Walking has long been considered an automated skill required for daily functioning and independent living. In reality, walking requires the complex neural coordination of visual, proprioceptive, and vestibular sensory incoming information (Beurskens and Bock, 2012). Furthermore, walking in everyday life is not performed as a single task since environmental demands require the ability to perform additional cognitive tasks at the same time (i.e., dual task), such as walking while talking (Yuan et al., 2015). Thus, walking depends on higher-order cognitive processes, known as executive functions (Yogev et al., 2008). Research consistently demonstrates that executive functions are important for successful dual task performance (Coppin et al., 2006).

The risk for developing impaired mobility (e.g., slow walking) increases with age (Verghese et al., 2009; Seidler et al., 2010). Falls are a significant consequence of impaired mobility (Fuller, 2000) with the majority of falls occurring during dual task conditions, such as walking while performing a secondary task (Tideiksaar, 1996). Thus, it is hypothesized that falls may not be a result of balance deficits in isolation, but the inability to effectively allocate attention to postural stability in dual task situations (Lajoie et al., 1996). This hypothesis is supported by observations such as older adults who stop walking while engaged in conversation (i.e., less able to dual task) are more likely to fall than those who continue walking (Lundin-Olsson et al., 1997). Recent evidence also highlights the importance of maintaining dual task ability in older adults beyond falls risk; Montero-Odasso et al. (2017) demonstrated that reduced dual task gait performance among older adults with mild cognitive impairment (MCI) was associated with progression to dementia.

MCI is considered the prodromal stage for dementia and is characterized by global brain atrophy and cognitive decline beyond normal aging but that does not impact daily living (Feldman and Jacova, 2005). Older adults with MCI are also found to have a greater decline in physical functioning (Aggarwal et al., 2006) and poorer performance under dual task conditions (Nascimbeni et al., 2015). Consequently, those with MCI are five times as likely to fall compared to cognitively intact older adults (Tinetti et al., 1988). Despite this, knowledge regarding the underlying neural correlates associated with dual task and gait performance in MCI is lacking. A better understanding of the neural basis for impaired dual task and mobility in this population can inform future strategies to prevent the progression of MCI and subsequently reduce the risk of mobility disability and falling.

Current evidence suggests that both the cognitive and motor impairments associated with MCI have a common neurobiological basis (Silbert et al., 2008; Callisaya et al., 2013). Of relevance, aging is characterized by disruptions in the functional connectivity of neural networks that support both cognitive and motor functions. Recent evidence highlights the potential involvement of the default mode network (DMN) and the frontoparietal network (FPN). The DMN is involved in autobiographical memory, memory consolidation, and self-referential thought (Andrews-Hanna et al., 2007; Buckner et al., 2008). It is active during rest and deactivates during task-oriented processes, to maintain attention and stay on task (Raichle et al., 2001). However, the DMN shows less deactivation on task for older adults and those with MCI (Lustig et al., 2003; Mevel et al., 2011). Although findings are inconsistent, it is thought this lack of deactivation may be due to greater resting state DMN functional connectivity (Mevel et al., 2011). It is suggested that as the posterior components of the network are among the first brain regions to be affected by both age and MCI-related atrophy (Buckner et al., 2005; Choo et al., 2010), compensation occurs as connections between the DMN and frontal regions increase (Davis et al., 2008), leading to a net increase in resting state functional connectivity and a lack of deactivation on task. This lack of deactivation while on task is theorized to increase distraction and reduce cognitive performance (Gardini et al., 2014). With age, walking becomes more dependent on executive functioning (Coppin et al., 2006). Therefore a decline in cognitive performance is subsequently hypothesized to also have a detrimental impact on walking in older adults and those with MCI.

The FPN is involved in top-down attentional control and allocation of available neural resources to important cognitive processes (Corbetta, 1998; Scolari et al., 2015), as well as in motor planning and motor execution (Ptak et al., 2017). It has also been associated with dual task walking performance. Compared to normal walking, performance on a walking while talking task was associated with greater functional connectivity between the prefrontal and supplementary motor areas (SMA) of the FPN and sensorimotor network, respectively (Yuan et al., 2015).

Much like the FPN, the SMA is a key structure for the execution and control of voluntary movement, motor planning (Roland et al., 1980; Eccles, 1982), and for maintaining attention on a motor task (Johansen-Berg and Matthews, 2000). Unlike some of the deeper brain structures, functional activation of the SMA can be assessed while walking using imaging techniques such as near infrared spectroscopy. Research using this technique, has found that an increase in activation of the SMA was associated with declines in gait performance under dual task conditions (Lu et al., 2015). Thus, the SMA may play an important role in the maintenance of gait performance during dual task walking.

Research focusing on the relationship between the DMN and the task-related networks responsible for motor functioning is lacking. However, one study did find that greater functional connectivity between the DMN and FPN was associated with reduced performance on a finger-tapping task in older fallers, indicative of an inability to focus attention on the task (Hsu et al., 2014). It is theorized that in older adults, this greater connectivity between the DMN and task-related networks at rest may suggest the DMN is remaining active on task, competing for resources with the task-related networks, and subsequently causing a decline in performance. This competition for resources is likely exacerbated under dual task conditions (Tombu and Jolicœur, 2003) and is hypothesized to underlie the relationship between cognitive and motor functioning decline, leading to an increased risk of falls and mobility disability in older adults and those with MCI. However, to our knowledge, no research has yet investigated the effect of the functional between-network connectivity of the DMN in relation to dual task walking paradigms.

In addition to reduced dual task performance, slower gait speed and increased postural sway are considered major factors contributing to an increased risk of falls. Older adults with MCI were found to have significantly slower gait speed (Eggermont et al., 2010) and greater postural sway (Liu-Ambrose et al., 2008) compared to healthy older adults. Both the SMA and components of the FPN have been associated with having a functional role in controlling gait speed (Harada et al., 2009; Yuan et al., 2015) and maintaining postural stability (Mihara et al., 2008). Therefore, maladaptive connectivity between these regions and the DMN may also negatively impact gait speed and postural stability, further contributing to an increased risk of falling. No research has yet investigated this potential relationship.

Consequently, the primary objective of this study was to investigate the relationship between the functional connectivity within the DMN and the between-network connectivity of the DMN with both the FPN and SMA (i.e., DMN–FPN and DMN–SMA) during a dual task walking paradigm in older adults with MCI. In addition, the secondary objective was to explore the association of within-network and between-network connectivity with gait speed and postural sway.

We hypothesized that greater functional connectivity within the DMN, between DMN–FPN, as well as between the DMN–SMA will be associated with poorer dual task walking performance, slower gait speed and greater postural sway. The rationale and hypotheses for this study are highlighted in Figure 1.

We found that greater resting state DMN functional connectivity was significantly associated with greater dual task cost among community-dwelling older adults with MCI. It was previously discussed that greater DMN connectivity at rest is indicative of a lack of deactivation on task in older adults with MCI (Lustig et al., 2003; Mevel et al., 2011), which would subsequently result in a decrease in task performance. The association between greater DMN connectivity and reduced dual task performance found in this study is therefore likely evidence for a lack of deactivation of the DMN on task.

One possible explanation for greater dual task cost being associated with greater DMN connectivity is provided by the capacity sharing theory. This theory states that because attentional resources are limited, when a person is required to perform two attention-demanding tasks simultaneously, the performance of at least one of these tasks will deteriorate (Tombu and Jolicœur, 2003). Therefore, if the greater DMN connectivity at rest were evidence of a lack of deactivation on task, this would suggest that the DMN is providing additional competition for resources, reducing those available for task-related functioning and subsequently reducing performance on the task. In addition to competing for resources generally, the DMN would be using these resources to fuel a functional process responsible for increasing distraction from the task, further amplifying its negative impact on task performance.

Walking in everyday life is not performed as a single task. Hence, the dual task walking measure has been associated with replicating the demands of the environment whilst walking (Tideiksaar, 1996). Findings from the current study indicate that patients with MCI are unable to meet the cognitive demands required to maintain walking performance in everyday life due to a lack of resources available and reduced ability to maintain attention on the task. It is plausible that an increase in distraction whilst attempting dual task walking may contribute to the increase in falls risk seen in older adults with MCI.

In addition, we also found that the functional connectivity between the DMN and SMA was significantly associated with slower gait speed and increased postural sway. Given that the DMN would normally deactivate on task (Raichle et al., 2001), intact resting state DMN–SMA connectivity would usually be an indication that during task performance, when the SMA is active, the DMN is deactivated. In this case, greater connectivity between these brain regions at rest is negatively correlated with gait speed and thus suggests there is interference between these networks on task; most plausibly that the DMN is influencing the SMA by remaining active on task.

The SMA has been shown to play an active role in motor planning (Roland et al., 1980), and in maintaining attention on a motor task (Johansen-Berg and Matthews, 2000). Through the DMN remaining active, increasing competition for resources and reducing the ability to maintain attention on the task, the SMA is unable to create motor plans with as much accuracy or efficiency. This would likely result in reduced quality of gait control and subsequently, gait speed. In addition, this decline in gait speed will further contribute to an increased risk of falls (Verghese et al., 2009; Espy et al., 2010). It would be useful to establish whether interventions targeted at increasing gait speed are also found to alter this maladaptive connectivity in order to provide support for this theory.

The SMA has also been implicated in postural control. Findings from Viallet et al. (1992) lead to the suggestion that the SMA was responsible for selecting the relevant circuits of phasic postural adjustments in order to maintain posture. Consequently, if an increase in DMN activity on task were to add competition for resources to the SMA, it is likely that there would be a decrease in the ability to select the correct postural adjustments to maintain balance, leading to the increased sway evident in this study.

It is important to note that greater connectivity between the DMN and SMA only correlated with performance on the floor sway task and not performance on the foam sway task. It is thought this is because the foam sway task may require more overt attention. Whereas, the participants may be less inclined to intentionally focus on the floor sway task because they are not so obviously unstable. This may be better explained by the capacity model of attention, which states that when a task is less cognitively demanding, more resources remain available to be allocated to task-irrelevant networks (Kahneman, 1973). In this case, the additional resources are allocated to the DMN. It may therefore be that when performing the foam sway task, participants were able to overcome the DMN interference by actively allocating more resources to the task-related regions than when performing the floor sway task. However, this can only be considered a speculative explanation as participants were not asked about how much attention they paid to the task and task-related neuronal activity could not be measured. This theory is also contradictory to the capacity sharing theory that has been used to explain the findings in relation to dual task performance, known to be a cognitively demanding task. Investigating the effect of the DMN–SMA connectivity over progressively more challenging postural tasks would be beneficial to determine which theory is most likely to explain these results.

There was no relationship found between connectivity of the DMN and FPN at rest and any of the behavioral measures. Due to the lack of previous literature in this area it is not completely clear why this may be. One of the studies that did find increased connectivity between the DMN and FPN during the performing of a motor task, found the increase in connectivity was specific to older adults classified as fallers rather than non-fallers (Hsu et al., 2014). Consequently, it may be that the FPN is more resistant to the influence of the DMN until a later stage in mobility decline. Further research is required to investigate the effect of the DMN on the FPN across several stages of falls risk, ideally comparing healthy older adults to those with MCI and those classified as fallers in order to establish at what stage connectivity between the DMN and FPN may become detrimental, if at all.

This study only compared resting state brain network connectivity with behavioral measures and did not investigate brain activity on task. Due to the limitations of functional MRI (fMRI) scanning (i.e., sensitivity to movement artifacts), and the lack of MRI-safe apparatus it was not possible for us to assess activity of the DMN and associated networks while performing motor tasks such as walking. Thus, caution must be taken when interpreting these results, as they are only correlational. To support our findings, future studies should aim to investigate the functional connectivity both within and between these networks whilst simultaneously performing a motor task. It is also important to acknowledge that there may be other underlying factors that could account for these results. For example, this study did not investigate the impact of potential sex differences or the underlying structural integrity of these networks, which may have provided an alternative explanation of the findings. Consequently, future studies should aim to identify any sex differences and compare both the structural and functional associations between these networks and mobility factors in order to better understand the underlying neurobiological factors contributing to increased falls risk. In addition, our study consisted of community-dwelling older adults with MCI exclusively and we did not have ethical clearance to collect any data pertaining to the race or ethnicity of our participants, as this was not related to our hypotheses. Thus, we acknowledge that this may make the generalizability of our findings ambiguous. Furthermore, some evidence highlights the potential influence of the cerebellum on the networks discussed in this study (Habas et al., 2009). Although this was not one of the main focuses of the current study, it may be beneficial to additionally investigate the role of the cerebellum in relation to these networks in future research. Due to the exploratory nature of this study and the small sample size, no adjustments were made to the significance value in order to control for multiple comparisons. It is acknowledged that this increases the likelihood of a type I error and future studies with larger sample sizes are needed to confirm our current findings. Finally, walking was performed indoors on a GAITRite mat and a verbal subtraction task was used for the dual task, consistent with other studies using a dual task walking paradigm (Beurskens and Bock, 2012). However, it would be useful to determine the specific effects of other cognitive and motor tasks whilst walking over varying terrains as well, as this is likely to be more representative of real-world scenarios.

Our results show that increased resting state DMN connectivity is associated with a decrease in dual task performance, slower gait speed and increased postural sway in older adults with MCI; either via greater within network connectivity or between-network connectivity with the SMA. This supports the theory proposed initially in Figure 1. To our knowledge, this is the first study to investigate the role of the DMN on dual task performance and motor functioning in people with MCI. These findings can be used to determine if interventions targeted to improve gait, cognition, and/or dual tasking specifically, can reduce the maladaptive effects of greater DMN connectivity in older adults vulnerable to an increased risk of mobility disability.

RC, TL-A, and CLH were involved in designing and performing the study. All authors contributed to the data analysis. RC, TL-A, and CLH were involved in the interpretation of results. RC wrote the first draft of the manuscript. TL-A, JB, and CLH wrote portions of the manuscript and critically reviewed the manuscript. All authors have read and approved the manuscript.

TL-A is a Canada Research Chair in Physical Activity, Mobility and Cognitive Neuroscience. JB is a Canadian Institutes of Health Research and Michael Smith Foundation for Health Research Postdoctoral Fellow. CLH is an Alzheimer Society Research Program Doctoral Trainee. The other author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.