Abstract
Background. Intermittent sequential pneumatic compression (ISPC) can effectively promote blood flow and improve microcirculation. The increase in pressure gradient and blood flow velocity by ISPC has been suggested to be a possible mechanism to improve the microcirculation of patients. However, the effects of ISPC on cerebral oscillations are still unclear.Methods. The tissue concentration of oxyhemoglobin and deoxyhemoglobin oscillations were measured by functional near-infrared spectroscopy under resting and ISPC conditions in 27 right-handed adult patients with stroke. Five characteristic frequency signals (I, 0.6–2 Hz; II, 0.145–0.6 Hz; III, 0.052–0.145 Hz; IV, 0.021–0.052 Hz; and V, 0.0095–0.021 Hz) were identified using the wavelet method. The wavelet amplitude (WA) and laterality index (LI) were calculated to describe the frequency-specific cortical activities.Results. The WA values of the ipsilesional motor cortex (MC) in the frequency intervals III (F = 4.378, p = 0.041), IV (F = 4.281, p = 0.044), and V (F = 5.33, p = 0.025) and those of the contralesional MC in III (F = 10.122, p = 0.002), IV (F = 9.275, p = 0.004), and V (F = 8.373, p = 0.006) were significantly higher when the patients were under the ISPC state than when they were under the resting state. Also, the LI value of the prefrontal cortex (PFC) and MC of the patients decreased more obviously in the ISPC state compared with the resting state despite there was no significant difference.Conclusions. ISPC could induce the activation of bilateral MCs in myogenic and neurogenic innervations and endothelial cell metabolic activities. The decreased LI values in the PFC and MC indicated that the ISPC had a positive effect on these regions’ functional rehabilitation. The ISPC of 0.03 Hz is not suitable for all patients with stroke, and personalized treatment options should be considered in subsequent ISPC intervention. This study provides a method for assessing the effects of ISPC on cerebral oscillations, and the results benefit the optimization of ISPC parameters in the personalized treatment for the functional recovery of patients with stroke.
Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis
