An EEG Study of Children With and Without ADHD Symptoms: Between-Group Differences and Associations With Sluggish Cognitive Tempo Symptoms
JOURNAL OF ATTENTION DISORDERS
Authors: Jarrett, Matthew A.; Gable, Philip A.; Rondon, Ana T.; Neal, Lauren B.; Price, Hannah F.; Hilton, Dane C.
Abstract
Objective: We examined differences between those with and without ADHD symptoms on resting state electroencephalography (EEG) indices and unique relations with sluggish cognitive tempo (SCT) symptoms. Method: Children with ADHD symptoms (n = 21) and healthy controls (n = 20) were assessed using rating scales, a neuropsychological task measuring sustained attention and inhibitory control, and EEG activity during a resting state period. Between-group, correlational, and regression analyses were conducted. Results: Large differences (particularly for theta/beta ratio in frontal and frontocentral regions) were found on EEG measures between those with and without ADHD symptoms. While ADHD and SCT symptoms both related to sustained attention on a computerized task, only ADHD symptoms were related to frontal and frontocentral theta/beta ratio. Conclusion: Results support the conclusion that ADHD symptoms are strongly associated with theta/beta ratio in frontal and frontocentral regions. Future studies should explore unique neurophysiological correlates of SCT.
CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy
MEDICAL PHYSICS
Authors: Liu, Yingzi; Lei, Yang; Wang, Tonghe; Fu, Yabo; Tang, Xiangyang; Curran, Walter J.; Liu, Tian; Patel, Pretesh; Yang, Xiaofeng
Abstract
Purpose Current clinical application of cone-beam CT (CBCT) is limited to patient setup. Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT-based adaptive planning presently impractical. In this study, we developed a deep-learning-based approach to improve CBCT image quality and HU accuracy for potential extended clinical use in CBCT-guided pancreatic adaptive radiotherapy. Methods Thirty patients previously treated with pancreas SBRT were included. The CBCT acquired prior to the first fraction of treatment was registered to the planning CT for training and generation of synthetic CT (sCT). A self-attention cycle generative adversarial network (cycleGAN) was used to generate CBCT-based sCT. For the cohort of 30 patients, the CT-based contours and treatment plans were transferred to the first fraction CBCTs and sCTs for dosimetric comparison. Results At the site of abdomen, mean absolute error (MAE) between CT and sCT was 56.89 +/- 13.84 HU, comparing to 81.06 +/- 15.86 HU between CT and the raw CBCT. No significant differences (P > 0.05) were observed in the PTV and OAR dose-volume-histogram (DVH) metrics between the CT- and sCT-based plans, while significant differences (P < 0.05) were found between the CT- and the CBCT-based plans. Conclusions The image similarity and dosimetric agreement between the CT and sCT-based plans validated the dose calculation accuracy carried by sCT. The CBCT-based sCT approach can potentially increase treatment precision and thus minimize gastrointestinal toxicity.