Structurally Constrained Connectivity Explains Enhanced Visuomotor Decision-Making in Action Gamers
Poster No:
719
Submission Type:
Late-Breaking Abstract Submission
Authors:
Kyle Cahill1, Mukesh Dhamala2
Institutions:
1Georgia State University, ATLANTA, GA, 2Georgia State University, Atlanta, GA
First Author:
Co-Author:
Introduction:
Action video game (AVG) playing has been associated with cognitive enhancements, particularly in visual attention, decision-making, and visuomotor processing. However, the underlying neuroplastic changes driving these improvements remain unclear. This study investigates the impact of long-term AVG experience on brain connectivity by structurally constrained functional (SC-FC) and effective (SC-TGC) connectivity analyses. We hypothesize gamers exhibit more efficient processing of context dependent information in connections between key brain areas that allow greater cognitive flexibility compared to non-gamers, causing improvements in response times.
Methods:
Subject Data
47 right-handed participants were recruited. Groups were age-matched (gamers = 20.6 ± 2.4 years; non-gamers = 19.9 ± 2.6 years). Participants who indicated playing 5 h per week or more in one of four types of video game genres for the last two years were considered "gamers". The four types of action video game genres considered were First-Person Shooter (FPS), Real-Time Strategy (RTS), Multiplayer Online Battle Arena (MOBA), and Battle Royale (BR). Participants who were "non-gamers" in this study averaged less than 30 min per week in any video game over the last two years. A modified left–right moving-dot (MD) task was used to probe for differences in response time and accuracy between the cohorts. The effective number of participants for the SC-FC and SC-TGC analysis was 42 total participants (24 gamers and 18 non-gamers) with 40 total participants (23 gamers and 17 non-gamers) for brain–behavior regression between SC-FC & SC-TGC measures with response time.
MRI Data
Whole-brain structural and functional MR imaging was conducted on a 3 T Siemens Magnetom Prisma MRI scanner (Siemens, Atlanta, GA, USA) at the joint Georgia State University and Georgia Institute of Technology Center for Advanced Brain Imaging, Atlanta, GA, USA. First, high-resolution anatomical images were acquired using a T1-MEMPRAGE scan sequence (TR = 2530 ms; TE1-4: 1.69–7.27 ms; TI = 1260 ms; flip angle = 7 deg; voxel size 1 mm × 1 mm × 1 mm). Following, four functional runs used a T2*-weighted gradient echo-planar imaging sequence (TR = 535 ms; TE = 30 ms; flip angle = 46°; field of view = 240 mm; voxel Size = 3.8 mm × 3.8 mm × 4 mm; number of slices = 32, collected in an interleaved order; slice thickness = 4 mm) and acquired 3440 brain images while participants performed the behavioral tasks.
Connectivity Protocols and Analysis
Preprocessing of fMRI time series data was handled in AFNI. We extracted the time series for each region by overlaying the AAL3 atlas. We then parsed the time series with the task conditions to obtain the fMRI time series that correctly corresponded with the decision-making task and calculated undirected (FC) and directed (dFC) connectivity for each participant.
DSI Studio was utilized for diffusion MR image analysis. We used multi-shell diffusion with b-values of 300, 650, 1000, and 2000 s/mm². The acquisition parameters consisted of an in-plane resolution of 2 mm and a slice thickness of 2 mm. The QA threshold was 0.12. The angular threshold was 60 degrees. The step size was 1.00 mm. Tracks with length shorter than 10 mm or longer than 400 mm were discarded. A total of 5,000,000 tracts were calculated. We then binarized the structural matrix using QA >0.12 as a threshold and then constrained each participant's functional connectivity to only those with a valid structural connection.
47 right-handed participants were recruited. Groups were age-matched (gamers = 20.6 ± 2.4 years; non-gamers = 19.9 ± 2.6 years). Participants who indicated playing 5 h per week or more in one of four types of video game genres for the last two years were considered "gamers". The four types of action video game genres considered were First-Person Shooter (FPS), Real-Time Strategy (RTS), Multiplayer Online Battle Arena (MOBA), and Battle Royale (BR). Participants who were "non-gamers" in this study averaged less than 30 min per week in any video game over the last two years. A modified left–right moving-dot (MD) task was used to probe for differences in response time and accuracy between the cohorts. The effective number of participants for the SC-FC and SC-TGC analysis was 42 total participants (24 gamers and 18 non-gamers) with 40 total participants (23 gamers and 17 non-gamers) for brain–behavior regression between SC-FC & SC-TGC measures with response time.
MRI Data
Whole-brain structural and functional MR imaging was conducted on a 3 T Siemens Magnetom Prisma MRI scanner (Siemens, Atlanta, GA, USA) at the joint Georgia State University and Georgia Institute of Technology Center for Advanced Brain Imaging, Atlanta, GA, USA. First, high-resolution anatomical images were acquired using a T1-MEMPRAGE scan sequence (TR = 2530 ms; TE1-4: 1.69–7.27 ms; TI = 1260 ms; flip angle = 7 deg; voxel size 1 mm × 1 mm × 1 mm). Following, four functional runs used a T2*-weighted gradient echo-planar imaging sequence (TR = 535 ms; TE = 30 ms; flip angle = 46°; field of view = 240 mm; voxel Size = 3.8 mm × 3.8 mm × 4 mm; number of slices = 32, collected in an interleaved order; slice thickness = 4 mm) and acquired 3440 brain images while participants performed the behavioral tasks.
Connectivity Protocols and Analysis
Preprocessing of fMRI time series data was handled in AFNI. We extracted the time series for each region by overlaying the AAL3 atlas. We then parsed the time series with the task conditions to obtain the fMRI time series that correctly corresponded with the decision-making task and calculated undirected (FC) and directed (dFC) connectivity for each participant.
DSI Studio was utilized for diffusion MR image analysis. We used multi-shell diffusion with b-values of 300, 650, 1000, and 2000 s/mm². The acquisition parameters consisted of an in-plane resolution of 2 mm and a slice thickness of 2 mm. The QA threshold was 0.12. The angular threshold was 60 degrees. The step size was 1.00 mm. Tracks with length shorter than 10 mm or longer than 400 mm were discarded. A total of 5,000,000 tracts were calculated. We then binarized the structural matrix using QA >0.12 as a threshold and then constrained each participant's functional connectivity to only those with a valid structural connection.
·AAL3 Atlas Parcellation Categories for Connectivity Analysis
Results:
Behaviorally relevant connectivity differences found.
left parahippocampal -> left superior temporal pole dFC greater in Gamers
left mid temporal - left inferior temporal FC greater in non-Gamers
right insula -> right posterior orbitofrontal dFC greater in non-Gamers
left parahippocampal -> left superior temporal pole dFC greater in Gamers
left mid temporal - left inferior temporal FC greater in non-Gamers
right insula -> right posterior orbitofrontal dFC greater in non-Gamers
·Behaviorally Relevant Connectivity Differences between Gamers and Non-Gamers
Conclusions:
Connections between areas involving object recognition and interoception are dampened while the connection involved in processing contextual information is more prominent and explains the improved response times in AVGs.
Higher Cognitive Functions:
Decision Making 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
fMRI Connectivity and Network Modeling
Motion Correction and Preprocessing
Neuroinformatics and Data Sharing:
Brain Atlases
Novel Imaging Acquisition Methods:
BOLD fMRI
Diffusion MRI
Perception, Attention and Motor Behavior:
Perception: Visual
Motor Planning and Execution
Visuo-Motor Functions 2
Keywords:
ADULTS
Cognition
FUNCTIONAL MRI
Motor
NORMAL HUMAN
Perception
Plasticity
Statistical Methods
Vision
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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