阿尔兹海默病和轻度认知障碍的脑结构差异:一种自动量化脑容量测量工具的使用
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摘要
目的研究一种新型自动量化工具-AccuBrain在评估轻度认知功能障碍(MCI)、阿尔兹海默症(AD)患者的脑结构变化方面的诊断价值。方法采用Siemens 3.0 T磁共振扫描仪,对21例可能AD和24例可能轻度认知功能障碍(MCI)患者及与之年龄、性别相匹配24例老年健康人对照组(NC)行全脑扫描,应用磁化准备快速梯度回波成像序列获取三维结构图像。数据分析采用AccuBrain进行处理,它可自动分割20多个大脑区域,并测量其体积,量化其数值,同时可测得其所占全脑体积的比率,从而来得出差异性脑区。结果与NC组相比,在绝对体积方面,MCI组的显著差异性脑区是海马、双侧海马、双侧扣带回、双侧岛叶、双侧颞叶、脑室、侧脑室、第三脑室、侧脑室下角、双侧侧脑室、双侧侧脑室下角,而AD组的则是海马、杏仁核、壳核、双侧海马、双侧杏仁核、双侧壳核、双侧伏隔核、双侧额叶、双侧颞叶、双侧扣带回、双侧岛叶、脑室、侧脑室、第三脑室、侧脑室下角、双侧侧脑室、双侧侧脑室下角;考虑颅内总体积的情况,即相对体积方面,MCI组的海马、腹侧间脑、丘脑、双侧海马、左侧杏仁核、双侧腹侧间脑、双侧丘脑、双侧额叶、双侧颞叶、左侧扣带回、双侧岛叶、脑室、侧脑室、第三脑室、侧脑室下角、双侧侧脑室、双侧侧脑室下角具有显著差异性;而AD组的海马、杏仁核、腹侧间脑、丘脑、壳核、伏隔核、双侧海马、双侧杏仁核、双侧腹侧间脑、双侧丘脑、双侧壳核、双侧伏隔核、双侧额叶、双侧颞叶、左侧扣带回、双侧岛叶、脑室、侧脑室、第三脑室、侧脑室下角、双侧侧脑室、双侧侧脑室下角具有显著差异性。结论AccuBrain能自动分割全脑体积且量化其数值,方法上更直观,在辅助AD的临床早期诊断方面具有巨大的潜力和应用前景。
Objective To study the diagnostic value of AccuBrain, an automated quantitative tool in assessing the brain structure changes in patients with mild cognitive impairment(MCI) and Alzheimer's disease(AD). Methods 3D MRI of the whole brain was performed using a 3 T MR scanner(Siemens, Verio, Germany) on patients with AD(21) and MCI(24) as well as 24 age and sex matched healthy volunteers. The AccuBrain method was used to automatically segment more than 20 brain regions and measure their volume and volume ratio. Results Compared with the control group, the absolute volume was significantly different in the bilateral hippocampus, cingulate gyrus, insula, temporal lobe, and ventricles of patients with MCI whereas significant different volume was found in the bilateral amygdala, putamen, hippocampus, nucleus accumbens, frontal lobe, temporal lobe, cingulate gyrus, insula,and ventricles of patients with AD. Significant difference in volume ratio was found in the left amygdala, left cingulate gyrus, bilateral hippocampus, ventral diencephalon, thalamus, frontal lobe, temporal lobe, insula, and ventricles of patients with MCI whereas the volume ratio was significantly different in the left cingulate gyrus, bilateral hippocampus, amygdala, ventral diencephalon, thalamus,putamen, nucleus accumbens, frontal lobe, temporal lobe, insula, and ventricles of patients with AD. Conclusion AccuBrain can automatically segment the whole brain and quantify the volume. It may be useful in the early clinical diagnosis of AD.
Objective To study the diagnostic value of AccuBrain, an automated quantitative tool in assessing the brain structure changes in patients with mild cognitive impairment(MCI) and Alzheimer's disease(AD). Methods 3D MRI of the whole brain was performed using a 3 T MR scanner(Siemens, Verio, Germany) on patients with AD(21) and MCI(24) as well as 24 age and sex matched healthy volunteers. The AccuBrain method was used to automatically segment more than 20 brain regions and measure their volume and volume ratio. Results Compared with the control group, the absolute volume was significantly different in the bilateral hippocampus, cingulate gyrus, insula, temporal lobe, and ventricles of patients with MCI whereas significant different volume was found in the bilateral amygdala, putamen, hippocampus, nucleus accumbens, frontal lobe, temporal lobe, cingulate gyrus, insula,and ventricles of patients with AD. Significant difference in volume ratio was found in the left amygdala, left cingulate gyrus, bilateral hippocampus, ventral diencephalon, thalamus, frontal lobe, temporal lobe, insula, and ventricles of patients with MCI whereas the volume ratio was significantly different in the left cingulate gyrus, bilateral hippocampus, amygdala, ventral diencephalon, thalamus,putamen, nucleus accumbens, frontal lobe, temporal lobe, insula, and ventricles of patients with AD. Conclusion AccuBrain can automatically segment the whole brain and quantify the volume. It may be useful in the early clinical diagnosis of AD.
引文
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[16]Choi EJ,Son YD,Noh Y,et al.Glucose Hypometabolism in Hippocampal Subdivisions in Alzheimer's Disease:A Pilot Study Using High-Resolution18F-FDG PET and 7.0-T MRI.JClin Neurol,2018,14:158-164.
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[24]S覬rensen L,Igel C,Liv Hansen N,et al.Early detection of Alzheimer's disease using MRI hippocampal texture.Hum Brain Mapp,2016,37:1148-1161.
[25]Shaffer JL,Petrella JR,Sheldon FC,et al.Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid,MR Imaging,and PETBiomarkers.Radiology,2013,266:583-591.
[2]Vemuri P,Jack CR Jr.Role of structural MRI in Alzheimer's disease.Alzheimers Res Ther,2010,2:23.
[3]Jack CR Jr,Knopman DS,Jagust WJ,et al.Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade.Lancet Neurol,2010,9:119-128.
[4]Zhang T,Wang D,Zhang Q,et al.Supervoxel-based statistical analysis of diffusion tensor imaging in schizotypal personality disorder.Neuroimage,2017,163:368-378.
[5]Abrigo J,Shi L,Luo Y,et al.Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer's disease imaging biomarker.Acta Radiol,2018,5:284185118795327.
[6]陈庆华,张凤强,李立新,等.阿尔茨海默病发病机制和诊断技术研究进展.中华老年心脑血管病杂志,2018,20:108-110.
[7]McKhann GM,Knopman DS,Chertkow H,et al.The diagnosis of dementia due to Alzheimer’s disease:recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease.Alzheimers Dement,2011,7:263-269.
[8]Pruessner JC,Li LM,Serles W,et al.Volumetry of Hippocampus and Amygdala with High-resolution MRI and Three-dimensional Analysis Software:Minimizing the Discrepancies between Laboratories.Cereb Cortex,2000,10:433-442.
[9]Jack CR Jr,Petersen RC,O'Brien PC,et al.MR-based hippocampal volumetry in the diagnosis of Alzheimer's disease.Neurology,1992,42:183-188.
[10]Jack CR Jr,Slomkowski M,Gracon S,et al.MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD.Neurology,2003,60:253-260.
[11]Thompson PM,Mega MS,Woods RP,et al.Cortical change in Alzheimer's disease detected with a disease-specific population-based brain atlas.Cereb Cortex,2001,11:1-16.
[12]Thompson PM,Hayashi KM,de Zubicaray G,et al.Dynamics of gray matter loss in Alzheimer's disease.J Neurosci,2003,23:994-1005.
[13]Karas GB,Burton EJ,Rombouts SA,et al.A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry.Neuroimage,2003,18:895-907.
[14]Choo IH,Lee DY,Oh JS,et al.Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer's disease.Neurobiol of Aging,2010,31:772-779.
[15]Jacobs HI,Van Boxtel MP,Jolles J,et al.Parietal cortex matters in Alzheimer's disease:an overview of structural,functional and metabolic findings.Neurosci Biobehav Rev,2012,36:297-309.
[16]Choi EJ,Son YD,Noh Y,et al.Glucose Hypometabolism in Hippocampal Subdivisions in Alzheimer's Disease:A Pilot Study Using High-Resolution18F-FDG PET and 7.0-T MRI.JClin Neurol,2018,14:158-164.
[17]Karas GB,Scheltens P,Rombouts SA,et al.Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease.Neuroimage,2004,23:708-716.
[18]Kirova AM,Bays RB,Lagalwar S.Working Memory and Executive Function Decline across Normal Aging,Mild Cognitive Impairment,and Alzheimer’s Disease.Biomed Res Int,2015,2015:1-9.
[19]Lopez OL,Jagust WJ,Dulberg C,et al.Risk factors for mild cognitive impairment in the Cardiovascular Health Study Cognition Study:part 2.Arch Neurol,2003,60:1394-1399.
[20]Mitchell AJ,Shiri-Feshki M.Rate of progression of mild cognitive impairment to dementia--meta-analysis of 41 robust inception cohort studies.Acta Psychiatr Scand,2009,119:252-265.
[21]Petersen RC,Doody R,Kurz A,et al.Current Concepts in Mild Cognitive Impairment.Arch Neurol,2001,58:1985-1992.
[22]Attems J,Jellinger KA.Olfactory tau pathology in Alzheimer disease and mild cognitive impairment.Clin Neuropathol,2006,25:265-271.
[23]Sabbagh MN,Cooper K,Delange J,et al.Functional,Global and Cognitive Decline Correlates to Accumulation of Alzheimer’s Pathology in MCI and AD.Curr Alzheimer Res,2010,7:280-286.
[24]S覬rensen L,Igel C,Liv Hansen N,et al.Early detection of Alzheimer's disease using MRI hippocampal texture.Hum Brain Mapp,2016,37:1148-1161.
[25]Shaffer JL,Petrella JR,Sheldon FC,et al.Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid,MR Imaging,and PETBiomarkers.Radiology,2013,266:583-591.