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IntroductionGait disturbances significantly impact the mobility and quality of life of individuals with Parkinson’s disease (PD). This study aims to delve into the cortical mechanisms underlying gait disorders in PD, specifically focusing on the prefrontal cortex (PFC), premotor cortex (PMC), and primary somatosensory cortex (PSC).ObjectiveTo compare the functional connectivity of the PFC, PMC, and PSC regions during walking between individuals with PD and healthy controls.MethodsThe study included 30 individuals with PD (mean age 62.40 ± 7.16 years) and 22 healthy older adults (mean age 60.95 ± 6.34 years). All participants were requested to walk back and forth at a comfortable pace for 30 s over a 10-meter course three times. A mobile functional near-infrared spectroscopy (fNIRS) system was employed to evaluate the oxyhemoglobin concentration change (∆HbO2). To assess the interactions between the PFC, PMC, and PSC, the connectivity strength between different fNIRS channels was computed.ResultsIndividuals with PD in the off-state exhibited significantly decreased walking speed and shorter stride length compared to the healthy controls. For six brain regions including the left (L) and right (R) PFC, PMC, and PSC, no significant differences in functional connectivity within each region were found between the PD and control groups. However, when it comes to the functional connectivity between every two regions, the PD group exhibited stronger functional connectivity than the control group in the LPFC-LPMC, LPFC-RPMC, LPFC-LPSC, RPFC-LPMC, RPFC-LPSC, LPMC-LPSC, LPMC-RPSC, and RPMC-RPSC. Positive correlations were found between gait performance (speed and stride length) and functional connectivity within the RPMC as well as between the RPMC and the RPSC.ConclusionIndividuals with PD exhibit notable gait disturbances and increased functional connectivity in brain regions responsible for sensorimotor integration and motor function in their off-state. Strengthening the functional connectivity within the RPMC and between the RPMC and the RPSC could be a potential target for future treatments of gait impairments in PD.

Dopaminergic treatment has proved effective to Parkinson’s disease (PD), but the conventional treatment assessment is human-administered and prone to intra- and inter-assessor variability. In this paper, we propose a knowledge-driven framework and discover that brain ACtivation-Transition-Spectrum (ACTS) features achieve effective quantified assessments of dopaminergic therapy in PD. Firstly, brain activities of fifty-one PD patients during clinical walking tests under the OFF and ON states (without and with dopaminergic medication) were measured with functional near-infrared spectroscopy (fNIRS). Then, brain ACTS features were formulated based on the medication-induced variations in temporal features of brain regional activation, transition features of brain hemodynamic states, and graph spectrum of brain functional connectivity. Afterwards, a prior selection algorithm was constructed based on recursive feature elimination and graph spectrum analysis for the selection of principal discriminative features. Further, linear discriminant analysis was conducted to predict the treatment-induced improvements. The results demonstrated that the proposed method decreased the misclassification probability from 42% to 16% in the evaluations of dopaminergic treatment and outperformed existing fNIRS-based methods. Therefore, the proposed method promises a quantified and objective approach for dopaminergic treatment assessment, and our brain ACTS features may serve as promising functional biomarkers for treatment evaluation.

Deep brain stimulation (DBS) is establishing itself as a promising treatment for disorders of consciousness (DOC). Measuring consciousness changes is crucial in the optimization of DBS therapy for DOC patients. However, conventional measures use subjective metrics that limit the investigations of treatment-induced neural improvements. The focus of this study is to analyze the regulatory effects of DBS and explain the regulatory mechanism at the brain functional level for DOC patients. Specifically, this paper proposed a dynamic brain temporal-spectral analysis method to quantify DBS-induced brain functional variations in DOC patients. Functional near-infrared spectroscopy (fNIRS) that promised to evaluate consciousness levels was used to monitor brain variations of DOC patients. Specifically, a fNIRS-based experimental procedure with auditory stimuli was developed, and the brain activities during the procedure from thirteen DOC patients before and after the DBS treatment were recorded. Then, dynamic brain functional networks were formulated with a sliding-window correlation analysis of phase lag index. Afterwards, with respect to the temporal variations of global and regional networks, the variability of global efficiency, local efficiency, and clustering coefficient were extracted. Further, dynamic networks were converted into spectral representations by graph Fourier transform, and graph energy and diversity were formulated to assess the spectral global and regional variability. The results showed that DOC patients under DBS treatment exhibited increased global and regional functional variability that was significantly associated with consciousness improvements. Moreover, the functional variability in the right brain regions had a stronger correlation with consciousness enhancements than that in the left brain regions. Therefore, the proposed method well signifies DBS-induced brain functional variations in DOC patients, and the functional variability may serve as promising biomarkers for consciousness evaluations in DOC patients.

Neurovascular coupling (NVC) connects neural activity with hemodynamics and plays a vital role in sustaining brain function. Combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is a promising way to explore the NVC. However, the high-order property of EEG data and variability of hemodynamic response function (HRF) across subjects have not been well considered in existing NVC studies. In this study, we proposed a novel framework to enhance the subject-specific parametric modeling of NVC from simultaneous EEG-fNIRS measurement. Specifically, task-related tensor decomposition of high-order EEG data was performed to extract the underlying connections in the temporal-spectral-spatial structures of EEG activities and identify the most relevant temporal signature within multiple trials. Subject-specific HRFs were estimated by parameters optimization of a double gamma function model. A canonical motor task experiment was designed to induce neural activity and validate the effectiveness of the proposed framework. The results indicated that the proposed framework significantly improves the reproducibility of EEG components and the correlation between the predicted hemodynamic activities and the real fNIRS signal. Moreover, the estimated parameters characterized the NVC differences in the task with two speeds. Therefore, the proposed framework provides a feasible solution for the quantitative assessment of the NVC function.

Parkinson’s disease (PD) is a prevalent brain disorder, and PD diagnosis is crucial for treatment. Existing methods for PD diagnosis are mainly focused on behavior analysis, while the functional neurodegeneration of PD has not been well investigated. This paper proposes a method to signify functional neurodegeneration of PD with dynamic functional connectivity analysis. A functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation from 50 PD patients and 41 age-matched healthy controls in clinical walking tests. Dynamic functional connectivity was constructed with sliding-window correlation analysis, and k-means clustering was applied to generate the key brain connectivity states. Dynamic state features including state occurrence probability, state transition percentage and state statistical features were extracted to quantify the variations of brain functional networks. A support vector machine was trained to classify PD patients and healthy controls. Statistical analysis was conducted to investigate the difference between PD patients and healthy controls as well as the relationship between dynamic state features and the MDS-UPDRS sub-score of gait. The results showed that PD patients had a higher probability of transiting to brain connectivity states with high levels of information transmission compared with healthy controls. The MDS-UPDRS sub-score of gait and the dynamics state features showed a significant correlation. Moreover, the proposed method had better classification performances than the available fNIRS-based methods in terms of accuracy and F1 score. Thus, the proposed method well signified functional neurodegeneration of PD, and the dynamic state features may serve as promising functional biomarkers for PD diagnosis.

BackgroundCortical activation patterns in patients with Parkinson's disease (PD) may be influenced by postural strategies, but the underlying neural mechanisms remain unclear. Our aim is to examine the role of the fronto-parietal lobes in patients with PD adopting different postural strategies and the effect of dual task (DT) on fronto-parietal activation.MethodsTwo groups of patients with PD adopting either the posture first strategy (PD-PF) or the posture second strategy (PD-PS) were examined respectively when in the “OFF” state while single-walking task (SW) and DT. Frontal and parietal lobe activity was assessed by functional near infrared spectroscopy (fNIRS) and measuring gait parameters. Linear mixed models were used for analyses.ResultsPatients with PD who adopted PS had greater cortical activation than those who adopted PF, and there was no difference between PF and PS in the behavioral parameters. For oxyhemoglobin levels, the task condition (SW vs. DT) had a main effect in fronto-parietal lobes. Postural strategy (PD-PF vs. PD-PS) a main effect in the left prefrontal cortex (LPFC), left parietal lobe (LPL), and right parietal lobe (RPL) regions. In the task of walking with and without the cognitive task, patients with PD adopting PS had higher activation in the LPL than those adopting PF. In DT, only PD patients who adopted PS had elevated oxyhemoglobin levels in the LPFC, right prefrontal cortex (RPFC), and LPL compared with the SW, whereas patients with PD who adopted PF showed no differences in any region.ConclusionDifferent patterns of fronto-parietal activation exist between PD-PF and PD-PS. This may be because PD-PS require greater cortical functional compensation than those adopting PF.

Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimating human's intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use. Although the muscle synergy principle implies that it is possible to estimate human motion using sEMG signals from even one signal muscle, few studies investigated the feasibility of continuous motion estimation based on single-channel sEMG. In this study, a feature-guided convolutional neural network (FG-CNN) has been proposed to estimate human knee joint movement using single-channel sEMG. In the proposed FG-CNN, several handcrafted features have been fused into a CNN model to guide CNN feature extraction, and both handcrafted and CNN-extracted features were applied to a regression model, i.e., random forest regression, to estimate knee joint movements. Experiments with 8 healthy subjects were carried out, and sEMG signals measured from 6 muscles, i.e., vastus lateralis, vastus medialis, biceps femoris, semitendinosus, lateral or medial gastrocnemius (LG or MG), were separately evaluated for knee joint estimation using the proposed method. The experimental results demonstrated that the proposed FG-CNN method with single-channel sEMG signals from LG or MG can effectively estimate human knee joint movements. The average correlation coefficient between the measured and the estimated knee joint movements is 0.858 ± 0.085 for LG and 0.856 ± 0.057 for MG. Meanwhile, comparative studies showed that the combined handcrafted-CNN features outperform either the handcrafted features or the CNN features; the performance of the proposed signal-channel sEMG-based FG-CNN method is comparable to those of the traditional multi-channel sEMG-based methods. The outcomes of this study enable the possibility of developing a single-channel sEMG-based human-robot interface for knee joint movement estimation, which can facilitate the routine use of assistive robots.

The inverse finite element method (iFEM) is a novel method for reconstructing the full-field displacement of structures by discrete measurement strain. In practical engineering applications, the accuracy of iFEM is reduced due to the positional offset of strain sensors during installation and errors in structural installation. Therefore, a coarse and fine two-stage calibration (CFTSC) method is proposed to enhance the accuracy of the reconstruction of structures. Firstly, the coarse calibration is based on a single-objective particle swarm optimization algorithm (SOPSO) to optimize the displacement–strain transformation matrix related to the sensor position. Secondly, as selecting different training data can affect the training effect of self-constructed fuzzy networks (SCFN), this paper proposes to screen the appropriate training data based on residual analysis. Finally, the experiments of the wing-integrated antenna structure verify the efficiency of the method on the reconstruction accuracy of the structural body displacement field.

Abstract Background Deep brain stimulation (DBS) has proved effective for Parkinson’s disease (PD), but the identification of stimulation parameters relies on doctors’ subjective judgment on patient behavior. Methods Five PD patients performed 10-meter walking tasks under different brain stimulation frequencies. During walking tests, a wearable functional near-infrared spectroscopy (fNIRS) system was used to measure the concentration change of oxygenated hemoglobin (△H b O 2) in prefrontal cortex, parietal lobe and occipital lobe. Brain functional connectivity and global efficiency were calculated to quantify the brain activities. Results We discovered that both the global and regional brain efficiency of all patients varied with stimulation parameters, and the DBS pattern enabling the highest brain efficiency was optimal for each patient, in accordance with the clinical assessments and DBS treatment decision made by the doctors. Conclusions Task fNIRS assessments and brain functional connectivity analysis promise a quantified and objective solution for patient-specific optimization of DBS treatment. Trial registration Name: Accurate treatment under the multidisciplinary cooperative diagnosis and treatment model of Parkinson’s disease. Registration number is ChiCTR1900022715. Date of registration is April 23, 2019.

Diabetic cardiomyopathy (DCM) is an important cardiac disorder in patients with diabetes. High glucose (HG) levels lead to inflammation of cardiomyocytes, oxidative stress, and long-term activation of autophagy, resulting in myocardial fibrosis and remodelling. Astragaloside-IV (AS-IV) has a wide range of pharmacological effects. This study aimed to investigate the effects of AS-IV on injury induced by HG in rat cardiomyocytes (H9C2(2-1)) and the involvement of the miR-34a-mediated autophagy pathway. An AS-IV concentration of 100 μM was selected based on H9C2(2-1) cell viability using the cell counting kit-8 (CCK-8). We found that 33 mM HG induced a morphologic change in cells and caused excessive oxidative stress, whereas AS-IV inhibited lipid peroxidation and increased superoxide dismutase activity. In terms of mRNA expression, HG increased miR-34a and inhibited Bcl2 and Sirt1, whereas AS-IV and miR-34a-inhibitor reversed the above effects. Further, LC3-GFP adenovirus infection and western blotting showed that HG increased autophagy, which was reversed synergistically by AS-IV and miR-34a-inhibitor. Bcl2 and pAKT/AKT protein expressions in the HG group was significantly lower than that in controls, but AS-IV and miR-34a-inhibitor antagonized the process. Thus, AS-IV inhibits HG-induced oxidative stress and autophagy and protects cardiomyocytes from injury via the miR-34a/Bcl2/(LC3II/LC3I) and pAKT/Bcl2/(LC3II/LC3I) pathways.

Lung adenocarcinoma is one of the leading causes of cancer-related death worldwide. Low expression of Interleukin-33 (IL-33) was reported to be associated with the progression of pulmonary adenocarcinoma. However, the IL-33-mediated immunoregulation in pulmonary adenocarcinoma remains unclear. In this study, we found that IL-33 treatment evidently repressed tumour growth, induced CD4+ T cells infiltration and IL-17 expression in pulmonary adenocarcinoma. Notably, IL-33 treatment increased the number of Dendritic Cells (DCs) in pulmonary adenocarcinoma. More importantly, IL-33 induced maturation and regulated the function of DCs by increasing expression of DCs mature markers (CD40 and CD80, CD86) DCs-function-related gene including antigen presentation genes (HLA-DMA, HLA-DMB and CD74) and cytokines (IL-1β, IL-6 and TNF). Mechanistic studies demonstrated that IL-33 treatment induced DCs maturation by upregulating CYLD expression in DCs. In addition, CYLD played an important role in DCs-induced T cell proliferation and IL-17 secretion. In conclusion, our study demonstrated that IL-33 mediated immunoregulation in pulmonary adenocarcinoma by improving DC-induced T cell proliferation by upregulating CYLD expression.

C18H20ON4F12P2, triclinic, P1̄ (no. 2), a = 9.393(8) Å, b = 10.894(10) Å, c = 13.716(12) Å, α = 107.741(14)°, β = 100.379(15)°, γ = 92.869(14)°, V = 1306.7(19) Å3, Z = 2, Rgt(F) = 0.0916, wRref(F2) = 0.1970, T = 296(2) K.

C27H24N3OF6P, monoclinic, Cc (no. 9), a = 18.0589(18) Å, b = 12.5100(9) Å, c = 13.3659(12) Å, β = 124.522(7)°, V = 2487.9(4) Å3, Z = 4, Rgt(F) = 0.0437, wRref(F2) = 0.1141, T = 113(2) K.

C34H31N3O, monoclinic, P21/c (no. 14), a = 26.803(5) Å, b = 5.8243(12) Å, c = 16.893(3) Å, β = 98.53(3)°, V = 2608.1(9) Å3, Z = 4, Rgt(F) = 0.0759, wRref(F2) = 0.2028, T = 113(2) K.