Different activation dynamics in multiple neural systems during simulated driving

Abstract

Driving is a complex behavior that recruits multiple cognitive elements. We report on an imaging study of simulated driving that reveals multiple neural systems, each of which have different activation dynamics. The neural correlates of driving behavior are identified with fMRI and their modulation with speed is investigated. We decompose the activation into interpretable pieces using a novel, generally applicable approach, based upon independent component analysis. Some regions turn on or off, others exhibit a gradual decay, and yet others turn on transiently when starting or stopping driving. Signal in the anterior cingulate cortex, an area often associated with error monitoring and inhibition, decreases exponentially with a rate proportional to driving speed, whereas decreases in frontoparietal regions, implicated in vigilance, correlate with speed. Increases in cerebellar and occipital areas, presumably related to complex visuomotor integration, are activated during driving but not associated with driving speed.

Near-infrared spectroscopy as a tool for driving research

“…Similarly, Mader et al (2009) asked participants to watch videos of car driving on unfamiliar and familiar routes, while monitoring brain activations via fMRI. Results demonstrated that observing driving on a familiar route may cause less activations of the temporoparietal, occipital and inferior frontal areas, even with specially trained drivers, because of reduction in attention and perception processes (see also Calhoun et al, 2002;Callan et al, 2009;Just, Keller, and Cynkar, 2008; see Calhoun and Pearlson, 2012 for review). On the other hand, using EEG, Jäncke, Brunner, and Esslen (2008) examined the neural basis of driver's speeding behaviour, and revealed more α-band-related (8-13 Hz) activity (i.e., less neural activation) during fast driving, due to less neurophysiological effect in executive control (see also Kim et al, 2013;see Lal and Craig, 2001 for review).…”

“…In the case of fMRI, most often in the laboratory, studies have identified important brain regions underlying various driving actions or pertaining to increased risk (Calhoun et al, 2002;Callan et al, 2009;Just, Keller, and Cynkar, 2008;Mader et al, 2009;Spiers and Maguire, 2007; see Calhoun and Pearlson, 2012 for review). In the case of EEG, especially when paired with recent high-end driving simulators or in-car application, studies have led to advances in understanding the role of the brain when speeding, or in long-distance vehicle operation and resulting impact of fatigue (Boyle et al, 2008;Jäncke, Brunner, and Esslen, 2008;Lal et al, 2003;Zhao et al, 2012; see Lal and Craig, 2001 for review).…”

Preliminary Study on Human Brain Imaging by Reflectance Diffuse Optical Tomography

“…13 DOT can measure the both image of oxy and deoxy hemoglobin that are the great advantage for understanding the functional localization of the brain. There are the reports that the activity near the frontal region lowered under a video game task 14,15 , supporting our result.…”

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

“…For example, the initial use of ICA modeled the ICA time courses using a task-based approach, but then so-called transient task-related effects were observed, 7,27,43,44 including an early example of the now widely studied default mode network. 28 The use of ICA also identified spatially structured but non-task-related components within task-fMR imaging data and shortly thereafter in resting-state fMR imaging data.…”

“…In the early ICA studies of task-based designs, transient task-related activity was captured by independent components enabling investigators to better understand how the brain is responding in regions where activity does not perfectly temporally correspond with the task. 27,28 This property is likely one of the main reasons ICA is so widely used on resting fMR imaging data, because in this case there is no temporal model available because the subject is resting quietly without the presence of an externally controlled stimulus. ICA is free of assumptions about the temporal evolution of the data.…”