This study aimed to investigate the relationship between neural efficiency and the ability of an athlete to produce accurate efforts in different perceived intensity zones during a racing scenario. The α/β ratio was used to quantify the neural efficiency during cycling, as it traduced the degree of participants information processing activity with lower cortical activity possible. Twelve trained competitive male cyclists delimited their perceived intensity zones 2 to 6 on a scale to assess the rating of exercise intensity. Then, they performed a 30 min racing scenario during which they had to produce different perceived intensities. The ability of athletes to produce perceived effort with accuracy and their neural efficiency was quantified during the racing scenario. The increase in the neural efficiency with the increase in the effort intensity could partly explain the improvement in athletes' ability to produce accurately perceived efforts from intensity zones 3 to 6. Moreover, the neural efficiency during the racing scenario was significantly correlated to the ability to produce perceived effort with accuracy at submaximal intensities.
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http://dx.doi.org/10.1007/s10484-021-09517-z | DOI Listing |
Environ Monit Assess
January 2025
Department of Landscape Architecture, Remote Sensing and GIS Laboratory, University of Cukurova, Adana, 01330, Turkey.
Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. Image classification, a widely adopted for extracting valuable information, has seen a surge in the application of deep learning methodologies due to their effectiveness.
View Article and Find Full Text PDFHum Brain Mapp
January 2025
Center for MR Research, University Children's Hospital Zurich, Zurich, Switzerland.
The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD.
View Article and Find Full Text PDFCardiovasc Diagn Ther
December 2024
Operational Research Center in Healthcare, Near East University, Nicosia, Turkey.
Background: Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years.
View Article and Find Full Text PDFInt J Neural Syst
January 2025
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, P. R. China.
Visual semantic decoding aims to extract perceived semantic information from the visual responses of the human brain and convert it into interpretable semantic labels. Although significant progress has been made in semantic decoding across individual visual cortices, studies on the semantic decoding of the ventral and dorsal cortical visual pathways remain limited. This study proposed a graph neural network (GNN)-based semantic decoding model on a natural scene dataset (NSD) to investigate the decoding differences between the dorsal and ventral pathways in process various parts of speech, including verbs, nouns, and adjectives.
View Article and Find Full Text PDFNat Comput Sci
January 2025
Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.
The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges.
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