White Matter Tract-Cognitive Relationships in Children with High-Functioning Autism Spectrum Disorder

OBJECTIVE: The purpose of the present study was to clarify the relationship between white matter tracts and cognitive symptoms in children with high-functioning autism spectrum disorder (ASD). METHODS: We examined the cognitive functions of 17 children with high-functioning ASD and 18 typically developing (TD) controls and performed diffusion tensor imaging (DTI) tractography. We compared the results between the groups and investigated the correlations between the cognitive scores and DTI parameters within each group. RESULTS: The Comprehension scores in the ASD group exhibited a positive correlation with mean diffusivity (MD) in the forceps minor (F minor). In the TD group, the Comprehension scores were positively correlated with fractional anisotropy (FA) in the right inferior fronto-occipital fasciculus (IFO) and left anterior thalamic radiation (ATR), and negatively correlated with MD in the left ATR, radial diffusivity (RD) in the right IFO, and RD in the left ATR. Additionally, a positive correlation was observed between the Matching Numbers scores and MD in the left uncinate fasciculus and F minor, and RD in the F minor. Furthermore, the Sentence Questions scores exhibited a positive correlation with RD in the right inferior longitudinal fasciculus. Relative to TD controls, the specific tract showing a strong correlation with the cognitive scores was reduced in the ASD group. CONCLUSION: Our findings indicate that white matter tracts connecting specific brain areas may exhibit a weaker relationship with cognitive functions in children with ASD, resulting in less efficient cognitive pathways than those observed in TD children.

Sleep stage estimation method using a camera for home use

Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects' body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and fi ve parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these fi ve parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed suffi cient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device.