Measuring follow-up time in routinely-collected health datasets: Challenges and solutions.

A key requirement for longitudinal studies using routinely-collected health data is to be able to measure what individuals are present in the datasets used, and over what time period. Individuals can enter and leave the covered population of administrative datasets for a variety of reasons, including both life events and characteristics of the datasets themselves. An automated, customizable method of determining individuals' presence was developed for the primary care dataset in Swansea University's SAIL Databank. The primary care dataset covers only a portion of Wales, with 76% of practices participating. The start and end date of the data varies by practice. Additionally, individuals can change practices or leave Wales. To address these issues, a two step process was developed. First, the period for which each practice had data available was calculated by measuring changes in the rate of events recorded over time. Second, the registration records for each individual were simplified. Anomalies such as short gaps and overlaps were resolved by applying a set of rules. The result of these two analyses was a cleaned set of records indicating start and end dates of available primary care data for each individual. Analysis of GP records showed that 91.0% of events occurred within periods calculated as having available data by the algorithm. 98.4% of those events were observed at the same practice of registration as that computed by the algorithm. A standardized method for solving this common problem has enabled faster development of studies using this data set. Using a rigorous, tested, standardized method of verifying presence in the study population will also positively influence the quality of research.

An MEG investigation of the neural mechanisms subserving complex visuomotor coordination.

Fifteen human participants performed a manual and ocular tracking task with a continuously and unpredictably moving visual target, while magnetoencephalography (MEG) signals were recorded. Three-dimensional source reconstructions were generated from the MEG signals, using synthetic aperture magnetometry (SAM). The SAM images indicated main effects of alpha band (8-15 Hz) and beta band (15-30 Hz) source power decreases, for manual tracking in the sensorimotor and parietal cortices, and for ocular tracking in the parietal and occipital cortices. Additionally, the manual tracking task evoked a clear, contralateral motor cortex response in the form of high gamma band (60-90 Hz) source power increases. Time-frequency spectrograms revealed the induced gamma band power increases were sustained for the duration of each ten second trial demonstrating these oscillations are not simply transients associated with movement onset. The onset of the gamma band response was characterised by higher initial onset power and frequency but no correlations were observed between oscillatory power and successful tracking performance.

Adaptation to sensory-motor temporal misalignment: instrumental or perceptual learning?

Sensory-motor delays vary over the course of development and under different environmental conditions. Previous research has shown that humans can compensate for the resulting temporal misalignment while performing sensory-motor tasks (e.g., Cunningham, Billock, & Tsou, 2001a), but remains silent on the question of whether perceptual learning-similar to that involved in adaptation to spatial misalignment (e.g., Redding & Wallace, 1993) and in adaptation to purely intersensory misalignment (e.g., Fujisaki, Shimojo, Kashino, & Nishida, 2004)-is also involved in this adaptive response. Following an attempted replication of Cunningham et al.'s (2001a) study in a preliminary experiment, we present in this paper two experiments that demonstrate that after-effects of adaptation to temporal misalignment do not spontaneously decay. The literature on adaptation to spatial misalignment suggests that, while instrumental learning spontaneously decays in the absence of reinforcement, perceptual learning persists. Therefore our results are consistent with adaptation being effected through perceptual learning.