title: Cortical Dynamic Causality Network for Auditory-Motor Tasks.
abstract: Motor preparation and execution require the interactions of a large-scale brain network, while the study of the dynamic changes of their interactions could uncover the underlying neural mechanism of the corresponding information processing. This dynamic analysis requires high temporal resolution of the recorded signals. Electroencephalogram (EEG) with high temporal resolution has been widely used in related studies. However, studies based on scalp EEG always lead to distorted results, due to scalp volume conduction, compared with that of cortically recorded signals. In the current study, the dynamic networks of motor preparation and execution are investigated using Go/No-go tasks performed with the left/right hand. In the analysis, the EEG source localization and dynamic causal model are combined together to investigate the neural processes of motor preparation and execution. The results show that similar network patterns with nodes distributed in the bilateral occipital lobe, bilateral temporal lobe, bilateral dorsolateral prefrontal cortex, and contralateral supplementary motor area could be revealed for both the Go and No-go tasks. Statistical testing further indicates that stronger couplings with the supplementary motor area could be found in Go and right-hand response tasks compared with No-go and left-hand response tasks, respectively. The findings in the current study demonstrate that the information exchange within the motor related brain networks plays an important role for motor related functions, i.e., the different motor functions may have the different information exchange and processing network patterns.
authors.forename: Tiejun
authors.initials: T
authors.lastname: Liu
authors.forename: Fali
authors.initials: F
authors.lastname: Li
authors.forename: Yi
authors.initials: Y
authors.lastname: Jiang
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
authors.forename: Diankun
authors.initials: D
authors.lastname: Gong
authors.forename: Peiyang
authors.initials: P
authors.lastname: Li
authors.forename: Teng
authors.initials: T
authors.lastname: Ma
authors.forename: Kan
authors.initials: K
authors.lastname: Qiu
authors.forename: He
authors.initials: H
authors.lastname: Li
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
mesh_terms: D000328:Adult; D001307:Auditory Perception / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D009434:Neural Pathways; D011597:Psychomotor Performance / Q000502:physiology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult