4D dynamic system for visual-motor integration analysis

Wee, Yejin; Lee, Onseok

doi:10.1080/10255842.2022.2131400

Cited by 1 publication

(2 citation statements)

References 35 publications

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“…For complex figures, if they could not be completed within the timeframe (25 s), the corresponding data were excluded from the data analysis. In addition, previous research [ 20 ] found that using the 4D system, healthy adults required an average of 50.72 ± 22.22 s and 51.16 ± 18.15 s to complete Picture21 and Picture24, respectively. Thus, participants may not have been able to complete a single drawing within the 25 s. Consequently, data corresponding to these two figures were not used for the subsequent data processing.…”

Section: Methodsmentioning

confidence: 91%

“…Current computerized assessment methods for these VMI are limited to observations and descriptions of behavioral performance. Based on the Beery VMI test, Wee et al proposed a 4D dynamic analysis system that implements VMI testing in a 3D virtual space and obtains time-series data of hand joints and trajectories [ 20 ]. Nicholas et al used eye-tracking technology to observe the eye movements of children during the Beery VMI test, providing a new method for assessing visual-motor integration in real time [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Changes in effective connectivity during the visual-motor integration tasks: a preliminary f-NIRS study

Wang,

Li,

Wang

et al. 2024

Behav Brain Funct

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Background Visual-motor integration (VMI) is an essential skill in daily life. The present study aimed to use functional near-infrared spectroscopy (fNIRS) technology to explore the effective connectivity (EC) changes among brain regions during VMI activities of varying difficulty levels. Methods A total of 17 healthy participants were recruited for the study. Continuous Performance Test (CPT), Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A), and Beery VMI test were used to evaluate attention performance, executive function, and VMI performance. Granger causality analysis was performed for the VMI task data to obtain the EC matrix for all participants. One-way ANOVA analysis was used to identify VMI load-dependent EC values among different task difficulty levels from brain network and channel perspectives, and partial correlation analysis was used to explore the relationship between VMI load-dependent EC values and behavioral performance. Results We found that the EC values of dorsal attention network (DAN) → default mode network (DMN), DAN → ventral attention network (VAN), DAN → frontoparietal network (FPN), and DAN → somatomotor network (SMN) in the complex condition were higher than those in the simple and moderate conditions. Further channel analyses indicated that the EC values of the right superior parietal lobule (SPL) → right superior frontal gyrus (SFG), right middle occipital gyrus (MOG) → left SFG, and right MOG → right postcentral gyrus (PCG) in the complex condition were higher than those in the simple and moderate conditions. Subsequent partial correlation analysis revealed that the EC values from DAN to DMN, VAN, and SMN were positively correlated with executive function and VMI performance. Furthermore, the EC values of right MOG → left SFG and right MOG → right PCG were positively correlated with attention performance. Conclusions The DAN is actively involved during the VMI task and thus may play a critical role in VMI processes, in which two key brain regions (right SPL, right MOG) may contribute to the EC changes in response to increasing VMI load. Meanwhile, bilateral SFG and right PCG may also be closely related to the VMI performance.

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Section: Methodsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%