Objectives: To explore unprompted adherence to a personalized, home-based, computerized cognitive training program in patients with multiple sclerosis (MS), and to examine the impact of training on cognitive performance.
Methods: Participants were assigned to a training (n=59) or a control group (n=48). Those in the training group were instructed to train three times a week for 12 weeks. The control group received no training. All participants were evaluated with a Neuropsychological Examination (N-CPC) at baseline and at the end of the study.
Results: In the training group, 42 (71.2%) participants adhered to the training schedule and 22 (37.3%) completed the entire training regimen. In the control group, 24 (50.0%) participants agreed to be retested on the N-CPC. The training group showed a significant improvement over that shown by the control group in three memory-based cognitive abilities (general memory, visual working memory and verbal working memory). Post-hoc exploration of data from the N-CPC showed that cognitive training was also associated with increased naming speed, speed of information recall, focused attention and visuo-motor vigilance.
Conclusions: The appreciable rates of adherence and cognitive improvements observed indicate that personalized cognitive training is a practical and valuable tool to improve cognitive skills and encourage neuronal plasticity in patients with MS.
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http://dx.doi.org/10.3233/NRE-2010-0546 | DOI Listing |
Sci Rep
January 2025
School of Medicine, Yan'an University, Yan'an, 716000, China.
The study aims to develop and validate an effective model for predicting frailty risk in individuals with mild cognitive impairment (MCI). The cross-sectional analysis employed nationally representative data from CHARLS 2013-2015. The sample was randomly divided into training (70%) and validation sets (30%).
View Article and Find Full Text PDFJ Neural Eng
January 2025
Mechanical and Aerospace, Missouri University of Science and Technology, 400 W 13th St., Rolla, Missouri, 65409, UNITED STATES.
This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention. \emph{Approach}: Data are collected from participants operating an NVIDIA JetBot on a testbed simulating urban streets for three different scenarios.
View Article and Find Full Text PDFJ Neural Eng
January 2025
School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Objective Race driving is a complex motor task that involves multiple concurrent cognitive processes in different brain regions coordinated to maintain and optimize speed and control. Delineating the neuroplasticity accompanying the acquisition of complex and fine motor skills such as racing is crucial to elucidate how these are gradually encoded in the brain and inform new training regimes. This study aims, first, to identify the neural correlates of learning to drive a racing car using non-invasive electroencephalography (EEG) imaging and longitudinal monitoring.
View Article and Find Full Text PDFCommun Psychol
January 2025
Institute of Psychology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.
Learning an association does not always succeed on the first attempt. Previous studies associated increased error signals in posterior medial frontal cortex with improved memory formation. However, the neurophysiological mechanisms that facilitate post-error learning remain poorly understood.
View Article and Find Full Text PDFGastroenterology
January 2025
Department of Medicine, University of Rochester Medical Center, Rochester, New York.
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