Research on blood oxygen activity in cerebral cortical motor function areas with adjustment intention during gait

Abstract

BACKGROUND:

The study of the neural mechanism of human gait control can provide a theoretical basis for the treatment of walking disorders or the improvement of rehabilitation strategies, and further promote the functional rehabilitation of patients with movement disorders. However, the performance and changes of cerebral cortex activity corresponding to gait adjustment intentions are still not clear.

OBJECTIVE:

The purpose of this study was to detect the blood oxygen activation characterization of the cerebral cortex motor function area when people have the intention to adjust gait during walking.

METHODS:

Thirty young volunteers (21 $\pm$ 1 years old) performed normal walking, speed increase, speed reduction, step increase, and step reduction, during which oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total oxyhemoglobin (HbT) information in the prefrontal cortex (PFC), premotor cortex (PMC), supplementary motor area (SMA) was continuous monitored using near-infrared brain functional imaging.

RESULTS:

(1) With the intention to adjust gait, the HbO concentration in the SMA increased significantly, while the HbT concentration in the medial-PFC decreased significantly. (2) In the HbO concentration, step reduction is more activated than the step increase in the left-PMC ( $p=$ 0.0130); step adjustment is more activated than speed adjustment in the right-PMC ( $p=$ 0.0067). In the HbR concentration, the speed reduction is more activated than the speed increase in the left-PFC ( $p=$ 0.0103).

CONCLUSIONS:

When the intention of gait adjustment occurs, the increase of HbO concentration in the SMA indicates the initial stage of gait adjustment will increase the cognitive-locomotor demand of the brain. The left brain area meets the additional nerve needs of speed adjustment. The preliminary findings of this study can lay an important theoretical foundation for the realization of gait control based on fNIRS-BCI technology.

Keywords

Functional near-infrared spectroscopy adjustment intention gait speed step length cortical activation

1. Introduction

Patients with stroke have severe motor dysfunction, especially a severe decline in walking ability of the lower limbs [1]. Moreover, the instability of gait puts them in a dangerous state that is easy to fall [2]. The restoration of the walking ability of the elderly or patients with dysfunction plays an important role in the quality of life and reduces the socioeconomic burden.

There have been many studies on gait control and activation of brain regions. Different pace controls on a treadmill affect blood oxygen response in specific brain regions of a subject [3, 4]. Previous studies have shown that the reduction in stride and increase in walking speed can improve the gait stability of the elderly while walking and reduce the risk of falls. This shows that daily walking training is beneficial to improve the walking ability of patients with dysfunction [5]. But detection of motion intention faces two problems, one is recognition accuracy, and the other is time latency. Therefore, we hope to observe the characteristics of the activation and representation of motor functional regions of the brain when walking to find features that are conducive to detecting motor intentions.

In previous studies, multiple motion patterns of people can be identified through devices such as acceleration sensors [6, 7, 8]. However, participants in such studies are required to be healthy and have independent exercise ability. For patients with motor dysfunction, the use of such devices cannot effectively obtain motion data, which is not conducive to detecting the change of gait movement intention of patients. Based on the advantages of fNIRS equipment’s portability and low sensitivity to the environment [9], the real-time imaging technology of the brain during walking activities has been rapidly developed. Miyai et al. [10] detected medial primary sensorimotor cortices and supplementary motor areas (SMA) in young brain regions that were activated when walking on a treadmill at 1 km/h. Besides, SMA and prefrontal cortices (PFC) are involved in the motor preparation and execution stages [11, 12], and control the pace during walking [13, 14]. While premotor cortex (PMC) for healthy subjects and stroke patients plays an important role in bipedal walking [15], and can reflect upcoming changes in motion speed [16]. To verify the hypothesis, we used fNIRS equipment to detect changes in oxygenated hemoglobin (HbO), oxygenated hemoglobin (HbR), and total hemoglobin (HbT) information in the brain regions of PFC, SMA, and PMC from normal walking tasks to gait adjustment tasks.

2. Materials and methods

2.1 Subjects

Thirty young volunteers (21 $\pm$ 1 years old, male: female $=$ 8: 7) from Soochow University participated in this walking experiment. All volunteers were right-handed, in good health, and had no history of mental illness or cerebrovascular disease. All volunteers participated in this walking experiment for the first time, and they signed and informed consent prior to the experiment.

2.2 Experiment procedure

The experiment mainly includes two types of gait adjustments, the first type is the step adjustment, and the other type is the speed adjustment. To avoid the influence of the sequence of experiments, 30 young volunteers were randomly divided into two groups. The first group performed the step adjustment experiment before performing the speed adjustment experiment. In the second group, the speed is adjusted first and then the step is adjusted. Among them, step adjustment includes two states: step increase and step reduction; speed adjustment includes two states: speed increase and speed reduction. The walking experiment was performed in the corridor of a laboratory. The experimental environment is shown in Fig. 1a. The experimental walking distance was 21 m. Each corridor is about 10.5 m, a total distance of two corridors. The Mark point is set between two adjacent corridors. Each participant walked 10.5 m in a natural state, adjusted the gait at 10.5 m, and the remaining 10.5 m finished in the adjusted state. This requires that the subject’s adjustment range when gait changes are significantly different from the normal walking. A marking line is set at 10.5 m. When the participants start the walking task, walk to the middle marking line and end the walking task, the operator should use fNIRS software to mark a Mark respectively. After completing one walking task, participants were required to take a break and turn to prepare for the next walking task. The rest time after each turn was not less than 45 s. The experimental process is shown in Fig. 1c. When subjects adjusted the walking speed, the step size remained the same. Similarly, when the participants adjust the step size, the walking speed is unchanged, and each gait adjustment task is performed twice.

Figure 1.

a. Laboratory corridor environment; b. Experimental equipment; c. Experimental process, S for Start, E for End, R for Rest, NW for Normal Walk, DI for Speed Increase, DR for Speed reduction, PI stands for Step Increase, PR stands for Step Reduction, and TA stands for Turn Around.

In contrast to previous walking experiments on treadmills, to achieve natural walking tasks, this experiment did not deliberately require specific pace and step length. The experiment operator does not issue any instructions except for the two instructions “start of experiment” and “end of experiment” at the beginning and end of the experiment. The start, end, and rest duration of each walking task are completely controlled by the subject. All subjects performed 2–3 pre-walking experiments before the start of the real experiment to master the corresponding start-stop and rest time. We pay more attention to the changes of brain blood oxygen activation before and after the subjects adjust their gait. Therefore, the subjects are required to maintain their most natural walking state in the first stage of walking, and there must be obvious gait adjustments in the second stage.

2.3 NIRS imaging

The near-infrared brain imaging device used in this paper is a portable near-infrared brain imaging device (LIGHTNIRS) [17] of Shimadzu Corporation, Japan, as shown in Fig. 1b. The test wavelength is 780 nm, 805 nm, and 830 nm, and the sampling frequency is 13.33 Hz.

LightNIRS has 8 detectors and 8 emitters. Two 2 $\times$ 4 areas are measured before and after the experiment. The first area is used to measure the PFC, and the second area is used to measure the SMA and PMC. Each brain area requires 4 detectors and 4 emitters, and a complete head holder is used to hold the detector and transmitter. The experiment is based on the 10–20 system [18] to locate the brain area. The specific hood layout is shown in Fig. 2. Cz in the figure represents the vertical intersection of the anterior-posterior sagittal line and the left-right sagittal line of the human brain. Among them, the anterior-posterior sagittal line is the line connecting the nasal root and the posterior occipital bone, while the left-right sagittal line is the line connecting the left earlobe to the right earlobe. Channels 1–10 in the first area measure the blood oxygen content of the PFC, channels 11, 14, and 18 in the second area measure the left PMC, channels 13, 17, and 20 measure the right PMC, and 12, 15, 16, and 19 channels measure SMA. According to the Brodman brain area distribution, the distance between Cz and 19 channels is 30 mm, and the distance between adjacent emitters and receivers is also 30 mm.

Figure 2.

Measurement distribution of probe and brain area. Cz represents the vertical intersection of the anterior and posterior sagittal lines of the human brain with the left and right sagittal lines.

2.4 Data analysis

Walking tasks often cause motion artifacts and physical noise to be included in brain signals. At the same time, the continuous data monitoring process may cause zero drift [18]. To reduce these effects, combined filtering is used in this paper to remove related noise. The neural activity of the human brain dominates gait adjustment intention during walking, so it is hoped that the corresponding frequency band range that reflects nerve activity in the hemoglobin information of the brain is extracted, that is, the frequency band less than 0.145 Hz [19, 20]. A Chebyshev low-pass filter [21] with a second-order and a maximum ripple gain of 1 dB in the passband is used to filter out noise caused by breathing and heartbeat, and the cutoff frequency is set to 0.145 Hz. Then, a mathematical morphological filter (MMF) is used to eliminate baseline drift while maintaining the main morphology of blood oxygen signals. MMF can be expressed by the following formula:

$\displaystyle y=x-(\textit{filter}_{oc}(x)+\textit{filter}_{co}(x))/2$ (1)

$\textit{filter}_{oc}$ and $\textit{filter}_{co}$ represent morphological open filtering and morphological closed filtering, which are composed of corrosion and expansion [22].

To reduce the impact of different individuals’ skull differences, the regions of interest (ROI) divided in this study are shown in Table 1. To reduce the impact of noise such as body motion, and to find the highest commonality in walking tasks for the brain region, using the weight method to calculate the overall blood oxygen signal of the ROI brain region. The calculation formula is as follows:

$\displaystyle y=\sum_{j}X_{ij}\omega_{j}(i=1,2,\ldots,N,j=1,2,\ldots,M)$ (2)

$X$ represents the matrix of the original channel blood oxygen signal composed of $M$ channels and $N$ sampling points, and ${\omega}_{j}$ represents the weight of each channel calculated from the blood oxygen concentration information entropy of each channel [23].

Table 1

The division of the ROI brain

The number of ROIs	The name of ROI	Number of channels included
1	L-PFC	1, 4, 8
2	M-PFC	2, 5, 6, 9
3	R-PFC	3, 7, 10
4	L-PMC	11, 14, 18
5	SMA	12, 15, 16, 19
6	R-PMC	13, 17, 20

First, four different gait adjustment tasks are uniformly defined as gait adjustment tasks. Previous studies have shown that [24, 25] lower limb motor intention can usually be detected 0.5–2 s in advance. The blood oxygen data within 2 seconds before and after the mark position marked during the experiment is selected for analysis. The sliding-window method is used to calculate the time-domain characteristics of the blood oxygen signal. Among them, the window length is set to 40 sampling points, and the step size is set to 1 sampling point.

After finding the turning point of gait adjustment, in order to further distinguish the four states of speed increase, speed reduction, step increase, and step reduction, we calculated the original signal of blood oxygen concentration and the rate of change of blood oxygen concentration in the ROI. The concentration change rate is calculated as dx ${}_{\text{i}}$ $=$ x ${}_{\text{i}+1}$ $-$ x ${}_{\text{i}}$ , where x ${}_{\text{i}}$ represents the blood oxygen data at the i sampling point in the time window, x ${}_{\text{i}+1}$ represents the blood oxygen data at the $\text{i}+1$ sampling point in the time window. Then calculate the Pearson correlation coefficient as the feature score for each of the ROI of the original signal and the blood oxygen difference signal. A feature score corresponds to two brain regions, and it is added to the corresponding ROI. Observing the spatial distribution of the correlation characteristics of the brain area under the different state adjustment states.

To statistically compare the validity of the results of blood oxygen activation in the ROI between the normal walking task and the gait adjustment task, this paper uses the function ‘ttest_ind’ of the ‘stats.scipy’ in the python package to calculate the significance level p values. Each $t$ -test contains 30 sample data. If $p$ values less than 0.05 indicate rejection of the null hypothesis.

3. Results

Among the walking blood oxygen concentration data of 30 people in this experiment, one person’s channel blood oxygen data is many times larger than 29 others, and this person’s experimental data has been excluded. During normal walking and gait adjustment, two types of blood oxygen concentration activation in the ROI are shown in Fig. 3. Compared with the normal walking, the concentration of HbO of the SMA increased significantly during gait adjustment ( $t=$ 3.1158, $p=$ 0.0029). The concentration of HbT of the medial-PFC decreased significantly ( $t=$ 2.7164, $p=$ 0.0088).

Figure 3.

The spatial distribution of blood oxygen activation in the ROI before and after gait adjustment. The red color area indicates that the brain area is obviously activated. NW indicates normal walking, and GC indicates gait adjustment. ch1 represents channel 1 of blood oxygen, and so on.

Figure 4.

The spatial distribution of the correlation characteristics of the brain area during the four gait adjustment states under the HbO signals. The red color area indicates that the brain area is obviously activated. ch1 represents channel 1 of blood oxygen, and so on.

Figure 5.

The spatial distribution of brain activation in four gait adjustment states from the HbO and HbR information levels based on the correlation coefficient characteristics between brain regions are shown in Figs 4 and 5. Compared with normal walking, the sliding window method calculation found that in the 15th sample after the mark point the difference in activation of the brain area is the most significant within 2 seconds before and after the mark position. At the level of HbO information, compared with the step increase, the activation of left-PMC is significant when the step reduction ( $t=-$ 2.5670, $p=$ 0.0130, the average values are: 0.7471 and 0.8730 respectively). Compared with the speed increase, the activation of the right-PMC is significant when the step increase ( $t=-$ 2.2074, $p=$ 0.0314, the average values are: 0.5893 and 0.7937 respectively). Compared with speed reduction, left-PMC and right-PMC are significantly activated when the step reduction (left-PMC: $t=-$ 2.2572, $p=$ 0.0279, the average values are: 0.7538 and 0.8730 respectively; right-PMC: $t=-$ 2.8797, $p=$ 0.0056, the average values are: 0.7639 and 0.8898 respectively). In the HbR information level, compared with the speed increase, the left-PFC is significantly activated when the speed reduction ( $t=-$ 2.6106, $p=$ 0.0103, the average values are: 0.5923 and 0.7533 respectively).

4. Discussion

Compared with normal walking, the SMA is activated significantly when gait adjustment intent occurs. We know that SMA plays an important role in exercise planning [26, 27], and it may be that the subject planned the exercise in advance when the gait adjustment intention occurred. This process promoted the activation of the SMA. Compared with normal walking, the HBT in the medial-PFC decreased significantly during gait adjustment. This means that the brain region needs to consume more oxygenated hemoglobin when gait adjustment intention occurs. This result is consistent with previous studies [28] suggesting that the PFC plays an important role in preparing for the motor.

For the four gait adjustment task, experimental results show that the left-PFC was activated significantly at walking speed reduction compared to a walking speed increase. The PFC plays a role in cognitive needs [29], Taeko [4] pointed out that when walking on a treadmill, a greater increase in oxyHb in the left PFC during walking at 70% intensity than at 50 or 30%. The reason for the difference in the activation results of the brain regions may be that the experiment was performed in a natural environment and the precise walking speed control on the treadmill could not be achieved. Participants needed extra attention to deliberately maintain a slow speed, so the increased PFC activation might be related to its role in attention to action or in planning movement before the adjustment task. In addition, the left-PMC was significantly activated when the step reduction compared with the step increase. From normal walking to step reduction is a gait task that needs to be maintained deliberately, which will cause the subject to perform spontaneous fine motion control. From the perspective of physiological needs, intentional motion control requirements mean that more PMC resources need to be called [30]. Compared with the decrease of gait parameters, the activation of left-PFC was significant when the gait parameters were increased. It can be seen that the increase in walking speed has a significant effect on the activation of the brain region, indicating that an increase in gait parameters requires more blood oxygen metabolism in the brain to meet cognitive-locomotor demand.

This study also compares the differences in activation of the brain regions with changes in walking speed and step size. This study found that the right-PMC was significantly activated when the step increase compared with speed increase. The left-PMC and right-PMC were significantly activated when the step reduction compared to the speed reduction. Indicating that the PMC plays a promoting role in completing the step adjustment task. From a theoretical perspective, walking speed adjustment and step size adjustment belong to two different gait adjustment modes. The walking frequency determines the magnitude of the walking speed, and the step size changes depend on the distance between the two legs. This difference in walking posture control has led to different areas of brain activation to some extent.

5. Conclusion

This study observed the spatial distribution of cortical activity and brain region correlation characteristics in different gait tasks and gait adjustment states. During walking, an increase in HbO concentration in the SMA when gait adjustment intention occurs. There were also significant differences in the degree of blood oxygen activation in functional areas of the brain under different gait adjustment modes. The preliminary findings of this study can provide an important theoretical basis for implementing gait control based on fNIRS-BCI technology.

Footnotes

Acknowledgments

The research was supported by grants from the National Natural Science Foundation of China (61673286 and 62073228) and National Natural Science Foundation of China (U1713218).

Conflict of interest

None to report.

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