The basis of motor learning involves decomposing complete actions into a series of predictive individual components that form the whole. The present fMRI study investigated the areas of the human brain important for oculomotor short-term learning, by using a novel sequence learning paradigm that is equivalent in visual and temporal properties for both saccades and pursuit, enabling more direct comparisons between the oculomotor subsystems. In contrast with previous studies that have implemented a series of discrete ramps to observe predictive behaviour as evidence for learning, we presented a continuous sequence of interlinked components that better represents sequences of actions. We implemented both a classic univariate fMRI analysis, followed by a further multivariate pattern analysis (MVPA) within a priori regions of interest, to investigate oculomotor sequence learning in the brain and to determine whether these mechanisms overlap in pursuit and saccades as part of a higher order learning network. This study has uniquely identified an equivalent frontal-parietal network (dorsolateral prefrontal cortex, frontal eye fields and posterior parietal cortex) in both saccades and pursuit sequence learning. In addition, this is the first study to investigate oculomotor sequence learning during fMRI brain imaging, and makes significant contributions to understanding the role of the dorsal networks in motor learning.
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http://dx.doi.org/10.1016/j.neuropsychologia.2016.04.021 | DOI Listing |
Adv Sci (Weinh)
December 2024
Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Digital PCR (dPCR) has transformed nucleic acid diagnostics by enabling the absolute quantification of rare mutations and target sequences. However, traditional dPCR detection methods, such as those involving flow cytometry and fluorescence imaging, may face challenges due to high costs, complexity, limited accuracy, and slow processing speeds. In this study, SAM-dPCR is introduced, a training-free open-source bioanalysis paradigm that offers swift and precise absolute quantification of biological samples.
View Article and Find Full Text PDFEur J Neurol
January 2025
School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Background: The regulatory role of the apolipoprotein E (APOE) ε4 allele in the clinical manifestations of spinocerebellar ataxia type 3 (SCA3) remains unclear. This study aimed to evaluate the impact of the APOE ε4 allele on cognitive and motor functions in SCA3 patients.
Methods: This study included 281 unrelated SCA3 patients and 182 controls.
BMC Genomics
December 2024
School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
Background: The subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Orthodontics, Faculty of Dentistry, Mahidol University, Bangkok, Thailand.
This research evaluated the effectiveness of an online simulation-based serious game as a learning tool in diagnosis and treatment planning for oral lesions (SimOL) in comparison to a pre-recorded lecture-based approach and to determine its appropriate integration into the undergraduate dental curriculum. A crossover randomized control trial was conducted with a cohort of 77 dental undergraduates. They were randomly assigned into two groups.
View Article and Find Full Text PDFCommun Biol
December 2024
College of Computer Science and Technology, Ocean University of China, Qingdao, China.
Understanding the function of proteins is of great significance for revealing disease pathogenesis and discovering new targets. Benefiting from the explosive growth of the protein universal, deep learning has been applied to accelerate the protein annotation cycle from different biological modalities. However, most existing deep learning-based methods not only fail to effectively fuse different biological modalities, resulting in low-quality protein representations, but also suffer from the convergence of suboptimal solution caused by sparse label representations.
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