Multiarea Brain Activation and Gait Deterioration During a Cognitive and Motor Dual Task in Individuals With Parkinson Disease : Journal of Neurologic Physical Therapy

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Multiarea Brain Activation and Gait Deterioration During a Cognitive and Motor Dual Task in Individuals With Parkinson Disease

Liu, Yan-Ci PT, PhD; Yang, Yea-Ru PT, PhD; Yeh, Nai-Chen PT, MS; Ku, Pei-Hsin PT; Lu, Chia-Feng PhD; Wang, Ray-Yau PT, PhD

Author Information
Journal of Neurologic Physical Therapy: October 2022 - Volume 46 - Issue 4 - p 260-269
doi: 10.1097/NPT.0000000000000402

Abstract

INTRODUCTION

Performing a secondary task during walking, defined as dual-task walking, is necessary in daily living.1 According to the type of secondary task, dual-task walking can be classified as motor or cognitive. Motor dual-task walking refers to conducting motor tasks such as carrying or manipulating objects during walking,2 while cognitive dual-task walking implies conducting attentional tasks such as mental tracking, verbal fluency, conversational, or memory tasks during walking.3

Parkinson disease (PD) is a neurological degenerative disease, which leads to motor impairments such as difficulty with task switching, functional walking, and postural control. Cognitive impairments in executive function, memory, and attention have also been observed.4–6 The cognitive and sensorimotor impairments may therefore lead to difficulties during activities that require greater conscious control, such as dual-task walking.1,7–9 Previous studies found decreased gait speed and stride length and increased stride-to-stride variability and double support time during cognitive and motor dual-task walking in people with PD.1,10–13

The prefrontal cortex (PFC) is considered to play a crucial role in locomotion and dual tasks.14–17 Results from functional magnetic resonance imaging (fMRI) studies suggested that the PFC is an important mediator of cognitive dual tasks.14–16,18 Studies using functional near-infrared spectroscopy (fNIRS) showed that the PFC activated significantly during dual-task walking in healthy adults and the elderly.19–24 In people with PD, Nieuwhof et al25 reported that the HbO2 concentration in the PFC significantly increased during walking combined with a serial subtraction or digit span task, as compared with quiet standing. This may be attributed to an increased reliance on executive function to control movements that cannot be compensated due to basal ganglia dysfunction.26 On the other hand, Maidan et al24 found that the PFC did not activate significantly during walking combined with a serial subtraction task as compared with single walking (SW), which may indicate inability of the PFC to further recruit for difficult tasks due to an already high activation during SW.27 Although there are increasing fNIRS studies on PFC activity in individuals with PD, activity in other brain areas involved in adapting walking speed and posture, such as the premotor cortex (PMC) and supplementary motor area (SMA),28,29 was less studied.

The PMC and SMA are connected with the basal ganglia and thus have a pivotal function in the basal-ganglia cortical loop.30 In healthy individuals, decreased activity in the PMC and increased activity between the SMA and other motor areas were noted during automatic tasks.31 Recent studies also indicated that the SMA and PMC take part in walking with adjustment intention or with complex tasks.32,33 Our previous fNIRS study showed that, in addition to the PFC, higher activation was observed in the PMC and SMA during cognitive and motor dual-task walking in young adults.34 The SMA and PMC were also crucial during cognitive and motor dual-task walking in individuals with stroke.35 In individuals with PD, however, activity in the PMC and SMA to adapt to the challenges of dual-task walking is not immediately known. Therefore, the aim of this study was to investigate cognitive and motor dual-task walking performance and multiarea brain activities in individuals with PD.

METHODS

Participants

Individuals with PD were enrolled between March 2017 and November 2019. The inclusion criteria were (1) between 20 and 90 years of age, (2) diagnosed with idiopathic PD, with the diagnostic criteria defined as the presence of at least 2 of 4 features (resting tremor, bradykinesia, rigidity, and asymmetric onset), one of which had to be resting tremor or bradykinesia,36 (3) stages 1 to 3 of the Hoehn and Yahr scale, (4) be able to walk 10 m independently without an assistive device, (5) be able to use both upper extremities to hold a tray for the motor dual task, (6) is medically stable, and (7) has a Mini-Mental State Examination (MMSE) score of 24 or more. The exclusion criteria were (1) history of malignant tumors, (2) any neurological or orthopedic disease that might affect the experiment, (3) history of mental illness within 5 years, (4) alcohol addiction within the last 12 months, and (5) history of using drugs that may affect the central nervous system (eg, antiepileptic or antidepressant drugs) within the past month. All assessments were conducted while participants were in the “on-phase” of medication. Participants were informed about the research procedures and signed a written consent form. The study protocol was approved by the Institutional Review Board of National Yang-Ming University and was registered at http://www.clinicaltrials.in.th/ (TCTR20190118010). Sample size was calculated using the G*Power v3.1.9. The effect size was set at 0.4, power at 0.8, and α level at 0.05. The estimated total sample size was 24. Considering possible missing data, we recruited 28 participants for this study.

Study Design

This was a cross-sectional study. Characteristic data including age, gender, MMSE score, Hoehn and Yahr stage, and time since diagnosis of PD were obtained before the start of the trials. Participants were asked to walk on a walkway back and forth for 60 seconds under the 3 conditions described next. Gait performance and brain activity were recorded during each trial:

  1. Single-walking (SW). Participants were asked to walk at their comfortable speed.
  2. Walking while performing a cognitive task (WCT). Participants were asked to walk while serially subtracting 3 from a 3-digit number.
  3. Walking while performing a motor task (WMT). Participants were asked to walk while carrying a tray with a cup of water on it with both hands.

Each walking condition was repeated 2 times in random order, totaling 6 walking trials, with a 60-second rest period between each trial. Before each trial started, participants were asked to stand quietly for at least 15 seconds to stabilize their hemodynamics. Verbal instruction was provided by the assessor for each upcoming trial. To observe the natural performance of dual-task walking in individuals with PD, task prioritization was not emphasized before or during each walking condition.

Gait Performance

Gait performance was recorded and analyzed using the GAITRite system (CIR System, Inc, Havertown, Pennsylvania), which has a sensor pad connected to a laptop. The walkway was 4.75-m-long and 0.89-m-wide, with a 4.30-m-long and 0.61-m-wide pressure-sensitive area. Temporal and spatial gait parameters were recorded and analyzed, as each participant walked along the walkway. Participants were asked to walk from one end to the other back and forth, and to turn outside of the electronic walkway. Average values of the 2 trials from each condition were calculated for data analysis. Gait parameters of interest included speed (final common expression of locomotion, in cm/s), stride length (spatial characteristic of gait, in cm), stride time (temporal characteristic of gait, in seconds), swing cycle (associated with shuffling steps in individuals with PD, measured as percent of the gait cycle), and temporal and spatial variability (important predictor of falls).37 Temporal variability was calculated as the coefficient of variation (standard deviation / mean × 100%) of the stride time. Spatial variability was calculated as the coefficient of variation (standard deviation / mean × 100%) of the stride length. Dual-task cost (DTC) was calculated using the following formula to quantify the interference of each dual task on walking: (dual-task walking speed − single walking speed) / single walking speed × 100%.38

Brain Activation

A multichannel wearable fNIRS imaging system (NIRSport, NIRx Medical Technologies LLC, Glen Head, New York) was used to detect hemodynamics of the bilateral PFC, PMC, and SMA (Figure 1).34,35 The instrument exports and receives dual-wavelength (760 and 850 nm) near-infrared signals using 8 LED light sources and 8 detectors attached on the head cap, which is compatible with the international 10-5 system. Standard surface positions were set approximately 3.0 cm between every 2 adjacent positions.39 Fourteen source-detector channels with a sample rate of 7.81 Hz were used to detect changes in local blood hemodynamics. Locations of the fNIRS channels have been validated by structural T1-weighted MRI in our previous study.34 Participants were asked to wear a backpack that contained the fNIRS control box and a connected laptop for data acquisition. The entire data acquisition set weighed less than 1 kg, which exerted minimal influence on gait performance.

F1
Figure 1.:
Arrangement of opcodes. Locations of 8 sources and 8 detectors based on the international 10-5 system. “Fp” represents the placement on prefrontal, “AFp” represents the placement between prefrontal and frontal, “F” represents the placement on frontal, “FFC” represents the placement between frontal and frontal central, and “FC” represents the placement between frontal and central. Channel numbers are shown between the source-detector pair. PFC, prefrontal cortex; PMC, premotor cortex; SMA, supplementary motor area. This figure is available in color online (www.jnpt.org).

Procedures for signal processing in this study were the same as those of our previous study.35 To reduce physical artifacts, data were rejected based on the coefficient of variation, which was set at CVchan >15% and CVtrail >10%.34 Remaining fNIRS signals were bandpass-filtered (low-cut frequency of 0.005 Hz and high-cut frequency of 0.03 Hz) to eliminate the effects of heartbeat, respiration, and low-frequency signal drifts for each wavelength.40 Wavelet filtering was used to remove motion artifacts.41 Preprocessed signals for each channel were converted to concentration changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (f) using the modified Beer-Lambert law.42–44 The 5-second baseline collected before each walking trial showed the relative changes in HbO and HbR concentrations. To improve signal-to-noise ratio, we averaged the HbO and HbR changes over the 2 repetitions for each walking condition.40,44 The HOMER2 fNIRS processing package was used for filtering, artifact removal, and conversion of the signals for further analysis.45

Neuronal activation is typically coupled with a rapid increase in HbO and a relatively lower-amplitude reduction in HbR based on neurovascular coupling. As brain activation includes both HbO and HbR, the index of the hemoglobin differential (Hbdiff = HbO – HbR), which comprises both HbO and HbR values, was used to evaluate brain activation in this study.28,34,46,47 During the 60-second walking trial, cerebral hemodynamics showed that activation lasted about 30 to 40 seconds after the task started, with no significant activation thereafter. Therefore, the first 40 seconds of the hemodynamic data were used for data analysis and were divided into the early phase and the late phase. The period between 5 and 20 seconds after task onset was defined as the early phase to reflect immediate hemodynamic response to the walking condition. The period between 20 and 40 seconds after task onset was defined as the late phase to assess sustained activation.34 The fNIRS data were prepared for statistical analysis using a customized script developed on Matlab2013b (Matlab, The MathWorks Inc, Natick, Massachusetts).

Statistical Analysis

One-sided t test with false discovery rate correction (q = 0.05) of multiple comparisons for the 14 channels was used to determine significant increases in brain activation under each walking condition and during each phase (P < 0.023). One-way repeated-measures multivariate analyses of variance were used to determine differences in gait and brain activation during the 3 conditions. For gait performance, the 6 gait parameters were set as the dependent variables, and the 3 walking conditions were set as the independent variables. For brain activation, the Hbdiff values from the 14 channels were set as the dependent variables, and the 3 walking conditions set as the independent variables. For any significant difference in outcome parameters among the 3 walking conditions, a post hoc test with the Bonferroni correction was used for pairwise comparisons (SW and WCT, SW and WMT, and WCT and WMT). Reported P values were adjusted for the Bonferroni correction, and the significance level was set at P < 0.05. Dual-task costs during WCT and WMT were analyzed using 1-way repeated-measures analysis of variance, and the significance level was set at α = 0.05. The Pearson correlation coefficient was used to examine the correlation between brain activation in each channel and the gait parameters during the early and late phases of gait, respectively. The significance level was set at α = 0.05. All data were analyzed using the SPSS Version 24.0 software (SPSS Inc. Chicago, Illinois).

RESULTS

Twenty-eight individuals (17 men and 11 women) with PD participated in this study. The mean age was 66.1 years, and the mean time since diagnosis of PD was 74.6 months. The median of the Hoehn and Yahr stage was 1.5 (ranging from 1 to 3). Characteristic data of all participants are shown in Table 1.

Table 1. - Basic Data of Participants (N = 28)a
Variable Value
Age, y 66.1 ± 8.0
Gender, male/female 17/11
MMSE 28.0 ± 1.8
Onset duration, mo 74.6 ± 57.7
Median Hoehn and Yahr stage (range) 1.5 (1-3)
Abbreviation: MMSE, Mini-Mental State Examination.
aValues are mean ± SD or frequency.

Gait Performance

Gait performance under the 3 walking conditions is shown in Table 2. There was a statistically significant difference in gait parameters (P < 0.001) under the 3 conditions, including speed (P < 0.001), stride length (P < 0.001), stride time (P < 0.001), and swing cycle (P = 0.007). Participants with PD walked more slowly, with shorter stride length, shorter swing cycle, and longer stride time during both WCT and WMT as compared with SW (Table 2). Comparison between dual-task types showed that participants with PD walked with a longer stride time during WCT than WMT (P = 0.002). DTC was also greater during WCT than WMT (P = 0.029). There was no significant difference in gait variability (temporal variability: P = 0.080, spatial variability: P = 0.212) among the 3 conditions.

Table 2. - Gait Performance Under 3 Different Walking Conditions (N = 28)a
SW WCT WMT Pairwise Comparisons
SW vs WCTb P Valuec SW vs WMTb P Valuec WCT vs WMTb P Valuec
Speed, cm/s 94.1 ± 19.6 77.0 ± 23.3 81.7 ± 19.8 17.1
(10.8, 23.5)
<0.001 12.5
(7.7, 17.3)
<0.001 −4.7
(−11.2, 1.9)
0.241
Stride length, cm 104.3 ± 17.9 92.7 ± 21.4 92.3 ± 17.5 11.7
(6.3, 17.1)
<0.001 12.1
(8.3, 15.8)
<0.001 0.4
(−5.0, 5.7)
1.000
Stride time, s 1.11 ± 0.08 1.23 ± 0.16 1.14 ± 0.11 −0.12
(−0.18, −0.06)
<0.001 −0.03
(−0.06, 0)
0.050 0.09
(0.03, 0.15)
0.002
Swing cycle, % 33.9 ± 2.4 32.3 ± 3.2 32.5 ± 2.3 1.6
(0.4, 2.8)
0.010 1.4
(0.1, 2.6)
0.030 −0.2
(−1.7, 1.2)
1.000
Temporal variability 6.45 ± 9.37 9.29 ± 9.52 5.76 ± 5.27
Spatial variability 6.79 ± 5.27 9.41 ± 6.13 8.18 ± 6.36
Speed DTC, % 19.8 ± 13.4 13.6 ± 9.9 6.22
(0.01, 0.12)
0.029
Abbreviations: DTC, dual-task cost; SW, single walking; WCT, walking while performing cognitive task; WMT, walking while performing motor task.
aValues are mean ± SD.
bMean difference (95% confidence interval) for pairwise comparisons.
cAdjusted P value for Bonferroni correction.

Brain Activation

Participants with PD showed increased brain activation in most channels except for the right PFC (Ch. 2) in the early phase of SW, but no sustained activation was observed in any of the channels in the late phase (Figure 2a). WCT showed increased activation in the PFC, PMC, and SMA in the early phase, and sustained activation in the bilateral PMC (Ch. 6 and 7) and SMA (Ch. 13) in the late phase (Figure 2b). During WMT, significant increases in the left PFC (Ch. 1), bilateral PMC (Ch. 3 and 7 in left PMC, Ch. 5 and 6 in right PMC), and bilateral SMA (Ch. 11, 12, 13, and 14) were observed in the early phase, but there was no sustained activation in the late phase (Figure 2c).

F2
Figure 2.:
Illustrations of brain activation level: (a) during SW, (b) during WCT, and (c) during WMT. The t values of significant activation with FDR correction for the multiple comparison during early (5-20 second after task onset) or late (20-40 seconds after task onset) phase are color-coded under the axis for each channel. The x-axis depicts the time since the task onset and the y-axis depicts the hemoglobin. The dashed line separates the early and late phases. The hemoglobin differential level was calculated by an average of all participants. FDR, false discovery rate; Hbdiff, differential between HbO and HbR; HbO, oxygenated hemoglobin; HbR, deoxygenated hemoglobin; SW, single walking; WCT, walking while performing cognitive task; WMT, walking while performing motor task. This figure is available in color online (www.jnpt.org).

Pairwise comparisons of hemodynamic responses during the different walking conditions in the early and late phases are shown in Figure 3. Early activation did not differ between SW and WCT, but there was a higher level of Hbdiff sustained in the bilateral PMC (Ch. 3, 4, 7, and 8 in left PMC, Ch. 5, 6, 9, and 10 in the right PMC) and SMA (Ch. 11, 12, 13, and 14) in the late phase of WCT when compared with the late phase of SW (Figure 3a). As for the comparison between WMT and SW, there was no significant difference in any of the channels in either the early or the late phase of walking (Figure 3b). Higher cortical Hbdiff was demonstrated during WCT in the late phase in all the channels except for the bilateral PFC (Ch. 1 and 2) and the right PMC (Ch. 9), as compared with WMT (Figure 3c).

F3
Figure 3.:
Illustrations of brain activation level between different walking conditions: (a) during WCT and during SW, (b) during WMT and during SW, and (c) during WCT and WMT. The t values of significant activations with FDR correction for the multiple comparison during early (5-20 seconds after task onset) or late (20-40 seconds after task onset) phase are color-coded under the axis for each channel. The x-axis depicts the time since the task onset and the y-axis depicts the hemoglobin level. The dashed line separates the early and late phases. The hemoglobin differential level is shown, which was calculated by average of all participants. FDR, false discovery rate; SW, single walking; WCT, walking while performing cognitive task; WMT, walking while performing motor task. This figure is available in color online (www.jnpt.org).

The Relation Between Brain Activation and Gait Performance

The relation between brain activity in the early and late phases and gait performance during single walking and dual-task walking are shown in Tables 3 and 4. During SW, the early phase of brain activity in the right PFC (Ch. 2) and left PMC (Ch. 7) correlated with speed and stride time. In the late phase, bilateral PFC, PMC, and SMA (all channels except for Ch. 5, 6, 10, and 14) significantly correlated with speed, stride time, and stride length. During WCT, the right PMC (Ch. 6) positively correlated with spatial variability in the early phase. In the late phase, results showed that activity in the bilateral PFC correlated with speed, stride time, and stride length. There was a significant correlation in the activity of the right PFC (Ch. 2), left PMC (Ch. 3, 7, 8), and SMA (Ch. 11) with swing cycle. During WMT, speed, stride time, swing cycle, and stride length correlated with early phase activation in the bilateral PFC, PMC, and the left SMA. In the late phase, activation in the bilateral PFC, PMC, and SMA (all the channels except for Ch. 7, 9, and 10) correlated significantly with all the gait parameters.

Table 3. - Correlations Between Brain Activity in Different Brain Areas and Gait Parameters in Early Phase (From 5 to 20 Seconds After the Task Onset)a
Walking Condition Brain Area Gait Parameters Correlation Coefficient
SW Right PFC (Ch. 2) Cadence −0.559b
Right PFC (Ch. 2) Stride time 0.595b
Left PMC (Ch. 7) Speed −0.406c
Left PMC (Ch. 7) Stride time −0.423c
WCT Right PFC (Ch. 2) Cadence −0.418c
Right PMC (Ch. 6) Spatial variability 0.432c
WMT Left PFC (Ch. 1) Swing cycle −0.514c
Right PFC (Ch. 2) Swing cycle −0.443c
Left PMC (Ch. 4) Speed −0.402c
Right PMC (Ch. 5) Swing cycle −0.405c
Left SMA (Ch. 11) Speed −0.407c
Left SMA (Ch. 11) Stride time 0.410c
Left SMA (Ch. 13) Speed −0.507c
Left SMA (Ch. 13) Cadence −0.414c
Left SMA (Ch. 13) Stride length −0.431c
Left SMA (Ch. 13) Stride time 0.497c
Abbreviations: PFC, prefrontal cortex; PMC, premotor cortex; SMA, supplementary area; SW, single walking; WCT, walking while performing cognitive task; WMT, walking while performing motor task.
aThis table only shows the correlations with statistical significance.
bP < 0.01.
cP < 0.05.

Table 4. - Correlations Between Brain Activity in Different Brain Areas and Gait Parameters in Late Phase (From 20 to 40 Seconds After the Task Onset)a
Walking Condition Brain Area Gait Parameters Correlation Coefficient Brain Area Gait Parameters Correlation Coefficient
SW Left PFC (Ch. 1) Stride time 0.440b Left PMC (Ch. 7) Stride length −0.505c
Left PFC (Ch. 1) Swing cycle −0.416b Left PMC (Ch. 8) Speed −0.437b
Right PFC (Ch. 2) Speed −0.493b Left PMC (Ch. 8) Stride length −0.420b
Right PFC (Ch. 2) Cadence −0.464b Right PMC (Ch. 9) Speed −0.434b
Right PFC (Ch. 2) Stride length −0.412b Right PMC (Ch. 9) Stride length −0.406b
Right PFC (Ch. 2) Stride time 0.497b Left SMA (Ch. 11) Speed −0.474b
Left PMC (Ch. 3) Speed −0.441b Left SMA (Ch. 11) Stride length −0.428b
Left PMC (Ch. 3) Stride length −0.397b Right SMA (Ch. 12) Speed −0.414b
Left PMC (Ch. 4) Speed −0.449b Right SMA (Ch. 12) Stride length −0.433b
Left PMC (Ch. 4) Stride length −0.403b Left SMA (Ch. 13) Speed −0.441b
Left PMC (Ch. 7) Speed −0.437b Left SMA (Ch. 13) Stride time 0.408b
WCT Left PFC (Ch. 1) Speed −0.438b Right PFC (Ch. 2) Stride time 0.474b
Left PFC (Ch. 1) Cadence −0.425b Right PFC (Ch. 2) Swing cycle −0.505b
Left PFC (Ch. 1) Stride time 0.413b Left PMC (Ch. 3) Swing cycle −0.462b
Right PFC (Ch. 2) Speed −0.510b Left PMC (Ch. 7) Swing cycle −0.470b
Right PFC (Ch. 2) Cadence −0.473b Left PMC (Ch. 8) Swing cycle −0.453b
Right PFC (Ch. 2) Stride length −0.459b Left SMA (Ch. 11) Swing cycle −0.485b
WMT Left PFC (Ch. 1) Cadence −0.409b Left PMC (Ch. 8) Stride length −0.474b
Left PFC (Ch. 1) Stride time 0.496b Left PMC (Ch. 8) DTC 0.433b
Left PFC (Ch. 1) Swing cycle −0.439b Left SMA (Ch. 11) Speed −0.513c
Right PFC (Ch. 2) Speed −0.490b Left SMA (Ch. 11) Stride length −0.461b
Right PFC (Ch. 2) Stride length −0.438b Left SMA (Ch. 11) Stride time 0.478b
Right PFC (Ch. 2) Stride time 0.495b Left SMA (Ch. 11) Spatial variability 0.449b
Right PFC (Ch. 2) Swing cycle −0.478b Right SMA (Ch. 12) Speed −0.406b
Left PMC (Ch. 3) Speed −0.429b Right SMA (Ch. 12) Stride time 0.396b
Left PMC (Ch. 3) Stride time 0.440b Right SMA (Ch. 12) Spatial variability 0.411b
Left PMC (Ch. 3) DTC 0.402b Right SMA (Ch. 12) DTC 0.430b
Left PMC (Ch. 4) Speed −0.472b Left SMA (Ch. 13) Speed −0.530c
Left PMC (Ch. 4) Cadence −0.393b Left SMA (Ch. 13) Stride length −0.478b
Left PMC (Ch. 4) Stride time 0.493b Left SMA (Ch. 13) Stride time 0.493b
Left PMC (Ch. 4) DTC 0.474b Left SMA (Ch. 13) Spatial variability 0.428b
Right PMC (Ch. 5) DTC 0.498c Left SMA (Ch. 13) DTC 0.426b
Right PMC (Ch. 6) Stride time 0.442b Right SMA (Ch. 14) Stride time 0.426b
Right PMC (Ch. 6) DTC 0.565c Right SMA (Ch. 14) Spatial variability 0.439b
Left PMC (Ch. 8) Speed −0.522c
Abbreviations: DTC, dual-task cost; PFC, prefrontal cortex; PMC, premotor cortex; SMA, supplementary area; SW, single walking; WCT, walking while performing cognitive task; WMT, walking while performing motor task.
aThis table only shows the correlations with statistical significance.
bP < 0.05.
cP < 0.01.

DISCUSSION

To our knowledge, this is the first study to report multiarea brain activation and walking performance during different types of dual-task walking (cognitive and motor) in individuals with PD. Both types of dual tasks exerted a negative impact on gait parameters including speed, stride length, and swing and stride time. Interference of the cognitive task was greater than that of the motor task on gait performance, with higher and more sustained activation in the PMC and SMA during the late phase of WCT compared with WMT.

The effects of the motor and cognitive dual tasks on walking in this study were in line with previous results in individuals with PD.1,3,13 Cognitive dual tasks led to significantly decreased gait speed and stride length, and had an overall greater effect on gait performance compared with a motor dual task.48,49 Previous studies on healthy adults, healthy elderly, and people with stroke found that cognitive and motor secondary tasks exerted a similar negative influence on walking performance.34,35,50 Although the global cognitive function of the participants was similar to that of the participants in the present study (MMSE ≥24), the particularly negative impact of the cognitive dual task on gait may be specific to individuals with PD and associated with impairments in executive function. Executive function, which facilitates set shifting, dividing or alternating attentional resources, and response inhibition,51 may be limited in the PD population,48,52,53 leading to compensatory walking behavior. In the present study, higher DTC and slower walking speed, indicating less stability and greater fall risk,54 was found during WCT. Taking this together, dual-task walking, especially cognitive dual-task walking, may pose a greater challenge for participants with PD.

The PFC has been recognized as the key area for executive function,34,55,56 which is involved in many daily activities, especially walking. Another task in addition to walking increases attentional demand and prefrontal load, interfering with gait performance.25,34 However, brain activity may react differently in neurologically involved people, such as in individuals with PD. It is interesting to note that the bilateral PFC did not activate more during dual-task walking than during SW in the present study, although our previous study and other studies showed that the PFC activated significantly during dual-task walking in healthy adults and the elderly.21–23,34 In line with the results of the present study, Maidan et al24 reported no difference in PFC activation during cognitive dual-task walking as compared with SW in participants with PD, as well as hyperactivity in PFC during SW. It is possible that individuals with PD demonstrate higher PFC activation to compensate for deficits in automaticity.31,51,57,58 Higher brain activation in individuals with PD were noted even in relatively simple tasks.52,57 In an fMRI study, Wu and Hallett59 found that individuals with PD showed greater activity in the PFC, cerebellum, PMC, parietal cortex, and the precuneus while performing automatic movement as compared with healthy controls. This compensatory phenomenon of higher activation during a single task may reflect lowered efficiency of the neural network.60 Therefore, individuals with PD may recruit other brain areas for more complex activities. As shown in the present study, recruiting more PMC and SMA may be the strategy used to cope with the more challenging cognitive dual-task walking. We further noted that almost all the brain areas measured in this study were activated more during WCT than WMT. These results suggest that, even with increased activation in other brain areas during WCT, a secondary cognitive task would induce a greater interference and negative impact on walking compared with a secondary motor task in participants with PD.

The PMC and SMA were significantly activated during both the cognitive and motor dual-task walking in our previous study in healthy adults.34 Additional neural processing is known to occur in the PMC and SMA for dual-task walking. In participants with PD, however, activation of the PMC and SMA was significantly higher only during WCT, but not during WMT, when compared with SW. The SMA is involved mainly in programming, preparing, and executing highly practiced movement sequences,61,62 and is associated with internally driven actions.63,64 The PMC is suggested to be important for selecting movements in response to external cues.63,65 Greater activation of the PMC and SMA during WCT suggests that both areas are needed to continuously adapt walking speed and posture while the participant was simultaneously engaged with the cognitive task.28,34,66 No such increase was noted during the WMT, which may be due to the nature of the secondary motor task used in the present study. Holding a tray (with a glass of water on it) is not a movement sequence, but a fixed motor task. A reduced need for planning and guiding of movement may explain the lack of SMA and PMC activation in our results. Our study on healthy participants found greater activation during WCT compared with WMT.34 Similar results were seen in our current study in individuals with PD, suggesting that because greater movement processing was required to maintain continuous walking while executing the cognitive task, the secondary cognitive task may be more challenging for both healthy adults and individuals with PD compared with the secondary motor task. Considering the deficits in the basal ganglia-cortical loop in individuals with PD, it is possible that the secondary motor task in the present study did not require extra processing, thus bypassing the loop. Lastly, it is possible that greater activation occurred in other parts of the brain, such as the primary motor (M1) and somatosensory (S1) cortices. The M1 and S1, however, were not the focus of the present study, and further research is needed to confirm this possibility. Overall, there are still limited studies on different brain area activity during dual-task walking, and further elucidation is needed.

The results of our present study suggest that dual tasks led to decreased walking performance; however, it is reasonable to suggest that cognitive dual-task training may be a potential intervention for individuals for PD. Several pilot studies have indicated that dual-task training can lead to improved gait performance in participants with PD.67,68 Combining cognitive dual-task gait training with other interventions may promote neuroplasticity and improve walking ability in individuals with PD. Further controlled trials are necessary to confirm the potential benefits of dual-task training and its associated brain changes.

In this study, we also examined the relation between brain activation and gait performance and found a negative correlation: the higher the brain activation during single and dual-task walking, the worse the gait. These results were also noted in individuals with stroke.35 Therefore, we speculate that, in people with impaired neuromotor control, increased brain activation in the motor- and cognitive-related areas may not be able to sufficiently fulfill the demands of walking, even during single walking.

Limitations

The present study looked at different dual-task gait performances and multiarea brain activities in participants with PD, which has not yet been reported by previous studies. However, there are several limitations. First, the sample size of the present study was relatively small, and a study with a larger sample size is needed to validate our findings. Despite the small sample size, statistical power was greater than 0.99 (effect size ≈ 0.97, partial η² = 0.483), indicating that the possibility of a type 2 error was very low. Second, the clinical disability level of participants was mild to moderate (Hoehn and Yahr stages I to III), and generalizations of our findings are limited to individuals with mild to moderate PD. Third, all participants were tested during the “on-medication” status. It is unclear whether our results can be applied to the off-medication status. Fourth, we did not include short channels, and thus could not measure hemodynamics to regress out artifact hemodynamics, noise, or diode movement. Fifth, due to instrument limitations, gait performance could not be partitioned into different phases. Thus, the results of correlation and the proposed underlying mechanisms of motor control should be considered with caution. Lastly, we did not quantify participants' performance of the secondary task. Future research involving quantification of both primary and secondary tasks may provide greater insight on the dual-task capacity of individuals with PD.

CONCLUSION

In individuals with mild-to-moderate PD, motor and cognitive dual tasks exerted a negative impact on walking performance. WMT did not induce greater activation in the PMC and SMA when compared with SW. The impact of the secondary cognitive task was still more significant than that of the secondary motor task, as indicated by greater activation in the PMC and SMA, in individuals with PD.

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Keywords:

brain activities; cognitive dual task; gait performance; motor dual task; Parkinson disease

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