten neurologically intact subjects were scanned using 3D ultrasonography. In addition, three people with SCI having complete tetraplegia participated further to receive a 12-week treatment (TSCS with task-specific rehabilitation) after evaluation of sagittal spinal profile. Pre- and post-assessments were conducted to evaluate the differences in sagittal spinal alignment. The results showed that the TK and LL values for a person with SCI in a dependent seated posture were greater than those of normal subjects for: standing (by TK: 6.8° ± 1.6°; LL: 21.2° ± 1.9°), sitting straight (by TK: 10.0° ± 4.0°; LL: 1.7° ± 2.6°), and relaxed sitting (by TK: 3.9° ± 0.3°; LL: 7.7° ± 1.4°), respectively, indicating an increased risk for spinal deformity. In addition, TK decreased by 10.3° ± 2.3° after the TSCS treatment, showing a reversible change. These results suggest that the TSCS treatment could be used to restore normal sagittal spinal alignment for individuals with chronic SCI.

Change in the Quick Dementia Rating System Across Time in Older Adults with and without Cognitive Impairment.

The Quick Dementia Rating System (QDRS) is a brief, informant-reported dementia staging tool that approximates scores on the Clinical Dementia Rating Scale in patients with Alzheimer's disease (AD). The current study sought to examine change in the QDRS across time, which is necessary for clinical and research efforts. One-hundred ten older adults (intact, mild cognitive impairment [MCI], mild AD, classified with Alzheimer's Disease Neuroimaging Initiative criteria) were rated on the QDRS by an informant and had an amyloid positron emission tomography scan at baseline. The informant re-rated each participant on the QDRS after one year. Dependent t-tests compared the entire sample and various subgroups (e.g., cognitive status, amyloid status) on baseline and follow-up QDRS scores. In the entire sample, the Total score on the QDRS significantly increased (i.e., worsened) on follow-up (p < 0.001). When subgroups were analyzed, the MCI and mild AD subjects showed increasing (i.e., worsening) QDRS Total scores (both p < 0.001), but the intact subjects remained stable over time (p = 0.28). Additionally, those classified as being amyloid positive at baseline showed significantly increased QDRS Total scores at follow-up (p < 0.001) compared to those who were amyloid negative at baseline, whose QDRS Total scores remained stable over time (p = 0.63). The QDRS can potentially demonstrate worsening functioning status across one year, especially in those who have MCI or mild AD and those who are amyloid positive. Therefore, the current results preliminarily suggest that the QDRS may provide an efficient tool for tracking progression in clinical trials in AD.

Uneven Terrain Recognition Using Neuromorphic Haptic Feedback.

Recent years have witnessed relevant advancements in the quality of life of persons with lower limb amputations thanks to the technological developments in prosthetics. However, prostheses that provide information about the foot-ground interaction, and in particular about terrain irregularities, are still missing on the market. The lack of tactile feedback from the foot sole might lead subjects to step on uneven terrains, causing an increase in the risk of falling. To address this issue, a biomimetic vibrotactile feedback system that conveys information about gait and terrain features sensed by a dedicated insole has been assessed with intact subjects. After having shortly experienced both even and uneven terrains, the recruited subjects discriminated them with an accuracy of 87.5%, solely relying on the replay of the vibrotactile feedback. With the objective of exploring the human decoding mechanism of the feedback startegy, a KNN classifier was trained to recognize the uneven terrains. The outcome suggested that the subjects achieved such performance with a temporal dynamics of 45 ms. This work is a leap forward to assist lower-limb amputees to appreciate the floor conditions while walking, adapt their gait and promote a more confident use of their artificial limb.

Breathing stability during sleep and during hypercapnia is dependent on the retrotrapezoid nucleus in adult mice

Background: Eupneic breathing during sleep is determined by the chemical drive arising from central and peripheral chemoreceptors. The retrotrapezoid nucleus (RTN) contains chemoreceptor neurons that are proposed to be critical for ventilatory responses to CO 2 . RTN chemoreceptor neurons can be identified by expression of transcripts for Neuromedin B ( Nmb + ). We hypothesized that the ablation of RTN Nmb + neurons cause alveolar hypoventilation at rest and breathing instability during sleep. Normally, in intact subjects, increasing respiratory drive with hypercapnia produces a robust and more stable breathing pattern compared to resting condition. We furhter hypothesized that in the absence of RTN, hypercapnia will not increase breathing stability and the ventilatory response to CO 2 will be impaired. Methods: Bilateral microinjections of a designer cell-ablation virus, AAV5-Flex-Casp3-TEVP, were placed in the RTN in heterozygous Cre-positive (RTN lesion) and Cre-negative (controls) Nmb-Cre mice and EEG/neck EMG electrodes were implanted. One month later, mice were tested in an unrestrained plethysmography chamber for breathing and sleep/wake recordings in normoxic conditions. Mice were exposed to hyperoxic hypercapnia (F I CO 2 up to 0.09) to assess the central respiratory CO 2 chemoreflex after RTN lesion. Arterial blood gases were also collected in unrestrained unanesthetized conditions. Values are presented as mean ±SD. Results: Ablation of the RTN was complete (&lt;1% of total RTN Nmb + neurons remaining in RTN lesion group) and selective based on counts of neighboring catecholaminergic and serotonergic neurons. Arterial PCO 2 was increased in RTN-lesion mice (n=6) compared to controls (n=7) (49 ± 5 vs. 39 ± 3 mmHg, p&lt;0.001) and arterial PO 2 was reduced (75 ± 11 vs. 89 ± 7 mmHg, p=0.019). Minute-ventilation was reduced in RTN-lesion mice (n=13) compared to control (n=13) (1.7 ± 0.4 vs. 2.4 ± 0.3 mL/min/g, p&lt;0.0001) due to a reduction in tidal volume that was most evident during NREM sleep. Breathing variability was increased in RTN-lesion mice (n=9) compared to controls (n=10) during NREM (22 ± 5 vs. 10 ± 1% of inter-breath variability, p&lt;0.001) and REM sleep (37 ± 8 vs. 25 ±7 % of inter-breath variability, p&lt;0.01) but not during wakefulness (28 ± 9 vs. 21 ±10 % of inter-breath variability, p=0.32). The ventilatory response to CO 2 was attenuated in RTN lesion mice compared to controls (3.2 ±1 vs. 10.4 ±2 mL/min/g at FICO2 0.09, p&lt;0.0001).Finally, during hypercapnia, breathing was more variable in RTN-lesion mice (23 ±10 vs. 3.1 ±0.4 % of inter-breath variability, p&lt;0.0001) compared to control. Summary and conclusion: In sum, RTN neurons contribute to resting alveolar ventilation and eupneic breathing, especially during sleep. The hypercapnic ventilatory response is severely blunted and hypercapnia does not increase breathing stability in the absence of RTN. We conclude that breathing stability during sleep and during hypercapnia is dependent on the RTN. This work is funded by National Institutes of Health, R01HL148004 to Stephen BG Abbott and R01HL108609 to Douglas A Bayliss. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Continuous grip force estimation from surface electromyography using generalized regression neural network.

Grip force estimation is highly required in realizing flexible and accurate prosthetic control. This study presents a method to accurately estimate continuous grip force from surface electromyography (sEMG) under three forearm postures for unilateral amputees. Ten able-bodied subjects and a transradial amputee were recruited. sEMG signals were recorded from six forearm muscles on the dominant side of each able-bodied subject and the stump of amputee. Meanwhile, grip force was synchronously measured from the ipsilateral hands of able-bodied subjects and contralateral hand of amputee. Three force profiles (triangle, trapezoid, and fast triangle) were tested under three forearm postures (supination, neutral and pronation). Two algorithms (Generalized Regression Neural Network (GRNN) and Multilinear Regression Model (MLR)) were compared using several EMG features. The estimation performance was evaluated by coefficient of determination (R2) and mean absolute error (MAE). The optimal regressor combining TD and GRNN achieved R2= 96.33 ± 1.13% and MAE= 2.11 ± 0.52% for the intact subjects, and R2= 86.86% and MAE= 2.13% for the amputee. The results indicated that multiple grip force curves under three forearm postures could be accurately estimated for unilateral amputees using mirrored bilateral training. The proposed method has the potential for precise force control of prosthetic hands.

Conserved reduction of m6A RNA modifications during aging and neurodegeneration is linked to changes in synaptic transcripts

N6-methyladenosine (m6A) regulates mRNA metabolism. While it has been implicated in the development of the mammalian brain and in cognition, the role of m6A in synaptic plasticity, especially during cognitive decline, is not fully understood. In this study, we employed methylated RNA immunoprecipitation sequencing to obtain the m6A epitranscriptome of the hippocampal subregions CA1, CA3, and the dentate gyrus and the anterior cingulate cortex (ACC) in young and aged mice. We observed a decrease in m6A levels in aged animals. Comparative analysis of cingulate cortex (CC) brain tissue from cognitively intact human subjects and Alzheimer's disease (AD) patients showed decreased m6A RNA methylation in AD patients. m6A changes common to brains of aged mice and AD patients were found in transcripts linked to synaptic function including calcium/calmodulin-dependent protein kinase 2 (CAMKII) and AMPA-selective glutamate receptor 1 (Glua1). We used proximity ligation assays to show that reduced m6A levels result in decreased synaptic protein synthesis as exemplified by CAMKII and GLUA1. Moreover, reduced m6A levels impaired synaptic function. Our results suggest that m6A RNA methylation controls synaptic protein synthesis and may play a role in cognitive decline associated with aging and AD.

A Locomotion Mode Recognition Algorithm using adaptive Dynamic Movement Primitives.

Control systems of robotic prostheses should be designed to decode the users' intent to start, stop, or change locomotion; and to select the suitable control strategy, accordingly. This paper describes a locomotion mode recognition algorithm based on adaptive Dynamic Movement Primitive models used as locomotion templates. The models take foot-ground contact information and thigh roll angle, measured by an inertial measurement unit, for generating continuous model variables to extract features for a set of Support Vector Machines. The proposed algorithm was tested offline on data acquired from 10 intact subjects and 1 subject with transtibial amputation, in ground-level walking and stair ascending/descending activities. Following subject-specific training, results on intact subjects showed that the algorithm can classify initiatory and steady-state steps with up to 100.00% median accuracy medially at 28.45% and 27.40% of the swing phase, respectively. While the transitory steps were classified with up to 87.30% median accuracy medially at 90.54% of the swing phase. Results with data of the transtibial amputee showed that the algorithm classified initiatory, steady-state, and transitory steps with up to 92.59%, 100%, and 93.10% median accuracies medially at 19.48%, 51.47%, and 93.33% of the swing phase, respectively. The results support the feasibility of this approach in robotic prosthesis control.

Analysis of Gender Differences in Online Handwriting Signals for Enhancing e-Health and e-Security Applications

Handwriting is a complex perceptual–motor skill that is mastered around the age of 8. Although its computerized analysis has been utilized in many biometric and digital health applications, the possible effect of gender is frequently neglected. The aim of this paper is to analyze different online handwritten tasks performed by intact subjects and explore gender differences in commonly used temporal, kinematic, and dynamic features. The differences were explored in the BIOSECUR-ID database. We have identified a significant gender difference in on-surface/in-air time of genuine and skilled forgery signatures, on-surface time in cursive letters and numbers, and pressure, speed, and acceleration in text written in capital letters. Our findings accent the need to consider gender as an important confounding factor in studies dealing with online handwriting signal processing.