Brain Activity and Sustained Attention over the Adult Age Span: fMRI of Simulated Bus-Following
Poster No:
873
Submission Type:
Abstract Submission
Authors:
Felix Menze1, Nathan Churchill2, Tom Schweizer2, Simon Graham3
Institutions:
1University of Toronto, Toronto, Ontario, 2Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, Toronto, ONTARIO, 3Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, Toronto, ONTARIO
First Author:
Co-Author(s):
Nathan Churchill, PhD
Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute
Toronto, ONTARIO
Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute
Toronto, ONTARIO
Tom Schweizer
Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute
Toronto, ONTARIO
Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute
Toronto, ONTARIO
Introduction:
Sustained attention (the ability to focus on one task for a long time) is important for performing many naturalistic tasks, such as operating vehicles (Edkins & Pollock, 1997). Behavioral studies have shown that both young and old adults show decreased performance on sustained attention paradigms, compared to middle-aged adults (Fortenbaugh et al., 2015). This is of particular concern as attentional lapses may cause unsafe driving behaviour and in extreme cases fatal collisions (Strayer & Drew, 2004).
To date, very little neuroimaging research has investigated sustained attention during driving over the adult age span with high ecological validity. The present work fills this void by studying the brain activity supporting performance of a bus-following task using a highly novel, fMRI-compatible driving simulator (Kan et al., 2012). It is hypothesized that 1) brain regions associated with sustained attention show increased activity during periods of excessive speed (raising risk of a rear-end collision, thus increasing attention and leading to braking behavior); and 2) prefrontal activity is increased in young and old adults compared to middle-aged adults. Such work would provide an important scientific contribution towards improved understanding of sustained attentional processes in the real world.
To date, very little neuroimaging research has investigated sustained attention during driving over the adult age span with high ecological validity. The present work fills this void by studying the brain activity supporting performance of a bus-following task using a highly novel, fMRI-compatible driving simulator (Kan et al., 2012). It is hypothesized that 1) brain regions associated with sustained attention show increased activity during periods of excessive speed (raising risk of a rear-end collision, thus increasing attention and leading to braking behavior); and 2) prefrontal activity is increased in young and old adults compared to middle-aged adults. Such work would provide an important scientific contribution towards improved understanding of sustained attentional processes in the real world.
Methods:
Sixty active licensed drivers without major medical, psychiatric or neurological impairments were recruited across adult age (17-76yrs, 35% f). Participants performed a simulated bus-following task (~220s) during fMRI at 3.0 Tesla, that required them to drive at constant distance behind a bus of varying speed. The resulting fMRI data were denoised by Optimization of Preprocessing Pipelines for NeuroImaging (OPPNI) software (Churchill et al., 2015). Brain activity was estimated using participant-level general linear models (GLMs) which included regressors modelling excessive speed. Group-level GLMs then analyzed the effects of age as a quadratic polynomial including linear and mean response (intercept) terms across age. The resulting activation maps were corrected at false discovery rate q < 0.05 and regionally interpreted using the Brainnetome atlas (BNA; Fan et al., 2016).
Results:
The mean group activation associated with excessive car speed (Fig 1) implicated the dorsolateral prefrontal cortex (Brodmann areas A6dl, A9/46d), paracentral lobule (A1/2/3/4ll), superior parietal lobe (A7), anterior cingulate cortex (A23, A24), thalamus (BNA areas lPFtha, Stha), occipital gyrus (rLinG, V5/MT+) and cerebellum (I-VI, VIII-X, Crus I/II, Vermis).
Quadratic increases in brain activity with age (Fig 2a) were observed predominantly in prefrontal areas (A6, A8m, A10l, A9/46, A12/47, A14m, A44) with quadratic decreases (Fig 2b) in occipital areas (LinG, CunG, OccG, vmPOS, OPC). Linear effects largely overlapped with these regions.
Quadratic increases in brain activity with age (Fig 2a) were observed predominantly in prefrontal areas (A6, A8m, A10l, A9/46, A12/47, A14m, A44) with quadratic decreases (Fig 2b) in occipital areas (LinG, CunG, OccG, vmPOS, OPC). Linear effects largely overlapped with these regions.
Conclusions:
Uniquely, this study examines brain activity associated with sustained attention during simulated bus following across the adult age span. Widespread activations were associated with excessive speed followed by corrective braking (common effects of attentional lapse) in areas consistently engaged during sustained attention paradigms (Huang et al., 2023). Significant nonlinear aging effects were also found in prefrontal and occipital areas, analogous to u-shaped patterns of driving performance with age (Robertsen et al., 2022). Compared to baseline brain activity of middle-aged drivers, both young and old drivers showed increased prefrontal engagement and decreased engagement of visual processing areas, similar to compensatory shifts seen during driving with distraction (Schweizer et al., 2013). Future work will investigate how these effects are modulated by cognitive status and driving ability in elderly participants.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Motor Behavior:
Motor Planning and Execution
Perception, Attention and Motor Behavior:
Attention: Visual
Keywords:
Aging
Data analysis
Design and Analysis
FUNCTIONAL MRI
MRI
Other - Driving
1|2Indicates the priority used for review
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Provide references using APA citation style.
Edkins, G. D. (1997). The influence of sustained attention on Railway accidents. Accident Analysis & Prevention, 29(4), 533–539. https://doi.org/10.1016/S0001-4575(97)00033-X
Fan, L. (2016). The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cerebral Cortex, 26(8). https://doi.org/10.1093/cercor/bhw157
Fortenbaugh, F. C. (2015). Sustained Attention Across the Life Span in a Sample of 10,000. Psychological Science, 26(9), 1497–1510. https://doi.org/10.1177/0956797615594896
Huang, H. (2023). A review of visual sustained attention: neural mechanisms and computational models. PeerJ, 11, e15351. https://doi.org/10.7717/peerj.15351
Kan, K. (2012). Methodology for functional MRI of simulated driving. Medical Physics, 40(1), 012301. https://doi.org/10.1118/1.4769107
Robertsen, R. (2022). Aging and Driving: A Comparison of Driving Performance Between Older and Younger Drivers in an On-Road Driving Test. SAGE Open, 12(2), 215824402210961. https://doi.org/10.1177/21582440221096133
Schweizer, T. A. (2013). Brain activity during driving with distraction: an immersive fMRI study. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00053
Strayer, D. L. (2004). Profiles in Driver Distraction: Effects of Cell Phone Conversations on Younger and Older Drivers. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(4), 640–649. https://doi.org/10.1518/hfes.46.4.640.56806