Deglutition Disorders, Lateral Medullary Syndrome
Conditions
Brief summary
The goal of this observational study is to detect the alteration of cortical activation and functional connectivity during swallowing in patients with Lateral Medullary Syndrome (LMS) dysphagia by functional near infrared spectroscopy (fNIRS). The main questions it aims to answer are: * The alteration of cortical activation during swallowing in patients with LMS compared with healthy subjects. * The alteration of cortical functional connectivity during swallowing in patients with LMS compared with healthy subjects. fNIRS will be used to detect cortical activation and functional connectivity during swallowing tasks in LMS patients and healthy subjects, and to compare the differences between patients and healthy subjects.
Interventions
OTHERFunctional near-infrared spectroscopy (fNIRS) detection
fNIRS( NirScan Danyang Huichuang Medical Equipment Co. Ltd., China) will be used to detect changes in HbO and HbR during rest and voluntary swallowing. The swallowing procedure will be used consisted of three phases of manipulations: 30-s resting state periods, 30-s swallowing periods and 30-s resting state periods. A pre-collection will be conducted before the formal collection so that the subjects could understand the test process. After the pre-collection, the subjects will take a rest for 5 minutes before the formal collection.
Sponsors
The First Affiliated Hospital of Zhengzhou University
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
* The first ischemic stroke was confirmed by head MRI as LMS; * 18-80 years old, right-handed; * Dysphagia was confirmed by video fluoroscopic swallow study(VFSS); * Conscious enough to cooperate with fNIRS testing; * No previous neurological or mental illness.
Exclusion criteria
* Other central nervous system diseases (Parkinson's disease, Alzheimer's disease, intracranial tumors, etc.); * Other diseases causing dysphagia (tumors of the esophagus, larynx, nasopharynx, etc.); * Serious physical diseases (cancer, fracture, etc.); * Other mental disorders (mania, schizophrenia, etc.); * Unconscious, unable to cooperate with the examination
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| cortical activation during swallowing | day 1 | The NirSpark package will be used to preprocess and analyze the fNIRS data. The steps will be used to preprocess the data including: deleting irrelevant time intervals and unrelated artifacts; turning the intensity of light into optical density; choosing the band-pass filter to filter the noise and interference signals (0.01-0.2 Hz); translating optical density to the oxygen concentration in the blood; and setting the initial time of the hemodynamic response function (HRF) to 30 s and the end time to 60 s (the time for a single block paradigm). To analyze the HbO2 time-series data, a generalized linear model (GLM) will be used. The GLM could calculate the degree of matching between the experimental and ideal HRF values for each task and participant. The beta value, which represents the channel's level of cortical activation, will be utilized to estimate the HRF prediction of the HbO2 signal and can be used to represent the HRF function's peak value. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| functional connectivity during swallowing | day 1 | The Network module of NirSpark software was used to extract the HbO2 concentration change at each time point, analyze the Pearson correlation coefficient of HbO2 concentration in each channel on the time series, and define it as the functional connection strength between channels through Fisher-Z conversion. The Network module of NirSpark software was used to extract the HbO2 concentration change at each time point, analyze the Pearson correlation coefficient of HbO2 concentration in each channel on the time series, and define it as the functional connection strength between channels through Fisher-Z conversion. The Network module of NirSpark software was used to extract the HbO2 concentration change at each time point, analyze the Pearson correlation coefficient of HbO2 concentration in each channel on the time series, and define it as the functional connection strength between channels through Fisher-Z conversion. |
Countries
China
Contacts
Primary ContactYi Li, Master
Outcome results
None listed