This study explored how the neural efficiency and proficiency worked in athletes with different skill levels from the perspective of effective connectivity brain network in resting state. The deconvolved conditioned Granger causality (GC) analysis was applied to functional magnetic resonance imaging (fMRI) data of 35 elite athletes (EAs) and 42 student-athletes (SAs) of racket sports as well as 39 normal controls (NCs), to obtain the voxel-wised hemodynamic response function (HRF) parameters representing the functional segregation and effective connectivity representing the functional integration. The results showed decreased time-to-peak of HRF in the visual attention brain regions in the two athlete groups compared with NC and decreased response height in the advanced motor control brain regions in EA comparing to the nonelite groups, suggesting the neural efficiency represented by the regional HRF was different in early and advanced skill levels. GC analysis demonstrated that the GC values within the middle occipital gyrus had a linear trend from negative to positive, suggesting a stepwise "neural proficiency" of the effective connectivity from NC to SA then to EA. The GC values of the inter-lobe circuits in EA had the trend to regress to NC levels, in agreement with the neural efficiency of these circuits in EA. Further feature selection approach suggested the important role of the cerebral-brainstem GC circuit for discriminating EA. Our findings gave new insight into the complementary neural mechanisms in brain functional segregation and integration, which was associated with early and advanced skill levels in athletes of racket sports.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842890 | PMC |
http://dx.doi.org/10.1002/hbm.26057 | DOI Listing |
Elife
January 2025
Center for Medical Genetics Ghent, Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
Heritable fragile bone disorders (FBDs), ranging from multifactorial to rare monogenic conditions, are characterized by an elevated fracture risk. Validating causative genes and understanding their mechanisms remain challenging. We assessed a semi-high throughput zebrafish screening platform for rapid in vivo functional testing of candidate FBD genes.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu 641032 India.
Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
RPTU Kaiserslautern-Landau, Kaiserslautern, Germany.
The advent of in-memory computing has introduced a new paradigm of computation, which offers significant improvements in terms of latency and power consumption for emerging embedded AI accelerators. Nevertheless, the effect of the hardware variations and non-idealities of the emerging memory technologies may significantly compromise the accuracy of inferred neural networks and result in malfunctions in safety-critical applications. This article addresses the issue from three different perspectives.
View Article and Find Full Text PDFAnn Ital Chir
January 2025
Medical Department, Ningbo No.9 Hospital, 315020 Ningbo, Zhejiang, China.
Aim: This study aimed to develop a reliable and efficient system for predicting and locating rib fractures in medical images using an ensemble of convolutional neural networks (CNNs).
Methods: We employed five CNN architectures-Visual Geometry Group Network 16 (VGG16), Densely Connected Convolutional Network 169 (DenseNet169), Inception Version 4 (Inception V4), Efficient Network B7 (EfficientNet-B7), and Residual Network Next 50 layers (ResNeXt-50)-trained on a dataset of 840 grayscale computed tomography (CT) scan images in .jpg format collected from 42 patients at a local hospital.
Nanoscale
January 2025
Department of Biomedical Engineering, Sogang University, Seoul 04107, Korea.
The differentiation of human induced pluripotent stem cells (hiPSCs) into neural progenitor cells (NPCs) is a promising approach for the treatment of neurodegenerative diseases and regenerative medicine. Dual-SMAD inhibition using small molecules has been identified as a key strategy for directing the differentiation of hiPSCs into NPCs by regulating specific cell signaling pathways. However, conventional culture methods are time-consuming and exhibit low differentiation efficiency in neural differentiation.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!