Objective: Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disorder that is associated with cognitive disabilities, including attention and motor learning problems. These disabilities have been extensively studied in children with NF1 but limited studies have been performed in adults.
Method: Attention, motor learning and intellectual performance were studied with neuropsychological tasks in 32 adults with NF1 and 32 controls.
Results: The NF1 and control group performed similarly on attention and motor learning tasks, although controls had shorter reaction times than adults with NF1 during the motor learning task ([60] = -2.20, = .03). Measures of attention or motor learning were not significantly associated with reduced intellectual performance in NF1.
Conclusion: In contrast to many studies in children with NF1, our findings did not provide evidence for presence of attention or motor learning problems in adults with NF1 in neuropsychological tasks. Our observations may be of clinical importance to determine treatment focus in adults with NF1.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987013 | PMC |
| http://dx.doi.org/10.1177/10870547211012035 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Systematic Review of EEG-Based Imagined Speech Classification Methods.
Sensors (Basel)
December 2024
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance.
View Article and Find Full Text PDFEnhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.
Sensors (Basel)
December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.
View Article and Find Full Text PDFMachine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson's Disease Mobility Assessments.
Sensors (Basel)
December 2024
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials.
View Article and Find Full Text PDFImproving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.
Sensors (Basel)
December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
View Article and Find Full Text PDFMechanisms and Potential Benefits of Neuroprotective Agents in Neurological Health.
Nutrients
December 2024
Department of Molecular Biology and Genetics, Çanakkale Onsekiz Mart University, Çanakkale 17100, Turkey.
The brain contains many interconnected and complex cellular and molecular mechanisms. Injury to the brain causes permanent dysfunctions in these mechanisms. So, it continues to be an area where surgical intervention cannot be performed except for the removal of tumors and the repair of some aneurysms.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!