皮质下血管性认知障碍的临床表现、危险因素及其影像学特征
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摘要
血管性痴呆(vascular dementia, VD)是由脑血管疾病引起的缺血性、缺血-缺氧性或出血性脑损害所导致的认知功能障碍,是继阿尔茨海默病(Alzheimer’s disease, AD)之后老年期痴呆的第二常见原因。随着对认识损害研究的深入,以AD为模型的VD诊断标准存在的不足之处日趋明显。以往VD的定义以AD为模型,主要强调学习新知识障碍和记忆的缺陷,而不是典型VD所伴有的精神运动迟缓和执行功能障碍;其诊断要求患者认知损害达到影响日常生活能力程度,这样就会影响脑血管性疾病所致轻度认知功能损害的早期诊断,低估其发病率,耽误患者的早期预防和治疗。1993年Hachinski和Bowler提出用血管性认知障碍(vascular cognitive impairment, VCI)这一更为宽泛的概念代替或涵盖VD。VCI概念的提出对于痴呆的早期诊断、早期预防和治疗具有重要的临床意义。
VCI是指由血管因素引起的或与之伴随的认知功能损害。与以往的VD概念相比,其含义更加宽泛,包括各种脑血管及其危险因素导致的各种水平的认知功能损害,尤其是包括了未到达痴呆标准的轻度认知功能损害。VCI为一大类综合征,可分为三个主要亚型,即皮质型VCI、关键部位梗死型VCI和皮质下型VCI。不同类型VCI的病因和发病机制不同,其临床表现和治疗也不尽相同。因为血管性危险因素是可以治疗的,所以应该有可能预防、延缓或减轻VCI,但是由于缺乏统一的、令人满意的诊断标准,已使VCI的研究进展受到阻碍。故而有关VCI研究的重点逐渐向临床特征相对一致的皮质下型VCI转移。
皮质下缺血性脑血管病(subcortical ischemic vascular disease, SIVD)是造成皮质下型VCI最常见原因。目前认为SIVD的主要病因是小血管病变(cerebral small vessel disease, SVD),其发病可能是在多种因素的共同作用下引起脑小血管病变,最终导致小动脉阻塞和腔隙性脑梗死(lacunar infarcts, LI);或是由于深穿支动脉临界狭窄和低灌注引起广泛的白质不全梗死。SIVD的临床症状包括运动和认知执行速度减慢、健忘、构音障碍、情绪改变、小便失禁和步态异常等。高血压是其主要的危险因素,其他导致脑血管疾病的血管性危险因素如糖尿病、高脂血症和心脏疾病、贫血、吸烟、酗酒等,均可促进SIVD的发生。而并非所有SIVD均出现认知损害,徐群等报道44.5%SIVD患者可有血管性认知功能损害,那么以上SIVD危险因素与其认知损害的关系如何,尚少见报道。积极探讨和研究皮质下VCI的危险因素,进一步明确其与各种血管性危险因素的关系,有助于VCI患者的早期预防和治疗。
SIVD特征性病理表现为多发性LI和皮质下缺血性脑白质病变(white matter lesions, WML),后者以往称为白质疏松。WML在磁共振(magnetic resonance imaging, MRI)T2加权像和液体衰减反转恢复像(fliud attenuated inversion recovery, FLAIR )表现为脑白质高信号( white matter hyperintensities,WMH)。多年来,人们一直没有停止影像学改变的程度和类型与认知功能损害关系的研究,但没有明确的结论。早期研究结果表明,白质疏松与认知功能的相关性较弱。这与以往研究多采用定性分析不无关系。而近年来发展兴起的自动图像分割定位、定量技术,为神经影像学改变与认知功能损害关系的研究翻开了崭新的一页。Bowler综述近年来有关WML与认知功能关系的研究结果,提出"如忽视病变部位的重要性,而试图找到梗死、白质病变体积与认知改变的密切联系必然会失败"。综合定位、定量分析才有可能对SIVD患者早期认知损害,乃至其他类型VD的早期诊断提供可靠的帮助。
为此,本研究拟通过病例对照研究,从危险因素、结构性神经影像学病灶定位、定量等方面对SIVD进行对比观察,以便掌握皮质下VCI临床及辅助检查特征、LI和WML在认知损害中的作用及其相互影响。为建立统一的、可操作的诊断标准提供部分帮助,最终能为易感患者的早期预防、为可疑患者的早期诊断和早期治疗提供临床及辅助检查证据,避免或减缓痴呆的发生。
第一部分:皮质下缺血性脑血管病的临床特征及其认知损害的危险因素
目的:观察SIVD患者的临床特征,确定SIVD患者认知损害的危险因素,同时分析不同亚型SIVD的危险因素。
方法:急性缺血性脑血管病发病3个月以上的患者,由经过培训的专职医师、应用标准化语言对患者和家属进行访谈,应用标准化方法对患者进行详细查体,记录患者症状和体征,进行神经心理学评估并行头MRI扫描。根据Erkinjuntti提出MRI诊断标准最终入选SIVD患者70例,其中白质病变型(white matter lesions type SIVD, WML-SIVD)43例,腔隙性脑梗死突出型(lacunar infarcts type SIVD, LI-SIVD)27例。根据神经心理学评估分为认知正常组(no cognitive impairment, NCI)和血管性认知障碍组(VCI),详细记录其血管性危险因素;应用单因素和多因素Logistic逐步回归进行分析。
结果:70例SIVD患者平均年龄(66.89±7.03)岁,男性患者33例,女性37例,平均受教育时间为(2.81±2.37)年。NCI组和VCI组患者简明精神状态检查(mini-mental status examation, MMSE)得分分别为(24.45±2.95)和(19.81±2.56),VCI患者MMSE得分明显降低(P<0.05)。临床症状以肢体肌力减退为主,占81.43%(57例),行走不稳者40% (28例),构音障碍患者20%(14例),但症状较轻;其次为饮水呛咳和尿失禁各占14.29%(10例)。神经系统查体71.4%患者检出上运动神经元受损体征,14.9%假性延髓麻痹(10例),7例发现锥体外系体征(10%),而共济失调步态异常者仅占7.14%。不同亚型SIVD单因素分析显示WML-SIVD患者高血压发生率明显高于LI-SIVD患者(P<0.05),LI-SIVD患者糖尿病、高脂血症和冠心病发生率明显高于WML-SIVD患者(P<0.05),两种亚型在贫血、吸烟和饮酒史比率无统计学差异(P>0.05)。非条件Logistic逐步回归分析结果显示高血压病为WML-SIVD患者的危险因素[odds ratio (OR) 8.531 (1.676-43.41); P=0.008];而糖尿病[OR 0.082 (0.016 to 0.436); P=0.003]、高脂血症[OR 0.158 (0.035 to 0.720) ; P=0.019]和冠心病[OR 0.005 (0.007 to 0.336); P=0.002]在LI-SIVD的发生率更高。SIVD患者认知损害血管性危险因素单因素分析显示,VCI组患者高血压、糖尿病和高脂血症病史的发生率明显高于NCI组(P<0.05),提示存在高血压、糖尿病和高脂血症病史SIVD患者出现认知损害的危险增加,而心脏病史、贫血、饮酒和吸烟史在两组之间无显著差异(P>0.05)。校正其他因素影响进行非条件Logistic逐步回归分析,结果证实高血压、糖尿病、高脂血症SIVD患者更易出现认知损害(P<0.05),其OR值分别为高血压5.265(1.563-17.731),糖尿病3.445(1.008-11.772),高脂血症3.649(0.974-11.466);
结论:SIVD患者主要临床表现为肢体肌力减退、行走不稳、构音障碍、尿失禁和饮水呛咳,以及上运动神经元受损和假性延髓麻痹体征,部分患者可见锥体外系体征。两种SIVD亚型的危险因素不尽相同。高血压为WML-SIVD患者的危险因素,而LI-SIVD患者糖尿病、高脂血症和冠心病发生率明显高于WML-SIVD患者。SIVD患者出现认知损害的危险因素为高血压、糖尿病,其危险程度分别增加5.265倍和3.445倍。高脂血症对SIVD患者出现认知损害所起作用尚不能明确。年龄可能并不是SIVD认知损害的危险因素。
第二部分:皮质下血管性认知障碍与脑白质病变体积的关系
目的:确定SIVD患者缺血性WML体积与认知损害之间的关系。
方法:急性缺血性脑血管病发病3个月以上的患者,由经过培训的专职医师、应用标准化语言对患者和家属进行访谈,应用标准化方法对患者进行详细查体,记录患者症状和体征,进行神经心理学评估并行头MRI扫描。根据Erkinjuntti提出MRI诊断标准最终入选SIVD患者70例,根据神经心理学评估分为NCI组和VCI组。采用半自动定量测定方法,测定患者WMH体积。应用相关分析和分层多元回归分析认知功能与WMH体积的关系。
结果:70例患者平均WMH体积(29.56±15.58)cm3,范围4.20-86.01cm3。NCI组和VCI组平均WMH体积分别为(23.63±10.52)cm3和(34.84±17.50)cm3,VCI组WMH体积明显高于NCI组,统计学分析显示显著性差异(Z=-2.83,P=0.005)。单因素相关分析显示MMSE与教育呈正相关(r=0.49,P<0.001),而与年龄(r=-0.235,P=0.025)、WMH体积(r=-0.398,P<0.001)呈负相关,同时WMH体积与年龄呈正相关(r=0.279,P=0.01),而教育与年龄呈负相关,具有统计学差异。分层多元线性回归分析显示,首先进入模型的年龄、性别和受教育程度,仅受教育程度与简明精神状态检查得分有关(P<0.001)。当WMH体积进入模型后,简明精神状态检查得分仍与受教育程度有关(P<0.001)。校正年龄、性别与受教育程度的影响后,WMH体积与MMSE得分呈负相关(B=-0.077, t=-3.243,P=0.002),具有统计学显著性差异;WMH体积变化能解释简明精神状态评分改变的10.2%。
结论:ImageJ结合其Voxel Counter Plugins测定和计算脑白质高信号体积的方法应用方便,省时、准确,测定结果可靠,可操作性强。SIVD患者认知损害与白质病变程度密切相关,随着WML程度逐渐增加,SIVD患者认知功能将不断恶化。在校正年龄、性别和受教育程度的影响后,WMH体积仅能解释认知损害变化的10.2%。
第三部分:皮质下血管性认知障碍与脑白质病变部位的关系
目的:分析SIVD患者缺血性脑WML部位与认知功能损害的关系,同时对WMH体积测定与分级评分的相关性进行评价。
方法:急性缺血性脑血管病发病3个月以上的患者,由经过培训的专职医师、应用标准化语言对患者和家属进行访谈,应用标准化方法对患者进行详细查体,记录患者症状和体征,进行神经心理学评估并行头MRI扫描。根据Erkinjuntti提出MRI诊断标准最终入选SIVD患者70例,根据神经心理学评估分为NCI组和VCI组。本研究在前一部分研究结果的基础上,应用年龄相关白质改变分级方法,对WMH评分,应用相关分析和分层多元回归分析认知功能与WMH部位的关系,以及WMH体积和部位的相互影响。
结果:70例皮质下缺血性脑血管病患者白质高信号总平均分为(12.37±3.93)分,范围3-20。WMH体积和总评分相关分析结果显示,随着WMH体积增加,WMH总评分增多,两者呈正相关(r=0.879, P<0.001)。以白质高信号体积为自变量绘制散点图,显示WMH体积与WMH总评分呈曲线关系。VCI组WMH总平均分(13.65±3.51),较NCI组WMH总平均分(10.94±3.94)明显升高,具有统计学显著性差异(t=-3.043,P=0.003)。VCI组额区、顶枕区和基底节区WMH评分均较NCI组明显升高,具有显著性差异(P<0.05);而颞区和幕下区WMH评分两组无显著性差异(P>0.05)。分层多元线性回归分析显示结果。首先进入模型的年龄、性别和受教育程度,结果同前。当WMH总评分进入模型后,简明精神状态检查得分仍与受教育程度有关(P<0.001)。在校正年龄、性别与受教育程度的影响后,WMH总平均分与MMSE得分呈负相关(B=-0.270,t=-2.842, P=0.006),WMH总平均分仅能解释简明精神状态评分改变的8.1%,低于WMH体积的10.2%;而各脑区评分能解释简明精神状态评分改变的19.3%,明显高于WMH体积和总评分。其中额区和基底节区WMH评分与MMSE得分有关,具有统计学显著性差异(P<0.05),而顶枕区、颞区和幕下区WMH评分与MMSE得分无统计学差异(P>0.05)。
结论:白质病变体积与总评分呈正相关,两种方法均可用来评价白质病变的程度;而与评分方法相比,WMH体积测定能更加敏感地反映认知功能改变。SIVD患者认知功能与白质病变的部位密切相关,白质病变部位评价认知损害改变较体积测定更敏感。SIVD患者认知功能损害与额区、基底核区白质病变有关,而与顶枕区、颞区和幕下区白质病变无关。
第四部分:皮质下血管性认知障碍与腔隙性脑梗死的关系
目的:探讨腔隙性脑梗死与皮质下缺血性脑血管病认知损害的关系,及腔隙性脑梗死和脑白质病变之间的相互影响。
方法:急性缺血性脑血管病发病3个月以上的患者,由经过培训的专职医师、应用标准化语言对患者和家属进行访谈,应用标准化方法对患者进行详细查体,记录患者症状和体征,进行神经心理学评估并行头MRI扫描。根据Erkinjuntti提出MRI诊断标准最终入选SIVD患者70例,根据神经心理学评估分为NCI组和VCI组。应用相关分析和分层多元回归分析认知功能与LI数量和部位的关系,以及WMH和LI的相互影响。
结果:VCI组LI总平均数为(4.38±1.82),较NCI组的(3.39±1.71)明显升高,具有统计学显著性差异(Z=-2.202,P=0.028)。VCI组额区和基底节区LI数量均较NCI组明显升高,具有显著性差异(P<0.05);而顶枕区、颞区和幕下区LI数量两组无显著性差异,分层多元线性回归分析以简明精神状态量表得分为因变量,第一个模型:分析LI数量对认知损害的影响,首先进入模型的年龄、性别和受教育程度结果同前。当第二步LI总平均数进入模型后,简明精神状态检查得分仍与受教育程度有关(P<0.001)。在校正年龄、性别与受教育程度的影响后, LI数仅能解释简明精神状态评分改变的9.1%。第二个模型:分析LI部位对认知损害的影响;第一步同前,第二步不同部位LI同时进入模型,结果显示LI部位能解释简明精神状态评分改变的25.7%,明显高于第一模型中LI数量的作用。其中额区和基底节区LI与MMSE得分呈负相关,具有统计学显著性差异(P<0.05),而顶枕区、颞区和幕下区LI与MMSE得分无统计学差异。第三个模型:分析WMH体积和LI数量对认知损害的影响;在校正年龄、性别与受教育程度的影响后,WMH体积能解释简明精神状态评分改变的10.2%;而LI数量进入模型后R2变化0.112,高于LI数量独自进入模型的R2值(R2=0.091),与MMSE的相关性均有统计学显著性差异(P<0.001),两者均为SIVD患者认知损害的预测指标。第四个模型:分析白质病变部位和腔隙性脑梗死部位对认知损害的影响:结果显示仅基底节LI和额区缺血性WML为认知损害的独立预测因素(P<0.001)。
结论:LI数量和部位均与SIVD患者的认知损害有关,LI部位在SIVD认知功能损害中的作用比LI数量更大。LI数量和WMH体积均为SIVD认知损害的独立危险因素。校正年龄、性别、受教育程度、WML和LI部位后,仅基底节区LI和额区WML为SIVD患者认知功能损害的独立预测指标。LI数量增加和缺血性脑白质病变程度加重,尤其是基底节区腔梗增加和额叶WML程度加重将预示认知功能损害的恶化。
Vascular dementia (VD) is the second most common cause of dementia in elderly after Alzheimer’s disease (AD). It results from ischaemic, ischemic-hypoxic, or haemorrhagic brain injury that cuased by cerebrovascular disease, manifests a series of cognitive impairment syndromes. With the deeper development of cognitive impairment research, the criteria for VD based on AD presents its shortcomings. Based on the model of AD, the old criteria for VD emphasizes on new knowledge learning disability and memory loss, but not on psychomotor slowing and impairment of executive functioning which is the clinical features of the typical VD form. It also defines dementia as the level of cognitive impairment at which normal daily functions are impaired, therefore only late cases will be identified, so underestimating the prevalence of cognitive impairment due to vascular disease and denying early cases the benefit of timely preventative treatment. Vascular cognitive impairment (VCI), a new concept covering that of VD suggested by Hachinski and Bowler in 1993, is now widely accepted as a more appropriate alternative terminology than VD.
Cognitive impairment that is caused by or associated with vascular factors has been termed as VCI. It covers a wide spectrum of cognitive dysfunction associated with and presumed to be caused by cerebrovascular disease, what most important is that it included subtle and clinically often undetected cognitive impairment. VCI presents a clinical syndrome. The main subtypes of VCI included in current classification are cortical VCI, strategic infarct VCI and subcortical VCI. The clinical manifest and treatment of the subtype are different from each other for its difference at etiological aspects and pathogenesis. Because vascular risk factors are treatable, it should be possible to prevent, postpone, or mitigate VCI. However, progress in VCI research has been hindered by lack of unified and satisfactory diagnostic criteria for the condition. Research emphasis of VCI gradually turned to the relatively homogeneous subcortical VCI.
Subcortical ischemic vascular disease (SIVD) is the most common cause of subcortical VCI. It is accepted now that the pathogenesis of SIVD related to cerebral small vessel disease caused by multifactor. Two paths may be involved. First, occlusion of the arteriolar lumen due to arteriolosclerosis leads to the formation of lacunar infarcts (LI); second or critical stenosis and hypoperfusion of multiple medullary arterioles causes widespread incomplete infarction of deep white matter. Symptoms include motor and cognitive dysexecutive slowing, forgetfulness, dysarthria, mood changes, urinary symptoms, and short-stepped gait. Hypertension serves as the main risk factor of SIVD. Other vascular risk factor of cerebrovascular disease, such as diabetes millitus, hyperlipidemia, heart disease, anemia, smoking and excessive drinking, rise the incidence of SIVD. VCI can not be diagnosed to all patients with SIVD. XU et al reported that total of 44.5% patients with SIVD presented cognitive impairment at a successive clinical research. There were few reports considering the relationship between cognitive impairment and risk factor of SIVD. It is benefit to VCI patients’timely preventively treatment to further study on the risk factor of subcortical VCI and identify the relationships between cognitive impairment and vascular risk factors of SIVD.
SIVD is characterised by extensive cerebral white matter lesions (WML) and lacunar infarcts in deep grey and white matter structures. WML, which be called“leukoaraiosis”in the past, shows "white matter hyperintensities (WMH) on T2 and fliud attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) sequence. Researches of the relationship between cognitive impairment and the degree or type of lesions on MRI had been going on for the past many years, but no definite consensus had been to reach. There were not significant difference between leukoaraiosis and cognitive impairment at initial research reports. Those differet results some extent due to the qualitative analysis used at their research. With the progress of technique on automated image segmentation to quantitation and location, researches on relationships between cognitive impairment and neuroimaging changes are stepping into a new era. Bowler reviewed reports considering WML and cognitive impairment in the past few years, and mentioned that“attempts to correlate closely infarct volumes with cognitive change routinely fail because of the overriding importance of lesion location”. Combinated quantitation with qualitation analysis, it is possible to gain reliable data which is benefit to early cases diagnosis of cognitive impairtment in SIVD, and even to other types of VD.
The following 4 parts were designed to study on the risk factor, relationship between LI, WML and cognitive impairtment in SIVD.
Part 1: Clinical Feature and Risk Factor of Cognitive Impairment in Patients with Subcortical Ischemic Vascular Disease
Objectives:To investigate the clinical feature, the risk factor for cognitive impairment of SIVD, and the risk factor for its subtypes: white matter lesions type (WML-SIVD) and lacunar infarct type (LI-SIVD).
Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were divided into no cognitive impairment(NCI) and vascular cognitive impairment(VCI) group. Symptomes and signs, vascular risk factor profiles were recorded by interviewers and examiners, then risk factor profiles for cognitive impairment of SIVD or its subtypes were compared using univariate and multivariate logistic regression analysis.
Results: The subjects consisted of 33 males and 37 females; the mean age was 66.89±7.03 years, the mean educational attainment was 2.81±2.37 years. Mini-mental status examation (MMSE) scores of VCI group (19.81±2.56) was significant lower than that of NCI group (24.45±2.95). Of the 70 SIVD patients, 81.43%(57) patients complainted of limbs weakness, 40% (28) of instability walking, 20%(14) of mild dysarthria, and 14.29%(10) of difficulty with drinding or urinary incontinence. On nervous system examination, 71.4% patients with upper motor neuron damaged signs, 14.9% with pseudobulbar palsy signs and 10% with extrapyramidal signs were detected, while only 7.14% patients with ataxia gait。On comparison the subtype of SIVD, univariate analysis demonstrated significant associations between WM and hypertension (P<0.05), and between LI and diabetes mellitus, hyperlipidemia, coronary artery disease (P<0.05), but no significant associations with anemia, smoking, alcohol drinking (P>0.05). These associations persisted after multivariate stepwise Logistic analysis, Hypertension was more frequent in WML-SIVD than LI-SIVD [odds ratio (OR) 8.531 (1.676 to 43.41); P=0.008], whereas diabetes mellitus [OR 0.082 (0.016 to 0.436); P=0.003], hyperlipidemia [OR 0.158 (0.035 to 0.720) ; P=0.019], coronary artery disease [OR 0.005 (0.007 to 0.336); P=0.002] more frequent in LI-SIVD. As to the risk factor for cognitive impairment, hypertension [OR 5.265 (1.563 to 17.731); P=0.007], diabetes mellitus [OR 3.445 (1.008 to 11.772); P=0.049] and hyperlipidemia [OR3.649 (0.974 to 11.466); P=0.027] were associated with VCI patients, even in the multivariate stepwise Logistic analysis after controling for the effects of other variables, but no correlations was found between coronary artery disease, anemia, smoking, alcohol drinking and cognitive impairment.
Conclusions: Limbs weakness, instability walking, mild dysarthria, urinary incontinence, difficult with drinking, upper motor neuron damaged signs and pseudobulbar palsy are common clinical manifestation of subcortical ischemic vascular disease, although extrapyramidal signs may be detected. Hypertension associates with WML of SIVD, while diabetes mellitus, hyperlipidemia, coronary artery disease associates with LI-SIVD. Hypertension and diabetes mellitus are the main risk factors for cognitive impairment of SIVD, and the risk increase 5.265 and 3.445 times, respectively. The relationship between hyperlipidemia and cognitive impairment couldn’t be established from our data. It is likely that age is not correlated with cognitive impairment of SIVD patients.
Part 2: Contributions of White Matter Lesions Volume to Subcortical Vascular Cognitive
Objectives:To evaluate contributions of WML to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD).
Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into NCI and VCI groups. The volume of WML, which shows white matter hyperintensities (WMH) on MRI fluid-attenuated inversion recovery images, was measured using a semi-automated quantitative method. Finally, correlation analysis and hierarchical multiple regression analysis was used to examine the relationship between general cognitive function and the volume of WMH.
Results: The mean WMH volume of all SIVD patients was (29.56±15.58)cm3, ranged from 4.20cm3 to 86.01cm3. The mean WMH volume of VCI group was (34.84±17.50) cm3, increased remarkedly than that of NCI group (23.63±10.52) cm3, there was significant difference between groups(Z=-2.83, P=0.005). MMSE score was positively correlated with educational attainment(r=0.49, P<0.001), but negatively correlated with age(r=-0.235, P=0.025) and WMH volume(r=-0.398, P<0.001) in a univariate correlation analysis. And age was positively correlated with WMH volume(r=0.279, P=0.01), but negatively with educational attainment. In the hierarchical multiple regression analysis, age, gender and educational attainment was entered at the first step, but only the educational attainment was related to MMSE score(P<0.001). After the WMH volume entered at the second step, the relationship remain significant between MMSE score and eductional attainment(P<0.001). It was revealed that WMH volume was negatively correlated with MMSE score(B=-0.077, t=-3.243, P=0.002) after controlling for the effects of age, gender and educational attainment, WMH volume explained an additional 10.2% of the variance in MMSE.
Conclusions: It is convenient to measure and count the volume of white matter hyperintensities using the semi-automated quantitative method which is based on ImageJ and its Voxel Counter Plugins. The result is accurary and reliable, the method is time-saving and easy to manipulate. There is a highly significant negative correlation between MMSE score and WMH volume, but WMH volume explains only an additional 10.2% of the variance in MMSE independently.
Part 3: Contributions of White Matter Lesions Location to Subcortical Vascular Cognitive Impairment
Objectives:To evaluate contributions of WML location to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD), and to evaluate the correlation of volumetric quantitation and qualitation of WMH .
Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into no cognitive impairment(NCI) and vascular cognitive impairment(VCI) group. Age-related white matter change rating scale, which is a visual rating scale developed by Wahlund, was used to qualitative measure and locate the WML. Integrated with the data of part 2, using correlation analysis and hierarchical multiple regression analysis to examine the relationship between general cognitive function and the location of WMH, and the correlation of volumetric quantitation and qualitation.
Results: The mean WMH rating scores of all the SIVD patients was (12.37±3.93), ranged from 3 to 20. There was a highly significant positively correlation between total WMH volume and total WMH score(r=0.879, P<0.001), scatter diagram using WMH volume as independent variables reveal a curvilinear relationship between total WMH volume and total WMH score. The mean scores of WMH in all regions of VCI group was (13.65±3.51), higher than that of NCI group(t=-3.043,P=0.003), which was (10.94±3.94). As to each regions, the VCI group scores of frontal area, parieto-occipital area and basal ganglia area were remarkedly increased than that of NCI group(P<0.05), but no difference were noted at temporal area and infratentorial area. In the hierarchical multiple regression analysis, The result of first step was same to part 2. After the score of total WMH entered at the second step, the relationship remained significantly between MMSE score and eductional attainment(P<0.001). It was revealed that the score of total WMH was negatively correlated with MMSE score(B=-0.270,t=-2.842, P=0.006) after controlling for the effects of age, gender and educational attainment. The score of total WMH explained an additional 8.1% of the variance in MMSE,lower than the 10.2% of WMH volume. Moreover, when all the five regions enter instead of the score of total WMH at the second step, the R2 change was 19.3%. That is, all the five regions explained an additional 19.3% of the variance in MMSE,much higher than the effects of WMH volume and the score of total WMH. And the scores of frontal area and of basal ganglia area were the mainly variables(P<0.05), but no the scores of parieto-occipital area, infratentorial area and temporal area(P>0.05).
Conclusions: The volumetric quantitation of WMH was positively correlated with its volumetric qualitation. Each of the methods can be used to tract any changes of WML, except that the volumetric quantitation is more sensitivity. WML located at frontal area and basal ganglia area predicts cognitive decline, but not suits for WML located at parieto-occipital area, infratentorial area and temporal area.
Part 4: Contributions of Lacunar Infarcts to Subcortical Vascular Cognitive Impairment
Objectives:To explore contributions of LI to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD), and to evaluate the relationship of LI and WML and effects of each other.
Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and MRI scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into NCI and VCI groups. The number of LI was counted and the location of it was recorded according to the method using at part 3. Integrated with the data of ahead, using correlation analysis and hierarchical multiple regression analysis to examine the relationship between general cognitive function and the number and the location of LI, and the relationship of LI and WML.
Results: The mean number of LI in VCI group was (4.38±1.82), higher than that of NCI group(Z=-2.202,P=0.028), which was (3.39±1.71). As to each regions, the VCI group number of LI in frontal area and basal ganglia area were remarkedly increased than that of NCI group(P<0.05), but no difference was noted at temporal area, parieto-occipital area and infratentorial area. In the hierarchical multiple regression analysis, considered MMSE scores as dependent variables, the first step is same as part 2 and part 3. After the number of total LI entered at the second step, the relationship remain significant between MMSE score and eductional attainment(P<0.001). It was revealed that the number of total LI was negatively correlated with MMSE score(B=-0.270,t=-2.842, P=0.006) after controlling for the effects of age, gender and educational attainment, the number of total LI explained an additional 9.1% of the variance in MMSE. Another analysis then performed, all of the five regions enter instead of the number of total LI at the second step, the R2 change was 25.7%. That is, all of the five regions explained an additional 25.7% of the variance in MMSE,much higher than the effects of the number of total LI. And the number of LI at frontal area and basal ganglia area were the mainly negative variables(P<0.05) to predict cognitive decline of patients with SIVD, but no relationship was noted between MMSE and the number of LI at parieto-occipital area, infratentorial area or temporal area (P>0.05). The third hierarchical multiple regession analysis followed, at the first step, age, gender and education were entered; then WMH volume were entered at the second step, the result was same as part 2, and WMH volume make the model R2 change 10.2%; the number of total LI enter after WMH volume at step 3, an additional R2 change was 0.112, there was a negatively correlation between MMSE and WMH volume, MMSE and the number of LI, independently. At last, the fourth analysis performed, the location of WML and LI were entered the model at the second step after age, gender and education, it revealed that only WML located at frontal area and LI located at basal ganglia area predicted cognitive decline of patients with SIVD, independently.
Conclusions: The number and location of LI were related to cognitive impairment, the effect of LI location is greater than than of its number. The number of LI and WMH volume both are predictors of cognitive impairment in patients with SIVD, independently; and LI located at basal ganglia area and WML at frontal area are the best predictors.
VCI是指由血管因素引起的或与之伴随的认知功能损害。与以往的VD概念相比,其含义更加宽泛,包括各种脑血管及其危险因素导致的各种水平的认知功能损害,尤其是包括了未到达痴呆标准的轻度认知功能损害。VCI为一大类综合征,可分为三个主要亚型,即皮质型VCI、关键部位梗死型VCI和皮质下型VCI。不同类型VCI的病因和发病机制不同,其临床表现和治疗也不尽相同。因为血管性危险因素是可以治疗的,所以应该有可能预防、延缓或减轻VCI,但是由于缺乏统一的、令人满意的诊断标准,已使VCI的研究进展受到阻碍。故而有关VCI研究的重点逐渐向临床特征相对一致的皮质下型VCI转移。
皮质下缺血性脑血管病(subcortical ischemic vascular disease, SIVD)是造成皮质下型VCI最常见原因。目前认为SIVD的主要病因是小血管病变(cerebral small vessel disease, SVD),其发病可能是在多种因素的共同作用下引起脑小血管病变,最终导致小动脉阻塞和腔隙性脑梗死(lacunar infarcts, LI);或是由于深穿支动脉临界狭窄和低灌注引起广泛的白质不全梗死。SIVD的临床症状包括运动和认知执行速度减慢、健忘、构音障碍、情绪改变、小便失禁和步态异常等。高血压是其主要的危险因素,其他导致脑血管疾病的血管性危险因素如糖尿病、高脂血症和心脏疾病、贫血、吸烟、酗酒等,均可促进SIVD的发生。而并非所有SIVD均出现认知损害,徐群等报道44.5%SIVD患者可有血管性认知功能损害,那么以上SIVD危险因素与其认知损害的关系如何,尚少见报道。积极探讨和研究皮质下VCI的危险因素,进一步明确其与各种血管性危险因素的关系,有助于VCI患者的早期预防和治疗。
SIVD特征性病理表现为多发性LI和皮质下缺血性脑白质病变(white matter lesions, WML),后者以往称为白质疏松。WML在磁共振(magnetic resonance imaging, MRI)T2加权像和液体衰减反转恢复像(fliud attenuated inversion recovery, FLAIR )表现为脑白质高信号( white matter hyperintensities,WMH)。多年来,人们一直没有停止影像学改变的程度和类型与认知功能损害关系的研究,但没有明确的结论。早期研究结果表明,白质疏松与认知功能的相关性较弱。这与以往研究多采用定性分析不无关系。而近年来发展兴起的自动图像分割定位、定量技术,为神经影像学改变与认知功能损害关系的研究翻开了崭新的一页。Bowler综述近年来有关WML与认知功能关系的研究结果,提出"如忽视病变部位的重要性,而试图找到梗死、白质病变体积与认知改变的密切联系必然会失败"。综合定位、定量分析才有可能对SIVD患者早期认知损害,乃至其他类型VD的早期诊断提供可靠的帮助。
为此,本研究拟通过病例对照研究,从危险因素、结构性神经影像学病灶定位、定量等方面对SIVD进行对比观察,以便掌握皮质下VCI临床及辅助检查特征、LI和WML在认知损害中的作用及其相互影响。为建立统一的、可操作的诊断标准提供部分帮助,最终能为易感患者的早期预防、为可疑患者的早期诊断和早期治疗提供临床及辅助检查证据,避免或减缓痴呆的发生。
第一部分:皮质下缺血性脑血管病的临床特征及其认知损害的危险因素
目的:观察SIVD患者的临床特征,确定SIVD患者认知损害的危险因素,同时分析不同亚型SIVD的危险因素。
方法:急性缺血性脑血管病发病3个月以上的患者,由经过培训的专职医师、应用标准化语言对患者和家属进行访谈,应用标准化方法对患者进行详细查体,记录患者症状和体征,进行神经心理学评估并行头MRI扫描。根据Erkinjuntti提出MRI诊断标准最终入选SIVD患者70例,其中白质病变型(white matter lesions type SIVD, WML-SIVD)43例,腔隙性脑梗死突出型(lacunar infarcts type SIVD, LI-SIVD)27例。根据神经心理学评估分为认知正常组(no cognitive impairment, NCI)和血管性认知障碍组(VCI),详细记录其血管性危险因素;应用单因素和多因素Logistic逐步回归进行分析。
结果:70例SIVD患者平均年龄(66.89±7.03)岁,男性患者33例,女性37例,平均受教育时间为(2.81±2.37)年。NCI组和VCI组患者简明精神状态检查(mini-mental status examation, MMSE)得分分别为(24.45±2.95)和(19.81±2.56),VCI患者MMSE得分明显降低(P<0.05)。临床症状以肢体肌力减退为主,占81.43%(57例),行走不稳者40% (28例),构音障碍患者20%(14例),但症状较轻;其次为饮水呛咳和尿失禁各占14.29%(10例)。神经系统查体71.4%患者检出上运动神经元受损体征,14.9%假性延髓麻痹(10例),7例发现锥体外系体征(10%),而共济失调步态异常者仅占7.14%。不同亚型SIVD单因素分析显示WML-SIVD患者高血压发生率明显高于LI-SIVD患者(P<0.05),LI-SIVD患者糖尿病、高脂血症和冠心病发生率明显高于WML-SIVD患者(P<0.05),两种亚型在贫血、吸烟和饮酒史比率无统计学差异(P>0.05)。非条件Logistic逐步回归分析结果显示高血压病为WML-SIVD患者的危险因素[odds ratio (OR) 8.531 (1.676-43.41); P=0.008];而糖尿病[OR 0.082 (0.016 to 0.436); P=0.003]、高脂血症[OR 0.158 (0.035 to 0.720) ; P=0.019]和冠心病[OR 0.005 (0.007 to 0.336); P=0.002]在LI-SIVD的发生率更高。SIVD患者认知损害血管性危险因素单因素分析显示,VCI组患者高血压、糖尿病和高脂血症病史的发生率明显高于NCI组(P<0.05),提示存在高血压、糖尿病和高脂血症病史SIVD患者出现认知损害的危险增加,而心脏病史、贫血、饮酒和吸烟史在两组之间无显著差异(P>0.05)。校正其他因素影响进行非条件Logistic逐步回归分析,结果证实高血压、糖尿病、高脂血症SIVD患者更易出现认知损害(P<0.05),其OR值分别为高血压5.265(1.563-17.731),糖尿病3.445(1.008-11.772),高脂血症3.649(0.974-11.466);
结论:SIVD患者主要临床表现为肢体肌力减退、行走不稳、构音障碍、尿失禁和饮水呛咳,以及上运动神经元受损和假性延髓麻痹体征,部分患者可见锥体外系体征。两种SIVD亚型的危险因素不尽相同。高血压为WML-SIVD患者的危险因素,而LI-SIVD患者糖尿病、高脂血症和冠心病发生率明显高于WML-SIVD患者。SIVD患者出现认知损害的危险因素为高血压、糖尿病,其危险程度分别增加5.265倍和3.445倍。高脂血症对SIVD患者出现认知损害所起作用尚不能明确。年龄可能并不是SIVD认知损害的危险因素。
第二部分:皮质下血管性认知障碍与脑白质病变体积的关系
目的:确定SIVD患者缺血性WML体积与认知损害之间的关系。
方法:急性缺血性脑血管病发病3个月以上的患者,由经过培训的专职医师、应用标准化语言对患者和家属进行访谈,应用标准化方法对患者进行详细查体,记录患者症状和体征,进行神经心理学评估并行头MRI扫描。根据Erkinjuntti提出MRI诊断标准最终入选SIVD患者70例,根据神经心理学评估分为NCI组和VCI组。采用半自动定量测定方法,测定患者WMH体积。应用相关分析和分层多元回归分析认知功能与WMH体积的关系。
结果:70例患者平均WMH体积(29.56±15.58)cm3,范围4.20-86.01cm3。NCI组和VCI组平均WMH体积分别为(23.63±10.52)cm3和(34.84±17.50)cm3,VCI组WMH体积明显高于NCI组,统计学分析显示显著性差异(Z=-2.83,P=0.005)。单因素相关分析显示MMSE与教育呈正相关(r=0.49,P<0.001),而与年龄(r=-0.235,P=0.025)、WMH体积(r=-0.398,P<0.001)呈负相关,同时WMH体积与年龄呈正相关(r=0.279,P=0.01),而教育与年龄呈负相关,具有统计学差异。分层多元线性回归分析显示,首先进入模型的年龄、性别和受教育程度,仅受教育程度与简明精神状态检查得分有关(P<0.001)。当WMH体积进入模型后,简明精神状态检查得分仍与受教育程度有关(P<0.001)。校正年龄、性别与受教育程度的影响后,WMH体积与MMSE得分呈负相关(B=-0.077, t=-3.243,P=0.002),具有统计学显著性差异;WMH体积变化能解释简明精神状态评分改变的10.2%。
结论:ImageJ结合其Voxel Counter Plugins测定和计算脑白质高信号体积的方法应用方便,省时、准确,测定结果可靠,可操作性强。SIVD患者认知损害与白质病变程度密切相关,随着WML程度逐渐增加,SIVD患者认知功能将不断恶化。在校正年龄、性别和受教育程度的影响后,WMH体积仅能解释认知损害变化的10.2%。
第三部分:皮质下血管性认知障碍与脑白质病变部位的关系
目的:分析SIVD患者缺血性脑WML部位与认知功能损害的关系,同时对WMH体积测定与分级评分的相关性进行评价。
方法:急性缺血性脑血管病发病3个月以上的患者,由经过培训的专职医师、应用标准化语言对患者和家属进行访谈,应用标准化方法对患者进行详细查体,记录患者症状和体征,进行神经心理学评估并行头MRI扫描。根据Erkinjuntti提出MRI诊断标准最终入选SIVD患者70例,根据神经心理学评估分为NCI组和VCI组。本研究在前一部分研究结果的基础上,应用年龄相关白质改变分级方法,对WMH评分,应用相关分析和分层多元回归分析认知功能与WMH部位的关系,以及WMH体积和部位的相互影响。
结果:70例皮质下缺血性脑血管病患者白质高信号总平均分为(12.37±3.93)分,范围3-20。WMH体积和总评分相关分析结果显示,随着WMH体积增加,WMH总评分增多,两者呈正相关(r=0.879, P<0.001)。以白质高信号体积为自变量绘制散点图,显示WMH体积与WMH总评分呈曲线关系。VCI组WMH总平均分(13.65±3.51),较NCI组WMH总平均分(10.94±3.94)明显升高,具有统计学显著性差异(t=-3.043,P=0.003)。VCI组额区、顶枕区和基底节区WMH评分均较NCI组明显升高,具有显著性差异(P<0.05);而颞区和幕下区WMH评分两组无显著性差异(P>0.05)。分层多元线性回归分析显示结果。首先进入模型的年龄、性别和受教育程度,结果同前。当WMH总评分进入模型后,简明精神状态检查得分仍与受教育程度有关(P<0.001)。在校正年龄、性别与受教育程度的影响后,WMH总平均分与MMSE得分呈负相关(B=-0.270,t=-2.842, P=0.006),WMH总平均分仅能解释简明精神状态评分改变的8.1%,低于WMH体积的10.2%;而各脑区评分能解释简明精神状态评分改变的19.3%,明显高于WMH体积和总评分。其中额区和基底节区WMH评分与MMSE得分有关,具有统计学显著性差异(P<0.05),而顶枕区、颞区和幕下区WMH评分与MMSE得分无统计学差异(P>0.05)。
结论:白质病变体积与总评分呈正相关,两种方法均可用来评价白质病变的程度;而与评分方法相比,WMH体积测定能更加敏感地反映认知功能改变。SIVD患者认知功能与白质病变的部位密切相关,白质病变部位评价认知损害改变较体积测定更敏感。SIVD患者认知功能损害与额区、基底核区白质病变有关,而与顶枕区、颞区和幕下区白质病变无关。
第四部分:皮质下血管性认知障碍与腔隙性脑梗死的关系
目的:探讨腔隙性脑梗死与皮质下缺血性脑血管病认知损害的关系,及腔隙性脑梗死和脑白质病变之间的相互影响。
方法:急性缺血性脑血管病发病3个月以上的患者,由经过培训的专职医师、应用标准化语言对患者和家属进行访谈,应用标准化方法对患者进行详细查体,记录患者症状和体征,进行神经心理学评估并行头MRI扫描。根据Erkinjuntti提出MRI诊断标准最终入选SIVD患者70例,根据神经心理学评估分为NCI组和VCI组。应用相关分析和分层多元回归分析认知功能与LI数量和部位的关系,以及WMH和LI的相互影响。
结果:VCI组LI总平均数为(4.38±1.82),较NCI组的(3.39±1.71)明显升高,具有统计学显著性差异(Z=-2.202,P=0.028)。VCI组额区和基底节区LI数量均较NCI组明显升高,具有显著性差异(P<0.05);而顶枕区、颞区和幕下区LI数量两组无显著性差异,分层多元线性回归分析以简明精神状态量表得分为因变量,第一个模型:分析LI数量对认知损害的影响,首先进入模型的年龄、性别和受教育程度结果同前。当第二步LI总平均数进入模型后,简明精神状态检查得分仍与受教育程度有关(P<0.001)。在校正年龄、性别与受教育程度的影响后, LI数仅能解释简明精神状态评分改变的9.1%。第二个模型:分析LI部位对认知损害的影响;第一步同前,第二步不同部位LI同时进入模型,结果显示LI部位能解释简明精神状态评分改变的25.7%,明显高于第一模型中LI数量的作用。其中额区和基底节区LI与MMSE得分呈负相关,具有统计学显著性差异(P<0.05),而顶枕区、颞区和幕下区LI与MMSE得分无统计学差异。第三个模型:分析WMH体积和LI数量对认知损害的影响;在校正年龄、性别与受教育程度的影响后,WMH体积能解释简明精神状态评分改变的10.2%;而LI数量进入模型后R2变化0.112,高于LI数量独自进入模型的R2值(R2=0.091),与MMSE的相关性均有统计学显著性差异(P<0.001),两者均为SIVD患者认知损害的预测指标。第四个模型:分析白质病变部位和腔隙性脑梗死部位对认知损害的影响:结果显示仅基底节LI和额区缺血性WML为认知损害的独立预测因素(P<0.001)。
结论:LI数量和部位均与SIVD患者的认知损害有关,LI部位在SIVD认知功能损害中的作用比LI数量更大。LI数量和WMH体积均为SIVD认知损害的独立危险因素。校正年龄、性别、受教育程度、WML和LI部位后,仅基底节区LI和额区WML为SIVD患者认知功能损害的独立预测指标。LI数量增加和缺血性脑白质病变程度加重,尤其是基底节区腔梗增加和额叶WML程度加重将预示认知功能损害的恶化。
Vascular dementia (VD) is the second most common cause of dementia in elderly after Alzheimer’s disease (AD). It results from ischaemic, ischemic-hypoxic, or haemorrhagic brain injury that cuased by cerebrovascular disease, manifests a series of cognitive impairment syndromes. With the deeper development of cognitive impairment research, the criteria for VD based on AD presents its shortcomings. Based on the model of AD, the old criteria for VD emphasizes on new knowledge learning disability and memory loss, but not on psychomotor slowing and impairment of executive functioning which is the clinical features of the typical VD form. It also defines dementia as the level of cognitive impairment at which normal daily functions are impaired, therefore only late cases will be identified, so underestimating the prevalence of cognitive impairment due to vascular disease and denying early cases the benefit of timely preventative treatment. Vascular cognitive impairment (VCI), a new concept covering that of VD suggested by Hachinski and Bowler in 1993, is now widely accepted as a more appropriate alternative terminology than VD.
Cognitive impairment that is caused by or associated with vascular factors has been termed as VCI. It covers a wide spectrum of cognitive dysfunction associated with and presumed to be caused by cerebrovascular disease, what most important is that it included subtle and clinically often undetected cognitive impairment. VCI presents a clinical syndrome. The main subtypes of VCI included in current classification are cortical VCI, strategic infarct VCI and subcortical VCI. The clinical manifest and treatment of the subtype are different from each other for its difference at etiological aspects and pathogenesis. Because vascular risk factors are treatable, it should be possible to prevent, postpone, or mitigate VCI. However, progress in VCI research has been hindered by lack of unified and satisfactory diagnostic criteria for the condition. Research emphasis of VCI gradually turned to the relatively homogeneous subcortical VCI.
Subcortical ischemic vascular disease (SIVD) is the most common cause of subcortical VCI. It is accepted now that the pathogenesis of SIVD related to cerebral small vessel disease caused by multifactor. Two paths may be involved. First, occlusion of the arteriolar lumen due to arteriolosclerosis leads to the formation of lacunar infarcts (LI); second or critical stenosis and hypoperfusion of multiple medullary arterioles causes widespread incomplete infarction of deep white matter. Symptoms include motor and cognitive dysexecutive slowing, forgetfulness, dysarthria, mood changes, urinary symptoms, and short-stepped gait. Hypertension serves as the main risk factor of SIVD. Other vascular risk factor of cerebrovascular disease, such as diabetes millitus, hyperlipidemia, heart disease, anemia, smoking and excessive drinking, rise the incidence of SIVD. VCI can not be diagnosed to all patients with SIVD. XU et al reported that total of 44.5% patients with SIVD presented cognitive impairment at a successive clinical research. There were few reports considering the relationship between cognitive impairment and risk factor of SIVD. It is benefit to VCI patients’timely preventively treatment to further study on the risk factor of subcortical VCI and identify the relationships between cognitive impairment and vascular risk factors of SIVD.
SIVD is characterised by extensive cerebral white matter lesions (WML) and lacunar infarcts in deep grey and white matter structures. WML, which be called“leukoaraiosis”in the past, shows "white matter hyperintensities (WMH) on T2 and fliud attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) sequence. Researches of the relationship between cognitive impairment and the degree or type of lesions on MRI had been going on for the past many years, but no definite consensus had been to reach. There were not significant difference between leukoaraiosis and cognitive impairment at initial research reports. Those differet results some extent due to the qualitative analysis used at their research. With the progress of technique on automated image segmentation to quantitation and location, researches on relationships between cognitive impairment and neuroimaging changes are stepping into a new era. Bowler reviewed reports considering WML and cognitive impairment in the past few years, and mentioned that“attempts to correlate closely infarct volumes with cognitive change routinely fail because of the overriding importance of lesion location”. Combinated quantitation with qualitation analysis, it is possible to gain reliable data which is benefit to early cases diagnosis of cognitive impairtment in SIVD, and even to other types of VD.
The following 4 parts were designed to study on the risk factor, relationship between LI, WML and cognitive impairtment in SIVD.
Part 1: Clinical Feature and Risk Factor of Cognitive Impairment in Patients with Subcortical Ischemic Vascular Disease
Objectives:To investigate the clinical feature, the risk factor for cognitive impairment of SIVD, and the risk factor for its subtypes: white matter lesions type (WML-SIVD) and lacunar infarct type (LI-SIVD).
Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were divided into no cognitive impairment(NCI) and vascular cognitive impairment(VCI) group. Symptomes and signs, vascular risk factor profiles were recorded by interviewers and examiners, then risk factor profiles for cognitive impairment of SIVD or its subtypes were compared using univariate and multivariate logistic regression analysis.
Results: The subjects consisted of 33 males and 37 females; the mean age was 66.89±7.03 years, the mean educational attainment was 2.81±2.37 years. Mini-mental status examation (MMSE) scores of VCI group (19.81±2.56) was significant lower than that of NCI group (24.45±2.95). Of the 70 SIVD patients, 81.43%(57) patients complainted of limbs weakness, 40% (28) of instability walking, 20%(14) of mild dysarthria, and 14.29%(10) of difficulty with drinding or urinary incontinence. On nervous system examination, 71.4% patients with upper motor neuron damaged signs, 14.9% with pseudobulbar palsy signs and 10% with extrapyramidal signs were detected, while only 7.14% patients with ataxia gait。On comparison the subtype of SIVD, univariate analysis demonstrated significant associations between WM and hypertension (P<0.05), and between LI and diabetes mellitus, hyperlipidemia, coronary artery disease (P<0.05), but no significant associations with anemia, smoking, alcohol drinking (P>0.05). These associations persisted after multivariate stepwise Logistic analysis, Hypertension was more frequent in WML-SIVD than LI-SIVD [odds ratio (OR) 8.531 (1.676 to 43.41); P=0.008], whereas diabetes mellitus [OR 0.082 (0.016 to 0.436); P=0.003], hyperlipidemia [OR 0.158 (0.035 to 0.720) ; P=0.019], coronary artery disease [OR 0.005 (0.007 to 0.336); P=0.002] more frequent in LI-SIVD. As to the risk factor for cognitive impairment, hypertension [OR 5.265 (1.563 to 17.731); P=0.007], diabetes mellitus [OR 3.445 (1.008 to 11.772); P=0.049] and hyperlipidemia [OR3.649 (0.974 to 11.466); P=0.027] were associated with VCI patients, even in the multivariate stepwise Logistic analysis after controling for the effects of other variables, but no correlations was found between coronary artery disease, anemia, smoking, alcohol drinking and cognitive impairment.
Conclusions: Limbs weakness, instability walking, mild dysarthria, urinary incontinence, difficult with drinking, upper motor neuron damaged signs and pseudobulbar palsy are common clinical manifestation of subcortical ischemic vascular disease, although extrapyramidal signs may be detected. Hypertension associates with WML of SIVD, while diabetes mellitus, hyperlipidemia, coronary artery disease associates with LI-SIVD. Hypertension and diabetes mellitus are the main risk factors for cognitive impairment of SIVD, and the risk increase 5.265 and 3.445 times, respectively. The relationship between hyperlipidemia and cognitive impairment couldn’t be established from our data. It is likely that age is not correlated with cognitive impairment of SIVD patients.
Part 2: Contributions of White Matter Lesions Volume to Subcortical Vascular Cognitive
Objectives:To evaluate contributions of WML to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD).
Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into NCI and VCI groups. The volume of WML, which shows white matter hyperintensities (WMH) on MRI fluid-attenuated inversion recovery images, was measured using a semi-automated quantitative method. Finally, correlation analysis and hierarchical multiple regression analysis was used to examine the relationship between general cognitive function and the volume of WMH.
Results: The mean WMH volume of all SIVD patients was (29.56±15.58)cm3, ranged from 4.20cm3 to 86.01cm3. The mean WMH volume of VCI group was (34.84±17.50) cm3, increased remarkedly than that of NCI group (23.63±10.52) cm3, there was significant difference between groups(Z=-2.83, P=0.005). MMSE score was positively correlated with educational attainment(r=0.49, P<0.001), but negatively correlated with age(r=-0.235, P=0.025) and WMH volume(r=-0.398, P<0.001) in a univariate correlation analysis. And age was positively correlated with WMH volume(r=0.279, P=0.01), but negatively with educational attainment. In the hierarchical multiple regression analysis, age, gender and educational attainment was entered at the first step, but only the educational attainment was related to MMSE score(P<0.001). After the WMH volume entered at the second step, the relationship remain significant between MMSE score and eductional attainment(P<0.001). It was revealed that WMH volume was negatively correlated with MMSE score(B=-0.077, t=-3.243, P=0.002) after controlling for the effects of age, gender and educational attainment, WMH volume explained an additional 10.2% of the variance in MMSE.
Conclusions: It is convenient to measure and count the volume of white matter hyperintensities using the semi-automated quantitative method which is based on ImageJ and its Voxel Counter Plugins. The result is accurary and reliable, the method is time-saving and easy to manipulate. There is a highly significant negative correlation between MMSE score and WMH volume, but WMH volume explains only an additional 10.2% of the variance in MMSE independently.
Part 3: Contributions of White Matter Lesions Location to Subcortical Vascular Cognitive Impairment
Objectives:To evaluate contributions of WML location to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD), and to evaluate the correlation of volumetric quantitation and qualitation of WMH .
Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into no cognitive impairment(NCI) and vascular cognitive impairment(VCI) group. Age-related white matter change rating scale, which is a visual rating scale developed by Wahlund, was used to qualitative measure and locate the WML. Integrated with the data of part 2, using correlation analysis and hierarchical multiple regression analysis to examine the relationship between general cognitive function and the location of WMH, and the correlation of volumetric quantitation and qualitation.
Results: The mean WMH rating scores of all the SIVD patients was (12.37±3.93), ranged from 3 to 20. There was a highly significant positively correlation between total WMH volume and total WMH score(r=0.879, P<0.001), scatter diagram using WMH volume as independent variables reveal a curvilinear relationship between total WMH volume and total WMH score. The mean scores of WMH in all regions of VCI group was (13.65±3.51), higher than that of NCI group(t=-3.043,P=0.003), which was (10.94±3.94). As to each regions, the VCI group scores of frontal area, parieto-occipital area and basal ganglia area were remarkedly increased than that of NCI group(P<0.05), but no difference were noted at temporal area and infratentorial area. In the hierarchical multiple regression analysis, The result of first step was same to part 2. After the score of total WMH entered at the second step, the relationship remained significantly between MMSE score and eductional attainment(P<0.001). It was revealed that the score of total WMH was negatively correlated with MMSE score(B=-0.270,t=-2.842, P=0.006) after controlling for the effects of age, gender and educational attainment. The score of total WMH explained an additional 8.1% of the variance in MMSE,lower than the 10.2% of WMH volume. Moreover, when all the five regions enter instead of the score of total WMH at the second step, the R2 change was 19.3%. That is, all the five regions explained an additional 19.3% of the variance in MMSE,much higher than the effects of WMH volume and the score of total WMH. And the scores of frontal area and of basal ganglia area were the mainly variables(P<0.05), but no the scores of parieto-occipital area, infratentorial area and temporal area(P>0.05).
Conclusions: The volumetric quantitation of WMH was positively correlated with its volumetric qualitation. Each of the methods can be used to tract any changes of WML, except that the volumetric quantitation is more sensitivity. WML located at frontal area and basal ganglia area predicts cognitive decline, but not suits for WML located at parieto-occipital area, infratentorial area and temporal area.
Part 4: Contributions of Lacunar Infarcts to Subcortical Vascular Cognitive Impairment
Objectives:To explore contributions of LI to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD), and to evaluate the relationship of LI and WML and effects of each other.
Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and MRI scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into NCI and VCI groups. The number of LI was counted and the location of it was recorded according to the method using at part 3. Integrated with the data of ahead, using correlation analysis and hierarchical multiple regression analysis to examine the relationship between general cognitive function and the number and the location of LI, and the relationship of LI and WML.
Results: The mean number of LI in VCI group was (4.38±1.82), higher than that of NCI group(Z=-2.202,P=0.028), which was (3.39±1.71). As to each regions, the VCI group number of LI in frontal area and basal ganglia area were remarkedly increased than that of NCI group(P<0.05), but no difference was noted at temporal area, parieto-occipital area and infratentorial area. In the hierarchical multiple regression analysis, considered MMSE scores as dependent variables, the first step is same as part 2 and part 3. After the number of total LI entered at the second step, the relationship remain significant between MMSE score and eductional attainment(P<0.001). It was revealed that the number of total LI was negatively correlated with MMSE score(B=-0.270,t=-2.842, P=0.006) after controlling for the effects of age, gender and educational attainment, the number of total LI explained an additional 9.1% of the variance in MMSE. Another analysis then performed, all of the five regions enter instead of the number of total LI at the second step, the R2 change was 25.7%. That is, all of the five regions explained an additional 25.7% of the variance in MMSE,much higher than the effects of the number of total LI. And the number of LI at frontal area and basal ganglia area were the mainly negative variables(P<0.05) to predict cognitive decline of patients with SIVD, but no relationship was noted between MMSE and the number of LI at parieto-occipital area, infratentorial area or temporal area (P>0.05). The third hierarchical multiple regession analysis followed, at the first step, age, gender and education were entered; then WMH volume were entered at the second step, the result was same as part 2, and WMH volume make the model R2 change 10.2%; the number of total LI enter after WMH volume at step 3, an additional R2 change was 0.112, there was a negatively correlation between MMSE and WMH volume, MMSE and the number of LI, independently. At last, the fourth analysis performed, the location of WML and LI were entered the model at the second step after age, gender and education, it revealed that only WML located at frontal area and LI located at basal ganglia area predicted cognitive decline of patients with SIVD, independently.
Conclusions: The number and location of LI were related to cognitive impairment, the effect of LI location is greater than than of its number. The number of LI and WMH volume both are predictors of cognitive impairment in patients with SIVD, independently; and LI located at basal ganglia area and WML at frontal area are the best predictors.
引文
1 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37: 2220~2241
2 Hachinski VC, Bowler JV. Vascular cognitive impairment. Neurology, 1993, 43:2159~2160
3 Garde E, Mortensen EL, Rostrup E, et al. Decline in intelligence is associated with progression in white matter hyperintensity volume. J Neurol Neurosurg Psychiatry, 2005, 76:1289~1291
4 Román GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol, 2002, 1:426~436
5 Schulz UG, Rothwell PM. Differences in vascular risk factors between etiological subtypes of ischemic stroke: importance of population-based studies. Stroke, 2003, 34: 2050 ~ 2059
6 Hund-Georgiadis M, Ballaschke O, Scheid R, et al. Characterization of cerebral microangiopathy using 3 Tesla MRI: correlation With neurological impairment and vascular risk factors. J Magn Reson Imaging, 2002, 15: 1 ~ 7
7 Khan U, Porteous L, Hassan A, et al. Risk factor profile of cerebral small vessel disease and its subtypes. J Neurol Neurosurg Psychiatry, 2007, 78: 702 ~ 706
8 Erkinjuntti T, Inzitari D, Panton I, et al. Limitations of clinical criteria for the diagnosis of vascular dementia in clinical trials. Is a focus on subcortical vascular dementia a solution? Ann NY Acad Sci, 2000, 903: 262 ~ 272
9血管性认识功能损害专家共识组。血管性认识功能损害的专家共识。中华内科杂志,2007,46:1052 ~ 1055
10 Roman GC, Tetemichi TK, Erkinjuntti T, et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIRENInternational Workshop. Neurology, 1993, 43:250 ~ 260
11 Wentzel C, Darvesh S, Macknight C, et al. Inter-rater reliability of the diagnosis of vascular cognitive impairment at a memory clinic. Neuroepidemiology, 2000, 19:186 ~ 193
12张明园,瞿光业,金华,等。几种痴呆测试工具的比较。中华神经精神科杂志,1991,24: 194 ~ 196
13中华心血管病杂志编委会血脂异常防治对策专题组。中国成人血脂异常防治指南(修订版)。北京:人民卫生出版社,2007
14 Caplan LR. Binswanger’s disease revisited. Neurology, 1995, 45: 626~33
15 Pohjasvaara T, Mantyla R, Ylikoski R, et al. Clinical feature of MRI-defined subcortical vascular disease. Alzheimer Dis Assoc Disord, 2003,17:236~242
16 de Leeuw FE, de Groot JC, Oudkerk M, et al. Hypertension and cerebral white matter lesions in a prospective cohort study. Brain, 2002, 125:765~ 772
17 Launer LJ. Epidemiology of white-matter lesions. Int Psychogeriatr, 2003, 15(Suppl. 1):99~103
18 Richert A, Ansarin K, Baran AS. Sleep apnea and hypertension: pathophysiologic mechanisms. Semin Nephrol, 2002, 22: 71~77
19 ZuccalàG, Onder G, Pedone C, et al. Hypotension and cognitive impairment: selective association in patients with heart failure. Neurology, 2001, 57: 1986~1992
20 Pullicino PM, Hart J. Cognitive impairment in congestive heart failure? Embolism vs hypoperfusion. Neurology, 2001, 57: 1945~1946
21 de Leeuw FE, de Groot JC, Ouderk M, et al. Atrial fibrillation and the risk of cerebral white matter lesions. Neurology, 2000, 54: 1795~1801
22 Guo Z, Viitanen M, Fratiglioni L, et al. Low blood pressure and dementia in elderly people: the Kungsholmen project. BMJ, 1996, 312: 805~808
23 DeCarli C, Miller BL, Swan GE, et al. Predictors of brain morphology for the men of the NHLBI twin study. Stroke, 1999, 30:529~536
24 Jokinen H, Kalska H, Ylikoski R, et al. MRI-defined subcortical ischemicvascular disease: baseline clinical and neuropsychological findings. The LADIS Study. Cerebrovasc Dic, 2009, 27:336~344
25 Boiten J, Lodder J, Kessels F. Two clinically distinct lacunar infarct entities? A hypothesis. Stroke, 1993, 24:652~656
1 Inzitari D, Carlucci G,Pantoni L. White matter changes: the clinical consequences in the aging population. J Neural Transm Suppl, 2000, 59: 1~8
2 de Leeuw FE, de Groot JC, Achten E, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry, 2001, 70:9~14
3 Breteler MMB, van Swieten JC, Bots ML, et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study. Neurology, 1994, 44: 1246~1252
4 Ylikoski A, Erkinjuntti T, Raininko R, et al. White matter hyperintensities on the MRI in the neurologically non-demented elderly: analysis of cohorts of consecutive subjects aged 65 to 85 years living at home. Stroke, 1995, 26: 1781~1787
5 Lindgren A, Roijer A, Rudling O, et al. Cerebral lesions on magnetic resonance imaging, heart disease, and vascular risk factors in subjects without stroke: a population-based study. Stroke, 1994, 25:929~934
6 Liao D, Cooper L, Cai J, et al. The prevalence and severity of white matter lesions, their relationship with age, ethnicity, gender, and cardiovascular risk factors: the ARIC study. Neuroepidemiology, 1997, 16: 149~162
7 Gunning~Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology, 2000, 14:224~232
8 deGroot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and subjective cognitive dysfunction: The Rotterdam Scan Study. Neurology, 2001, 56:1539~1545
9 Cohen RA, Paul RH, Ott BR, et al. The relationship of subcortical MRI hyperintensities and brain volume to cognitive function in vasculardementia. J Intl Neuropsycholog Soc, 2002, 8:743~752
10 Kramer JH, Reed BR, Mungas D, Weiner MW, Chui HC. Executive dysfunction in subcortical ischaemic white matter disease. J Neurol Neurosurg Psychiatry, 2002, 72:217~220
11 van Dijk EJ, Prins ND, Vrooman HA, et al. Progression of cerebral small vessel disease in relation to risk factors and cognitive consequences: Rotterdam Scan study. Stroke, 2008, 39:2712~2719
12 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008, 39:397~402
13 Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless of their location. Neurology, 2004, 63:246~253
14 Mirsen TR, Lee DH, Wong CJ, et al. Clinical correlates of white-matter changes on magnetic resonance imaging scans of the brain. Arch Neurol, 1991, 48:1015~1021.
15 Erkinjuntti T, Gao F, Lee DH, et al. Lack of difference in brain hyperintensities between patients with early Alzheimer’s disease and control subjects. Arch Neurol, 1994, 51:260~268
16 Yoshita M, Fletcher E, DeCarli C. Current concepts of analysis of cerebral white matter hyperintensities on magnetic resonance imaging. Top Magn Reson Imaging. 2005;16:399~407
17 M?ntyl? R, Erkinjuntti T, Salonen O, et al. Variable agreement between visual rating scales for white matter hyperintensities on MRI. Comparison of 13 rating scales in a poststroke cohort. Stroke, 1997, 28:1614~1623
18 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37: 2220~2241
19 Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke,2001, 32:1318~1322
20 Mungas D, Jagust WJ, Reed BR, et al. MRI predictors of cognition in subcortical ischemic vascular disease and Alzheimer disease. Neurology, 2001, 57:2229~2235
21 Chui HC. Subcortical ischemic vascular dementia. Neurol Clin, 2007, 25: 717~740
22 Pantoni L, García JH. The significance of cerebral white matter abnormalities 100 years after Binswanger’s report: a review. Stroke, 1995, 26:1293~1301
23 Román GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol, 2002, 1:426~436
24 Caplan LR. Binswanger’s disease revisited. Neurology, 1995, 45: 626~633
25 Schneider R, Ringelstein EB, Zeumer H, et al. The role of plasma hyperviscosity in subcortical arteriosclerotic encephalopathy (Binswanger’s disease). J Neurol, 1987, 234: 67~73
26 DeReuck J, Decoo D, Hasenbroekx MC, et al. Acetazolamide vasoreactivity in vascular dementia: a positron emission tomographic study. Eur Neurol, 1999, 41: 31~36.
27 Wentzel C, Darvesh S, Macknight C, et al. Inter-rater reliability of the diagnosis of vascular cognitive impairment at a memory clinic. Neuroepidemiology, 2000, 19:186~193
28宋晓灵,陈俊抛。阿尔茨海默病和血管性痴呆患者颅脑海马、白质疏松定量研究。卒中与神经疾病,2006,13:180~182
29何国军,陈俊抛,温志波,等。阿尔茨海默病与血管性痴呆患者白质疏松的MRI定量研究。中国临床康复,2004 , 8: 6066~6067
1 van Dijk EJ, Prins ND, Vrooman HA, et al. Progression of cerebral small vessel disease in relation to risk factors and cognitive consequences: Rotterdam Scan study. Stroke, 2008, 39:2712~2719
2 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008, 39:397~402
3 Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless of their location. Neurology, 2004, 63: 246~253
4 Kramer JH, Reed BR, Mungas D, et al. Executive dysfunction in subcortical ischaemic white matter disease. J Neurol Neurosurg Psychiatry, 2002, 72:217~220
5 Erkinjuntti T, Gao F, Lee DH, et al. Lack of difference in brain hyperintensities between patients with early Alzheimer’s disease and control subjects. Arch Neurol, 1994, 51:260~268
6 Wentzel C, Darvesh S, Macknight C, et al. Inter-rater reliability of the diagnosis of vascular cognitive impairment at a memory clinic. Neuroepidemiology, 2000, 19:186~193
7 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37: 2220~2241.
8 Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke, 2001, 32:1318~1322
9 van Straaten EC, Fazekas F, Rostrup E, et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke, 2006, 37:836~840
10 Wentzel C, Darvesh S, Macknight C, et al. Inter-rater reliability of thediagnosis of vascular cognitive impairment at a memory clinic. Neuroepidemiology, 2000, 19:186~93
11 Swartz RH, Black SE. Anterior-medial thalamic lesions in dementia: frequent, and volume dependently associated with sudden cognitive decline. J Neurol Neurosurg Psychiatry, 2006, 77:1307~1312
12 Scheltens P, Barkhof F, Valk J, et al. White matter lesions on magnetic resonance imaging in clinically diagnosed Alzheimer’s disease. Evidence for heterogeneity. Brain, 1992,115:735~748
13 de Leeuw FE, de Groot JC, Achten E, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry, 2001,70:9~14
14 Longstreth W Jr, Manolio TA, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke, 1996, 27: 1274~1282
15 Liao D, Cooper L, Cai J, et al. Presence and severity of cerebral white matter lesions and hypertension, its treatment, and its control. The ARIC study. Atherosclerosis risk in communities study. Stroke, 1996, 27: 2262~2270
16 Sijens PE, Oudkerk M, De Leeuw FE, et al. 1H Chemical shift imaging of the human brain at age 60-90 years reveals metabolic differences between women and men. Magn Reson Med, 1999, 42:24~31
17 Garrett KD, Cohen RA, Paul RH, et al. Computer-mediated measurement and subjective ratings of white matter hyperintensities in vascular dementia: relationships to neuropsychological performance. Clin Neuropsychol, 2004, 18:50~52
18 Gold G, Kovari E, Hof PR, et al. Sorting out the clinical consequences of ischemic lesions in brain aging: a clinicopathological approach. J Neurol Sci, 2007, 257:17~22
1 Vermeer SE, Prins ND, den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med, 2003, 348: 1215~1222
2 Jellinger KA, Attems J. Incidence of cerebrovascular lesions in Alzheimer’s disease: a postmortem study. Acta Neuropathol, 2003, 105: 14 ~17
3 Snowdon DA, Greiner LH, Mortimer JA, et al. Brain infarction and the clinical expression of Alzheimer disease. The nun study. JAMA, 1997, 277:813~ 817
4 Gold G, Kovari E, Corte G, et al. Clinical validity of Aβ-protein deposition staging in brain aging and Alzheimer disease. J Neuropathol Exp Neurol, 2001, 60:946~952
5 Giannakopoulos P, Herrmann FR, Bussiere T, et al. Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology, 2003, 60:1495~1500
6 Kovari E, Gold G, Herrmann FR, et al. Cortical microinfarcts and demyelination significantly affect cognition in brain aging. Stroke, 2004, 35:410~414
7 de Groot JC, de Leeuw FE, Oudkerk M, et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol, 2002, 52:335~341
8 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008, 39:397~402
9 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37: 2220~2241
10 Jokinen H, Kalska H, Ylikoski R, et al. MRI-defined subcortical ischemicvascular disease: baseline clinical and neuropsychological findings. The LADIS Study. Cerebrovasc Dic, 2009, 27:336~344
11 Matsusue E, Sugihara S, Fujii S, et al. White matter changes in elderly people: MR-pathologic correlations. Magn Reson Med Sci, 2006, 5: 99~104
12 Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke, 2001, 32:1318~1322
13 Ross GW, Petrovitch H, White LR, et al. Characterization of risk factors for vascular dementia. The Honolulu Asia Aging Study. Neurology, 1999, 53:337~343
14 Garde E, Mortensen EL, Rostrup E, et al. Decline in intelligence is associated with progression in white matter hyperintensity volume. J Neurol Neurosurg Psychiatry, 2005, 76:1289~1291
15 Erkinjuntti T, Inzitari D, Panton I, et al. Limitations of clinical criteria for the diagnosis of vascular dementia in clinical trials. Is a focus on subcortical vascular dementia a solution? Ann NY Acad Sci, 2000, 903: 262~272
16 Chui HC. Subcortical ischemic vascular dementia. Neurol Clin, 2007, 25: 717~740
17 Roman GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol, 2002, 1:426~436
18 Sachdev PS, Brodaty H, Valenzuela MJ, et al. The neuropsychological profile of vascular cognitive impairment in stroke and TIA patients. Neurology, 2004, 62:912~919
19 Liem MK, van der Grond J, Haan J, et al. Lacunar infarcts are the main correlate with cognitive dysfunction in CADASIL. Stroke, 2007, 38: 923~928
20 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008, 39:397~402
21 Sabri O, Ringelstein EB, Hellwig D, et al. Neuropsychological impairment correlates with hypoperfusion and hypometabolism but not with severity of white matter lesions on MRI in patients with cerebral microangiopathy. Stroke, 1999, 30:556~566
22 de Groot JC, de Leeuw FE, Oudkerk M, et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol, 2002, 52:335~341
23 Vermeer SE, Prins ND, den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med, 2003, 348: 1215~1222
24 Prins ND, van Dijk EJ, den Heijer T, et al. Cerebral small-vessel disease and decline in information processing speed, executive function and memory. Brain, 2005, 128:2034~2041
25 Van Zandvoort MJ, De Haan EH, et al. Chronic cognitive disturbances after a single supratentorial lacunar infarct. Neuropsychol Behav Neurol, 2001, 14:98~102
26 Van der Werf YD, Scheltens P, Lindeboom J, et al. Deficits of memory, executive functioning and attention following infarction in the thalamus; a study of 22 cases with localised lesions. Neuropsychologia, 2003, 41: 1330~1344
27 Cummings JL. Frontal subcortical circuits and human behavior. Arch Neurol, 1993, 50:873~80
28 Reed BR, Eberling JL, Mungas D, et al. Effects of white matter lesions and lacunes on cortical function. Arch Neurol, 2004, 61:1545~1550.
29 van der Flier WM, van Straaten EC, Barkhof F, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke, 2005, 36:2116~2120
1 Bowler JV. Vascular cognitive impairment. J Neurol Neurosurg Psychiatry, 2005, 76(Suppl V):35 ~ 44
2 Hachinski VC, Bowler JV. Vascular cognitive impairment. Neurology, 1993, 43:2159 ~ 2160
3 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37:2220 ~ 2241
4 Ingles JL, Boulton DC, Fisk JD, et al. Preclinical vascular cognitive impairment and Alzheimer disease: neuropsychological test performance 5 years before diagnosis. Stroke, 2007, 38:1148~1153
5 O'Brien JT. Vascular cognitive impairment. Am J Geriatr Psychiatry, 2006, 14: 724 ~ 733
6 O’Brien JT, Erkinjuntti T, Reisberg B, et al. Vascular cognitive impairment. Lancet Neurology, 2003, 2:89 ~ 98
7 Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol, 2005, 62:1160 ~ 1163
8 Sachdev PS. Vascular cognitive disorder. Int J Geriatr Psychiatry, 1999, 14: 402 ~ 403
9 Rockwood K, Howard K, MacKnight C, et al. Spectrum of disease in vascular cognitive impairment. Neuroepidemiology, 1999, 18:248 ~ 254
10 Leblanc GG, Meschia JF, Stuss DT, et al. Genetics of Vascular Cognitive Impairment. Stroke, 2006, 37:248 ~ 255
11 Feldman H, Levy AR, Hsiung GY, et al. A Canadian Cohort Study of Cognitive Impairment and Related Dementias (ACCORD): Study methods and baseline results. Neuroepidemiology, 2003, 22:265 ~ 274
12 Garrett KD, Browndyke JN, Whelihan W, et al. The neuropsychological profile of vascular cognitive impairment–no dementia: Comparisons to patients at risk for cerebrovascular disease and vascular dementia. ArchClin Neuropsychol, 2004, 19:745~757
13 Sachdev PS, Brodaty H, Valenzuela MJ, et al. The neuropsychological profile of vascular cognitive impairment in stroke and TIA patients. Neurology, 2004, 62: 912 ~ 919
14 Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke, 2001, 32:1318 ~ 1322
15 M?ntyl? R, Erkinjuntti T, Salonen O, et al. Variable Agreement Between Visual Rating Scales for White Matter Hyperintensities on MRI: Comparison of 13 Rating Scales in a Poststroke Cohort. Stroke, 1997, 28:1614 ~ 1623
16 Frisoni GB, P Scheltens, S Galluzzi, et al. Neuroimaging tools to rate regional atrophy, subcortical cerebrovascular disease, and regional cerebral blood flow and metabolism: consensus paper of the EADC. J Neurol Neurosurg Psychiatry, 2003, 74:1371 ~ 1381
17 Nys GM, van Zandvoort MJ, de Kort PL, et al. Cognitive disorders in acute stroke: prevalence and clinical determinants. Cerebrovasc Dis, 2007, 23: 408 ~ 416
18 Kuller LH, Lopez OL, Jagust WJ, et al. Determinants of vascular dementia in the Cardiovascular Health Study. Neurology, 2005, 64:1548 ~ 1552
19何国军,陈俊抛,温志波,等。阿尔茨海默病与血管性痴呆患者侧脑室、外侧裂的定量测定及影像学特征。中国临床康复,2004, 8: 2450~2451
20 Grau-Olivares M, Bartrés-Faz D, Arboix A, et al. Mild cognitive impairment after lacunar infarction: voxel-based morphometry and neuropsychological assessment. Cerebrovasc Dis, 2007, 23: 353 ~ 361
21 Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless their location. Neurology, 2004, 63:246 ~ 253
22何国军,陈俊抛,温志波,等。阿尔茨海默病与血管性痴呆患者白质疏松的MRI定量研究。中国临床康复,2004;8:6066 ~ 6067
23 Au R, Massaro JM, Wolf PA, et al. Association of White Matter Hyperintensity Volume With Decreased Cognitive Functioning: The Framingham Heart Study. Arch Neurol, 2006, 63:246 ~ 250
24 Bowler JV. Vascular Cognitive Impairment. Stroke, 2004, 35:386 ~ 388
25 Yoshita M, Fletcher E, Harvey D, et al. Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurology, 2006, 67:2192 ~ 2198
26 Yoshikawa T, Murase K, Oku N, et al. Statistical image analysis of cerebral blood flow in vascular dementia with small-vessel disease. J Nucl Med, 2003, 44:505 ~ 511
27 Kwan LT, Reed BR, Eberling JL, et al. Effects of subcortical cerebral infarction on cortical glucose metabolism and cognitive function. Arch Neurol, 1999, 56:809 ~ 814
28 Reed BR, Eberling JL, Mungas D, et al. Frontal lobe hypometabolism predicts cognitive decline in patients with lacunar infarction. Arch Neurol, 2001, 58:493 ~ 497
29 DeCarli C, Murphy DGM, Tranh M, et al. The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology, 1995,45:2077 ~ 2084
30 Sabri O, Ringelstein EB, Hellwig D, et al. Neuropsychological impairment correlates with hypoperfusion and hypometabolism but not with severity of white matter lesions on MRI in patients with cerebral microangiopathy. Stroke, 1999, 30:556 ~ 566
31 Behrens TE, Johansen-Berg H, Woolrich MW, et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci, 2003, 6:750 ~ 757
32 O’Sullivan M, Summers PE, Jones DK, et al. Normal-appearing white matter in ischemic leukoaraiosis: a diffusion tensor MRI study. Neurology, 2001, 57:2307~2310
33 Wardlaw JM, Sandercock PA, Dennis MS, et al. Is breakdown of theblood-brain barrier responsible for lacunar stroke, leukoaraiosis, and dementia? Stroke, 2003, 34:806~812
34 Wallin A, Sjogren M. Cerebrospinal fluid cytoskeleton proteins in patients with subcortical white-matter dementia. Mech Ageing Dev, 2001, 122: 1937 ~ 1949
1 Roman GC. Vascular dementia revisited: diagnosis, pathogenesis, treatment, and prevention. Med Clin North Am, 2002,86:477~499
2 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorders and Stroke-Canadian Stroke Network vascular cognitive impairment harmonization standards. Stroke, 2006, 37:2220~2241
3 O'Brien JT, Erkinjuntti T, Reisberg B, et al. Vascular cognitive impairment. Lancet Neurol, 2003,2:89~98
4 Erkinjuntti T. Subcortical ischemic vascular disease and dementia. Int Psychogeriatr, 2003,15 Suppl 1:23~26
5 Chui H. Dementia due to subcortical ischemic vascular disease. Clin Cornerstone, 2001,3:40~51
6 Roman GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol, 2002,1:426~436
7 Shinkawa A, Ueda K, Kiyohara Y, et al. Silent cerebral infarction in a community-based autopsy series in Japan. The Hisayama Study. Stroke, 1995, 26:380~385
8 Longstreth WT Jr, Bernick C, Manolio TA, et al. Lacunar infarcts defined by magnetic resonance imaging of 3660 elderly people: the Cardiovascular Health Study. Arch Neurol, 1998,55:1217~1225
9 Longstreth WT Jr, Manolio TA, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke, 1996, 27:1274~1282
10 Garcia JH, Lassen NA, Weiller C, et al. Ischemic stroke and incomplete infarction. Stroke, 1996,27:761~765
11 Pantoni L, Garcia JH. The significance of cerebral white matter abnormalities 100 years after Binswanger's report. A review. Stroke, 1995, 26:1293~1301
12 Tanaka K, Wada N, Hori K, et al. Chronic cerebral hypoperfusion disrupts discriminative behavior in acquired-learning rats. J Neurosci Methods, 1998,84:63~68
13 Kudo T, Takeda M, Tanimukai S, et al. Neuropathologic changes in the gerbil brain after chronic hypoperfusion. Stroke, 1993, 24:259~264; discussion 265
14 Tanaka K, Wada N, Ogawa N. Chronic cerebral hypoperfusion induces transient reversible monoaminergic changes in the rat brain. Neurochem Res, 2000,25:313~320.
15 De Reuck J, Decoo D, Hasenbroekx MC, et al. Acetazolamide vasoreactivity in vascular dementia: a positron emission tomographic study. Eur Neurol, 1999,41:31~36
16 Kuwabara Y, Ichiya Y, Sasaki M, et al. Time dependency of the acetazolamide effect on cerebral hemodynamics in patients with chronic occlusive cerebral arteries. Early steal phenomenon demonstrated by [15O]H2O positron emission tomography. Stroke, 1995,26:1825~1829
17 Yao H, Sadoshima S, Kuwabara Y, et al. Cerebral blood flow and oxygen metabolism in patients with vascular dementia of the Binswanger type. Stroke, 1990,21:1694~1699
18 Nakane H, Ibayashi S, Fujii K, et al. Cerebral blood flow and metabolism in patients with silent brain infarction: occult misery perfusion in the cerebral cortex. J Neurol Neurosurg Psychiatry, 1998,65:317~321
19 Smith EE, Rosand J, Knudsen KA, et al. Leukoaraiosis is associated with warfarin-related hemorrhage following ischemic stroke. Neurology, 2002, 59:193~197
20 Uggetti C, Egitto MG, Pichiecchio A, et al. Subcortical dementia associated with striking enlargement of the Virchow-Robin spaces and transneural degeneration of the left mammillo-thalamic tract. Cerebrovasc Dis, 2001,12:287~90
21 Capizzano AA, Schuff N, Amend DL, et al. Subcortical ischemic vascular dementia: assessment with quantitative MR imaging and 1H MRspectroscopy. AJNR Am J Neuroradiol, 2000,21:621~630
22 Dichgans M, Herzog J, Gasser T. NOTCH3 mutation involving three cysteine residues in a family with typical CADASIL. Neurology, 2001, 57: 1714~1717
23 Pezzini A, Padovani A. Cerebral amyloid angiopathy-related hemorrhages. Neurol Sci, 2008,29 Suppl 2:S260~263
24 Maat-Schieman M, Roos R, van Duinen S. Hereditary cerebral hemorrhage with amyloidosis-Dutch type. Neuropathology, 2005, 25:288~297
25 Wallin A, Edman A, Blennow K, et al. Stepwise comparative status analysis (STEP): a tool for identification of regional brain syndromes in dementia. J Geriatr Psychiatry Neurol, 1996,9:185~99.
26 Mega MS, Cummings JL. Frontal-subcortical circuits and neuropsychiatric disorders. J Neuropsychiatry Clin Neurosci, 1994, 6: 358~370
27 Kramer JH, Reed BR, Mungas D, et al. Executive dysfunction in subcortical ischaemic vascular disease. J Neurol Neurosurg Psychiatry, 2002, 72:217~220
28 Alexopoulos GS, Kiosses DN, Klimstra S, et al. Clinical presentation of the "depression-executive dysfunction syndrome" of late life. Am J Geriatr Psychiatry, 2002,10:98~106
29 Looi JC, Sachdev PS. Differentiation of vascular dementia from AD on neuropsychological tests. Neurology, 1999,53:670~678
30 Gunning-Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology, 2000, 14:224~232
31 de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and subjective cognitive dysfunction: the Rotterdam Scan Study. Neurology, 2001,56:1539~1545
32 Cohen RA, Paul RH, Ott BR, et al. The relationship of subcortical MRI hyperintensities and brain volume to cognitive function in vasculardementia. J Int Neuropsychol Soc, 2002,8:743~752
33 Vermeer SE, Prins ND, den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med, 2003, 348: 1215~1222
34 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008,39:397~402.
35 Fein G, Di Sclafani V, Tanabe J, et al. Hippocampal and cortical atrophy predict dementia in subcortical ischemic vascular disease. Neurology, 2000,55:1626~1635
36 Du AT, Schuff N, Laakso MP, et al. Effects of subcortical ischemic vascular dementia and AD on entorhinal cortex and hippocampus. Neurology, 2002,58:1635~1641
37 Frisoni GB, Scheltens P, Galluzzi S, et al. Neuroimaging tools to rate regional atrophy, subcortical cerebrovascular disease, and regional cerebral blood flow and metabolism: consensus paper of the EADC. J Neurol Neurosurg Psychiatry, 2003,74:1371~1381
38 Mantyla R, Erkinjuntti T, Salonen O, et al. Variable agreement between visual rating scales for white matter hyperintensities on MRI. Comparison of 13 rating scales in a poststroke cohort. Stroke, 1997, 28: 1614~1623
39 Erkinjuntti T, Inzitari D, Pantoni L, et al. Research criteria for subcortical vascular dementia in clinical trials. J Neural Transm Suppl, 2000, 59: 23~30
2 Hachinski VC, Bowler JV. Vascular cognitive impairment. Neurology, 1993, 43:2159~2160
3 Garde E, Mortensen EL, Rostrup E, et al. Decline in intelligence is associated with progression in white matter hyperintensity volume. J Neurol Neurosurg Psychiatry, 2005, 76:1289~1291
4 Román GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol, 2002, 1:426~436
5 Schulz UG, Rothwell PM. Differences in vascular risk factors between etiological subtypes of ischemic stroke: importance of population-based studies. Stroke, 2003, 34: 2050 ~ 2059
6 Hund-Georgiadis M, Ballaschke O, Scheid R, et al. Characterization of cerebral microangiopathy using 3 Tesla MRI: correlation With neurological impairment and vascular risk factors. J Magn Reson Imaging, 2002, 15: 1 ~ 7
7 Khan U, Porteous L, Hassan A, et al. Risk factor profile of cerebral small vessel disease and its subtypes. J Neurol Neurosurg Psychiatry, 2007, 78: 702 ~ 706
8 Erkinjuntti T, Inzitari D, Panton I, et al. Limitations of clinical criteria for the diagnosis of vascular dementia in clinical trials. Is a focus on subcortical vascular dementia a solution? Ann NY Acad Sci, 2000, 903: 262 ~ 272
9血管性认识功能损害专家共识组。血管性认识功能损害的专家共识。中华内科杂志,2007,46:1052 ~ 1055
10 Roman GC, Tetemichi TK, Erkinjuntti T, et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIRENInternational Workshop. Neurology, 1993, 43:250 ~ 260
11 Wentzel C, Darvesh S, Macknight C, et al. Inter-rater reliability of the diagnosis of vascular cognitive impairment at a memory clinic. Neuroepidemiology, 2000, 19:186 ~ 193
12张明园,瞿光业,金华,等。几种痴呆测试工具的比较。中华神经精神科杂志,1991,24: 194 ~ 196
13中华心血管病杂志编委会血脂异常防治对策专题组。中国成人血脂异常防治指南(修订版)。北京:人民卫生出版社,2007
14 Caplan LR. Binswanger’s disease revisited. Neurology, 1995, 45: 626~33
15 Pohjasvaara T, Mantyla R, Ylikoski R, et al. Clinical feature of MRI-defined subcortical vascular disease. Alzheimer Dis Assoc Disord, 2003,17:236~242
16 de Leeuw FE, de Groot JC, Oudkerk M, et al. Hypertension and cerebral white matter lesions in a prospective cohort study. Brain, 2002, 125:765~ 772
17 Launer LJ. Epidemiology of white-matter lesions. Int Psychogeriatr, 2003, 15(Suppl. 1):99~103
18 Richert A, Ansarin K, Baran AS. Sleep apnea and hypertension: pathophysiologic mechanisms. Semin Nephrol, 2002, 22: 71~77
19 ZuccalàG, Onder G, Pedone C, et al. Hypotension and cognitive impairment: selective association in patients with heart failure. Neurology, 2001, 57: 1986~1992
20 Pullicino PM, Hart J. Cognitive impairment in congestive heart failure? Embolism vs hypoperfusion. Neurology, 2001, 57: 1945~1946
21 de Leeuw FE, de Groot JC, Ouderk M, et al. Atrial fibrillation and the risk of cerebral white matter lesions. Neurology, 2000, 54: 1795~1801
22 Guo Z, Viitanen M, Fratiglioni L, et al. Low blood pressure and dementia in elderly people: the Kungsholmen project. BMJ, 1996, 312: 805~808
23 DeCarli C, Miller BL, Swan GE, et al. Predictors of brain morphology for the men of the NHLBI twin study. Stroke, 1999, 30:529~536
24 Jokinen H, Kalska H, Ylikoski R, et al. MRI-defined subcortical ischemicvascular disease: baseline clinical and neuropsychological findings. The LADIS Study. Cerebrovasc Dic, 2009, 27:336~344
25 Boiten J, Lodder J, Kessels F. Two clinically distinct lacunar infarct entities? A hypothesis. Stroke, 1993, 24:652~656
1 Inzitari D, Carlucci G,Pantoni L. White matter changes: the clinical consequences in the aging population. J Neural Transm Suppl, 2000, 59: 1~8
2 de Leeuw FE, de Groot JC, Achten E, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry, 2001, 70:9~14
3 Breteler MMB, van Swieten JC, Bots ML, et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study. Neurology, 1994, 44: 1246~1252
4 Ylikoski A, Erkinjuntti T, Raininko R, et al. White matter hyperintensities on the MRI in the neurologically non-demented elderly: analysis of cohorts of consecutive subjects aged 65 to 85 years living at home. Stroke, 1995, 26: 1781~1787
5 Lindgren A, Roijer A, Rudling O, et al. Cerebral lesions on magnetic resonance imaging, heart disease, and vascular risk factors in subjects without stroke: a population-based study. Stroke, 1994, 25:929~934
6 Liao D, Cooper L, Cai J, et al. The prevalence and severity of white matter lesions, their relationship with age, ethnicity, gender, and cardiovascular risk factors: the ARIC study. Neuroepidemiology, 1997, 16: 149~162
7 Gunning~Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology, 2000, 14:224~232
8 deGroot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and subjective cognitive dysfunction: The Rotterdam Scan Study. Neurology, 2001, 56:1539~1545
9 Cohen RA, Paul RH, Ott BR, et al. The relationship of subcortical MRI hyperintensities and brain volume to cognitive function in vasculardementia. J Intl Neuropsycholog Soc, 2002, 8:743~752
10 Kramer JH, Reed BR, Mungas D, Weiner MW, Chui HC. Executive dysfunction in subcortical ischaemic white matter disease. J Neurol Neurosurg Psychiatry, 2002, 72:217~220
11 van Dijk EJ, Prins ND, Vrooman HA, et al. Progression of cerebral small vessel disease in relation to risk factors and cognitive consequences: Rotterdam Scan study. Stroke, 2008, 39:2712~2719
12 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008, 39:397~402
13 Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless of their location. Neurology, 2004, 63:246~253
14 Mirsen TR, Lee DH, Wong CJ, et al. Clinical correlates of white-matter changes on magnetic resonance imaging scans of the brain. Arch Neurol, 1991, 48:1015~1021.
15 Erkinjuntti T, Gao F, Lee DH, et al. Lack of difference in brain hyperintensities between patients with early Alzheimer’s disease and control subjects. Arch Neurol, 1994, 51:260~268
16 Yoshita M, Fletcher E, DeCarli C. Current concepts of analysis of cerebral white matter hyperintensities on magnetic resonance imaging. Top Magn Reson Imaging. 2005;16:399~407
17 M?ntyl? R, Erkinjuntti T, Salonen O, et al. Variable agreement between visual rating scales for white matter hyperintensities on MRI. Comparison of 13 rating scales in a poststroke cohort. Stroke, 1997, 28:1614~1623
18 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37: 2220~2241
19 Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke,2001, 32:1318~1322
20 Mungas D, Jagust WJ, Reed BR, et al. MRI predictors of cognition in subcortical ischemic vascular disease and Alzheimer disease. Neurology, 2001, 57:2229~2235
21 Chui HC. Subcortical ischemic vascular dementia. Neurol Clin, 2007, 25: 717~740
22 Pantoni L, García JH. The significance of cerebral white matter abnormalities 100 years after Binswanger’s report: a review. Stroke, 1995, 26:1293~1301
23 Román GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol, 2002, 1:426~436
24 Caplan LR. Binswanger’s disease revisited. Neurology, 1995, 45: 626~633
25 Schneider R, Ringelstein EB, Zeumer H, et al. The role of plasma hyperviscosity in subcortical arteriosclerotic encephalopathy (Binswanger’s disease). J Neurol, 1987, 234: 67~73
26 DeReuck J, Decoo D, Hasenbroekx MC, et al. Acetazolamide vasoreactivity in vascular dementia: a positron emission tomographic study. Eur Neurol, 1999, 41: 31~36.
27 Wentzel C, Darvesh S, Macknight C, et al. Inter-rater reliability of the diagnosis of vascular cognitive impairment at a memory clinic. Neuroepidemiology, 2000, 19:186~193
28宋晓灵,陈俊抛。阿尔茨海默病和血管性痴呆患者颅脑海马、白质疏松定量研究。卒中与神经疾病,2006,13:180~182
29何国军,陈俊抛,温志波,等。阿尔茨海默病与血管性痴呆患者白质疏松的MRI定量研究。中国临床康复,2004 , 8: 6066~6067
1 van Dijk EJ, Prins ND, Vrooman HA, et al. Progression of cerebral small vessel disease in relation to risk factors and cognitive consequences: Rotterdam Scan study. Stroke, 2008, 39:2712~2719
2 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008, 39:397~402
3 Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless of their location. Neurology, 2004, 63: 246~253
4 Kramer JH, Reed BR, Mungas D, et al. Executive dysfunction in subcortical ischaemic white matter disease. J Neurol Neurosurg Psychiatry, 2002, 72:217~220
5 Erkinjuntti T, Gao F, Lee DH, et al. Lack of difference in brain hyperintensities between patients with early Alzheimer’s disease and control subjects. Arch Neurol, 1994, 51:260~268
6 Wentzel C, Darvesh S, Macknight C, et al. Inter-rater reliability of the diagnosis of vascular cognitive impairment at a memory clinic. Neuroepidemiology, 2000, 19:186~193
7 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37: 2220~2241.
8 Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke, 2001, 32:1318~1322
9 van Straaten EC, Fazekas F, Rostrup E, et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke, 2006, 37:836~840
10 Wentzel C, Darvesh S, Macknight C, et al. Inter-rater reliability of thediagnosis of vascular cognitive impairment at a memory clinic. Neuroepidemiology, 2000, 19:186~93
11 Swartz RH, Black SE. Anterior-medial thalamic lesions in dementia: frequent, and volume dependently associated with sudden cognitive decline. J Neurol Neurosurg Psychiatry, 2006, 77:1307~1312
12 Scheltens P, Barkhof F, Valk J, et al. White matter lesions on magnetic resonance imaging in clinically diagnosed Alzheimer’s disease. Evidence for heterogeneity. Brain, 1992,115:735~748
13 de Leeuw FE, de Groot JC, Achten E, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry, 2001,70:9~14
14 Longstreth W Jr, Manolio TA, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke, 1996, 27: 1274~1282
15 Liao D, Cooper L, Cai J, et al. Presence and severity of cerebral white matter lesions and hypertension, its treatment, and its control. The ARIC study. Atherosclerosis risk in communities study. Stroke, 1996, 27: 2262~2270
16 Sijens PE, Oudkerk M, De Leeuw FE, et al. 1H Chemical shift imaging of the human brain at age 60-90 years reveals metabolic differences between women and men. Magn Reson Med, 1999, 42:24~31
17 Garrett KD, Cohen RA, Paul RH, et al. Computer-mediated measurement and subjective ratings of white matter hyperintensities in vascular dementia: relationships to neuropsychological performance. Clin Neuropsychol, 2004, 18:50~52
18 Gold G, Kovari E, Hof PR, et al. Sorting out the clinical consequences of ischemic lesions in brain aging: a clinicopathological approach. J Neurol Sci, 2007, 257:17~22
1 Vermeer SE, Prins ND, den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med, 2003, 348: 1215~1222
2 Jellinger KA, Attems J. Incidence of cerebrovascular lesions in Alzheimer’s disease: a postmortem study. Acta Neuropathol, 2003, 105: 14 ~17
3 Snowdon DA, Greiner LH, Mortimer JA, et al. Brain infarction and the clinical expression of Alzheimer disease. The nun study. JAMA, 1997, 277:813~ 817
4 Gold G, Kovari E, Corte G, et al. Clinical validity of Aβ-protein deposition staging in brain aging and Alzheimer disease. J Neuropathol Exp Neurol, 2001, 60:946~952
5 Giannakopoulos P, Herrmann FR, Bussiere T, et al. Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology, 2003, 60:1495~1500
6 Kovari E, Gold G, Herrmann FR, et al. Cortical microinfarcts and demyelination significantly affect cognition in brain aging. Stroke, 2004, 35:410~414
7 de Groot JC, de Leeuw FE, Oudkerk M, et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol, 2002, 52:335~341
8 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008, 39:397~402
9 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37: 2220~2241
10 Jokinen H, Kalska H, Ylikoski R, et al. MRI-defined subcortical ischemicvascular disease: baseline clinical and neuropsychological findings. The LADIS Study. Cerebrovasc Dic, 2009, 27:336~344
11 Matsusue E, Sugihara S, Fujii S, et al. White matter changes in elderly people: MR-pathologic correlations. Magn Reson Med Sci, 2006, 5: 99~104
12 Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke, 2001, 32:1318~1322
13 Ross GW, Petrovitch H, White LR, et al. Characterization of risk factors for vascular dementia. The Honolulu Asia Aging Study. Neurology, 1999, 53:337~343
14 Garde E, Mortensen EL, Rostrup E, et al. Decline in intelligence is associated with progression in white matter hyperintensity volume. J Neurol Neurosurg Psychiatry, 2005, 76:1289~1291
15 Erkinjuntti T, Inzitari D, Panton I, et al. Limitations of clinical criteria for the diagnosis of vascular dementia in clinical trials. Is a focus on subcortical vascular dementia a solution? Ann NY Acad Sci, 2000, 903: 262~272
16 Chui HC. Subcortical ischemic vascular dementia. Neurol Clin, 2007, 25: 717~740
17 Roman GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol, 2002, 1:426~436
18 Sachdev PS, Brodaty H, Valenzuela MJ, et al. The neuropsychological profile of vascular cognitive impairment in stroke and TIA patients. Neurology, 2004, 62:912~919
19 Liem MK, van der Grond J, Haan J, et al. Lacunar infarcts are the main correlate with cognitive dysfunction in CADASIL. Stroke, 2007, 38: 923~928
20 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008, 39:397~402
21 Sabri O, Ringelstein EB, Hellwig D, et al. Neuropsychological impairment correlates with hypoperfusion and hypometabolism but not with severity of white matter lesions on MRI in patients with cerebral microangiopathy. Stroke, 1999, 30:556~566
22 de Groot JC, de Leeuw FE, Oudkerk M, et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol, 2002, 52:335~341
23 Vermeer SE, Prins ND, den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med, 2003, 348: 1215~1222
24 Prins ND, van Dijk EJ, den Heijer T, et al. Cerebral small-vessel disease and decline in information processing speed, executive function and memory. Brain, 2005, 128:2034~2041
25 Van Zandvoort MJ, De Haan EH, et al. Chronic cognitive disturbances after a single supratentorial lacunar infarct. Neuropsychol Behav Neurol, 2001, 14:98~102
26 Van der Werf YD, Scheltens P, Lindeboom J, et al. Deficits of memory, executive functioning and attention following infarction in the thalamus; a study of 22 cases with localised lesions. Neuropsychologia, 2003, 41: 1330~1344
27 Cummings JL. Frontal subcortical circuits and human behavior. Arch Neurol, 1993, 50:873~80
28 Reed BR, Eberling JL, Mungas D, et al. Effects of white matter lesions and lacunes on cortical function. Arch Neurol, 2004, 61:1545~1550.
29 van der Flier WM, van Straaten EC, Barkhof F, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke, 2005, 36:2116~2120
1 Bowler JV. Vascular cognitive impairment. J Neurol Neurosurg Psychiatry, 2005, 76(Suppl V):35 ~ 44
2 Hachinski VC, Bowler JV. Vascular cognitive impairment. Neurology, 1993, 43:2159 ~ 2160
3 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorder and Stroke-Canadian Stroke Network Vascular cognitive impairment harmonization standards. Stroke, 2006, 37:2220 ~ 2241
4 Ingles JL, Boulton DC, Fisk JD, et al. Preclinical vascular cognitive impairment and Alzheimer disease: neuropsychological test performance 5 years before diagnosis. Stroke, 2007, 38:1148~1153
5 O'Brien JT. Vascular cognitive impairment. Am J Geriatr Psychiatry, 2006, 14: 724 ~ 733
6 O’Brien JT, Erkinjuntti T, Reisberg B, et al. Vascular cognitive impairment. Lancet Neurology, 2003, 2:89 ~ 98
7 Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol, 2005, 62:1160 ~ 1163
8 Sachdev PS. Vascular cognitive disorder. Int J Geriatr Psychiatry, 1999, 14: 402 ~ 403
9 Rockwood K, Howard K, MacKnight C, et al. Spectrum of disease in vascular cognitive impairment. Neuroepidemiology, 1999, 18:248 ~ 254
10 Leblanc GG, Meschia JF, Stuss DT, et al. Genetics of Vascular Cognitive Impairment. Stroke, 2006, 37:248 ~ 255
11 Feldman H, Levy AR, Hsiung GY, et al. A Canadian Cohort Study of Cognitive Impairment and Related Dementias (ACCORD): Study methods and baseline results. Neuroepidemiology, 2003, 22:265 ~ 274
12 Garrett KD, Browndyke JN, Whelihan W, et al. The neuropsychological profile of vascular cognitive impairment–no dementia: Comparisons to patients at risk for cerebrovascular disease and vascular dementia. ArchClin Neuropsychol, 2004, 19:745~757
13 Sachdev PS, Brodaty H, Valenzuela MJ, et al. The neuropsychological profile of vascular cognitive impairment in stroke and TIA patients. Neurology, 2004, 62: 912 ~ 919
14 Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke, 2001, 32:1318 ~ 1322
15 M?ntyl? R, Erkinjuntti T, Salonen O, et al. Variable Agreement Between Visual Rating Scales for White Matter Hyperintensities on MRI: Comparison of 13 Rating Scales in a Poststroke Cohort. Stroke, 1997, 28:1614 ~ 1623
16 Frisoni GB, P Scheltens, S Galluzzi, et al. Neuroimaging tools to rate regional atrophy, subcortical cerebrovascular disease, and regional cerebral blood flow and metabolism: consensus paper of the EADC. J Neurol Neurosurg Psychiatry, 2003, 74:1371 ~ 1381
17 Nys GM, van Zandvoort MJ, de Kort PL, et al. Cognitive disorders in acute stroke: prevalence and clinical determinants. Cerebrovasc Dis, 2007, 23: 408 ~ 416
18 Kuller LH, Lopez OL, Jagust WJ, et al. Determinants of vascular dementia in the Cardiovascular Health Study. Neurology, 2005, 64:1548 ~ 1552
19何国军,陈俊抛,温志波,等。阿尔茨海默病与血管性痴呆患者侧脑室、外侧裂的定量测定及影像学特征。中国临床康复,2004, 8: 2450~2451
20 Grau-Olivares M, Bartrés-Faz D, Arboix A, et al. Mild cognitive impairment after lacunar infarction: voxel-based morphometry and neuropsychological assessment. Cerebrovasc Dis, 2007, 23: 353 ~ 361
21 Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless their location. Neurology, 2004, 63:246 ~ 253
22何国军,陈俊抛,温志波,等。阿尔茨海默病与血管性痴呆患者白质疏松的MRI定量研究。中国临床康复,2004;8:6066 ~ 6067
23 Au R, Massaro JM, Wolf PA, et al. Association of White Matter Hyperintensity Volume With Decreased Cognitive Functioning: The Framingham Heart Study. Arch Neurol, 2006, 63:246 ~ 250
24 Bowler JV. Vascular Cognitive Impairment. Stroke, 2004, 35:386 ~ 388
25 Yoshita M, Fletcher E, Harvey D, et al. Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurology, 2006, 67:2192 ~ 2198
26 Yoshikawa T, Murase K, Oku N, et al. Statistical image analysis of cerebral blood flow in vascular dementia with small-vessel disease. J Nucl Med, 2003, 44:505 ~ 511
27 Kwan LT, Reed BR, Eberling JL, et al. Effects of subcortical cerebral infarction on cortical glucose metabolism and cognitive function. Arch Neurol, 1999, 56:809 ~ 814
28 Reed BR, Eberling JL, Mungas D, et al. Frontal lobe hypometabolism predicts cognitive decline in patients with lacunar infarction. Arch Neurol, 2001, 58:493 ~ 497
29 DeCarli C, Murphy DGM, Tranh M, et al. The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology, 1995,45:2077 ~ 2084
30 Sabri O, Ringelstein EB, Hellwig D, et al. Neuropsychological impairment correlates with hypoperfusion and hypometabolism but not with severity of white matter lesions on MRI in patients with cerebral microangiopathy. Stroke, 1999, 30:556 ~ 566
31 Behrens TE, Johansen-Berg H, Woolrich MW, et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci, 2003, 6:750 ~ 757
32 O’Sullivan M, Summers PE, Jones DK, et al. Normal-appearing white matter in ischemic leukoaraiosis: a diffusion tensor MRI study. Neurology, 2001, 57:2307~2310
33 Wardlaw JM, Sandercock PA, Dennis MS, et al. Is breakdown of theblood-brain barrier responsible for lacunar stroke, leukoaraiosis, and dementia? Stroke, 2003, 34:806~812
34 Wallin A, Sjogren M. Cerebrospinal fluid cytoskeleton proteins in patients with subcortical white-matter dementia. Mech Ageing Dev, 2001, 122: 1937 ~ 1949
1 Roman GC. Vascular dementia revisited: diagnosis, pathogenesis, treatment, and prevention. Med Clin North Am, 2002,86:477~499
2 Hachinski V, Iadecola C, Petersen RC, et al. National Institute of Neurological Disorders and Stroke-Canadian Stroke Network vascular cognitive impairment harmonization standards. Stroke, 2006, 37:2220~2241
3 O'Brien JT, Erkinjuntti T, Reisberg B, et al. Vascular cognitive impairment. Lancet Neurol, 2003,2:89~98
4 Erkinjuntti T. Subcortical ischemic vascular disease and dementia. Int Psychogeriatr, 2003,15 Suppl 1:23~26
5 Chui H. Dementia due to subcortical ischemic vascular disease. Clin Cornerstone, 2001,3:40~51
6 Roman GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol, 2002,1:426~436
7 Shinkawa A, Ueda K, Kiyohara Y, et al. Silent cerebral infarction in a community-based autopsy series in Japan. The Hisayama Study. Stroke, 1995, 26:380~385
8 Longstreth WT Jr, Bernick C, Manolio TA, et al. Lacunar infarcts defined by magnetic resonance imaging of 3660 elderly people: the Cardiovascular Health Study. Arch Neurol, 1998,55:1217~1225
9 Longstreth WT Jr, Manolio TA, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke, 1996, 27:1274~1282
10 Garcia JH, Lassen NA, Weiller C, et al. Ischemic stroke and incomplete infarction. Stroke, 1996,27:761~765
11 Pantoni L, Garcia JH. The significance of cerebral white matter abnormalities 100 years after Binswanger's report. A review. Stroke, 1995, 26:1293~1301
12 Tanaka K, Wada N, Hori K, et al. Chronic cerebral hypoperfusion disrupts discriminative behavior in acquired-learning rats. J Neurosci Methods, 1998,84:63~68
13 Kudo T, Takeda M, Tanimukai S, et al. Neuropathologic changes in the gerbil brain after chronic hypoperfusion. Stroke, 1993, 24:259~264; discussion 265
14 Tanaka K, Wada N, Ogawa N. Chronic cerebral hypoperfusion induces transient reversible monoaminergic changes in the rat brain. Neurochem Res, 2000,25:313~320.
15 De Reuck J, Decoo D, Hasenbroekx MC, et al. Acetazolamide vasoreactivity in vascular dementia: a positron emission tomographic study. Eur Neurol, 1999,41:31~36
16 Kuwabara Y, Ichiya Y, Sasaki M, et al. Time dependency of the acetazolamide effect on cerebral hemodynamics in patients with chronic occlusive cerebral arteries. Early steal phenomenon demonstrated by [15O]H2O positron emission tomography. Stroke, 1995,26:1825~1829
17 Yao H, Sadoshima S, Kuwabara Y, et al. Cerebral blood flow and oxygen metabolism in patients with vascular dementia of the Binswanger type. Stroke, 1990,21:1694~1699
18 Nakane H, Ibayashi S, Fujii K, et al. Cerebral blood flow and metabolism in patients with silent brain infarction: occult misery perfusion in the cerebral cortex. J Neurol Neurosurg Psychiatry, 1998,65:317~321
19 Smith EE, Rosand J, Knudsen KA, et al. Leukoaraiosis is associated with warfarin-related hemorrhage following ischemic stroke. Neurology, 2002, 59:193~197
20 Uggetti C, Egitto MG, Pichiecchio A, et al. Subcortical dementia associated with striking enlargement of the Virchow-Robin spaces and transneural degeneration of the left mammillo-thalamic tract. Cerebrovasc Dis, 2001,12:287~90
21 Capizzano AA, Schuff N, Amend DL, et al. Subcortical ischemic vascular dementia: assessment with quantitative MR imaging and 1H MRspectroscopy. AJNR Am J Neuroradiol, 2000,21:621~630
22 Dichgans M, Herzog J, Gasser T. NOTCH3 mutation involving three cysteine residues in a family with typical CADASIL. Neurology, 2001, 57: 1714~1717
23 Pezzini A, Padovani A. Cerebral amyloid angiopathy-related hemorrhages. Neurol Sci, 2008,29 Suppl 2:S260~263
24 Maat-Schieman M, Roos R, van Duinen S. Hereditary cerebral hemorrhage with amyloidosis-Dutch type. Neuropathology, 2005, 25:288~297
25 Wallin A, Edman A, Blennow K, et al. Stepwise comparative status analysis (STEP): a tool for identification of regional brain syndromes in dementia. J Geriatr Psychiatry Neurol, 1996,9:185~99.
26 Mega MS, Cummings JL. Frontal-subcortical circuits and neuropsychiatric disorders. J Neuropsychiatry Clin Neurosci, 1994, 6: 358~370
27 Kramer JH, Reed BR, Mungas D, et al. Executive dysfunction in subcortical ischaemic vascular disease. J Neurol Neurosurg Psychiatry, 2002, 72:217~220
28 Alexopoulos GS, Kiosses DN, Klimstra S, et al. Clinical presentation of the "depression-executive dysfunction syndrome" of late life. Am J Geriatr Psychiatry, 2002,10:98~106
29 Looi JC, Sachdev PS. Differentiation of vascular dementia from AD on neuropsychological tests. Neurology, 1999,53:670~678
30 Gunning-Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology, 2000, 14:224~232
31 de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and subjective cognitive dysfunction: the Rotterdam Scan Study. Neurology, 2001,56:1539~1545
32 Cohen RA, Paul RH, Ott BR, et al. The relationship of subcortical MRI hyperintensities and brain volume to cognitive function in vasculardementia. J Int Neuropsychol Soc, 2002,8:743~752
33 Vermeer SE, Prins ND, den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med, 2003, 348: 1215~1222
34 Carey CL, Kramer JH, Josephson SA, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke, 2008,39:397~402.
35 Fein G, Di Sclafani V, Tanabe J, et al. Hippocampal and cortical atrophy predict dementia in subcortical ischemic vascular disease. Neurology, 2000,55:1626~1635
36 Du AT, Schuff N, Laakso MP, et al. Effects of subcortical ischemic vascular dementia and AD on entorhinal cortex and hippocampus. Neurology, 2002,58:1635~1641
37 Frisoni GB, Scheltens P, Galluzzi S, et al. Neuroimaging tools to rate regional atrophy, subcortical cerebrovascular disease, and regional cerebral blood flow and metabolism: consensus paper of the EADC. J Neurol Neurosurg Psychiatry, 2003,74:1371~1381
38 Mantyla R, Erkinjuntti T, Salonen O, et al. Variable agreement between visual rating scales for white matter hyperintensities on MRI. Comparison of 13 rating scales in a poststroke cohort. Stroke, 1997, 28: 1614~1623
39 Erkinjuntti T, Inzitari D, Pantoni L, et al. Research criteria for subcortical vascular dementia in clinical trials. J Neural Transm Suppl, 2000, 59: 23~30