阻塞性睡眠呼吸暂停低通气综合征的静息态脑功能磁共振成像研究
发布时间：2018-08-05 15:05
【摘要】：第一部分阻塞性睡眠呼吸暂停低通气综合征的静息态脑网络研究 目的： 运用静息态功能磁共振成像(resting-state functional magnetic resonance imaging, rs-fMRI)技术,采用独立成分分析(independent component analysis,ICA)获取阻塞性睡眠呼吸暂停低通气综合征(obstructive sleep apnea-hypopnea syndrome, OSAHS)患者和正常对照组的静息态脑网络,研究OSAHS患者与正常人的静息态脑网络功能连接差异及其与OSAHS严重程度的关系,并运用基于体素形态学(voxel-based morphometry, VBM)分析两组之间功能连接差异的结构基础,从而阐明OSAHS患者认知及运动功能障碍的脑网络机制。 材料与方法： 选择符合入组标准的OSAHS患者24例,年龄：31-59岁,平均44.6±7.4岁；健康志愿者21例,年龄：30-60岁,平均40.6±11.4岁；OSAHS患者与正常对照组均为男性、右利手,且两组之间年龄、教育年限无统计学差异(P0.05),OSAHS患者的体重指数、呼吸暂停低通气指数(apnea-hypopnea index, AHI)、(?)氧饱和度低于90%的睡眠时间比例(%TST90%)、Epworth嗜睡评分(Epworth sleepiness score, ESS)均显著高于正常对照组(P0.001),但是OSAHS患者MMSE评分低于正常对照组(P0.01)。采用GE3.0T磁共振扫描仪对所有受试者行rs-fMRI扫描。应用基于Matlab平台的SPM8及MICA软件进行预处理、独立成分分析和统计。预处理包括时间校正、头动校正、空间标准化、空间平滑、去线性漂移、低频滤波和去除协变量。运用三步主成分分析将功能数据分解为30个成分并重建为功能连接图,ICA运算次数为100。根据视觉观察,选择与文献报道的常见静息态脑网络一致的七个成分。统计分析采用单样本t检验,分别得到每个成分的模板,在此模板基础上采用两样本t检验比较OSAHS患者与正常对照组的脑网络功能连接差异,多重比较采用AphaSim校正。为了分析OSAHS严重程度对脑网络功能连接的影响,以年龄为控制变量,采用偏相关分析OSAHS患者AHI、%TST90%、ESS评分与差异脑区功能连接的相关性。为了阐明OSAHS脑网络功能连接改变的结构基础,以两组之间功能连接差异区域作为感兴趣区(region of interest, ROI),采用SPSS两样本检验比较两组之间ROI的灰质体积(grey matter volume, GMV)差异。 结果： 1.除视觉网络和听觉网络以外,其他静息态脑网络在OSAHS患者和正常志愿者之间均存在显著差异； 2. OSAHS患者的内侧前额叶和左侧背外侧前额叶的功能连接及灰质体积均减小,提示功能和结构损害；右侧背外侧前额叶和左侧中央前回的功能连接减低而灰质体积正常,提示功能损害；右侧后扣带的功能连接增强而灰质体积正常,提示功能代偿； 3. OSAHS患者AHI与右侧额顶网络的背外侧前额叶功能连接存在显著负相关。 结论： 1.静息态脑网络的功能与结构改变反映了OSAHS患者的记忆、执行、注意及运动功能缺陷。 2.右侧额顶网络功能连接改变能够反映OSAHS患者的病情严重程度。 第二部分阻塞性睡眠呼吸暂停低通气综合征的全脑局部一致性研究 目的： 基于rs-fMRI数据,利用考察局部脑区低频振荡信号同步性的局部一致性(regional homogeneity, ReHo)算法,研究OSAHS患者与健康志愿者的全脑ReHo值差异以及同GMV改变之间的关系。 材料与方法： 选择符合入组标准的OSAHS患者24例、健康志愿者21例,采用GE3.0T磁共振扫描仪进行rs-fMRI检查,应用基于Matlab平台的SPM8及REST软件进行预处理和统计分析。预处理包括时间校正、头动校正、空间标准化、空间平滑、去线性漂移、低频滤波和去除协变量。预处理后数据用REST软件计算每个体素的ReHo值,通过逐个体素的分析得到每个人的ReHo图。为了消除个体差异的影响,我们计算出标准化的ReHo值,通过基于体素的分析,获得两组之间ReHo值存在显著差异的脑区,多重比较采用AphaSim校正。为了阐明OSAHS患者全脑ReHo值改变的结构基础,将两组之间ReHo值差异脑区作为ROI,采用SPSS两样本t检验比较两组之间ROI的GMV差异。为了分析OSAHS严重程度对全脑ReHo改变的影响,以年龄为控制变量,采用偏相关分析OSAHS患者AHI、%TST90%、ESS评分与差异脑区ReHo值及GMV的相关性。 结果： 1. OSAHS患者ReHo值升高区域包括右侧小脑半球、海马旁回、颞上回、壳核、双侧中央前回、中央后回及辅助运动区,主要位于感觉运动相关脑区,提示OSAHS患者的脑功能代偿； 2. OSAHS患者ReHo值减低区域包括左侧颞下回、双侧小脑半球、前额叶、楔前叶及角回,主要位于认知相关脑区,提示OSAHS患者的认知功能损害； 3.在DSAHS患者ReHo升高脑区中,右侧壳核ReHo值与患者的%TST90%、ESS呈显著正相关,右侧海马旁回GMV与患者的ESS呈显著负相关；在OSAHS患者ReHo减低脑区中,双侧角回ReHo值与患者的%TST90%呈显著负相关,左侧楔前叶ReHo值与患者的%TST90%呈显著负相关,内侧前额叶ReHo值、GMV均与患者的ESS呈显著负相关,提示以上脑区的功能和结构改变能够反映OSAHS严重程度。 结论： 1. OSAHS患者与正常对照组的全脑ReHo值存在显著性差异,其中患者ReHo值升高主要位于运动感觉相关脑区,ReHo值减低主要位于认知相关脑区； 2. OSAHS患者ReHo值改变与日间嗜睡及夜间缺氧程度具有显著相关性。 第三部分阻塞性睡眠呼吸暂停低通气综合征的全脑低频振幅研究 目的： 基于rs-fMRI数据,利用考察静息状态下脑自发性神经元活动的低频振幅(amplitude of low frequency fluctuations, ALFF)算法,研究OSAHS患者与健康志愿者的全脑ALFF值差异以及同GMV改变之间的关系。 材料与方法： 选择符合入组标准的OSAHS患者24例、健康志愿者21例,采用GE3.0T磁共振扫描仪进行静息态fMRI检查,应用基于Matlab平台的SPM8及REST软件进行预处理和统计分析。预处理包括时间校正、头动校正、空间标准化、空间平滑、去线性漂移、低频滤波和去除协变量。预处理后数据用REST软件计算每个体素的ALFF值,通过逐个体素的分析得到每个人的ALFF图。为了消除个体差异的影响,我们计算出标准化的ALFF值,通过基于体素的分析,获得两组之间ALFF值存在显著差异的脑区,多重比较采用AphaSim校正。为了阐明OSAHS患者全脑ALFF值改变的结构基础,将两组之间ALFF值差异脑区作为ROI,采用SPSS两样本t-检验比较两组之间ROI的GMV差异。为了分析OSAHS严重程度对全脑ALFF改变的影响,以年龄为控制变量,采用偏相关分析OSAHS患者AHI、%TST90%、ESS评分与差异脑区ALFF值及GMV的相关性。 结果： 1. OSAHS患者ALFF值升高区域包括右侧海马旁回、梭状回及颞下回,颞上回、顶上回、右侧中央前后回、左侧中央旁小叶、中扣带和辅助运动区、中央后回,主要位于感觉运动相关脑区,提示OSAHS患者的脑功能代偿机制； 2. OSAHS患者ALFF值减低区域包括双侧前额叶、后扣带及楔前叶,主要位于认知相关脑区,代表OSAHS患者的认知功能损害； 3. OSAHS患者双侧楔前叶ALFF值与ESS评分呈负相关,提示患者楔前叶功能损害与日间嗜睡程度有关。 结论： 1. OSAHS患者与正常对照组的全脑ALFF值存在显著性差异,其中患者ALFF值升高主要位于感觉运动相关脑区,ALFF值减低主要位于认知相关脑区； 2. OSAHS患者日间嗜睡与双侧楔前叶ALFF值改变具有显著相关性。
[Abstract]:Part one resting state brain network study of obstructive sleep apnea hypopnea syndrome
Objective:
The resting state functional magnetic resonance imaging (resting-state functional magnetic resonance imaging, rs-fMRI) technique was used to obtain the rest of the obstructive sleep apnea hypopnea syndrome (obstructive sleep) (obstructive sleep) patients and the normal control group by the independent component analysis (independent component analysis, ICA). The state brain network studies the differences in the resting state brain network function connection between OSAHS patients and normal people and their relationship with the severity of OSAHS, and analyzes the structural basis of the functional connectivity differences between the two groups based on the voxel morphology (voxel-based morphometry, VBM), thus clarifying the brain network mechanism of cognitive and motor dysfunction in OSAHS patients.
Materials and methods:
24 cases of OSAHS patients, aged 31-59 years old, with an average of 44.6 + 7.4 years of age, were selected to conform to the standard of entry, 21 healthy volunteers, 30-60 years old and 40.6 + 11.4 years old. OSAHS patients and normal control groups were male, right hand, and age of two groups without statistical difference (P0.05), body mass index of OSAHS patients and apnea low The ventilation index (apnea-hypopnea index, AHI), (?) the proportion of oxygen saturation less than 90% (%TST90%), Epworth lethargy score (Epworth sleepiness score, ESS) were significantly higher than that of the normal control group (P0.001), but the MMSE score of OSAHS patients was lower than that of the normal control group. SPM8 and MICA software based on Matlab platform are used for preprocessing, independent component analysis and statistics. Preprocessing includes time correction, head motion correction, spatial standardization, spatial smoothing, de linear drift, low frequency filtering and removal of covariance. Three steps principal component analysis is used to decompose functional data into 30 components and be rebuilt into functional connections. Figure, the number of ICA operations is 100. according to visual observation, select seven components consistent with the common resting brain network reported in the literature. Statistical analysis uses a single sample t test to get the template of each component. On the basis of this template, two samples of t test are used to compare the differences in the functional connection of the brain network between the OSAHS patients and the normal control group. AphaSim correction was used. In order to analyze the effect of OSAHS severity on functional connectivity of the brain network, the correlation between OSAHS patients' AHI,%TST90%, ESS score and the functional connection of different brain regions was analyzed with age as the control variable. In order to clarify the structural basis of the functional connection changes of the OSAHS brain network, the functional connection between the two groups was poor. Different regions of interest (ROI) were used to compare the differences of grey matter volume (GMV) of ROI between the two groups by SPSS.
Result:
1. Except for visual and auditory networks, resting brain networks were significantly different between OSAHS patients and normal volunteers.
2. the functional connection and gray matter volume of the medial prefrontal lobes and the left dorsolateral prefrontal lobes decreased, indicating the functional and structural damage. The functional connection of the right dorsolateral prefrontal lobe and the left precentral gyrus was reduced and the volume of gray matter was normal, suggesting the function damage; the functional connection of the right posterior cingulate band was normal and the volume of gray matter was normal. Functional compensatory function;
3. there was a significant negative correlation between AHI and right frontal frontal network in OSAHS patients.
Conclusion:
1. the functional and structural changes of resting brain networks reflect memory, executive, attention and motor deficits in OSAHS patients.
2. the functional connectivity changes on the right front-end network can reflect the severity of OSAHS patients.
The second part of the whole brain regional coherence study of obstructive sleep apnea hypopnea syndrome
Objective:
Based on rs-fMRI data, using the local consistency (regional homogeneity, ReHo) algorithm to investigate the synchronization of low frequency oscillations in local brain region (ReHo), the relationship between ReHo and GMV changes in the whole brain of OSAHS patients and healthy volunteers was studied.
Materials and methods:
24 patients with OSAHS, 21 healthy volunteers, and 21 healthy volunteers, rs-fMRI examination by GE3.0T magnetic resonance scanner, pre processing and statistical analysis using SPM8 and REST software based on Matlab platform. The preprocessing includes time correction, head motion correction, spatial standardization, spatial smoothing, de linear drift, low-frequency filtering and removal. Covariant quantity. The pre processed data is calculated by REST software for each voxel's ReHo value, and each person's ReHo diagram is obtained by a voxel analysis. In order to eliminate the influence of individual differences, we calculate the standardized ReHo values and obtain the brain regions with significant differences in ReHo values between the two groups by the voxel based analysis. Multiple comparison uses AphaSim Correction. In order to clarify the structural basis of the changes in the ReHo value of the whole brain of OSAHS patients, the ReHo value difference between the two groups was taken as ROI, and the GMV difference between the two groups was compared with the SPSS two sample t test. The correlation between the score and the difference of ReHo and GMV in the brain region.
Result:
1. the elevation of ReHo value in 1. OSAHS patients included right cerebellar hemisphere, parahippocampal gyrus, upper temporal gyrus, putamen, bilateral central anterior gyrus, posterior central gyrus and auxiliary motor area, which were mainly located in the related brain regions of sensory movement, suggesting the compensatory brain function of OSAHS patients.
2. OSAHS patients with decreased ReHo value included left temporal gyrus, bilateral cerebellar hemisphere, prefrontal lobe, prefrontal lobe, and angular gyrus, mainly in cognitive related brain regions, suggesting cognitive impairment in OSAHS patients.
3. in the ReHo elevation of the DSAHS patients, the ReHo value of the right putamen was significantly positively correlated with the patient's%TST90% and ESS, and the right parahippocampal gyrus GMV was negatively correlated with the patient's ESS, and the bilateral angular gyrus ReHo value was negatively correlated with the patient's%TST90% in the ReHo reduced brain area of OSAHS patients. The left anterior lobe ReHo value was significantly negative to the patient. ReHo and GMV in medial prefrontal lobe were negatively correlated with ESS, suggesting that functional and structural changes in the above brain regions could reflect the severity of OSAHS.
Conclusion:
There was a significant difference in the ReHo value of the whole brain between 1. OSAHS patients and the normal control group. The increase of the ReHo value in the patients was mainly located in the motor related brain region, and the decrease of the ReHo value was mainly in the cognitive related brain area.
2. the change of ReHo in OSAHS patients was significantly correlated with daytime sleepiness and nocturnal hypoxia.
The third part of the whole brain low frequency amplitude study of obstructive sleep apnea hypopnea syndrome
Objective:
Based on rs-fMRI data, the amplitude of low frequency fluctuations (ALFF) algorithm was used to study the low frequency amplitude (ALFF) of spontaneous neuron activity in resting state (ALFF). The relationship between the whole brain ALFF difference and the GMV changes in OSAHS patients and healthy volunteers was studied.
Materials and methods:
24 OSAHS patients and 21 healthy volunteers were selected in accordance with the standard of the group. The GE3.0T magnetic resonance scanner was used for resting state fMRI examination. The SPM8 and REST software based on the Matlab platform were used for preprocessing and statistical analysis. The preprocessing includes time correction, head motion correction, space standardization, spatial smoothing, de linear drift, low-frequency filtering, and The ALFF value of each voxel is calculated by REST software and the ALFF diagram of each person is obtained by the analysis of the voxel. In order to eliminate the influence of individual differences, we calculate the standardized ALFF values and obtain the brain regions with significant differences in ALFF values between the two groups by the analysis of voxels, and the multiple comparison uses Ap. HaSim correction. In order to clarify the structural basis of the changes in the ALFF value of the whole brain of OSAHS patients, the difference of the brain region between the two groups was taken as ROI, and the GMV difference between the two groups was compared with the SPSS two sample t- test. In order to analyze the effect of OSAHS severity on the ALFF changes in the whole brain, the age was used as the controlled variable, and the partial correlation analysis was used. 0%, the correlation between ESS score and ALFF value and GMV in different brain regions.
Result:
1. the elevation of ALFF value in 1. OSAHS patients included right parahippocampal gyrus, fusiform gyrus and inferior temporal gyrus, upper temporal gyrus, upper parietal gyrus, right central posterior gyrus, left paracentral lobule, cingulate belt and auxiliary motor area, central posterior gyrus, mainly located in the related brain area of sensory movement, suggesting the compensatory mechanism of brain function in OSAHS patients.
2. The decreased ALFF values in OSAHS patients include bilateral prefrontal lobes, posterior cingulate and anterior cuneate lobes, mainly located in the cognitive-related brain regions, representing cognitive impairment in OSAHS patients.
3. The ALFF value of bilateral anterior wedge lobe in OSAHS patients was negatively correlated with ESS score, suggesting that the impairment of anterior wedge lobe function was related to daytime sleepiness.
Conclusion:
There was a significant difference in the ALFF value of the whole brain between 1. OSAHS patients and the normal control group. The increase of the ALFF value in the patients was mainly located in the sensory motor related brain region, and the decrease of the ALFF value was mainly located in the cognitive related brain area.
2. the daytime sleepiness of OSAHS patients was significantly correlated with the change of ALFF value in bilateral anterior wedge.
【学位授予单位】：天津医科大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：R766;R445.2
本文编号：2166159
[Abstract]:Part one resting state brain network study of obstructive sleep apnea hypopnea syndrome
Objective:
The resting state functional magnetic resonance imaging (resting-state functional magnetic resonance imaging, rs-fMRI) technique was used to obtain the rest of the obstructive sleep apnea hypopnea syndrome (obstructive sleep) (obstructive sleep) patients and the normal control group by the independent component analysis (independent component analysis, ICA). The state brain network studies the differences in the resting state brain network function connection between OSAHS patients and normal people and their relationship with the severity of OSAHS, and analyzes the structural basis of the functional connectivity differences between the two groups based on the voxel morphology (voxel-based morphometry, VBM), thus clarifying the brain network mechanism of cognitive and motor dysfunction in OSAHS patients.
Materials and methods:
24 cases of OSAHS patients, aged 31-59 years old, with an average of 44.6 + 7.4 years of age, were selected to conform to the standard of entry, 21 healthy volunteers, 30-60 years old and 40.6 + 11.4 years old. OSAHS patients and normal control groups were male, right hand, and age of two groups without statistical difference (P0.05), body mass index of OSAHS patients and apnea low The ventilation index (apnea-hypopnea index, AHI), (?) the proportion of oxygen saturation less than 90% (%TST90%), Epworth lethargy score (Epworth sleepiness score, ESS) were significantly higher than that of the normal control group (P0.001), but the MMSE score of OSAHS patients was lower than that of the normal control group. SPM8 and MICA software based on Matlab platform are used for preprocessing, independent component analysis and statistics. Preprocessing includes time correction, head motion correction, spatial standardization, spatial smoothing, de linear drift, low frequency filtering and removal of covariance. Three steps principal component analysis is used to decompose functional data into 30 components and be rebuilt into functional connections. Figure, the number of ICA operations is 100. according to visual observation, select seven components consistent with the common resting brain network reported in the literature. Statistical analysis uses a single sample t test to get the template of each component. On the basis of this template, two samples of t test are used to compare the differences in the functional connection of the brain network between the OSAHS patients and the normal control group. AphaSim correction was used. In order to analyze the effect of OSAHS severity on functional connectivity of the brain network, the correlation between OSAHS patients' AHI,%TST90%, ESS score and the functional connection of different brain regions was analyzed with age as the control variable. In order to clarify the structural basis of the functional connection changes of the OSAHS brain network, the functional connection between the two groups was poor. Different regions of interest (ROI) were used to compare the differences of grey matter volume (GMV) of ROI between the two groups by SPSS.
Result:
1. Except for visual and auditory networks, resting brain networks were significantly different between OSAHS patients and normal volunteers.
2. the functional connection and gray matter volume of the medial prefrontal lobes and the left dorsolateral prefrontal lobes decreased, indicating the functional and structural damage. The functional connection of the right dorsolateral prefrontal lobe and the left precentral gyrus was reduced and the volume of gray matter was normal, suggesting the function damage; the functional connection of the right posterior cingulate band was normal and the volume of gray matter was normal. Functional compensatory function;
3. there was a significant negative correlation between AHI and right frontal frontal network in OSAHS patients.
Conclusion:
1. the functional and structural changes of resting brain networks reflect memory, executive, attention and motor deficits in OSAHS patients.
2. the functional connectivity changes on the right front-end network can reflect the severity of OSAHS patients.
The second part of the whole brain regional coherence study of obstructive sleep apnea hypopnea syndrome
Objective:
Based on rs-fMRI data, using the local consistency (regional homogeneity, ReHo) algorithm to investigate the synchronization of low frequency oscillations in local brain region (ReHo), the relationship between ReHo and GMV changes in the whole brain of OSAHS patients and healthy volunteers was studied.
Materials and methods:
24 patients with OSAHS, 21 healthy volunteers, and 21 healthy volunteers, rs-fMRI examination by GE3.0T magnetic resonance scanner, pre processing and statistical analysis using SPM8 and REST software based on Matlab platform. The preprocessing includes time correction, head motion correction, spatial standardization, spatial smoothing, de linear drift, low-frequency filtering and removal. Covariant quantity. The pre processed data is calculated by REST software for each voxel's ReHo value, and each person's ReHo diagram is obtained by a voxel analysis. In order to eliminate the influence of individual differences, we calculate the standardized ReHo values and obtain the brain regions with significant differences in ReHo values between the two groups by the voxel based analysis. Multiple comparison uses AphaSim Correction. In order to clarify the structural basis of the changes in the ReHo value of the whole brain of OSAHS patients, the ReHo value difference between the two groups was taken as ROI, and the GMV difference between the two groups was compared with the SPSS two sample t test. The correlation between the score and the difference of ReHo and GMV in the brain region.
Result:
1. the elevation of ReHo value in 1. OSAHS patients included right cerebellar hemisphere, parahippocampal gyrus, upper temporal gyrus, putamen, bilateral central anterior gyrus, posterior central gyrus and auxiliary motor area, which were mainly located in the related brain regions of sensory movement, suggesting the compensatory brain function of OSAHS patients.
2. OSAHS patients with decreased ReHo value included left temporal gyrus, bilateral cerebellar hemisphere, prefrontal lobe, prefrontal lobe, and angular gyrus, mainly in cognitive related brain regions, suggesting cognitive impairment in OSAHS patients.
3. in the ReHo elevation of the DSAHS patients, the ReHo value of the right putamen was significantly positively correlated with the patient's%TST90% and ESS, and the right parahippocampal gyrus GMV was negatively correlated with the patient's ESS, and the bilateral angular gyrus ReHo value was negatively correlated with the patient's%TST90% in the ReHo reduced brain area of OSAHS patients. The left anterior lobe ReHo value was significantly negative to the patient. ReHo and GMV in medial prefrontal lobe were negatively correlated with ESS, suggesting that functional and structural changes in the above brain regions could reflect the severity of OSAHS.
Conclusion:
There was a significant difference in the ReHo value of the whole brain between 1. OSAHS patients and the normal control group. The increase of the ReHo value in the patients was mainly located in the motor related brain region, and the decrease of the ReHo value was mainly in the cognitive related brain area.
2. the change of ReHo in OSAHS patients was significantly correlated with daytime sleepiness and nocturnal hypoxia.
The third part of the whole brain low frequency amplitude study of obstructive sleep apnea hypopnea syndrome
Objective:
Based on rs-fMRI data, the amplitude of low frequency fluctuations (ALFF) algorithm was used to study the low frequency amplitude (ALFF) of spontaneous neuron activity in resting state (ALFF). The relationship between the whole brain ALFF difference and the GMV changes in OSAHS patients and healthy volunteers was studied.
Materials and methods:
24 OSAHS patients and 21 healthy volunteers were selected in accordance with the standard of the group. The GE3.0T magnetic resonance scanner was used for resting state fMRI examination. The SPM8 and REST software based on the Matlab platform were used for preprocessing and statistical analysis. The preprocessing includes time correction, head motion correction, space standardization, spatial smoothing, de linear drift, low-frequency filtering, and The ALFF value of each voxel is calculated by REST software and the ALFF diagram of each person is obtained by the analysis of the voxel. In order to eliminate the influence of individual differences, we calculate the standardized ALFF values and obtain the brain regions with significant differences in ALFF values between the two groups by the analysis of voxels, and the multiple comparison uses Ap. HaSim correction. In order to clarify the structural basis of the changes in the ALFF value of the whole brain of OSAHS patients, the difference of the brain region between the two groups was taken as ROI, and the GMV difference between the two groups was compared with the SPSS two sample t- test. In order to analyze the effect of OSAHS severity on the ALFF changes in the whole brain, the age was used as the controlled variable, and the partial correlation analysis was used. 0%, the correlation between ESS score and ALFF value and GMV in different brain regions.
Result:
1. the elevation of ALFF value in 1. OSAHS patients included right parahippocampal gyrus, fusiform gyrus and inferior temporal gyrus, upper temporal gyrus, upper parietal gyrus, right central posterior gyrus, left paracentral lobule, cingulate belt and auxiliary motor area, central posterior gyrus, mainly located in the related brain area of sensory movement, suggesting the compensatory mechanism of brain function in OSAHS patients.
2. The decreased ALFF values in OSAHS patients include bilateral prefrontal lobes, posterior cingulate and anterior cuneate lobes, mainly located in the cognitive-related brain regions, representing cognitive impairment in OSAHS patients.
3. The ALFF value of bilateral anterior wedge lobe in OSAHS patients was negatively correlated with ESS score, suggesting that the impairment of anterior wedge lobe function was related to daytime sleepiness.
Conclusion:
There was a significant difference in the ALFF value of the whole brain between 1. OSAHS patients and the normal control group. The increase of the ALFF value in the patients was mainly located in the sensory motor related brain region, and the decrease of the ALFF value was mainly located in the cognitive related brain area.
2. the daytime sleepiness of OSAHS patients was significantly correlated with the change of ALFF value in bilateral anterior wedge.
【学位授予单位】：天津医科大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：R766;R445.2
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