Age-related changes in midfrontal theta activity during steering control: A driving simulator study

As a result of population aging and its associated socioeconomic implications, it is important that older adults remain independent for as long as possible (Eby et al., 2008). One essential aspect of independence, and by extension quality of life, is mobility. The ability to drive a motor vehicle enables a person to perform both practical tasks, such as getting groceries or going to doctor's visits, and recreational activities, such as visiting family and social events. Accordingly, research indicates that driving cessation at a higher age is a risk factor for depression and can lead to admission into a retirement facility (Chihuri et al., 2016; Windsor et al., 2007). However, participating in traffic might not be a given for every older adult as they are not always able to drive successfully, since it has been documented that older drivers have an increased risk of being involved in (at-fault) motor vehicle accidents, that could be injury-inflicting, or even fatal due to increased frailty (Baldock et al., 2002; Falkenstein et al., 2020; Hakamies-Blomqvist, 2004; Langford et al., 2006; Liu et al., 2020). This increased accident risk has been found to be associated with declines in functional abilities (Anstey et al., 2012; Karthaus and Falkenstein, 2016).

Healthy aging is typically associated with changes in perceptual, cognitive and motor functions (Karthaus and Falkenstein, 2016). These can be considered essential for performing a complex task such as driving a car (Holland, 2001). Motor control is a ubiquitous part of driving, since the driver has to coordinate the movements of upper and lower limbs in order to adequately navigate the vehicle. Moreover, functional brain imaging studies have demonstrated that older adults exhibit substantial changes in brain activation compared to younger adults during the execution of relatively easy motor tasks, such as the coordination of cyclical flexion/extension movements of hands and feet (Heuninckx et al., 2008; Heuninckx et al., 2005).

In these motor tasks, older adults perform the movement at a comparable level as younger adults, yet exhibit overactivation in frontal and parietal brain regions, reflecting respectively a higher need for cognitive control and sensorimotor processing (Seidler et al., 2010). Thus, in older adults, a shift is seen from automatic to more cognitively controlled processing, which might be a compensatory strategy to maintain or reduce further decline in motor function (Cabeza et al., 2018; Cabeza et al., 2002; Heuninckx et al., 2008; Heuninckx et al., 2005; Reuter-Lorenz and Cappell, 2008). However, this over activation has also been observed to correlate with performance deterioration, rather pointing to dedifferentiation than compensation, due to non-selective recruitment of brain regions and/or deficits in inhibitory neurotransmission (Bernard and Seidler, 2012; Seidler et al., 2010; Zapparoli et al., 2022). Yet, dedifferentiation in the older adult brain might occur in addition to compensation, and compensation might also have occurred up to some extent even when performance did not improve, indicating that it was unsuccessful to a certain degree. Therefore, the compensation and dedifferentiation hypotheses are not necessarily mutually exclusive (Burianová et al., 2013; Cabeza, 2002), and both might fall short taking into account task complexity. Following the theoretical framework of the Compensation-Related Utilization of Neural Circuit Hypothesis (CRUNCH) (Reuter-Lorenz and Cappell, 2008), the aging brain might be able to successfully recruit additional neural resources when task demands are low, hence maintaining behavioral performance. However, when task demands increase, the aging brain reaches a ceiling in neural resources, thus causing a deterioration in task performance compared to younger adults (Reuter-Lorenz and Cappell, 2008; Seidler et al., 2010; Zapparoli et al., 2022). As an extension, the Scaffolding Theory of Aging and Cognition model (STAC) introduces the concept of scaffolding, that is neural dynamics in response to challenges across the entire lifespan (Park and Reuter-Lorenz, 2009). For younger adults, learning new skills or performing demanding tasks is initially associated with overactivation of neural regions. With successful learning or less demanding tasks, there is a shift from a broad to a focal neural network (Park and Reuter-Lorenz, 2009). Due to the decline in neural efficiency, an older adult might need to rely on this broad network once again. These scaffolded networks are, however, less efficient and when the need for compensation exceeds the available scaffolding capacity, this could cause performance declines in older adults (Park and Reuter-Lorenz, 2009). Correspondingly, it has been found that with aging, the ability to perform a cognitive task while walking decreases. This suggests that both motor and cognitive function require shared resources, or that more cognitive resources, that is increased mental effort, are required to protect gait function (Li and Lindenberger, 2002). During driving, less automatic and more cognitively controlled motor-related processing could limit the availability of cognitive resources needed to react promptly and adequately in critical and complex driving situations. Therefore, there is a need for a better understanding of age-related changes in motor-related neural processing in the context of driving, and to which extent cognitive resources are recruited even during a “simple” driving task without cognitive distraction.

An effective technique to measure neurophysiological brain dynamics while driving is electroencephalography (EEG). EEG allows us to analyze oscillatory power of different frequency bands in different brain regions. For evaluating motor processes and mental effort, specifically the theta band (4-8 Hz) and alpha band (8-12 Hz) seem to be of interest. First, theta in midfrontal brain regions has been previously linked with processes of cognitive and motor control, mental effort and sensorimotor integration (Cavanagh and Frank, 2014; Cruikshank et al., 2012; Klimesch, 1999; Van der Lubbe et al., 2021; Yordanova et al., 2020). In the latter, theta seems to be essential for the integration of various brain regions into a cortical attention network (Kaiser and Schütz-Bosbach, 2021; Klimesch, 1999; Scheeringa et al., 2008; von Stein and Sarnthein, 2000). In younger adults, increased midfrontal theta power indicates enhanced cognitive control when task difficulty is increased (Cavanagh and Frank, 2014; Cruikshank et al., 2012; Duprez et al., 2020; Klimesch, 1999). Furthermore, there is evidence that overall, EEG spectral power is sensitive to normal age-related changes over the adult lifespan, in which reduced overall power with increasing age has been established (Cummins and Finnigan, 2007; Polich, 1997). This general age-related decrease in power is most prominent for theta power in a midfrontal region (Anguera et al., 2013; Cummins and Finnigan, 2007). Interestingly, older adults do not necessarily demonstrate increased theta power with increasing task difficulty, indicating age-related changes in the functioning of the underlying neural networks, which might be considered compensatory when task accuracy is maintained (Heuninckx et al., 2008; Heuninckx et al., 2005; McEvoy et al., 2001; Reuter-Lorenz and Cappell, 2008).

However, when accompanied by a decline in performance, this lack of theta power increase could point towards dedifferentiation (Cummins and Finnigan, 2007; Kardos et al., 2014; Seidler et al., 2010; Zapparoli et al., 2022) or, on the other hand, indicate a possible plateau in resource allocation or limitation in capacity, thus restricting compensation (Park and Reuter-Lorenz, 2009). Moreover, this possible plateau effect for theta power has also been found in younger adults, when participants reached an overload while performing multiple concurrent tasks. This was postulated to reflect a saturation in cognitive resource allocation (Park and Reuter-Lorenz, 2009; Puma et al., 2018). Younger adults that could not perform many concurrent tasks, reached this theta power plateau sooner than young adults that were able to manage more concurrent tasks (Puma et al., 2018).

Second, alpha in posterior parietal regions has been linked to processes of attention allocation and withdrawal, with increased alpha power indicating attentional withdrawal or even boredom during monotonous tasks (Borghini et al., 2014; Herrmann and Knight, 2001; Klimesch, 2012; Smith et al., 1999). In contrast, a decrease in alpha power in both younger and older adults has been associated with increased mental workload during complex and cognitively demanding tasks, along with increased task accuracy (Borghini et al., 2014; Klimesch, 1999). Also, younger adults were found to withdraw attention, that is,higher alpha, during less demanding driving situations, while older adults required sustained attention even in these less demanding situations (Getzmann et al., 2018).

Previous driving research has mostly focused on the impact of age-related cognitive changes on driving performance, with only few studies investigating the neural correlates of cognition and its association with driving (for a review see (Depestele et al., 2020)). Moreover, research on age-related changes in brain dynamics of motor control in a driving context is even more sparse. Anguera et al. (2013) found age-related changes in the neural signature of cognitive control during a joystick-controlled visuomotor driving task (Anguera et al., 2013). Similar results were found during simulated steering tasks, with older adults requiring more cognitive control than younger adults to keep the car on track during road curves or when crosswind was present (Getzmann et al., 2018; Karthaus et al., 2018). To focus on the motor aspect of driving, this study evaluated steering control during simulated driving in a highly controlled virtual environment with little to no cognitive distraction. To assess if age-related neurophysiological changes could be considered a compensational mechanism, we needed to ensure that all participants performed the task at a similar accuracy to increase the likelihood that all participants experienced a similar level of difficulty.

Also, previous research on driving in an older population mostly focused on the differences between a young age group, often with limited driving experience, and an older age group. Changes across the entire adult lifespan are rarely taken into account. As previous research indicated that age-related neurophysiological changes are already - to a lesser extent - present in middle-aged adults, while performance remains similar to younger adults (Berchicci et al., 2012; McEvoy et al., 2001), we included non-novice adults from three different age groups: younger adults (YA), middle-aged adults (MAA) and older adults (OA). Including middle-aged adults can further improve our comprehension of age-related changes in brain dynamics, as aging does not necessarily entail a linear deterioration across the lifespan, with a steeper deterioration after the age of 65 (Fjell et al., 2013; Salthouse et al., 2003; van der Willik et al., 2020). The addition of middle-aged adults provides a more complete picture of the aging process, and can provide additional support for the current theories regarding age-related brain changes (Grady et al., 2006).

Since research in brain dynamics of older adults in the context of driving is sparse (Depestele et al., 2020), this study evaluated power dynamics of neural oscillations while driving through curved and straight road segments in three age groups in a motor control driving task. We formulated four hypotheses. First, we hypothesized that irrespective of age, midfrontal theta power would be higher when driving through more demanding curved segments, relative to less demanding straight road segments. In addition, we hypothesized that even when older persons demonstrate a motor task performance similar to younger adults, they would show different task-related midfrontal theta dynamics. In this regard, based on previous literature, it is possible to formulate two mutually exclusive hypotheses. Based on the findings regarding increased theta power for more difficult tasks in younger adults, we could hypothesize that older adults would exhibit a higher increase in midfrontal theta activity during curved relative to straight road segments compared to younger adults, to account for the need of more cognitive control during the more difficult road segments. Alternatively, the midfrontal theta increase could also be attenuated in older adults, which would argue for possible midfrontal theta plateauing due to a limit in capacity, or the reliance on different neural strategies. Third, we hypothesized that while driving in straight road segments, younger adults would exhibit increased alpha activity over posterior parietal regions relative to curved segments. This increase would likely not be present in older adults, as an indication of the recruitment of additional attentional resources even during less demanding driving situations. Fourth and finally, we hypothesized that these expected age-related neurophysiological changes in alpha and theta power would also be apparent in middle-aged adults, but to a lesser extent, indicating that age-related neural changes are already present at middle-age (Berchicci et al., 2012; McEvoy et al., 2001).

留言 (0)

沒有登入
gif