scholarly journals Skill-Specific Changes in Somatosensory Nogo Potentials in Baseball Players

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142581 ◽  
Author(s):  
Koya Yamashiro ◽  
Daisuke Sato ◽  
Hideaki Onishi ◽  
Kazuhiro Sugawara ◽  
Sho Nakazawa ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Koya Yamashiro ◽  
Yudai Yamazaki ◽  
Kanako Siiya ◽  
Koyuki Ikarashi ◽  
Yasuhiro Baba ◽  
...  

AbstractLong-term skills training is known to induce neuroplastic alterations, but it is still debated whether these changes are always modality-specific or can be supramodal components. To address this issue, we compared finger-targeted somatosensory-evoked and auditory-evoked potentials under both Go (response) and Nogo (response inhibition) conditions between 10 baseball players, who require fine hand/digit skills and response inhibition, to 12 matched track and field (T&F) athletes. Electroencephalograms were obtained at nine cortical electrode positions. Go potentials, Nogo potentials, and Go/Nogo reaction time (Go/Nogo RT) were measured during equiprobable somatosensory and auditory Go/Nogo paradigms. Nogo potentials were obtained by subtracting Go trial from Nogo trial responses. Somatosensory Go P100 latency and Go/Nogo RT were significantly shorter in the baseball group than the T&F group, while auditory Go N100 latency and Go/Nogo RT did not differ between groups. Additionally, somatosensory subtracted Nogo N2 latency was significantly shorter in the baseball group than the T&F group. Furthermore, there were significant positive correlations between somatosensory Go/Nogo RT and both Go P100 latency and subtracted Nogo N2 latency, but no significant correlations among auditory responses. We speculate that long-term skills training induce predominantly modality-specific neuroplastic changes that can improve both execution and response inhibition.


PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0146379
Author(s):  
Koya Yamashiro ◽  
Daisuke Sato ◽  
Hideaki Onishi ◽  
Kazuhiro Sugawara ◽  
Sho Nakazawa ◽  
...  

2008 ◽  
Vol 17 (4) ◽  
pp. 473-482
Author(s):  
임승길 ◽  
김병곤 ◽  
Kimyoungjae
Keyword(s):  

Author(s):  
Garrett S. Bullock ◽  
Edward C. Beck ◽  
Gary S. Collins ◽  
Stephanie R. Filbay ◽  
Kristen F. Nicholson

Author(s):  
Hyeuk Kim

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.


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