| Alzheimer’s disease(AD)is a neurodegenerative disease with insidious onset and progressive development,which has a high incidence and mortality worldwide.Due to the lack of effective treatment methods,finding objective,accurate and non-invasive detection methods is an important way to improve the accuracy of early AD screening,promote the level of early AD intervention,and delay the progression of AD disease.The existing AD detection methods are still inadequate in sensitivity,specificity,objectivity,convenience and other aspects,and cannot meet the needs of early screening and long-term examination of AD patients.Therefore,it is of great significance to find new detection and evaluation techniques for AD.Precision grip is a movement behavior characterized by the interaction between fingertips and objects.It is the most unique motor function of human beings compared with other primates,and also an important basis for various fine and dexterous operations.Accurate grasping usually includes the stage of reaching out and the stage of grasping.In the stage of reaching out,it is necessary to accurately perceive the spatial position of the target,clearly control the motion posture of the hand and the relative position of the hand and the target.In the grasping stage,it is necessary to coordinate the strength of each finger to match the grip force with the load force,and flexibly adjust the finger force according to the task requirements and the weight and centroid of the object.In the process of precise grasping,sensory information of visual,tactile and proprioceptive modes is involved,and it is integrated with the fine motor control process of the central-peripheral neuromuscular system.This sensorimotor fusion mechanism is not only an important physiological mechanism for precise grasping,but also provides an important potential way to examine and evaluate the function of the nervous system.Since previous studies have found that grasping function changes even earlier than cognitive dysfunction,the establishment of a precise grasping sensory-motor function test analysis technology will be helpful to realize the early noninvasive quantitative evaluation of AD.Based on the above thinking,this paper revolves around AD early oriented research,accurately grasping the sensorimotor dysfunction design accurately grasping the test analysis system,discusses the AD early in the kinematics and dynamics characteristics under different visual feedback,analysis of the cerebral cortex in the brain electricity and muscle electricity neural activity and muscle activation information system motion control mechanism,The changes of characteristic parameters closely related to AD were found,which laid a foundation for establishing a non-invasive,quantitative and accurate detection method for early AD screening.In view of the above problems,the thesis carries out the following research work:(1)kinematics and kinetics regulation of precision grip for the early AD in a physical environment.In this thesis,we assessed grasping function in early AD by using a reach-tograsp experimental paradigm under different visual feedback,including kinematics and kinetics regulation.Object manipulation requires well fine motor control involving both kinematics and kinetics regulation.Despite extensive studies revealed that sensory-motor dysfunctions are often associated with AD,relatively little is known how the cognitive impairment could affect the grasping performance.Therefore,this study aims to examine the cognition-related and task-complexity-related changes during precision grasping movements.Seventeen AD patients and controls performed grasping tasks with and without visual feedback.The means and standard deviations of kinematic and kinetic data were applied to quantify the fine motor control between the groups and different movement patterns.Results showed that AD patients demonstrated remarkably decrease the reaching speed,reduce the grasping accuracy,augment the transportation variability and decreased grip force(GF)-load force(LF)coordination for grasping tasks than controls(p<0.05).Meanwhile,the non-bellshaped profile of AD patients showed that they rely more on feedback control and showed characteristic similar to that of children(p<0.05).By examining the correlation with the neuropsychological assessment,we were able to interpret some deficits in terms of global cognitive functioning and grasping function.These results imply that quantifying the characteristics of grasping performance of AD patients helps us to further understand the fine motor control mechanism of AD.This preliminary investigation can be used as supplementary information in identifying individuals with AD.Studies have shown that grasping parameters are moderately to highly correlated with AD’s Mini-Mental State Examination(MMSE)、Montreal Cognitive Assessment(MoCA)、Hamilton Anxiety Scale(HAMA)and Hamilton Depression Scale(HAMD),which may help explain that cognitive deficits in AD contribute to the decline in precise grasping.(2)the muscle synergy of precision grip in virtual reality(VR).VR is an effective complement to existing treatments for a variety of diseases,including neurodegenerative diseases,traumatic brain injury,and stroke.Precision grasp is a standard action for upper limb neurorehabilitation based on virtual environment.VR has shown great application potential in the field of cognitive assessment.The VR system also provides greater flexibility for changes in visual feedback.Electroencephalogram(EEG)and electromyography(EMG)analysis system is introduced,an effective way of peripheral nerve activity,which helps to provide a more precise theoretical reference for the early diagnosis of AD.In this study,healthy subjects were initially selected to perform a precision grasp task,and the changes in the position of the centroid and the visual feedback of the centroid were added to the original experimental paradigm.The results showed that loading phase were significantly increased under the low-prediction condition,which may reflect inefficiencies in the programming of subjects’ hand closure and difficulties in forming smooth transitions between individual motor phases.High-prediction conditions help to generate more accurate predictions for precise control of GF,and low-prediction conditions result in increased GF.Furthermore,visual feedback from the moving hand and the centroid had different effects on GF.Muscle synergy analysis revealed that neuromuscular control strategies change under different visual feedback modalities.(3)the granger causality network of brain regions based on precision grasp in VR.This chapter mainly analyzes the control mechanism of precision grip from the perspectives of wavelet packet energy entropy of myoelectric signals,spatial distribution of multi-channel electroencephalogram energy and causal network.The results show that healthy adults’muscle activation is less affected by visual feedback.The research has proved that the abductor pollicis brevis and the dorsal first interosseous muscle play the main role in the double-finger grasping task.The results of brain region energy analysis have proved that βband is the characteristic band of precision grasp,and the central region and frontal parietal region are its characteristic brain regions.Electroencephalogram map shows that the visual feedback of the moving hand and the center of mass have different effects on the activated areas of the brain,which helps to reveal the complex neurophysiological mechanisms.Causal connection analysis among multiple brain regions showed that the region with the strongest causal connection was located in the central region,followed by the parietal and frontal regions,the weakest region was the temporal region.In conclusion,grasping is not only a key behavior of human interaction with the environment,but also an important experimental test to explore sensory-motor impairment in early AD.This study elucidates the complex sensory-motor processes involved in grasping behavior from the perspectives of kinematics,kinetics,EEG and EMG,helping to link basic motor research to early AD diagnosis. |
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