Research Of Evaluating State Of Consciousness And Rehabilitation Of Patients With Severe Disorders Of Consciousness Based On EEG And FMRI

Patients with severe disorders of consciousness (DOC) refer to patients who has low awarenessof the environment and the self, it showed severe brain dysfunction in the clinical. These patientsmay be caused by a variety of reasons, including severe traumatic brain injury, cerebral hemorrhageor infarction, electrical burns and other factors. According to the degree of the consciousness, DOCpatients can be grouped into minimally conscious state (MCS) and vegetative state (VS). How toevaluate effective the rehabilitation process with DOC patients is a challenge faced by the medicalprofession. Conscious of behavior rating scale is an evaluation method commonly used in thehospital, but it would depend on the operator’s experience and may have some subjectivity.Therefore, it has an important clinical significance to explore a more objective and effectiveevaluation method with patient’s brain information.Electroencephalogram (EEG) has many advantages, such as: harmless, low cost, easy to get,and convenient to bedside detection, etc. EEG can reflect the state of brain function objectively andaccurately by a higher temporal resolution. Brain fMRI has the advantage of non-invasive, noradiation. It can reflect the connection of brain function or the activation of barin by a higher spatialresolution. EEG and fMRI technology are used on my research, and the importance of them when inthe evaluating the state of consciousness and rehabilitation process of DOC patients is studied bythe correlation between EEG nonlinear characteristics and CRS-R score, and also thecommoncharacteristic parameters of fMRI signal.The main contents are as follows:(1) Background and introduction: This part mainly introduces the basic knowledges of severedisorders of consciousness, the method of evaluating consciousness, and the present situation ofEEG and fMRI in DOC patients.(2) Basic knowledge of EEG and fMRI, experimental program: The generation mechanism andcharacteristics, pre-procescesing and analysis methods of EEG and fMRI are introduced. At thesame time, this part introduces the choice of subjects and the paramenters of experimentalequipment.(3) Correlation between EEG nonlinear characteristics and CRS-R score: We took approximatentropy, sample entropy, permutation entropy and complexity LZC as characteristics of EEG. First,the difference of EEG at T1time and T2time in different group (all, MCS and VS) was analyzed.Then, the correlation between EEG nonlinear characteristics change and CRS-R score change wasstudied. At last, the approximat entropy was used to draw a brain mapping for visual characterization. The results showed that: The CRS-R score changes significantly both for MCS andVS patients (P<0.01). Approximate entropy and complexity LZC changes significantly for MCSpatients (P<0.05), while only approximate entropy changes significantly for VS patients (P<0.05).There has significantly positive correlation of score changes between CRS-R and nonlinearcharacteristics (approximate entropy for both MCS and VS, complexity LZC for MCS only), thecorrelation coefficients were calculated,0.851and0.693for MCS (approximate entropy andcomplexity LZC), and0.778for VS (approximate entropy). Approximate entropy can be used tovisualize patient’s brain function mapping. Approximate entropy could be used as a potentialmethod to evaluate the state and the rehabilitation process for patients with awareness.(4) Analysis of fMRI signal: We took regional homogeneity (ReHo), amplitude of lowfrequency fluctuation (ALFF) and small world as characteristics of fMRI. First, the significantdifference of MCS, VS, Health and VS at T1time and T2time was analyzed by thesecharacteristics. Then, the characteristics of small world and the degree were calculated. At last, theadjacency matrix and3D Brain function network were drawed. The results showed that: ReHo andALFF significantly increase or decrease both for MCS and VS when relativing to the Health groupand ALFF decrease significantly for two VS patients at T2time and T2time. The shortest path (onecharacteristic of small world) and the degree show there is significant difference between VS andHealth. Clustering coefficient (other characteristic) show that both VS and MCS were significantdifferent to Health. The adjacency matrix and3D Brain function network show an increase of brainconnected numbers but the intensity has weakened in VS patient one, an enhancement ofconnection strength enhancement and significantly increased of brain connected numbers int VSpatient two. Brain function network can used for visual characterizatior of brain activity as the sameof EEG brain mapping. To some extent, ReHo, ALFF, degree, small world and brain functionnetwork can reflect the state of patient’s consciousness.