Attention Training Improves Executive Functions and Ameliorates Behavioral Symptoms in Children with Attention-Deficit Hyperactivity Disorder: Implication of Tele-Cognitive-Rehabilitation in the Era of Coronavirus Disease

Background: Children with attention-deficit hyperactivity disorder (ADHD) struggle with impaired attention, leading to impaired executive function and behavioral symptoms. In this study, we aimed to evaluate the effect of attention training on executive functions and behavioral symptoms in children with ADHD, in a tele-cognitive-rehabilitation setting. Methods: Thirty children (mean age: 9.93 ± 1.68 years, 21 boys) with ADHD were randomly assigned to 2 equal groups of attention training and active control group. Attentive Rehabilitation and Improvement of Attention (ARIA) and a class of storytelling were used for intervention in two groups, in an online platform. Continuous performance test, one-back test, Wisconsin card sorting test (WCST), Conner's parent rating scale, and behavioral rating inventory of executive function (BRIEF) were used for assessment in three-baseline, postintervention, and follow-up sessions. Repeated measures analysis of variances were used for analysis. Results: ARIA leads to significant improvement in omission error (P < 0.001), commission error (P = 0.006), and response time (P = 0.005) of continuous performance test, cluster (P = 0.001), but not preservation error (P = 0.110) of WCST, accuracy of NBT (P = 0.004) and the score of Conner's parent rating scale (P < 0.001) and BRIEF (P < 0.001). These results indicate improved attention and executive functions, amelioration of ADHD symptoms, and improved behavioral performance. Conclusion: This study suggests that attention can be trained through tele-cognitive rehabilitation using a remediation program in children with ADHD. The effectiveness of this training can be confirmed by examining the transfer of training effects to other untrained cognitive domains, executive functions, symptoms of ADHD, and behavioral performance.

Association between in-scanner head motion with cerebral white matter microstructure: a multiband diffusion-weighted MRI study

Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) has emerged as the most popular neuroimaging technique used to depict the biological microstructural properties of human brain white matter. However, like other MRI techniques, traditional DW-MRI data remains subject to head motion artifacts during scanning. For example, previous studies have indicated that, with traditional DW-MRI data, head motion artifacts significantly affect the evaluation of diffusion metrics. Actually, DW-MRI data scanned with higher sampling rate are important for accurately evaluating diffusion metrics because it allows for full-brain coverage through the acquisition of multiple slices simultaneously and more gradient directions. Here, we employed a publicly available multiband DW-MRI dataset to investigate the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS). The diffusion metrics used in this study included not only the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also newly proposed inter-voxel metric, local diffusion homogeneity (LDH). We found that the motion effects in FA and MD seems to be mitigated to some extent, but the effect on MD still exists. Furthermore, the effect in LDH is much more pronounced. These results indicate that researchers shall be cautious when conducting data analysis and interpretation. Finally, the motion-diffusion association is discussed.