Neurophysiological correlates of visuo-motor learning through mental and physical practice.

We have previously shown that mental rehearsal can replace up to 75% of physical practice for learning a visuomotor task (Allami, Paulignan, Brovelli, & Boussaoud, (2008). Experimental Brain Research, 184, 105-113). Presumably, mental rehearsal must induce brain changes that facilitate motor learning. We tested this hypothesis by recording scalp electroencephalographic activity (EEG) in two groups of subjects. In one group, subjects executed a reach to grasp task for 240 trials. In the second group, subjects learned the task through a combination of mental rehearsal for the initial 180 trials followed by the execution of 60 trials. Thus, one group physically executed the task for 240 trials, the other only for 60 trials. Amplitudes and latencies of event-related potentials (ERPs) were compared across groups at different stages during learning. We found that ERP activity increases dramatically with training and reaches the same amplitude over the premotor regions in the two groups, despite large differences in physically executed trials. These findings suggest that during mental rehearsal, neuronal changes occur in the motor networks that make physical practice after mental rehearsal more effective in configuring functional networks for skilful behaviour.

Multivoxel pattern analysis for FMRI data: a review.

Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves.

Which method to measure dentomaxillary discrepancy?

Tooth-size assessment is a useful indicator of dental crowding which constitutes one of the main parameters in tooth-size arch-length discrepancy (TSALD). Systematic TSALD evaluation is imperative in all patients consulting for dentofacial orthopedic treatment. The present study aims to compare four measurement techniques for calibrating tooth size. It was performed on casts taken from 30 Moroccan patients consulting at the Dentofacial Orthopedics Department of the Rabat School of Dentistry. Four measurement methods were used to calculate the largest mesiodistal diameter of mandibular teeth mesial to the first molars. Statistical analysis showed that the methods using a digital caliper, the analog vernier caliper and the computerized technique are reliable, accurate, and interchangeable. In contrast, the analog caliper with no vernier is less accurate.