Contribution of the brain-derived neurotrophic factor and neurometabolites to the motor performance

Individual motor performance ability is affected by various factors. Although the key factor has not yet completely been elucidated, the brain-derived neurotrophic factor (BDNF) genotype as well as neurometabolites may become contibuting factors depending on the learning stage. We investigated the effects of the Met allele of the BDNF gene and those of the neurometabolites on visuomotor learning. In total, 43 healthy participants performed a visuomotor learning task consisting of 10 blocks using the right index finger (Val66Val, n = 15; Val66Met, n = 15; and Met66Met, n = 13). Glutamate plus glutamine (Glx) concentrations in the primary motor cortex, primary somatosensory cortex (S1), and cerebellum were evaluated using 3-T magnetic resonance spectroscopy in 19 participants who participated in the visuomotor learning task. For the learning stage, the task error (i.e., learning ability) was significantly smaller in the Met66Met group compared with that observed in the remaining groups, irrespective of the learning stage (all p values < 0.003). A significant difference was observed between the Val66Val and Met66Met groups in the learning slope (i.e., learning speed) in the early learning stage (p = 0.048) but not in the late learning stage (all p values> 0.54). Moreover, positive correlations were detected between the learning slope and Glx concentrations in S1 only in the early learning stage (r = 0.579, p = 0.009). The BDNF genotype and Glx concentrations in S1 partially contribute to interindividual variability on learning speed in the early learning stage.

Learning curves, potential and speed in training of laparoscopic skills: a randomised comparative study in a box trainer

Background

The effectiveness of practical surgical training is characterised by an inherent learning curve. Decisive are individual initial starting capabilities, learning speed, ideal learning plateaus, and resulting learning potentials. The quantification of learning curves requires reproducible tasks with varied levels of difficulty. The hypothesis of this study is that the use of three-dimensional (3D) vision is more advantageous than two-dimensional vision (2D) for the learning curve in laparoscopic training.

Methods

Forty laparoscopy novices were recruited and randomised to a 2D Group and a 3D Group. A laparoscopy box trainer with two standardised tasks was used for training of surgical tasks. Task 1 was a positioning task, while Task 2 called for laparoscopic knotting as a more complex process. Each task was repeated at least ten times. Performance time and the number of predefined errors were recorded. 2D performance after 3D training was assessed in an additional final 2D cycle undertaken by the 3D Group.

Results

The calculated learning plateaus of both performance times and errors were lower for 3D. Independent of the vision mode the learning curves were smoother (exponential decay) and efficiency was learned faster than precision. The learning potentials varied widely depending on the corresponding initial values and learning plateaus. The final 2D performance time of the 3D-trained group was not significantly better than that of the 2D Group. The final 2D error numbers were similar for all groups.

Conclusions

Stereoscopic vision can speed up laparoscopic training. The 3D learning curves resulted in better precision and efficiency. The 3D-trained group did not show inferior performance in the final 2D cycle. Consequently, we encourage the training of surgical competences like suturing and knotting under 3D vision, even if it is not available in clinical routine.

Cumulative Instructional Time and Relative Effectiveness Conclusions: Extending Research on Response Intervals, Learning, and Measurement Scale

Adapted alternating treatments designs were used to evaluate three computer-based flashcard reading interventions (1-s, 3-s, or 5-s response intervals) across two students with disabilities. When learning was plotted with cumulative instructional sessions on the horizontal axis, the session-series graphs suggest that the interventions were similarly effective. When the same data were plotted as a function of cumulative instructional seconds, time-series graphs suggest that the 1-s intervention caused the most rapid learning for one student. Discussion focuses on applied implications of comparative effectiveness studies and why measures of cumulative instructional time are needed to identify the most effective intervention(s).Comparative effectiveness studies may not identify the intervention which causes the most rapid learning.Session-series repeated measures are not the same as time-series repeated measures.Measuring the time students spend in each intervention (i.e., cumulative instructional seconds) allows practitioners to identify interventions that enhance learning most rapidly.Student time spent working under interventions is critical for drawing applied conclusions.