Associations between Turning Characteristics and Corticospinal Inhibition in Young and Older Adults

NEUROSCIENCE RESEARCH ARTICLE C. W. Swanson, B. W. Fling / Neuroscience 425 (2020) 59–67

Associations between Turning Characteristics and Corticospinal Inhibition in Young and Older Adults Clayton W. Swanson a and Brett W. Fling a,b* a

Department of Health & Exercise Science, Colorado State University, Fort Collins, CO, USA

b

Molecular, Cellular, and Integrative Neuroscience Program, Colorado State University, Fort Collins, CO, USA

Abstract—The effects of aging are multifaceted including deleterious changes to the structure and function of the nervous system which often results in reduced mobility and quality of life. Turning while walking (dynamic) and in-place (stable) are ubiquitous aspects of mobility and have substantial consequences if performed poorly. Further, turning is thought to require higher cortical control compared to bouts of straight-ahead walking. This study sought to understand how relative amounts of corticospinal inhibition as measured by transcranial magnetic stimulation and the cortical silent period within the primary motor cortices are associated with various turning characteristics in neurotypical young (YA) and older adults (OA). In the current study, OA had reduced peak turn velocity and increased turn duration for both dynamic and stable turns. Further, OA demonstrated significantly reduced corticospinal inhibition within the right motor cortex. Finally, all associations between corticospinal inhibition and turning performance were specific to the right hemisphere, reflecting that those OA who maintained high levels of inhibition performed turning similar to their younger counterparts. These results compliment the right hemisphere model of aging and lateralization specification of cortically regulated temporal measures of dynamic movement. While additional investigations are required, these pilot findings provide an additional understanding as to the neural control of dynamic movements. Ó 2019 IBRO. Published by Elsevier Ltd. All rights reserved. Key words: aging, turning, cortical silent period, TMS, wearable sensors, inhibition.

falling occurs during both dynamic and stable bouts of mobility, reports have shown that, 42% of falls occur during transfers (sit-to-stand) or body weight shifts such as turning (Robinovitch et al., 2013). Furthermore, a fall while turning increases an individual’s risk of a hip fracture by a factor of eight compared to a fall while walking straight ahead (Cumming and Klineberg, 1994; Feldman and Robinovitch, 2007). The vast majority of gait research has focused on linear walking; however, turning is a ubiquitous daily task. Turns are performed during nearly all activities with the typical adult executing nearly 900 turns per day (Leach et al., 2018; Mancini et al., 2016). Turning is the act of a whole-body change in trajectory and requires a considerable amount of lower extremity coordination to successfully accomplish. During a turn, it is essential for the legs to be independently controlled both spatially and temporally, allowing one leg to cover more distance at a faster rate, while the opposing leg covers less distance at a slower rate. Various characteristics of turns have been shown to distinguish between neurotypical and atypical populations when standard gait metrics (e.g. gait speed) do not. For example, turning speed and number of steps to complete a turn delineated between recently diagnosed Parkinson’s patients

INTRODUCTION The effects of healthy aging are multifaceted, resulting in a number of deleterious changes throughout the lifespan. One primary maladaptive change is a general decrease in mobility (walking and balancing), resulting in a loss of autonomy and an increased risk of falling. These adaptations have evolved into a major public health concern with projected costs of fatal and non-fatal fall injuries reaching an estimated $52 billion by the start of the next decade in the United States alone (Englander et al., 1996). As aging advances, so does the risk of a falls, with 33% of adults over the age of 65 experiencing a fall each year and an additional 12% falling at least twice (Leach et al., 2018; Mancini et al., 2016; Milat et al., 2011; Salva et al., 2004; Shumway-Cook et al., 2000). While

*Correspondence to: B.W. Fling, Department of Health & Exercise Science, Colorado State University, 1582 Campus Delivery, Moby B201A, Fort Collins, CO 80523, USA. E-mail address: [email protected] (B. W. Fling). Abbreviations: cSP, cortical silent period; GABA, gamma-aminobutyric acid; MEPs, motor evoked potentials; MVC, maximal voluntary contractions; OA, older adults; TMS, transcranial magnetic stimulation; VMO, vastus medialis oblique; YA, young. https://doi.org/10.1016/j.neuroscience.2019.10.051 0306-4522/Ó 2019 IBRO. Published by Elsevier Ltd. All rights reserved. 59

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and neurotypical adults when no differences in gait speed for linear walking were observed (King et al., 2012). Further, older adults increase their step width and reduce their gait speed to a greater extent leading into a turn compared to neurotypical young adults (Paquette et al., 2008). These adaptations suggest a more cautious turning pattern as older adults turn slower and increase their base of support. While these studies describe adaptations of turning performance, they neglect to describe the neural underpinnings regulating these changes. A mounting body of research has been conducted aimed at identifying the neural mechanisms involved in controlling gait, balance, and coordination (Richmond and Fling, 2019). While there are clear limitations to various neuroimaging approaches in assessing the neural control of balance and gait, research has begun to elucidate various aspects of neuroanatomy and neurophysiology to describe age-related mobility adaptations. One such neuroimaging approach that has been employed to study the neural mechanisms underlying upper and lower extremity control is transcranial magnetic stimulation (TMS) (Fling and Seidler, 2012; Fujiyama et al., 2009; Fujiyama et al., 2012; Swanson and Fling, 2018). TMS offers a non-invasive method for studying the motor cortex, and can be manipulated to assess both inhibitory and excitatory neurophysiology within the central nervous system (CNS), which is known to be important for proper manipulation of both upper and lower extremities (Bhandari et al., 2016; Cash et al., 2017; Kujirai et al., 1993; Ziemann et al., 1996a,b). A common method for studying gamma-aminobutyric acid (GABA) or inhibitory neurotransmission is the cortical silent period (cSP), which is thought to specifically assess GABAB neurotransmission of corticospinal neurons (Werhahn et al., 1999). Recent work from our group demonstrates age-related differences in the inhibitory capacity of motor cortex neurons projecting to lower extremity muscles, and differences in the inhibitory contributions of the motor cortex when coordinating the lower extremities during typical, over-ground walking (Swanson and Fling, 2018). Although various turning characteristics have been shown to distinguish between disease and typical aging, research has yet to elucidate the underlying neural mechanisms regarding turning performance. Therefore, the purpose of this study was to assess the effects of corticospinal inhibition on turning characteristics in both healthy young (YA) and older adults (OA). We hypothesized that turning duration (s) and peak turn velocity (deg/s) would be significantly reduced in OA, and that OA would demonstrate reduced corticospinal inhibition compared to their younger counterparts. Furthermore, based on prior research in the upper (Fling and Seidler, 2012) and lower limbs (Swanson and Fling, 2018), we hypothesized that less inhibition (i.e. shorter silent periods) would be correlated with reduced turning performance in OA while in the YA group the inverse would be observed (i.e. more inhibition would be associated with reduced turning performance).

EXPERIMENTAL PROCEDURES Participants A total of twenty-nine healthy individuals completed the study; 14 YA (8 males; mean age, 24.4 ± 3.6 years; age range, 20–31 years) and 15 OA (9 males; mean age 72.3 ± 5.7 years; age range, 65–83 years). All participants were fully ambulatory and able to walk independently without use of assistance. Additionally, all participants had no acute fall history (prior 6 months) and no medical disorders such as, diagnosed neuromuscular, neurodegenerative, cognitive, orthopedic, or other comorbidities that would impact their mobility or risk of TMS. Prior to enrollment, all participants completed the Mini Mental State Exam (MMSE) (YA; MMSE score range, 28–30; mean score 28.43 ± 0.76; OA; MMSE score range, 27–30; mean score 28.87 ± 0.92) to assess cognitive status (Tombaugh and McIntyre, 1992). This study was performed in accordance with the Declaration of Helsinki and approved by the Colorado State University Institutional Review Board (#17-7053H), all participants provided written informed consent prior to participating. Participants came into the lab for two testing sessions which occurred on separate days no more than 10 days apart. Testing sessions included an instrumented assessment of dynamic and stable turning and TMS testing. To complete the instrumented assessment, six wireless inertial sensors were positioned on each foot, around the waist at the level of L5, on the sternum, and around each wrist (Mancini et al., 2011; Swanson et al., 2019). All sensors were attached to the body using Velcro and elastic straps. Sensors were fit tight enough to limit unwanted sensor movement without being cumbersome or uncomfortable for the participant. Turning assessment Dynamic turns were characterized as 180° turns while conducting a six-minute self-selected pace walk test. Prior to the trial, a foot template was placed (then removed) to ensure consistent foot placement for all trials and participants. Participants were asked to walk back and forth down a hall of 30 m in length. The walkway was marked with high visibility tape at each end to cue participants on where to turn around. Participants were asked to turn around naturally, to mimic forgetting something in a room they had just come from. Stable turns were characterized as a 360° turn clockwise with an immediate 360° turn anticlockwise as fast as possible. Prior to the trial a foot template was placed (then removed) to ensure consistent foot placement for all trials and participants. Once the trial was initiated participants were asked to step through the consecutive turns. However, participants were instructed not to spin on their toes, heals, or conduct a military style turn. These turns were conducted in an open space barefoot on linoleum flooring with a research assistant present as a spotter in case of balance loss. These turns were done to simulate a turn occurring in a space such as a kitchen or bathroom.

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cSP assessment The cSP procedures for each participant have been published prior (Swanson and Fling, 2018). Briefly, participants were seated in an adjustable upright chair so that their legs were hanging off the ground. Motor evoked potentials (MEPs) were elicited in the vastus medialis oblique (VMO) muscle as this particular muscle is important for the final 30° of knee extension and commonly utilized during a turning movement. To produce MEPs in each leg a MagPro x100 stimulator (MagVenture, Farum, Denmark) and a 2
95 mm angled butterfly coil (120degree, Cool D-B80) were used. For consistency across participants the scalp was drawn on using permanent marker. The center of the head (Cz) was determined by measuring the center distance between the nasion to inion and tragus to tragus of each ear (Homan et al., 1987). Following Cz marking, 2 cm marks were made laterally of Cz and anterior 5.5 cm (Groppa et al., 2012). The coil was placed tangentially against the scalp at approximately 45° from the mid-sagittal line as to be perpendicular to the central sulcus allowing for ideal current direction for oncoming cortical stimulus (Groppa et al., 2012). With participants seated and relaxed, the ‘hot spot’ for cortical stimulation of the VMO was determined. To limit potential for contralateral transmission during testing trials MEPs during hot spot detection were assessed bilaterally. Hot spots were determined when the contralateral VMO was isolated on the electromyography (EMG) trace for the active leg. The resting motor threshold (RMT) was determined bilaterally and defined as the lowest stimulus intensity which evoked a response 50 lV for five out of ten stimulations. To determine maximal force output of the knee extensors participants were asked to produce a series of maximal voluntary contractions (MVC). Participants’ legs (independently) were secured to the chair using a strap around the distal posterior shank and attached to a force transducer and bar beneath the participant. All participants completed between two – five MVC’s until the two highest forces were within 10% of each other. The same process was replicated for the opposing leg. The cSP was tested bilaterally with the stimulated hemisphere randomized across participants. To elicit the cSP, participants were asked to maintain an isometric contraction at 15% of their MVC. Participants were provided visual biofeedback on a forward display. The display depicted a vertical bar that grew or shrunk simultaneously to the present force being produced. A linear guide line (i.e. 15% of MVC) was placed in the middle of the display so participants had a target to match in producing their force for the entirety of the trial. Each trial lasted 2 minutes, during which time the researcher gave a stimulation at 120% of the RMT every 7–10 s with an average of 12 stimulations on each hemisphere (24 total).

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(Version 2) (Opal Sensors, APDM Inc., Portland, OR) was used to automatically stream and export turning metrics (El-Gohary et al., 2014; Horak et al., 2015). The primary turning metrics for each type (dynamic or stable) of turn included turn duration (s) and peak turn velocity (deg/s). cSP analysis Cortical silent period duration was the primary TMS measure. Details regarding the analysis of this measure have been published elsewhere (Swanson and Fling, 2018). cSP identification was calculated using a custom MATLAB (MathWorks, Nantick, MA) script, which identified individual cSPs. Once all individual cSPs were identified, they were then averaged to determine the duration of the silent period. Onset of the silent period was determined as the point when the EMG trace dipped below 1.5 standard deviations (SD) of the pre-stimulation mean. Offset of the silent period was determined as the point when the EMG went above the 1.5 SD pre-stimulation mean for five consecutive data points (Fling and Seidler, 2012; Swanson and Fling, 2018). Statistical analysis All statistical analysis was conducted in JMP Pro 13 with an alpha level set at 0.05. An exploratory outlier test was conducted, which resulted in one cSP (right hemisphere, OA) data point to be removed because it was greater than three interquartile ranges past the 25th and 75th percentile tails, and, at over 240 ms, was clearly outside of established ranges for cSP of lower limb muscles (Epstein et al., 2012). Additionally, two YA participants had non-quantifiable EMG data from their left hemisphere, however their right hemisphere cSP data was maintained for statistical analyses. Post data cleansing, normality was assessed using a Shapiro-Wilks test which demonstrated normal distribution for all variables. Between-group differences for demographic variables were assessed using independent t-tests. For turning analyses, an analysis of variance (ANOVA) was used to assess between group differences. For cSP duration a 2
2 ANOVA was used to assess for significant main effects (group and/or hemisphere) and interactions (group
hemisphere). Post-hoc analysis of interactions was assessed using a one-way ANOVA. Linear regression was used to assess correlations between hemisphere specific cSP durations and stable/dynamic turning metrics. Correlation strength between the cSP duration and turning metrics were calculated using Pearson correlation coefficients and a matrix of cSP durations and turning metric values. All data are presented as mean ± SD unless noted otherwise.

RESULTS Data analysis

Participant characteristics

Turning analysis. Dynamic and stable turning data were collected using previously validated Opal wireless inertial sensors (128 Hz), and Mobility Lab software

Participant characteristics are presented in Table 1. The two groups were significantly different with a mean age difference of 48.0 years (t(27) = 26.97, p < 0.001). When asked about exercise, YA reported exercising at

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Dynamic turning Dynamic turning metrics indicated group differences for turn duration and peak turn velocity (Fig. 1). For turn duration, OA demonstrated a significantly longer time to complete 180° comparted to the YA (F(1,28) = 11.63, p = 0.002). Accordingly, peak turn velocity was significantly different between the groups with OA turning at a slower speed compared to the YA (F(1,28) = 11.37, p = 0.002). These findings indicate that when walking at a natural, self-selected pace then turn around, OA demonstrate both a longer and slower 180° turn.

A3

B 260

*

Dynamic Turn Velocity (˚/s)

a higher intensity on average compared to the OA (F(1,27) = 4.46, p = 0.044), furthermore YA observed less exertion as measured using the Borg rate of perceived exertion scale post testing (F(1,27) = 8.13, p = 0.008). No other characteristics were statistically different.

Dynamic Turn Duration (s)

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2.8 2.6 2.4 2.2 2 1.8

Young Adult

*

240 220 200 180 160 140 120

Old Adult

Young Adult

Old Adult

Fig. 1. Boxplots of dynamic turning metrics between groups. (A) Demonstrates a significant difference between young and older adults for turn duration (p = 0.002) and (B) demonstrates a significant difference between young and older adults for peak turn velocity (p = 0.002).

Stable turning

Strength measures for the VMO were statistically different between age groups with OA demonstrating an overall weaker leg strength (t(56) = 5.313, p < 0.001). No significant difference for RMT was observed between age groups or between leg. Additionally, OA did not require higher stimulator intensity in eliciting MEPs (left VMO, t(27) = 1.583, p = 0.125; right VMO, t(27) = 1.99, p = 0.056). Silent period duration analysis confirmed a significant interaction: (F(1,24) = 14.347, p < 0.001). Post-hoc analysis was performed to evaluate both

B 500

*

6

Stable Turn Velocity (˚/s)

cSP

A Stable Turn Duration (s)

Stable turning demonstrated significant differences between groups for both turn duration and peak turn velocity (Fig. 2). Specifically, we report significantly increased turn duration (F(1,28) = 11.69, p = 0.002) and a decreased peak turn velocity (F(1,28) = 20.41, p < 0.001) in older adults. Taken together these results suggest that when asked to complete two consecutive 360° turns (clockwise then anticlockwise) as fast as possible, OA take more time and turn at a slower speed compared to their younger counterparts.

5.5 5 4.5 4 3.5 3 2.5 2 1.5

*

450 400 350 300 250 200 150 100

Young Adult

Old Adult

Young Adult

Old Adult

Fig. 2. Boxplots demonstrating the differences for stable turning metrics between groups. (A) Demonstrates a significant difference between young and older adults for turn duration (p = 0.002) and (B) demonstrates a significant difference between young and older adults for turn peak velocity (p < 0.001).

Table 1. Participant characteristics: demographic, anthropometric, activity information, and rate of perceived exertion scale (RPE) Characteristics

Younger Adults (n = 14)

Older Adults (n = 15)

Gender (n, % female) Age (years) Height (cm) Mass (kg) Dominate Leg (n, % Right) Activity Frequency (days) Activity Duration (mins) RPE of Average Activity Intensity RPE Post Test

6 (35.70) 24.36 ± 3.56 172.72 ± 8.10 69.04 ± 13.77 11 (78.57) 4.57 ± 1.41 65.36 ± 18.76 14.36 ± 1.74 7.71 ± 1.27

6 (40.00) 72.33 ± 5.68 172.05 ± 11.06 79.29 ± 16.17 15 (100.00) 4.23 ± 1.95 57.00 ± 33.16 12.07 ± 3.69 9.77 ± 2.40

Values are mean ± SD unless otherwise noted; p values refer to the independent t-test results.

p-value

<0.001 0.855 0.078 0.600 0.415 0.044 0.008

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200

Young Adult

Old Adult

* Cortical Silent Period (ms)

160

* 120

63

p = 0.029) (Fig. 4). While not significant, turn duration for the OAs demonstrated a moderate negative correlation with right hemisphere cSP duration (r = 0.51, p = 0.062). No other correlations were significant for either group in regard to hemispheric cSP duration (right or left) and dynamic turning metrics. However, interestingly the YA group demonstrated trends in the opposing direction only within the right hemisphere when compared to the OA, suggesting inhibitory control may shift with advancing age. These results indicated that longer cSPs (i.e. greater motor cortex inhibition) in the OA are associated with turning strategies more aligned with the YA. Correlations between stable turning metrics and cSP durations

80

40

0

Left

Right Hemisphere cSP

Fig. 3. Cortical silent period (cSP) demonstrated a significant difference between the right hemispheres for young and older adults. Also, cSP demonstrated a significant difference between the right and left hemisphere within older adults. * = p < 0.05.

interaction terms (hemisphere
group and group
hemisphere) (Fig. 3). The hemisphere
group interaction indicated that OA have a significant reduction in corticospinal inhibition for the right hemisphere compared to YA (F(1,27) = 4.45, p = 0.045). While the groups did not demonstrate a significant difference in inhibition between left hemispheres (F(1,26) = 1.63, p = 0.213). Further, the group
hemisphere interaction revealed that OA have significantly reduced inhibition in their right hemisphere compared to their left hemisphere (F(1,28) = 4.30, p = 0.048). YA demonstrated no differences between hemispheres (F(1,25) = 1.22, p = 0.280). These results indicate that OA have a reduction of inhibition in the right hemisphere compared to the YA and furthermore, OA have less inhibition in the right hemisphere compared to the left hemisphere.

Correlations between dynamic turning metrics and cSP durations Of the dynamic turning metrics, turn velocity for the OA group was the only metric significantly correlated with cSP duration (Table 2). Specifically, the right hemisphere cSP duration demonstrated a significant correlation to turn velocity for the OA (r = 0.58,

Right hemisphere cSP duration for the OA demonstrated a significant correlation for turn duration (r = 0.55, p = 0.043) (Fig. 5), and near significance for peak turn velocity (r = 0.53, p = 0.052). Young adults demonstrated a significant correlation for right hemispheric cSP duration and turn duration (r = 0.56, p = 0.036). No other turning metrics were significantly associated with cSP duration in right hemisphere. As for the left hemisphere neither group demonstrated a significant correlation with any of the turning metrics (Table 2). These results suggest that longer cSP durations (i.e. more corticospinal inhibition) in the OA specifically for the right hemisphere perform turns more characteristic of their younger counterparts.

DISCUSSION The aim of the current study was to assess the effects of aging on stable and dynamic turning characteristics, corticospinal inhibition, and the associations between these measures. Both dynamic and stable turning measures were obtained using six wireless inertial sensors secured to the body. Dynamic turning characteristics were measured while participants walked up and down a corridor performing 180° turns at their self-selected pace. Stable turn metrics were quantified while participants turned 360° clockwise then immediately 360° anticlockwise in place at their selfselected fast pace. Older adults performed both dynamic and stable turns at a reduced speed and increased duration when compared to their younger counterparts. Motor cortex inhibition was measured via the cSP duration and revealed a significant interaction. The right hemisphere demonstrated a significantly shorter SP duration indicative of less corticospinal inhibition compared to the left hemisphere in OA. Additionally, YA demonstrated significantly longer SPs and more inhibition when comparing the right hemispheres between groups. Furthermore, for both dynamic and stable turns, peak velocity demonstrated a positive relationship with cSP duration in OA whereas a negative correlation for the same measures were observed in the YA. Conversely, the opposite associations were observed for turn duration (dynamic and stable), a negative correlation with cSP duration in

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Table 2. Associations between dynamic and stable turning characteristics and hemispheric cSP durations Left Hemisphere cSP Young Adult

Right hemisphere cSP Old Adult

Young Adult

Old Adult

Turn Type

Variable

R

p-value

R

p-value

R

p-value

R

p-value

Dynamic

Duration (s) Velocity (°/s) Duration (s) Velocity (°/s)

0.003 0.009 0.306 0.319

0.994 0.978 0.333 0.312

0.252 0.299 0.284 0.321

0.366 0.28 0.306 0.244

0.332 0.353 0.562 0.438

0.246 0.216 0.036 0.117

0.51 0.582 0.546 0.528

0.062 0.029 0.043 0.052

Stable

of age, with this association bolstered with increased age. The YA were better able to maintain lower extremity control with reduced inhibition and OA with greater inhibition performed turns akin to their younger counterparts. Additionally, both groups demonstrated more robust correlations with the right hemisphere cSP duration for all turning metrics compared to 280 Young Adult Old Adult the left hemisphere, which revealed no significance in either 260 age group. 240 As expected, the OA in our study employed turning character220 istics which previous studies have 200 demonstrated to be biomechanically less advantageous or stable 180 (Akram et al., 2010; Hollands et al., 2014; Thigpen et al., 2000). 160 For individuals who display diffi140 culty while turning, this suggests an impairment of their dynamic bal120 ance which increases their risk of 100 falling (Leach et al., 2018). Similar 40 60 80 100 120 140 160 to prior studies, our results illuscSP Duration (ms) trate differences in turning quality Fig. 4. Correlation of dynamic peak turn velocity and right hemisphere cortical silent period (cSP) between YA and OA suggesting duration. A positive correlation was observed between variables for older adults (r = 0.58, an impairment associated with p = 0.029). Young adults demonstrated a negative relationship between dynamic peak turn velocity age (Leach et al., 2018; Mancini and cSP (r = 0.35, p = 0.216). et al., 2016). Specifically, turn duration and peak velocity during 7 Young Adult Old Adult the dynamic turns were significantly different between groups. Both turning characteristics were 6 significantly different between young and old during stable turns. 5 The reduction of peak turn velocity and increased turn duration sug4 gests a more cautious and simple turning strategy that accompanies 3 advanced age (Leach et al., 2018). While our OA were not char2 acterized as fallers, these results coincide with previous reports of 1 non-fallers, single fallers, and reoccurring fallers who demonstrate 0 reduced turn velocity and 40 60 80 100 120 140 160 increased turn duration (Mancini cSP Duration (ms) et al., 2016). Furthermore, Wright Fig. 5. Correlation of stable turn duration and right hemisphere cortical silent period (cSP) duration. A et al. (2012) reported that OA who negative correlation was observed between variables for older adults (r = 0.55, p = 0.043) and a have a future propensity of falling

Stable Turn Duration (s)

Dynamic Peak Turn Velocity (˚/s)

the OA and a positive correlation in the YA. These results are two-fold, suggesting a fundamental difference in the utilization of motor cortex inhibition and lower extremity control with age and a right hemispheric dominance for the control of lower extremity movements independent

positive relationship for young adults (r = 0.56, p = 0.036).

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demonstrate a more simplified en-bloc turning strategy while completing a 360° turn (Wright et al., 2012). Although, age related neurophysiological changes have been reported and associated with upper and lower extremity performance, there remains a shortage of conclusive research. Using TMS to assess relative amounts of GABA within the motor cortex provides the ability to assess corticospinal inhibition as it relates to various motor components. It is worth noting there are inconsistencies in reporting age related inhibitory adaptations with some studies reporting decreases, increases, or no apparent age associated changes (McGinley et al., 2010; Oliviero et al., 2006; Smith et al., 2011). This is likely a product of the natural aging process and the variability that exists as a result of the different age-related time courses that individuals experience. This may contribute to the inconsistent agerelated adaptations documented in the literature and is particularly notable as the majority of age related TMS studies typically only report one cortical hemisphere. That being said, our results demonstrate reduced inhibition for the OA in the right hemisphere when compared to the YA. A particularly notable finding of the current study is the inverse associations between motor cortex inhibitory capacity and turning performance in YA and OA. We report that YA demonstrated better turning performance with less corticospinal inhibition whereas OA performed turns better with more corticospinal inhibition. It is worth noting similar associations have been documented elsewhere in the aging literature for both the upper and lower extremities. Specifically, Fujiyama and colleagues documented a reduced ability to coordinate the upper and lower limbs was associated with decreased corticospinal inhibition in OA (Fujiyama et al., 2009, 2012). Furthermore, these results are consistent with findings from Swanson et al. (2018) where bilateral gait coordination in OA was worse in the presence of reduced inhibitory capacity while YA with less inhibition demonstrated better gait coordination (Swanson and Fling, 2018). The results from the present study display similar observations; however, the underlying mechanisms have yet to be elucidated. We postulate that YA may rely on different neural resources such as subcortical and/or spinal level modulation for successful bilateral movement control. Conversely, OA may rely on cortical inhibitory resources to a greater extent to properly coordinate such goal directed, bilateral movements. Interestingly, all significant associations between turning characteristics for both dynamic and stable turning and corticospinal inhibition in the current study were limited to the right hemisphere. Prior literature demonstrates that hemispheric dominance is associated with inhibitory laterality, or the expression of imbalanced inhibition between hemispheres (Netz et al., 1995). For example, inhibitory imbalances for OA have been observed where OA demonstrate reduced inhibition in the right hemisphere (Bashir et al., 2014), which has also been associated with reduced motor performance in recent TMS aging literature (Coppi et al., 2014). The results from the current paper supplement this neuro-

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physiological support for a right hemispheric aging model, which suggests that the right hemisphere is more susceptible and affected by the overall aging process when compared to the left hemisphere (Bashir et al., 2014; Dolcos et al., 2002; Seidler et al., 2010). The right hemispheric model for aging applies broadly to cortical areas, including the primary motor cortices, which is where we focused our stimulation due to turning being an motor task (Dolcos et al., 2002). Additional support for the age-related role of the right hemisphere in these functional, whole body turning tasks is provided from the current correlation analyses (Table 2). Hemispheric lateralization and specialization are not new concepts, in-fact they have been documented since the 18000 s (Manning and ThomasAnterion, 2011). Further, lateralization is a fundamental feature for neural organization mediating functions including language (Hutsler and Galuske, 2003; Manning and Thomas-Anterion, 2011), proprioception (Fling et al., 2014; Goble et al., 2012), postural control (Fling et al., 2014), and inhibition (Bashir et al., 2014). In addition, various aspects of motor control are thought to be lateralized, for example the left hemisphere is thought to provide visual feedback for movement, while the right hemisphere processes proprioceptive sensory feedback (Goble and Brown, 2008). Significant progress has been made in understanding the right hemispheres role in upper extremity movements (Mani et al., 2013; Schaefer et al., 2012). These studies demonstrate that the right hemisphere plays a significant role in movement timing and movement correction. While limited, there are studies supporting similar roles for the right hemisphere in controlling the lower extremities for postural control and locomotion. Specifically, that the right hemisphere engages in spatial orientation, programing, and executing familiar automated movements such as walking (Fling et al., 2014; Goble and Brown, 2008; Wolpert et al., 1998). Further, Fling et al. (2014) demonstrated significant associations between postural balance control and right hemispheric fiber tract structure where poorer white matter microstructural integrity was strongly associated with reduced proprioception in neurotypical and atypical individuals (Fling et al., 2014). The results of the current study compliment the right hemisphere dominance for processing temporally sensitive movements (e.g. turning) for the lower extremities where the older adults who maintained higher levels of inhibition performed turning similar to their younger counterparts. While the current study is taking a novel approach at elucidating the neural underpinnings of lower extremity control there are several limitations. The sample size was relatively small for this study; however, this study reveals promising results and suggests the need for a larger cross-sectional study. The number of TMS pulses to establish an average silent period is small; however, it has been documented that MEPs are more variable and thus require more trials than SPs, which Rossini et al. (2015) suggests 6–8 trials is enough to acquire a reliable SP average (Rossini et al., 2015; Sˇkarabot et al., 2019). The measure of cSP while participants are seated is considered standard practice; however, it is a limitation for most TMS studies due to the inability of stim-

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ulating the motor cortex while participants are conducting turns. Finally, the stable turning protocol assessed turning at the participants fastest self-selected pace, whereas the dynamic turning was assessed at the participants selfselected normal pace. Despite these differences in turning speed, we see similar age-related differences in turn duration, peak velocity and error in both the stable and dynamic turns. In summary, typical aging results in a number of functional and neural adaptations, which often produce maladaptive health consequences. The OA demonstrated reduced turning performance for both dynamic and stable turning characteristics when compared to YA, suggesting a more cautious turning strategy. Furthermore, we report intracortical inhibitory differences between the age groups and hemispheres, where the OA demonstrate reduced corticospinal inhibition of the right hemisphere compared to the left and OA had reduced corticospinal inhibition within the right hemisphere compared to YA. We suggest these results compliment a burgeoning body literature regarding a right-hemispheric model of aging, which indicates the right hemisphere is more susceptible to and affected by the overall aging process when compared to the left hemisphere. Finally, we extend the current literatures’ construct of hemispheric lateralization, where the right hemisphere is more adept at controlling temporal characteristics of movement by providing the novel perspective that lower extremity movement within OA who maintain higher levels of inhibition perform stable and dynamic turns similar to their younger counterparts. Future work will benefit from further investigation into the various neural mechanisms underlying efficacious mobility.

ACKNOWLEDGMENTS We would like to thank all of the individuals who participated in the study, the rocky mountain chapter of the American College of Sports Medicine for their student grant support, and Dr. Ben Sharp for his statistics consulting contribution.

DISCLOSURE STATEMENT Neither of the authors have any actual or potential conflicts of interest to declare.

FUNDING This research did not receive any specific grant from funding agencies in the public, commercial, or not-forprofit sectors.

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(Received 23 August 2019, Accepted 30 October 2019) (Available online 22 November 2019)