Infinite Biomedical Technologies (United States)

Sarcopenia, the age-related loss of muscle strength and mass, contributes to adverse health outcomes in older adults. While exercise mitigates sarcopenia by transiently activating calcium (Ca2+)- and reactive oxygen species (ROS)-dependent signaling pathways that enhance muscle performance and adaptation, these same signals become chronically elevated in aged skeletal muscle and promote functional decline. Ca2+/calmodulin-dependent protein kinase II (CaMKII) is a key transducer of both Ca2+ and ROS signals during exercise. Here we show that CaMKII is chronically activated in aged muscles, promoting muscle dysfunction. Muscle-specific expression of a constitutively active CaMKII construct in young mice recapitulates features of aging muscles, including impaired contractility, progressive atrophy, mitochondrial disorganization, formation of tubular aggregates, and an older transcriptional profile characterized by the activation of inflammatory and stress response pathways. Mediation analysis identified altered heme metabolism as a potential mechanism of CaMKII-induced weakness, independent of muscle atrophy. Conversely, partial inhibition of CaMKII in aged muscle improved contractile function and shifted the transcriptome toward a more youthful state without inducing hypertrophy. These findings identify chronic CaMKII activation as a driver of functional and molecular muscle aging and support the concept that CaMKII exemplifies antagonistic pleiotropy, whereby its beneficial roles in promoting muscle performance and adaptation during youth may incur deleterious consequences in aging. We propose that persistent CaMKII activation in aged skeletal muscle reflects unresolved cellular stress and promotes maladaptive remodeling. Enhancing physiological reserve capacity through exercise, in combination with temporally targeted CaMKII inhibition, may help restore adaptive CaMKII signaling dynamics and preserve muscle function in aging.

Contributing author was missing from the above-named paper.

Technological advances in multi-articulated prosthetic hands have outpaced the development of methods to intuitively control these devices. In fact, prosthetic users often cite "difficulty of use" as a key contributing factor for abandoning their prostheses. To overcome the limitations of the currently pervasive myoelectric control strategies, namely unintuitive proportional control of multiple degrees-of-freedom, we propose a novel approach: proprioceptive sonomyographiccontrol. Unlike myoelectric control strategies which measure electrical activation of muscles and use the extracted signals to determine the velocity of an end-effector; our sonomyography-based strategy measures mechanical muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Therefore, our sonomyography-based control is congruent with a prosthetic user’s innate proprioception of muscle deformation in the residual limb. In this work, we evaluated proprioceptive sonomyographic control with 5 prosthetic users and 5 able-bodied participants in a virtual target achievement and holding task for 5 different hand motions. We observed that with limited training, the performance of prosthetic users was comparable to that of able-bodied participants and thus conclude that proprioceptive sonomyographic control is a robust and intuitive prosthetic control strategy.

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant (p<;0.001) performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.

Adjusting to an amputation can often times be difficult for the body. Post-surgery, amputees not only have to incur expensive rehabilitation treatment costs, but also have to wait for up to several months before receiving a properly fitted prosthesis. We developed a mixed-reality training environment where amputees can train, at their own time and convenience, and interact with holographic objects, while also receiving tactile and proprioceptive feedback. We incorporate positional information through inertial sensors, touch and proprioception information through vibrational feedback, all integrated into an augmented-reality (AR) environment viewed through the Microsoft HoloLens TM. Training tasks were designed to account for limb rotation and object relocation in a three-dimensional space with a correct palm orientation essential for an intuitive grasp and release of objects. Our results showed an improved performance in training time, overshoot and completion rate with vibratory feedback (of both touch and proprioception) over without feedback. Furthermore, EMG activity was analyzed to estimate the muscular effort during each task.

The human body is a template for many state-of-the-art prosthetic devices and sensors. Perceptions of touch and pain are fundamental components of our daily lives that convey valuable information about our environment while also providing an element of protection from damage to our bodies. Advances in prosthesis designs and control mechanisms can aid an amputee's ability to regain lost function but often lack meaningful tactile feedback or perception. Through transcutaneous electrical nerve stimulation (TENS) with an amputee, we discovered and quantified stimulation parameters to elicit innocuous (non-painful) and noxious (painful) tactile perceptions in the phantom hand. Electroencephalography (EEG) activity in somatosensory regions confirms phantom hand activation during stimulation. We invented a multilayered electronic dermis (e-dermis) with properties based on the behavior of mechanoreceptors and nociceptors to provide neuromorphic tactile information to an amputee. Our biologically inspired e-dermis enables a prosthesis and its user to perceive a continuous spectrum from innocuous to noxious touch through a neuromorphic interface that produces receptor-like spiking neural activity. In a Pain Detection Task (PDT), we show the ability of the prosthesis and amputee to differentiate non-painful or painful tactile stimuli using sensory feedback and a pain reflex feedback control system. In this work, an amputee can use perceptions of touch and pain to discriminate object curvature, including sharpness. This work demonstrates possibilities for creating a more natural sensation spanning a range of tactile stimuli for prosthetic hands.

EEG and field potential rhythms established in the cortex and thalamus may accommodate the propagation of seizures. This article describes the interaction between thalamus and cortex during pentylenetetrazol (PTZ) seizures in rats with and without prior treatment with ethosuximide (ESM), a well-known antiepileptic drug (AED) that raises the threshold for seizures, was given before PTZ. The AED was given before PTZ convulsant administration. We track this thalamo-cortical association with a novel measure we have called the cross-bicoherence gain, or BISCOH. This quantity allows us to measure the spectral coherence in a purely higher order spectralmethodology. BISCOH is able to track the formation of nonlinearities at specific frequencies in the recorded EEG. BISCOH showed a strong increase in low alpha wave harmonic generationat 10 and 12.5 Hz after ESM treatment (p < 0.02 and p < 0.007, respectively). Conventional coherence failed to show distinctive and significant changes in thalamo-cortical coupling after ESM treatment at those frequencies and instead showed changes at 5 Hz. This rise in cortical rhythms is evidence of harmonic generation or new frequency formation in the thalamo-cortical system withAED therapy. BISCOH could become a powerful tool in unraveling changes in coherence due to neuroelectric modulation resulting from drug treatment or electrical stimulation.

A quantitative electroencephalogram (qEEG) index for the evaluation and monitoring of cortical health in ischemic brain injury is presented. The proposed qEEG index, called the subbands spectral complexity distance (SSCD), measures the distance between a vector of subbands spectral complexities computed from the investigated EEG and a one computed from normal EEG. For the computation of the SSCD, the spontaneous power spectral density of the EEG signal is estimated using the time-varying autoregressive (TV-AR) model, decomposed into subbands and the spectral complexity of each subband is computed. Results of evaluating human EEG data are presented to show the usefulness of the proposed SSCD index. It is shown that the index increases during the time-segments of ischemic brain injury while decreases during the normal and recovery segments. This can be explained by the fact that the ischemic brain injury may increase the irregularity of the EEG signal and therefore the power spectral density becomes more complex with ischemic brain injury.

Background Cardiac arrest (CA) can lead to significant neurological damage following resuscitation. A real-time tool for monitoring of neurological status in CA survivors is likely to have a significant impact on post-CPR therapy. To avoid typical subjective prediction of outcome, we have developed a quantitative EEG-based Cerebral Health Index (CHI), a multiparametric algorithm combining several separate spectral, temporal and probabilistic features. CHI is a measure of outcome that evaluates and scores the EEG post-resuscitation from CA. In the present study, we compared CHI to a species-specific behavioral outcome scoring measure, the Neurological Deficit Scores (NDS). Previously calibrated and tested, NDS includes multiple generalized neural, motor, sensory and behavioral subscores. We demonstrated the utility of CHI as a measure of monitoring neurological status as given by NDS. Methods CHI is an empirically trained algorithm that allows for parameter weighting to uniquely characterize the recovering EEG. We investigated the utility of CHI by comparing 72-h CHI values to 72-h of an asphyxial CA rodent model. Sixteen rats underwent a baseline EEG recording followed by 7- or 9-min asphyxia and anesthesia washout which led to CA. The NDS was scored at 72 hr after CA with concurrent 30-min EEG recording. The rats were divided into two groups: 8 rats with known NDS outcomes were used for training the CHI algorithm and 8 rats were used for testing the algorithm in a single blinded fashion. CHI values of the testing group were then compared to their corresponding NDS outcomes via Receiver Operator Characteristic (ROC) curve calculation. Results Physiological measures and arrest times were comparable in both poor and good outcome animals. On a scale basis of 0–80, average CHI values were 70 (±3.8) and 51 (±4.4) for the good and bad outcome animals, respectively. This translated to respective t-test p-values of p≤0.05. ROC analyses on testing cohorts at 72 hr showed an average area under the curve of 0.9. Conclusion CHI is a multi-feature index for accurately describing neurological status after CA in rats. CHI can potentially be used to supplement the traditional neurological exam to make EEG characterization a routine part of post-CA assessments.