Search for: All records

Abstract Changes in brain mitochondrial metabolism are coincident with functional decline; however, direct links between the two have not been established. Here, we show that mitochondrial targeting via the adiponectin receptor activator AdipoRon (AR) clears neurofibrillary tangles (NFTs) and rescues neuronal tauopathy-associated defects. AR reduced levels of phospho-tau and lowered NFT burden by a mechanism involving the energy-sensing kinase AMPK and the growth-sensing kinase GSK3b. The transcriptional response to AR included broad metabolic and functional pathways. Induction of lysosomal pathways involved activation of LC3 and p62, and restoration of neuronal outgrowth required the stress-responsive kinase JNK. Negative consequences of NFTs on mitochondrial activity, ATP production, and lipid stores were corrected. Defects in electrophysiological measures (e.g., resting potential, resistance, spiking profiles) were also corrected. These findings reveal a network linking mitochondrial function, cellular maintenance processes, and electrical aspects of neuronal function that can be targeted via adiponectin receptor activation. 

Abstract Objective. Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. Approach. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant’s vPCG neural activity, and evaluated performance on a series of target selection tasks. Main results. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. Significance. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click. (BrainGate2 ClinicalTrials.gov ID NCT00912041) 

Abstract Upper limb based neuromuscular interfaces aim to provide a seamless way for humans to interact with technology. Among noninvasive interfaces, surface electromyogram (EMG) signals hold significant promise. However, their sensitivity to physiological and anatomical factors remains poorly understood, raising questions about how these factors influence gesture decoding across individuals or groups. To facilitate the study of signal distribution shifts across individuals or groups of individuals, we present a dataset of upper limb EMG signals and physiological measures from 91 demographically diverse adults. Participants were selected to represent a range of ages (18 to 92 years) and body mass indices (healthy, overweight, and obese). The dataset also includes measures such as skin hydration and elasticity, which may affect EMG signals. This dataset provides a basis to study demographic confounds in EMG signals and serves as a benchmark to test the development of fair and unbiased algorithms that enable accurate hand gesture decoding across demographically diverse subjects. Additionally, we validate the quality of the collected data using state-of-the-art gesture decoding techniques. 

Abstract Objective. Intracortical brain–computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as ‘click-and-hold’ or ‘drag-and-drop’.Approach. Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel ‘latch decoder’ that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task.Main results. Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control.Significance. This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI. 

Abstract Children born with congenital upper limb absence exhibit consistent and distinguishable levels of biological control over their affected muscles, assessed through surface electromyography (sEMG). This represents a significant advancement in determining how these children might utilize sEMG-controlled dexterous prostheses. Despite this potential, the efficacy of employing conventional sEMG classification techniques for children born with upper limb absence is uncertain, as these techniques have been optimized for adults with acquired amputations. Tuning sEMG classification algorithms for this population is crucial for facilitating the successful translation of dexterous prostheses. To support this effort, we collected sEMG data from a cohort of N = 9 children with unilateral congenital below-elbow deficiency as they attempted 11 hand movements, including rest. Five classification algorithms were used to decode motor intent, tuned with features from the time, frequency, and time–frequency domains. We derived the congenital feature set (CFS) from the participant-specific tuned feature sets, which exhibited generalizability across our cohort. The CFS offline classification accuracy across participants was 73.8% ± 13.8% for the 11 hand movements and increased to 96.5% ± 6.6% when focusing on a reduced set of five movements. These results highlight the potential efficacy of individuals born with upper limb absence to control dexterous prostheses through sEMG interfaces. 

Abstract Potato virus Y(PVY,Potyviridae) is among the most important viral pathogens of potato. The potato resistance geneNy_tbrconfers hypersensitive resistance to the ordinary strain of PVY (PVY^O), but not the necrotic strain (PVY^N). Here, we unveil that residue 247 of PVY helper component proteinase (HCPro) acts as a central player controllingNy_tbrstrain‐specific activation. We found that substituting the serine at 247 in the HCPro of PVY^O(HCPro^O) with an alanine as in PVY^NHCPro (HCPro^N) disruptsNy_tbrrecognition. Conversely, an HCPro^Nmutant carrying a serine at position 247 triggers defence. Moreover, we demonstrate that plant defences are induced against HCPro^Omutants with a phosphomimetic or another phosphorylatable residue at 247, but not with a phosphoablative residue, suggesting that phosphorylation could modulateNy_tbrresistance. Extending beyond PVY, we establish that the same response elicited by the PVY^OHCPro is also induced by HCPro proteins from other members of thePotyviridaefamily that have a serine at position 247, but not by those with an alanine. Together, our results provide further insights in the strain‐specific PVY resistance in potato and infer a broad‐spectrum detection mechanism of plant potyvirus effectors contingent on a single amino acid residue. 

Abstract In designing femoral components, which restore native (i.e., healthy) knee kinematics, the flexion–extension (F-E) axis of the tibiofemoral joint should match that of the native knee. Because the F–E axis is governed by the curvature of the femoral condyles in the sagittal plane, the primary objective was to determine the variation in radii of curvature. Eleven high accuracy three-dimensional (3D) femur models were generated from ultrahigh resolution CT scans. The sagittal profile of each condyle was created. The radii of curvature at 15 deg increments of arc length were determined based on segment circles best-fit to ±15 deg of arc at each increment. Results were standardized to the radius of the best-fit overall circle to 15 deg–105 deg for the femoral condyle having a radius closest to the mean radius. Medial and lateral femoral condyles exhibited multiradius of curvature sagittal profiles where the radius decreased at 30 deg flexion by 10 mm and at 15 deg flexion by 8 mm, respectively. On either side of the decrease, radii of segment circles were relatively constant. Beyond the transition angles where the radii decreased, the anterior-posterior (A-P) positions of the centers of curvature varied 4.8 mm and 2.3 mm for the medial and lateral condyles, respectively. A two-radius of curvature profile approximates the radii of curvature of both native femoral condyles, but the transition angles differ with the transition angle of the medial femoral condyle occurring about 15 deg later in flexion. Owing to variation in A-P positions of centers of curvature, the F-E axis is not strictly fixed in the femur. 

Abstract Intracellular calcium (Ca²⁺) is ubiquitous to cell signaling across biology. While existing fluorescent sensors and reporters can detect activated cells with elevated Ca²⁺levels, these approaches require implants to deliver light to deep tissue, precluding their noninvasive use in freely behaving animals. Here we engineered an enzyme-catalyzed approach that rapidly and biochemically tags cells with elevated Ca²⁺in vivo. Ca²⁺-activated split-TurboID (CaST) labels activated cells within 10 min with an exogenously delivered biotin molecule. The enzymatic signal increases with Ca²⁺concentration and biotin labeling time, demonstrating that CaST is a time-gated integrator of total Ca²⁺activity. Furthermore, the CaST readout can be performed immediately after activity labeling, in contrast to transcriptional reporters that require hours to produce signal. These capabilities allowed us to apply CaST to tag prefrontal cortex neurons activated by psilocybin, and to correlate the CaST signal with psilocybin-induced head-twitch responses in untethered mice. 

Abstract The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations. 

Hand gesture classification is crucial for the control of many modern technologies, ranging from virtual and augmented reality systems to assistive mechatronic devices. A prominent control technique employs surface electromyography (EMG) and pattern recognition algorithms to identify specific patterns in muscle electrical activity and translate these to device commands. While being well established in consumer, clinical, and research applications, this technique suffers from misclassification errors caused by limb movements and the weight of manipulated objects, both vital aspects of how we use our hands in daily life. An emerging alternative control technique is force myography (FMG) which uses pattern recognition algorithms to predict hand gestures from the axial forces present at the skin’s surface created by contractions of the underlying muscles. As EMG and FMG capture different physiological signals associated with muscle contraction, we hypothesized that each may offer unique additional information for gesture classification, potentially improving classification accuracy in the presence of limb position and object loading effects. Thus, we tested the effect of limb position and grasped load on 3 different sensing modalities: EMG, FMG, and the fused combination of the two. 27 able-bodied participants performed a grasp and release task with 4 hand gestures at 8 positions and under 5 object weight conditions. We then examined the effects of limb position and grasped load on gesture classification accuracy across each sensing modality. It was found that position and grasped load had statistically significant effects on the classification performance of the 3 sensing modalities and that the combination of EMG and FMG provided the highest classification accuracy of hand gesture, limb position, and grasped load combinations (97.34%) followed by FMG (92.27%) and then EMG (82.84%). This points to the fact that the addition of FMG to traditional EMG control systems offers unique additional data for more effective device control and can help accommodate different limb positions and grasped object loads.