Novel motor learning

BACKGROUND: Cognitive impairment is one of the most common non-motor symptoms of Parkinson's disease (PD). Previous studies have demonstrated that low-intensity transcranial ultrasound stimulation can significantly suppress the motor symptoms of PD. However, whether ultrasound stimulation can improve cognitive ability in PD and the related neural oscillation mechanism remain unclear to date. OBJECTIVE: To evaluate the effect of ultrasound stimulation on memory ability in PD and explore its neural oscillation mechanism...

INTRODUCTION: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery...

Diabetes mellitus and chronic kidney disease represent escalating global epidemics with comorbidities akin to neuropathies, resulting in various neuromuscular symptoms that impede daily performance. Interestingly, previous studies indicated differing sensorimotor functions within these conditions. If assessing sensorimotor features can effectively distinguish between diabetes mellitus and chronic kidney disease, it could serve as a valuable and non-invasive indicator for early detection, swift screening, and ongoing monitoring, aiding in the differentiation between these diseases...

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front...

Accurate movements of the upper limb require the integration of various forms of sensory feedback (e.g., visual and postural information). The influence of these different sensory modalities on reaching movements has been largely studied by assessing endpoint errors after selectively perturbing sensory estimates of hand location. These studies have demonstrated that both vision and proprioception make key contributions in determining the reach endpoint. However, their influence on motor output throughout movement remains unclear...

In this study, we conducted an examination of knowledge integration concerning action information and assessed the impact of operational on this process. Additionally, we delved into the underlying mechanisms of how operational encoding influences the processing of knowledge integration of action information, utilizing the event-related potential technique. The results of our investigation revealed that operational encoding, encompassing the observed operational encoding and the imagined operational encoding, exhibited superior performance in the integration of action knowledge compared to verbal encoding...

Motor sequence learning gradually quickens reaction time, suggesting that sequence learning alters motor preparation processes. Interestingly, evidence has shown that preparing sequence movements decreases short intracortical inhibition (SICI) in the contralateral motor cortex (M1), but also that sequence learning alters motor preparation processes in both the contralateral and ipsilateral M1s. Therefore, one possibility is that sequence learning alters the SICI decreases occurring during motor preparation in bilateral M1s...

OBJECTIVE: Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers...

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification...

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques...

The L1 cell adhesion molecule (L1) has demonstrated a range of beneficial effects in animal models of spinal cord injury, neurodegenerative disease, and ischemia; however, the role of L1 in TBI has not been fully examined. Mutations in the L1 gene affecting the extracellular domain of this type 1 transmembrane glycoprotein have been identified in patients with L1 syndrome. These patients suffer from hydrocephalus, MASA (mental retardation, adducted thumbs, shuffling gait, aphasia) symptoms, and corpus callosum agenesis...

Neuroscience heavily relies on animal welfare in laboratory rodents as it can significantly affect brain development, cognitive function and memory formation. Unfortunately, laboratory animals are often raised in artificial environments devoid of physical and social stimuli, potentially leading to biased outcomes in behavioural assays. To assess this effect, we examined the impact of social and physical cage enrichment on various forms of motor coordination. Our findings indicate that while enriched-housed animals did not exhibit faster learning in eyeblink conditioning, the peak timing of their conditioned responses was slightly, but significantly, improved...

Loss of dopamine neurons causes motor deterioration in Parkinson's disease patients. We have previously reported that in addition to acute motor impairment, the impaired motor behavior is encoded into long-term memory in an experience-dependent and task-specific manner, a phenomenon we refer to as aberrant inhibitory motor learning. Although normal motor learning and aberrant inhibitory learning oppose each other and this is manifested in apparent motor performance, in the present study, we found that normal motor memory acquired prior to aberrant inhibitory learning remains preserved in the brain, suggesting the existence of independent storage...

OBJECTIVE: Cerebral cavernous malformations (CMs) are pathological lesions that cause discrete cortical disruption with hemorrhage, and their transcortical resections can cause additional iatrogenic disruption. The analysis of microsurgically treated CMs might identify areas of "eloquent noneloquence," or cortex that is associated with unexpected deficits when injured or transgressed. METHODS: Patients from a consecutive microsurgical series of superficial cerebral CMs who presented to the authors' center over a 13-year period were retrospectively analyzed...

Maternal 15q11.2-13.1 duplication syndrome, or Dup15q syndrome (Dup15q), is a rare neurodevelopmental disorder affecting as many as 1 in 5000 to 1 in 20,000 children worldwide. Autism and seizures are two of the most commonly observed phenotypes in Dup15q, with intellectual disability, hypotonia, gastrointestinal distress, and substantial fine and gross motor deficits also commonly reported. The community that is now known as the Dup15q Alliance started in 1994 as a small group of families raising children with chromosome 15q duplications...

PURPOSE: Evidence suggests that transcranial direct current stimulation (tDCS) can enhance motor performance and learning of hand tasks in persons with chronic stroke (PCS). However, the effects of tDCS on the locomotor tasks in PCS are unclear. This pilot study aimed to: (1) determine aggregate effects of anodal tDCS combined with step training on improvements of the neural and biomechanical attributes of stepping initiation in a small cohort of persons with chronic stroke (PCS) over a 4-week training program; and (2) assess the feasibility and efficacy of this novel approach for improving voluntary stepping initiation in PCS...

Achieving accurate position tracking for robotics and industrial servo systems is an extremely challenging task, particularly when dealing with control saturation, parameter perturbation, and external disturbance. To address these challenges, a predefined time convergent sliding mode adaptive controller (PTCSMAC) has been proposed for a permanent magnet linear motor (PMLM). A novel sliding mode surface (SMS) with predefined time convergence PDTC has been constructed, which ensures that the error converges to zero within the prescribed time...

This study introduces PDMotion, a mobile application comprising 11 digital tests, including those adapted from the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III and novel assessments, for remote Parkinson's Disease (PD) motor symptoms evaluation. Employing machine learning techniques on data from 50 PD patients and 29 healthy controls, PDMotion achieves accuracies of 0.878 for PD status prediction and 0.715 for severity assessment. A post-hoc explanation model is employed to assess the importance of features and tasks in diagnosis and severity evaluation...

When learning a new motor skill, people often must use trial and error to discover which movement is best. In the reinforcement learning framework, this concept is known as exploration and has been linked to increased movement variability in motor tasks. For locomotor tasks, however, increased variability decreases upright stability. As such, exploration during gait may jeopardize balance and safety, making reinforcement learning less effective. Therefore, we set out to determine if humans could acquire and retain a novel locomotor pattern using reinforcement learning alone...

Hippocampus-dependent spatial learning in rodents has been tested using a variety of methods. These include the Morris water maze (MWM), Y-maze, and novel object location (NOL) tasks. More recently, the active place avoidance (APA) task has been developed as an alternative to these more traditional approaches. In the APA task, mice must use spatial cues placed around a rotating arena to avoid a stationary shock zone. Due to the multiple parameters that can be adjusted, the APA task has been demonstrated to be a very versatile approach...