Movement-related potentials in high-functioning autism and Asperger's disorder

Autism and Asperger's disorder (AD) are neurodevelopmental conditions that affect cognitive and social-communicative function. Using a movement-related potential (MRP) paradigm, we investigated the clinical and neurobiological issue of 'disorder separateness' versus 'disorder variance' in autism and AD. This paradigm has been used to assess basal ganglia/supplementary motor functioning in Parkinson's disease. Three groups (high functioning autism [HFA]: 16 males, 1 female; mean age 12y 5mo [SD 4y 4mo]; AD: 11 males, 2 females; mean age 13y 5mo [SD 3y 8mo]; comparison group: 13 males, 8 females; mean age 13y 10mo, [SD 3y 11 mo]) completed a cued motor task during electroencephalogram recording of MRPs. The HFA group showed reduced peak amplitude at Cz, indicating less activity over the supplementary motor area during movement preparation. Although an overall significant between-group effect was found for early slope and peak amplitude, subanalysis revealed that the group with AD did not differ significantly from either group. However, it is suggested that autism and AD may be dissociated on the basis of brain-behaviour correlations of IQ with specific neurobiological measures. The overlap between MRP traces for autism and Parkinson's disease suggests that the neurobiological wiring of motor functioning in autism may bypass the supplementary motor area/primary motor cortex pathway.

Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering

The quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns. © 2014 Taylor & Francis.