Membaca membuka pintu dunia: Model yang jelas dan kuat untuk meningkatkan kemampuan membaca anak sekolah dasar

xix, 562 hlm.: 23 c

How are the motor system activity and functional connectivity between the cognitive and sensorimotor systems modulated by athletic expertise?

Expertise offers a unique insight into how our brain functions. The purpose of this experiment was to determine if motor system activity and functional connectivity between the cognitive system and sensorimotor system is differentially modulated by an individual's level of expertise. This goal was achieved through the acquisition of functional neuroimaging data in 10 expert volleyball players and 10 novice individuals who were presented with a series of sentences describing possible technical volleyball-specific motor acts and acts that cannot be performed as positive ("Do \u2026!") or negative ("Don't \u2026") commands, while they were silently reading them and deciding whether the actions were technically feasible or not. Compared with novices, experts' activity in the left primary motor cortex hand area (M1) and in the left premotor cortex (Pm) was decreased by impossible actions presented as positive commands. Sensorimotor activation in response to action-related stimuli is not that automatic as held since we found that these areas were deactivated during the task, and their functional connectivity to the primary visual cortex was strengthened for possible actions presented as positive commands, reflecting the neural processes underlying the interaction between motor and visual imagery. These results suggest that the neural activity within the key areas implicitly triggered by motor simulation is a function of the expertise, action feasibility, and context. \ua9 2013 Elsevier B.V

Computational approaches to fMRI analysis

Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex - and distinctly human - signals in the brain: acts of cognition such as thoughts, intentions and memories