基于不同频段时频特征的阿尔兹海默症脑功能异常分析
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摘要
目的采用信号的时频分析理论,在不同频段构建静息态功能磁共振成像(rs-fMRI)数据的特征图,从而分频段分析阿尔兹海默症(AD)的脑功能异常区域。方法针对55例AD患者和52例年龄、性别相匹配的正常被试者的rs-fMRI数据,分Slow-5和Slow-4两个频段,构建时频特征图,再分别进行双样本t检验和多重比较校正,获得AD相对于正常被试者的显著差异脑区。提取差异脑区内的特征值,分别与AD患者的简易精神状态检测(MMSE)和临床痴呆检测(CDR)量表值求皮尔森相关系数,分析差异脑区的病理学意义。结果在Slow-5频段,相比于正常被试者,AD患者左脑核间小叶的特征值明显升高,而右小脑后叶和左脑枕叶舌回明显降低;并且左脑核间小叶的特征值与MMSE明显正相关,左脑枕叶舌回的特征值与MMSE明显正相关,与CDR明显负相关;在Slow-4频段,两组特征图差异无统计学意义(P>0.05)。结论本研究结果提供了AD患者脑内异常神经元自发活动的经验证据,从而为揭示AD的潜在神经生理学机制提供信息,并展示了利用时频分析理论设计rs-fMRI数据特征应用于AD临床研究的可能性。
Objective To investigate the brain dysfunction regions of Alzheimer's disease(AD)by constructing the characteristic maps of the resting state functional magnetic resonance imaging(rs-fMRI)data in different frequency Bands based on the theory of time-frequency signal analysis.Methods The rs-fMRI data were obtained from 55 AD patients and 52 demographically-matched healthy subjects and used to construct the timefrequency characteristic maps at the Slow-5 and Slow-4 frequency bands.Then the double-sample t-test and the comparative analysis and correction method were used to analyze the significant difference of the brain regions between the AD and healthy subjects.The characteristic values of differential brain regions were obtained to analyze the Pearson correlation coefficients with the values of mini-mental state examination(MMSE)and clinical dementia review(CDR)in AD patients and the pathological significance of the differential brain regions.Results Compared with the healthy subjects,the characteristic values of left internuclear lobules increased significantly while those of right posterior lobe and left occipital lobe lingual gyrus decreased significantly in AD patients at the Slow-5 frequency band,and there were positive correlations between MMSE and the characteristic values in the left internuclear lobules and left occipital lobe lingual gyrus and negative correlations between CDR and the characteristic values in the left occipital lobe lingual gyrus.No significant differences between the two groups were observed at the Slow-4 frequency band(P>0.05).Conclusion The results of this study provide the empirical evidence for the spontaneous activity of abnormal neurons in the brain of AD patients,which provides the information for revealing the potential neurophysiological mechanism of AD and demonstrates the possibility of using the time-frequency analysis theory to design the rs-fMRI data characteristics for the clinical research of AD.
Objective To investigate the brain dysfunction regions of Alzheimer's disease(AD)by constructing the characteristic maps of the resting state functional magnetic resonance imaging(rs-fMRI)data in different frequency Bands based on the theory of time-frequency signal analysis.Methods The rs-fMRI data were obtained from 55 AD patients and 52 demographically-matched healthy subjects and used to construct the timefrequency characteristic maps at the Slow-5 and Slow-4 frequency bands.Then the double-sample t-test and the comparative analysis and correction method were used to analyze the significant difference of the brain regions between the AD and healthy subjects.The characteristic values of differential brain regions were obtained to analyze the Pearson correlation coefficients with the values of mini-mental state examination(MMSE)and clinical dementia review(CDR)in AD patients and the pathological significance of the differential brain regions.Results Compared with the healthy subjects,the characteristic values of left internuclear lobules increased significantly while those of right posterior lobe and left occipital lobe lingual gyrus decreased significantly in AD patients at the Slow-5 frequency band,and there were positive correlations between MMSE and the characteristic values in the left internuclear lobules and left occipital lobe lingual gyrus and negative correlations between CDR and the characteristic values in the left occipital lobe lingual gyrus.No significant differences between the two groups were observed at the Slow-4 frequency band(P>0.05).Conclusion The results of this study provide the empirical evidence for the spontaneous activity of abnormal neurons in the brain of AD patients,which provides the information for revealing the potential neurophysiological mechanism of AD and demonstrates the possibility of using the time-frequency analysis theory to design the rs-fMRI data characteristics for the clinical research of AD.
引文
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[2]HoltzmanD M,Morris J C,GoateA M.Alzheimer's disease:The challenge of the second century[J].Sci Transl Med,2011,3(77):77sr1.
[3]Cummings J L,Frank J C,Cherry D,et al.Guidelines for managing alzheimer's disease:part I.assessment[J].Am Fam Physician,2002,65(11):2263-2272.
[4]GomarJ J,Ragland J D,Ulu A M,et al.Differential medial temporal lobe morphometric predictors of item-and relational-encoded memories in healthy individuals and in individuals with mild cognitive impairment and Alzheimer's disease[J].Alzheimers Dement(NY),2017,3(2):238-246.
[5]Zhang J T,Guo Z W,Liu X Z,et al.Abnormal functional connectivity of the posterior cingulate cortex is associated with depressive symptoms in patients with Alzheimer's disease[J].Neuropsychiatr Dis Treat,2017,13:2589-2598.
[6]Zang Y F,Jiang T Z,Lu Y L,et al.Regional homogeneity approach to fMRI data analysis[J].Neuroimage,2004,22(1):394-400.
[7]Zang Y F,He Y,Zhu C Z,et al.Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI[J].Brain Dev,2007,29(2):83-91.
[8]Buzsáki G,Draguhn A.Neuronal oscillations in cortical networks[J].Science,2004,304(5679):1926-1929.
[9]FristonK J,Williams S,Howard R,et al.Movement-related effects in fMRI time-series[J].Magn Reson Med,1996,35(3):346-355.
[10]Yan C G,Cheung B,Kelly C,et al.A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics[J].Neuroimage,2013,76:183-201.
[11]Bajaj V,Pachori R B.Automatic classification of sleep stages based on the time-frequency image of EEG signals[J].Comput Methods Programs Biomed,2013,112(3):320-328.
[12]Chen X,Lu B,Yan C G.Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes[J].Hum Brain Mapp,2018,39(1):300-318.
[13]Zuo X N,Di Martino A,Kelly C,et al.The oscillating brain:Complex and reliable[J].Neuroimage,2010,49(2):1432-1445.
[14]Egorova N,Veldsman M,Cumming T,et al.Fractional amplitude of low-frequency fluctuations(fALFF)in poststroke depression[J].Neuroimage Clin,2017,16:116-124.
[15]Liu X N,Wang S Q,Zhang X Q,et al.Abnormal amplitude of low-frequency fluctuations of intrinsic brain activity in Alzheimer's disease[J].J Alzheimers Dis,2014,40(2):387-397.
[16]Bajaj V,Rai K,Kumar A,et al.Time-frequency image based features for classification of epileptic seizures from EEGsignals[J].Biomed Phys Eng Express,2017,3(1):015012.
[17]Boashash B,Azemi G,Ali Khan N.Principles of timefrequency feature extraction for change detection in nonstationary signals:Applications to newborn EEG abnormality detection[J].Pattern Recognition,2015,48(3):616-627.
[18]Chang C T,Glover G H.Time-frequency dynamics of resting-state brain connectivity measured with fMRI[J].Neuroimage,2010,50(1):81-98.
[19]Mayberg H S.Modulating limbic-cortical circuits in depression:Targets of antidepressant treatments[J].Semin Clin Neuropsychiatry,2002,7(4):255-268.
[20]Peng H J,Ning Y P,Zhang Y,et al.White-matter density abnormalities in depressive patients with and without childhood neglect:A voxel-based morphometry(VBM)analysis[J].Neurosci Lett,2013,550:23-28.
[21]Li H J,Hou X H,Liu H H,et al.Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease:A meta-analysis of 75fMRI studies[J].Hum Brain Mapp,2015,36(3):1217-1232.
[22]Gallagher D,Herrmann N.Agitation and aggression in Alzheimer's disease:An update on pharmacological and psychosocial approaches to care[J].Neurodegener Dis Manag,2015,5(1):75-83.
[23]Chi S,Yu J T,Tan M S,et al.Depression in Alzheimer's disease:Epidemiology,mechanisms,and management[J].JAlzheimers Dis,2014,42(3):739-755.
[24]Bogousslavsky J,Miklossy J,Deruaz J P,et al.Lingual and fusiform gyri in visual processing:Aclinico-pathologic study of superior altitudinal hemianopia[J].J Neurol Neurosurg Psychiatry,1987,50(5):607-614.
[25]Hahn B,Ross T J,Stein E A.Neuroanatomical dissociation between bottom-up and top-down processes of visuospatial selective attention[J].Neuroimage,2006,32(2):842-853.
[26]Phillips J S,Das S R,McMillan C T,et al.Tau PETimaging predicts cognition in atypical variants of Alzheimer's disease[J].Hum Brain Mapp,2018,39(2):691-708.
[27]Li H J,Hou X H,Liu H H,et al.Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease:A meta-analysis of 75fMRI studies[J].Hum Brain Mapp,2015,36(3):1217-1232.
[28]赵彬,商秀丽,何志义,等.阿尔茨海默病的静息态fMRI低频振幅研究[J].中国医科大学学报,2012,41(4):329-332.
[29]Sj9beck M,Englund E.Alzheimer's disease and the cerebellum:A morphologic study on neuronal and glial changes[J].Dement Geriatr Cogn Disord,2001,12(3):211-218.
[30]Stoodley C J,Schmahmann J D.Functional topography in the human cerebellum:A meta-analysis of neuroimaging studies[J].Neuroimage,2009,44(2):489-501.