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This study tested the two following hypotheses: (i) compared with non-athletes, elite athletes are characterized by a reduced cortical activation during the preparation of precise visuo-motor performance; (ii) in elite athletes, an optimal visuo-motor performance is related to a low cortical activation. To this aim, electroencephalographic (EEG; 56 channels; Be Plus EB-Neuro) data were recorded in 18 right-handed elite air pistol shooters and 10 right-handed non-athletes. All subjects performed 120 shots. The EEG data were spatially enhanced by surface Laplacian estimation. With reference to a baseline period, power decrease/increase of alpha rhythms during the preshot period indexed the cortical activation/deactivation (event-related desynchronization/synchronization, ERD/ERS). Regarding the hypothesis (i), low- (about 8-10 Hz) and high-frequency (about 10-12 Hz) alpha ERD was lower in amplitude in the elite athletes than in the non-athletes over the whole scalp. Regarding the hypothesis (ii), the elite athletes showed high-frequency alpha ERS (about 10-12 Hz) larger in amplitude for high score shots (50%) than for low score shots; this was true in right parietal and left central areas. A control analysis confirmed these results with another indicator of cortical activation (beta ERD, about 20 Hz). The control analysis also showed that the amplitude reduction of alpha ERD for the high compared with low score shots was not observed in the non-athletes. The present findings globally suggest that in elite athletes (experts), visuo-motor performance is related to a global decrease of cortical activity, as a possible index of spatially selective cortical processes ("neural efficiency").

Background: Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances.