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. 2023 Feb 6:14:1042254.
doi: 10.3389/fneur.2023.1042254. eCollection 2023.

Upper limb intelligent feedback robot training significantly activates the cerebral cortex and promotes the functional connectivity of the cerebral cortex in patients with stroke: A functional near-infrared spectroscopy study

Hao Li  1   2 , Xuefeng Fu  2 , Lijun Lu  2 , Hua Guo  1   2 , Wen Yang  1 , Kaifeng Guo  1 , Zhen Huang  1
Affiliations

Upper limb intelligent feedback robot training significantly activates the cerebral cortex and promotes the functional connectivity of the cerebral cortex in patients with stroke: A functional near-infrared spectroscopy study

Hao Li et al. Front Neurol. .

Abstract

Background: Upper limb intelligence robots are widely used to improve the upper limb function of patients with stroke, but the treatment mechanism is still not clear. In this study, functional near-infrared spectroscopy (fNIRS) was used to evaluate the concentration changes of oxygenated hemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) in different brain regions and functional connectivity (FC) of the cerebral cortex in patients with stroke.

Method: Twenty post-stroke patients with upper limb dysfunction were included in the study. They all received three different types of shoulder joint training, namely, active intelligent feedback robot training (ACT), upper limb suspension training (SUS), and passive intelligent feedback robot training (PAS). During the training, activation of the cerebral cortex was detected by fNIRS to obtain the concentration changes of hemoglobin and FC of the cerebral cortex. The fNIRS signals were recorded over eight ROIs: bilateral prefrontal cortices (PFC), bilateral primary motor cortices (M1), bilateral primary somatosensory cortices (S1), and bilateral premotor and supplementary motor cortices (PM). For easy comparison, we defined the right hemisphere as the ipsilesional hemisphere and flipped the lesional right hemisphere in the Nirspark.

Result: Compared with the other two groups, stronger cerebral cortex activation was observed during ACT. One-way repeated measures ANOVA revealed significant differences in mean oxy-Hb changes among conditions in the four ROIs: contralesional PFC [F(2, 48) = 6,798, p < 0.01], ipsilesional M1 [F(2, 48) = 6.733, p < 0.01], ipsilesional S1 [F(2, 48) = 4,392, p < 0.05], and ipsilesional PM [F(2, 48) = 3.658, p < 0.05]. Oxy-Hb responses in the contralesional PFC region were stronger during ACT than during SUS (p < 0.01) and PAS (p < 0.05). Cortical activation in the ipsilesional M1 was significantly greater during ACT than during SUS (p < 0.01) and PAS (p < 0.05). Oxy-Hb responses in the ipsilesional S1 (p < 0.05) and ipsilesional PM (p < 0.05) were significantly higher during ACT than during PAS, and there is no significant difference in mean deoxy-Hb changes among conditions. Compared with SUS, the FC increased during ACT, which was characterized by the enhanced function of the ipsilesional cortex (p < 0.05), and there was no significant difference in FC between the ACT and PAS.

Conclusion: The study found that cortical activation during ACT was higher in the contralesional PFC, and ipsilesional M1 than during SUS, and showed tighter cortical FC between the cortices. The activation of the cerebral cortex of ACT was significantly higher than that of PAS, but there was no significant difference in FC. Our research helps to understand the difference in cerebral cortex activation between upper limb intelligent feedback robot rehabilitation and other rehabilitation training and provides an objective basis for the further application of upper limb intelligent feedback robots in the field of stroke rehabilitation.

Keywords: cerebral cortex activation; functional connectivity; functional near-infrared spectroscopy; shoulder joint training; stroke; upper limb intelligent feedback robot.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Experimental setup. (A) Experimental setup from a sagittal view. The patient sat in a chair and two meters in front was a display screen. (B) Experimental setup from a coronal view. All parts of the affected limb were fixed and shoulder adductor abduction movement was performed in the task state.
Figure 2
Figure 2
Experimental procedure. There are three states of fNIRS testing, namely, the baseline, the task state (TS), and the resting state (RS). The first 10 s before the test were taken as the baseline. Then, start three tests, each including a 30-s task and a 30-s resting.
Figure 3
Figure 3
fNIRS data acquisition. (A) fNIRS optode layout design. Blue and purple-filled circles represent light sources and detectors, respectively. Gray rectangles represent long separation channels. The midline central point (Cz) is located at the middle point of optode S13 and optode D12. (B) Regions of interest and the channel setting. The probes were located over bilateral PFC (CPFC and IPFC); PM (CPM and IPM); bilateral M1 (CM1 and IM1); bilateral S1 (CS1 and IS1). C, contralesional; I, ipsilesional; PFC, prefrontal cortex; PM, premotor area and supplementary motor cortex; M1, primary motor cortex; S1, primary somatosensory cortex.
Figure 4
Figure 4
(A) Activation maps of the mean oxy-Hb changes under three different conditions. (B) Average changes in oxy-Hb concentration of ROIs for three conditions. Comparisons of execution-related oxy-Hb changes of ROIs for ACT, PAS, and SUS. C, contralesional; I, ipsilesional; oxy-Hb, oxy-hemoglobin; ROIs, regions of interest; PFC, prefrontal cortex; M1, primary motor cortex; S1, primary somatosensory cortex PM, premotor area, and supplementary motor cortex. *p < 0.05, **p < 0.01. Data are expressed as the mean with standard error (SE).
Figure 5
Figure 5
(A) Functional connectivity (FC) of ROIs during ACT, PAS, and SUS. (B) Channels with differences in FC between ACT and SUS (Pearson correlation), and there was no significant difference in FC between ACT and PAS. ROIs, regions of interest.
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Grants and funding

This study was supported by the Guangzhou Municipal Science and Technology Bureau (202201011729).
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