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Efficient non-noble metal bifunctional electrocatalysts can increase the conversion rate of electric energy in the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). Herein, a ball & sheet MoS2/Ni3S2 composite with wide-layer-spacing and high 1T-rich MoS2 is assembled on nickel foam (NF) via a two-step solvothermal method with polymeric sulfur (S-r-DIB) as the sulfur source. The obtained material serves as both the cathode and the anode toward overall water splitting in an alkaline electrolyte. The results proved that the interpenetration of MoS2/Ni3S2-p with a ball and sheet structure increased the material active surface area and exposed more catalytic active sites, which contributed to the penetration of solution and the transfer of charge/hydrion. Meanwhile, two different semiconductors of MoS2 and Ni3S2 along with the presence of ample active sulfur edge sites and few-layer, wide-layer-spacing structures of MoS2 lead to an outstanding electrocatalytic activity. In particular, the electrodes of MoS2/Ni3S2-p only need a battery voltage of 1.55 V at 10 mA cm-2. The bifunctional electrocatalyst MoS2/Ni3S2-p also shows excellent stability at large current densities during the electrochemical test.
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The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson's disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.
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Enfermedad de Parkinson , Humanos , Anciano , Enfermedad de Parkinson/diagnóstico , Envejecimiento , Algoritmos , Marcha , Extremidad InferiorRESUMEN
Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the target domain. This paper proposed an unsupervised end-to-end domain adaptation medical image segmentation method based on the generative adversarial network (GAN). A network training and adjustment model was designed, including segmentation and discriminant networks. In the segmentation network, the residual module was used as the basic module to increase feature reusability and reduce model optimization difficulty. Further, it learned cross-domain features at the image feature level with the help of the discriminant network and a combination of segmentation loss with adversarial loss. The discriminant network took the convolutional neural network and used the labels from the source domain, to distinguish whether the segmentation result of the generated network is from the source domain or the target domain. The whole training process was unsupervised. The proposed method was tested with experiments on a public dataset of knee magnetic resonance (MR) images and the clinical dataset from our cooperative hospital. With our method, the mean Dice similarity coefficient (DSC) of segmentation results increased by 2.52% and 6.10% to the classical feature level and image level domain adaptive method. The proposed method effectively improves the domain adaptive ability of the segmentation method, significantly improves the segmentation accuracy of the tibia and femur, and can better solve the domain transfer problem in MR image segmentation.
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Procesamiento de Imagen Asistido por Computador , Redes Neurales de la Computación , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética , Rodilla , Articulación de la RodillaRESUMEN
The paper designs a control system of radiation head of radiotherapy simulator. Using a driver to control two stepper motors, through the design of the PLC program, to achieve the simultaneous movement of the two delineators. It improves the control precision and saves the manufacturing cost of radiotherapy simulator. In clinical practice, it can improve the quality of treatment planning, and reduce patient X-ray exposure.
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Exposición a la Radiación , Planificación de la Radioterapia Asistida por Computador , Humanos , Radioterapia , Tomografía Computarizada por Rayos X , Rayos XRESUMEN
Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs), and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training.
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Mano/fisiopatología , Corteza Sensoriomotora/fisiopatología , Rehabilitación de Accidente Cerebrovascular/instrumentación , Accidente Cerebrovascular/fisiopatología , Actividades Cotidianas , Gestos , Humanos , Movimiento/fisiología , Recuperación de la Función/fisiología , Rehabilitación de Accidente Cerebrovascular/métodosRESUMEN
In neurorehabilitation, longitudinal assessment of arm movement related brain function in patients with motor disability is challenging due to variability in task performance. MRI-compatible robots monitor and control task performance, yielding more reliable evaluation of brain function over time. The main goals of the present study were first to define the brain network activated while performing active and passive elbow movements with an MRI-compatible arm robot (MaRIA) in healthy subjects, and second to test the reproducibility of this activation over time. For the fMRI analysis two models were compared. In model 1 movement onset and duration were included, whereas in model 2 force and range of motion were added to the analysis. Reliability of brain activation was tested with several statistical approaches applied on individual and group activation maps and on summary statistics. The activated network included mainly the primary motor cortex, primary and secondary somatosensory cortex, superior and inferior parietal cortex, medial and lateral premotor regions, and subcortical structures. Reliability analyses revealed robust activation for active movements with both fMRI models and all the statistical methods used. Imposed passive movements also elicited mainly robust brain activation for individual and group activation maps, and reliability was improved by including additional force and range of motion using model 2. These findings demonstrate that the use of robotic devices, such as MaRIA, can be useful to reliably assess arm movement related brain activation in longitudinal studies and may contribute in studies evaluating therapies and brain plasticity following injury in the nervous system.
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Mapeo Encefálico/instrumentación , Encéfalo/fisiología , Imagen por Resonancia Magnética/instrumentación , Movimiento , Adulto , Brazo , Femenino , Humanos , Masculino , Red Nerviosa/fisiología , Reproducibilidad de los Resultados , Robótica , Análisis y Desempeño de Tareas , Adulto JovenRESUMEN
Introduction: The tendon-sheath actuated bending-tip system (TAB) has been widely applied to long-distance transmission scenes due to its high maneuverability, safety, and compliance, such as in exoskeleton robots, rescue robots, and surgical robots design. Due to the suitability of operation in a narrow or tortuous environment, TAB has demonstrated great application potential in the area of minimally invasive surgery. However, TAB involves highly non-linear behavior due to hysteresis, creepage, and non-linear friction existing on the tendon routing, which is an enormous challenge for accurate control. Methods: Considering the difficulties in the precise modeling of non-linearity friction, this paper proposes a novel fuzzy control scheme for the Euler-Lagrange dynamics model of TAB for achieving tracking performance and providing accurate friction compensation. Finally, the asymptotic stability of the closed-loop system is proved theoretically and the effectiveness of the controller is verified by numerical simulation carried out in MATLAB/Simulink. Results: The desired angle can be reached quickly within 3 s by adopting the proposed controller without overshoot or oscillation in Tracking Experiment, demonstrating the regulation performance of the proposed control scheme. The proposed method still achieves the desired trajectory rapidly and accurately without steady-state errors in Varying-friction Experiment. The angle errors generated by external disturbances are < 1 deg under the proposed controller, which returns to zero in 2 s in Anti-disturbance Experiment. In contrast, comparative controllers take more time to be steady and are accompanied by oscillating and residual errors in all experiments. Discussion: The proposed method is model-free control and has no strict requirement for the dynamics model and friction model. It is proved that advanced tracking performance and real-time response can be guaranteed under the presence of unknown bounded non-linear friction and time-varying non-linear dynamics.
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PURPOSE: The technological advancements in surgical robots compatible with magnetic resonance imaging (MRI) have created an indispensable demand for real-time deformable image registration (DIR) of pre- and intra-operative MRI, but there is a lack of relevant methods. Challenges arise from dimensionality mismatch, resolution discrepancy, non-rigid deformation and requirement for real-time registration. METHODS: In this paper, we propose a real-time DIR framework called MatchMorph, specifically designed for the registration of low-resolution local intraoperative MRI and high-resolution global preoperative MRI. Firstly, a super-resolution network based on global inference is developed to enhance the resolution of intraoperative MRI to the same as preoperative MRI, thus resolving the resolution discrepancy. Secondly, a fast-matching algorithm is designed to identify the optimal position of the intraoperative MRI within the corresponding preoperative MRI to address the dimensionality mismatch. Further, a cross-attention-based dual-stream DIR network is constructed to manipulate the deformation between pre- and intra-operative MRI, real-timely. RESULTS: We conducted comprehensive experiments on publicly available datasets IXI and OASIS to evaluate the performance of the proposed MatchMorph framework. Compared to the state-of-the-art (SOTA) network TransMorph, the designed dual-stream DIR network of MatchMorph achieved superior performance with a 1.306 mm smaller HD and a 0.07 mm smaller ASD score on the IXI dataset. Furthermore, the MatchMorph framework demonstrates an inference speed of approximately 280 ms. CONCLUSIONS: The qualitative and quantitative registration results obtained from high-resolution global preoperative MRI and simulated low-resolution local intraoperative MRI validated the effectiveness and efficiency of the proposed MatchMorph framework.
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Algoritmos , Imagen por Resonancia Magnética , Cirugía Asistida por Computador , Humanos , Imagen por Resonancia Magnética/métodos , Cirugía Asistida por Computador/métodos , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
Understanding the response of the injured brain to different transcranial direct current stimulation (tDCS) montages may help explain the variable tDCS treatment results on poststroke motor gains. Cortical connectivity has been found to reflect poststroke motor gains and cortical plasticity, but the changes in connectivity following tDCS remain unknown. We aimed to investigate the relationship between tDCS-induced changes in cortical connectivity and poststroke motor gains. In this study, participants were assigned to receive four tDCS montages (anodal, cathodal, bilateral, and sham) over the primary motor cortex (M1) according to a single-blind, randomized, crossover design. Electroencephalography (EEG) and Jebsen-Taylor hand function test (JTT) were performed before and after the intervention. Motor cortical connectivity was measured using beta-band coherence with the ipsilesional and contralesional M1 as seed regions. Motor gain was evaluated based on the JTT completion time. We examined the relationship between baseline connectivity and clinical characteristics and that between changes in connectivity and motor gains after different tDCS montages. Baseline functional connectivity, motor impairment, and poststroke duration were correlated. High ipsilesional M1-frontal-temporal connectivity was correlated with a good baseline motor status, and increased connectivity was accompanied by good functional improvement following anodal tDCS treatment. Low contralesional M1-frontal-central connectivity was correlated with a good baseline motor status, and decreased connectivity was accompanied by good functional improvement following cathodal tDCS treatment. In conclusion, EEG-based motor cortical connectivity was correlated with stroke characteristics, including motor impairment and poststroke duration, and motor gains induced by anodal and cathodal tDCS.
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Estudios Cruzados , Electroencefalografía , Accidente Cerebrovascular Isquémico , Corteza Motora , Estimulación Transcraneal de Corriente Directa , Humanos , Corteza Motora/fisiopatología , Estimulación Transcraneal de Corriente Directa/métodos , Masculino , Femenino , Persona de Mediana Edad , Accidente Cerebrovascular Isquémico/fisiopatología , Accidente Cerebrovascular Isquémico/terapia , Método Simple Ciego , Anciano , Rehabilitación de Accidente Cerebrovascular/métodos , Adulto , Plasticidad Neuronal/fisiologíaRESUMEN
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.
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Acoplamiento Neurovascular , Humanos , Acoplamiento Neurovascular/fisiología , Reproducibilidad de los Resultados , Espectroscopía Infrarroja Corta/métodos , Electroencefalografía/métodos , Hemodinámica/fisiologíaRESUMEN
Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.
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Encéfalo , Enfermedad de Parkinson , Humanos , Enfermedad de Parkinson/fisiopatología , Enfermedad de Parkinson/tratamiento farmacológico , Masculino , Femenino , Encéfalo/fisiopatología , Persona de Mediana Edad , Anciano , Hemodinámica/fisiología , Hemodinámica/efectos de los fármacos , Espectroscopía Infrarroja Corta/métodos , Red Nerviosa/fisiopatología , Red Nerviosa/efectos de los fármacos , Dopaminérgicos/administración & dosificación , Caminata/fisiologíaRESUMEN
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.
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Trastornos de la Conciencia , Estado de Conciencia , Humanos , Trastornos de la Conciencia/terapia , EncéfaloRESUMEN
BACKGROUND: Early identification of mild cognitive impairment (MCI) is essential for its treatment and the prevention of dementia in Parkinson's disease (PD). Existing approaches are mostly based on neuropsychological assessments, while brain activation and connection have not been well considered. NEW METHOD: This paper presents a neuroimaging-based graph frequency analysis method and the generated features to quantify the brain functional neurodegeneration and distinguish between PD-MCI patients and healthy controls. The Stroop color-word experiment was conducted with 20 PD-MCI patients and 34 healthy controls, and the brain activation was recorded with functional near-infrared spectroscopy (fNIRS). Then, the functional brain network was constructed based on Pearson's correlation coefficient calculation between every two fNIRS channels. Next, the functional brain network was represented as a graph and decomposed in the graph frequency domain through the graph Fourier transform (GFT) to obtain the eigenvector matrix. Total variation and weighted zero crossings of eigenvectors were defined and integrated to quantify functional interaction between brain regions and the spatial variability of the brain network in specific graph frequency ranges, respectively. After that, the features were employed in training a support vector machine (SVM) classifier. RESULTS: The presented method achieved a classification accuracy of 0.833 and an F1 score of 0.877, significantly outperforming existing methods and features. COMPARISON WITH EXISTING METHODS: Our method provided improved classification performance in the identification of PD-MCI. CONCLUSION: The results suggest that the presented graph frequency analysis method well identify PD-MCI patients and the generated features promise functional brain biomarkers for PD-MCI diagnosis.
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Disfunción Cognitiva , Enfermedad de Parkinson , Humanos , Enfermedad de Parkinson/complicaciones , Enfermedad de Parkinson/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Disfunción Cognitiva/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , NeuroimagenRESUMEN
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.
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Accurate human motion estimation is crucial for effective and safe human-robot interaction when using robotic devices for rehabilitation or performance enhancement. Although surface electromyography (sEMG) signals have been widely used to estimate human movements, conventional sEMG-based methods, which need sEMG signals measured from multiple relevant muscles, are usually subject to some limitations, including interference between sEMG sensors and wearable robots/environment, complicated calibration, as well as discomfort during long-term routine use. Few methods have been proposed to deal with these limitations by using single-channel sEMG (i.e., reducing the sEMG sensors as much as possible). The main challenge for developing single-channel sEMG-based estimation methods is that high estimation accuracy is difficult to be guaranteed. To address this problem, we proposed an sEMG-driven state-space model combined with an sEMG decomposition algorithm to improve the accuracy of knee joint movement estimation based on single-channel sEMG signals measured from gastrocnemius. The effectiveness of the method was evaluated via both single- and multi-speed walking experiments with seven and four healthy subjects, respectively. The results showed that the normal root-mean-squared error of the estimated knee joint angle using the method could be limited to 15%. Moreover, this method is robust with respect to variations in walking speeds. The estimation performance of this method was basically comparable to that of state-of-the-art studies using multi-channel sEMG.
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Articulación de la Rodilla , Músculo Esquelético , Humanos , Electromiografía/métodos , Músculo Esquelético/fisiología , Movimiento/fisiología , AlgoritmosRESUMEN
Gait impairments are among the most common hallmarks of Parkinson's disease (PD), usually appearing in the early stage and becoming a major cause of disability with disease progression. Accurate assessment of gait features is critical to personalized rehabilitation for patients with PD, yet difficult to be routinely carried out as clinical diagnosis using rating scales relies heavily on clinical experience. Moreover, the popular rating scales cannot ensure fine quantification of gait impairments for patients with mild symptoms. Developing quantitative assessment methods that can be used in natural and home-based environments is highly demanded. In this study, we address the challenges by developing an automated video-based Parkinsonian gait assessment method using a novel skeleton-silhouette fusion convolution network. In addition, seven network-derived supplementary features, including critical aspects of gait impairment (gait velocity, arm swing, etc.), are extracted to provide continuous measures enhancing low-resolution clinical rating scales. Evaluation experiments were conducted on a dataset collected with 54 patients with early PD and 26 healthy controls. The results show that the proposed method accurately predicted the patients' unified Parkinson's disease rating scale (UPDRS) gait scores (71.25% match on clinical assessment) and discriminated between PD patients and healthy subjects with a sensitivity of 92.6%. Moreover, three proposed supplementary features (i.e., arm swing amplitude, gait velocity, and neck forward bending angle) turned out to be effective gait dysfunction indicators with Spearman correlation coefficients of 0.78, 0.73, and 0.43 matching the rating scores, respectively. Since the proposed system requires only two smartphones, it holds a significant benefit for home-based quantitative assessment of PD, especially for detecting early-stage PD. Furthermore, the proposed supplementary features can enable high-resolution assessments of PD for providing subject-specific accurate treatments.
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Trastornos Neurológicos de la Marcha , Enfermedad de Parkinson , Humanos , Enfermedad de Parkinson/complicaciones , Enfermedad de Parkinson/diagnóstico , Marcha , Esqueleto , Trastornos Neurológicos de la Marcha/diagnóstico , Trastornos Neurológicos de la Marcha/etiologíaRESUMEN
BACKGROUND AND OBJECTIVE: The simultaneous execution of a motor and cognitive dual task may lead to the deterioration of task performance in one or both tasks due to cognitive-motor interference (CMI). Neuroimaging techniques are promising ways to reveal the underlying neural mechanism of CMI. However, existing studies have only explored CMI from a single neuroimaging modality, which lack built-in validation and comparison of analysis results. This work is aimed to establish an effective analysis framework to comprehensively investigate the CMI by exploring the electrophysiological and hemodynamic activities as well as their neurovascular coupling. METHODS: Experiments including an upper limb single motor task, single cognitive task, and cognitive-motor dual task were designed and performed with 16 healthy young participants. Bimodal signals of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded simultaneously during the experiments. A novel bimodal signal analysis framework was proposed to extract the task-related components for EEG and fNIRS signals respectively and analyze their correlation. Indicators including within-class similarity and between-class distance were utilized to validate the effectiveness of the proposed analysis framework compared to the canonical channel-averaged method. Statistical analysis was performed to investigate the difference in the behavior and neural correlates between the single and dual tasks. RESULTS: Our results revealed that the extra cognitive interference caused divided attention in the dual task, which led to the decreased neurovascular coupling between fNIRS and EEG in all theta, alpha, and beta rhythms. The proposed framework was demonstrated to have a better ability in characterizing the neural patterns than the canonical channel-averaged method with significantly higher within-class similarity and between-class distance indicators. CONCLUSIONS: This study proposed a method to investigate CMI by exploring the task-related electrophysiological and hemodynamic activities as well as their neurovascular coupling. Our concurrent EEG-fNIRS study provides new insight into the EEG-fNIRS correlation analysis and novel evidence for the mechanism of neurovascular coupling in the CMI.
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Acoplamiento Neurovascular , Humanos , Acoplamiento Neurovascular/fisiología , Espectroscopía Infrarroja Corta/métodos , Electroencefalografía/métodos , Hemodinámica/fisiología , CogniciónRESUMEN
OBJECTIVE: While deep brain stimulation (DBS) has proved effective for certain patients with disorders of consciousness (DOC), the working neural mechanism is not clear, the response varies for patients, and the assessment is inadequate. This paper aims to quantify the DBS-induced changes of consciousness in DOC patients at the neural functional level. METHODS: Ten DOC patients were included for DBS surgery. The DBS target was the right centromedian-parafascicular (CM-pf) nuclei for four patients and the bilateral CM-pf nuclei for six patients. Functional near-infrared spectroscopy (fNIRS) was taken to measure the neural activation of patients, in parallel with Coma Recovery Scale-Revised (CRS-R), before the DBS surgery and one month after. The fNIRS signals were recorded from the frontal, parietal, and occipital lobes. Functional connectivity analysis quantified the communication between brain regions, area communication strength, and global communication efficiency. Linear regression analysis was conducted between the changes of indices based on functional connectivity analysis and the changes of the CRS-R index. RESULTS: Patients with trauma (n = 4) exhibited a greater increase of CRS-R scores after DBS treatment compared with patients with hemorrhage (n = 4) and brainstem infarction (n = 2). Global communication efficiency changed consistently with the CRS-R index (slope = 57.384, p < 0.05, R2=0.483). No significant relationship was found between the changes of area communication strength of six brain lobes and the changes of the CRS-R index. CONCLUSIONS: The cause of DOC is essential for the outcome of DBS treatment, and brain communication efficiency is a promising functional marker for DOC recovery. SIGNIFICANCE: fNIRS-based functional connectivity analysis on brain network signifies changes of consciousness in DOC patients after DBS treatment.
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Trastornos de la Conciencia , Estimulación Encefálica Profunda , Humanos , Encéfalo , Estado de Conciencia , ComaRESUMEN
BACKGROUND: In Parkinson's disease (PD), walking may depend on the activation of the cerebral cortex. Understanding the patterns of interaction between cortical regions during walking tasks is of great importance. OBJECTIVE: This study investigated differences in the effective connectivity (EC) of the cerebral cortex during walking tasks in individuals with PD and healthy controls. METHODS: We evaluated 30 individuals with PD (62.4±7.2 years) and 22 age-matched healthy controls (61.0±6.4 years). A mobile functional near-infrared spectroscopy (fNIRS) was used to record cerebral oxygenation signals in the left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left parietal lobe (LPL), and right parietal lobe (RPL) and analyze the EC of the cerebral cortex. A wireless movement monitor was used to measure the gait parameters. RESULTS: Individuals with PD demonstrated a primary coupling direction from LPL to LPFC during walking tasks, whereas healthy controls did not demonstrate any main coupling direction. Compared with healthy controls, individuals with PD showed statistically significantly increased EC coupling strength from LPL to LPFC, from LPL to RPFC, and from LPL to RPL. Individuals with PD showed decreased gait speed and stride length and increased variability in speed and stride length. The EC coupling strength from LPL to RPFC negatively correlated with speed and positively correlated with speed variability in individuals with PD. CONCLUSION: In individuals with PD, the left prefrontal cortex may be regulated by the left parietal lobe during walking. This may be the result of functional compensation in the left parietal lobe.
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Enfermedad de Parkinson , Humanos , Persona de Mediana Edad , Anciano , Enfermedad de Parkinson/diagnóstico por imagen , Caminata/fisiología , Marcha/fisiología , Movimiento , Lóbulo Parietal/diagnóstico por imagenRESUMEN
Plantar pressure can signify the gait performance of patients with Parkinson's disease (PD). This study proposed a plantar pressure analysis method with the dynamics feature of the sub-regions plantar pressure signals. Specifically, each side's plantar pressure signals were divided into five sub-regions. Moreover, a dynamics feature extractor (DFE) was designed to extract features of the sub-regions signals. The radial basis function neural network (RBFNN) was used to learn and store gait dynamics. And a classification mechanism based on the output error in RBFNN was proposed. The classification accuracy of the proposed method achieved 100.00% in PD diagnosis and 95.89% in severity assessment on the online dataset, and 96.00% in severity assessment on our dataset. The experimental results suggested that the proposed method had the capability to signify the gait dynamics of PD patients.