Diffusion tensor imaging and Tract-Based Spatial Statistics in Alzheimer's disease and mild cognitive impairment

We aimed to explore the changes in fractional anisotropy (FA) in subjects with mild cognitive impairment (MCI) and Alzheimer's disease (AD) by analyzing diffusion tensor imaging (DTI) data using the Tract-Based Spatial Statistics (TBSS). DTI data were collected from 17 AD patients, 27 MCI subjects and 19 healthy controls. Voxel-based analysis with TBSS was used to compare FA among the three groups. Additionally, guided by TBSS findings, a region of interest (ROI)-based analysis along the TBSS skeleton was performed on group-level and the accuracy of the method was assessed by the back-projection of ROIs to the native space FA. Neurofiber tracts with decreased FA included: the parahippocampal white matter, cingulum, uncinate fasciculus, inferior and superior longitudinal fasciculus, corpus callosum, fornix, tracts in brain stem, and cerebellar tracts. Quantitative ROI-analysis further demonstrated the significant decrease on FA values in AD patients relative to controls whereas FA values of MCI patients were found in between the controls and AD patients. We conclude that TBSS is a promising method in examining the degeneration of neurofiber tracts in MCI and AD patients.     

White matter changes in mild cognitive impairment and AD: A diffusion tensor imaging study

Diffusion tensor imaging (DTI) can detect, in vivo, the directionality of molecular diffusion and estimate the microstructural integrity of white matter (WM) tracts. In this study, we examined WM changes in patients with Alzheimer's disease (AD) and in subjects with amnestic mild cognitive impairment (MCI) who are at greater risk for developing AD. A DTI index of WM integrity, fractional anisotropy (FA), was calculated in 14 patients with probable mild AD, 14 participants with MCI and 21 elderly healthy controls (NC). Voxel-by-voxel comparisons showed significant regional reductions of FA in participants with MCI and AD compared to controls in multiple posterior white matter regions. Moreover, there was substantial overlap of locations of regional decrease in FA in the MCI and AD groups. These data demonstrate that white matter changes occur in MCI, prior to the development of dementia.     

Cingulum fiber diffusivity and CSF T-tau in patients with subjective and mild cognitive impairment

Diffusion tensor imaging (DTI) and CSF biomarkers are useful diagnostic tools to differentiate patients with mild cognitive impairment (MCI) from normal controls, and may help predict conversion to dementia. Total Tau protein (T-tau) and DTI parameters are both markers for axonal damage, thus it is of interest to determine if DTI parameters are associated with elevated CSF T-tau levels in patients with cognitive impairment. For this purpose, patients with subjective cognitive impairment (SCI) and MCI were recruited from a university based memory clinic.Regions of interest were used to determine fractional anisotropy (FA), radial diffusivity (DR) and axial diffusivity (DA) in known white matter tracts in patients with MCI (n = 39) and SCI (n = 8) and 26 cognitively healthy controls. Significant lower FA and higher DR values were observed in patients with pathological vs. patients with normal CSF T-tau levels and vs. controls in left posterior cingulum fibers. T-tau values were negatively correlated with FA and positively correlated with DR values in the posterior cingulum fibers.Cingulum fiber diffusivity was related to T-tau pathology in SCI/MCI patients and altered DR may suggest that loss of myelin contributes to early white matter changes in patients at risk of developing Alzheimer's disease (AD).     

Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson's correlation

The aim of this paper is to obtain discriminant features from two scalar measures of Diffusion Tensor Imaging (DTI) data, Fractional Anisotropy (FA) and Mean Diffusivity (MD), and to train and test classifiers able to discriminate Alzheimer's Disease (AD) patients from controls on the basis of features extracted from the FA or MD volumes. In this study, support vector machine (SVM) classifier was trained and tested on FA and MD data. Feature selection is done computing the Pearson's correlation between FA or MD values at voxel site across subjects and the indicative variable specifying the subject class. Voxel sites with high absolute correlation are selected for feature extraction. Results are obtained over an on-going study in Hospital de Santiago Apostol collecting anatomical T1-weighted MRI volumes and DTI data from healthy control subjects and AD patients. FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in several cross-validation studies, supporting the usefulness of DTI-derived features as an image-marker for AD and to the feasibility of building Computer Aided Diagnosis systems for AD based on them.     

Diagnostic utility of hippocampal size and mean diffusivity in amnestic MCI

Hippocampus atrophy is a frequent finding in mild cognitive impairment (MCI), whereas diffusion-tensor-imaging (DTI) has demonstrated its value to detect subtle brain tissue changes in several neuropsychiatric diseases including MCI. To compare the diagnostic accuracy of both methods, high resolution MRI scans for hippocampus volumetry, and co-registered DTI-scans for ROI-based mean diffusivity (MD) and fractional anisotropy (FA) were carried out in 18 patients with amnestic MCI (7 females, age 67.3+/-8.7 years, MMSE 25.2+/-2.2) and 18 controls (age 66.9+/-9.0 years, MMSE 28.7+/-1.0). Diagnostic properties of normalized hippocampus volume (HV) and DTI measures with regard to MCI status were estimated by receiver operating characteristics (ROC) analyses and logistic regression. Parameters of the left hippocampus showed superior predictive power when compared to the right. At a specificity set to 80%, left HV had low sensitivity (50%); left hippocampal MD values revealed superior sensitivity (89%), similar to left hippocampal FA (78%). The results demonstrate higher sensitivity of DTI-derived left hippocampal parameters than volume measures in detecting subtle hippocampal abnormalities related to MCI.     

Longitudinal CSF and MRI biomarkers improve the diagnosis of mild cognitive impairment

The diagnosis of Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI) is limited because it is based on non-specific behavioral and neuroimaging findings. The lesions of Alzheimer's disease: amyloid beta (Abeta) deposits, tau pathology and cellular oxidative damage, affect the hippocampus in the earlier stages causing memory impairment. In a 2-year longitudinal study of MCI patients and normal controls, we examined the hypothesis that cerebrospinal fluid (CSF) markers for these pathological features improve the diagnostic accuracy over memory and magnetic resonance imaging (MRI)-hippocampal volume evaluations. Relative to control, MCI patients showed decreased memory and hippocampal volumes and elevated CSF levels of hyperphosphorylated tau and isoprostane. These two CSF measures consistently improved the diagnostic accuracy over the memory measures and the isoprostane measure incremented the accuracy of the hippocampal volume achieving overall diagnostic accuracies of about 90%. Among MCI patients, over 2 years, longitudinal hippocampal volume losses were closely associated with increasing hyperphosphorylated tau and decreasing amyloid beta-42 levels. These results demonstrate that CSF biomarkers for AD contribute to the characterization of MCI.     

Prediction of Alzheimer's disease in mild cognitive impairment: a prospective study in Taiwan

The relationship between apolipoprotein E (ApoE) and clinical manifestations of mild cognitive impairment (MCI) has not been investigated in non-Caucasian populations. This prospective study was conducted in an ethnic Chinese population to evaluate the correlations of ApoE genotype, cognitive performance, medial temporal structure volumes, and clinical outcome in amnestic MCI. Twenty normal elders, 58 MCI, and 20 mild Alzheimer's disease (AD) patients received neuropsychological, MRI, and ApoE genotype assessments at baseline. Patients with MCI had intermediate cognitive performance and hippocampal volumes between those in normal and AD groups. In each diagnostic group, 4 carriers (E4+) consistently had smaller hippocampal volume than non-carriers (E4−) did. Nineteen MCI subjects (32.7%) converted to AD during the 3-year study period. Compared with MCI non-converters and E4− MCI converters, E4+ MCI converters had the smallest hippocampal volume. However, 4 was not a predictor for AD. Both cognitive performance and hippocampal volume were predictive for progression to AD. However, stepwise Cox regression model integrating both neuropsychological and radiological variables showed that global cognitive performance was the only significant predictor for AD. A poor global cognitive score may be more crucial than a small hippocampal volume in the prediction of AD.     

基于极限学习机的阿尔兹海默病辅助诊断

阿尔兹海默病是一种渐进发展式的痴呆疾病, 其脑部随着病情发展逐渐出现萎缩。利用磁共振脑图像解剖学特征的变化, 提出一种使用极限学习机来诊断阿尔兹海默病以及轻度认知障碍的方法。采用FreeSurfer软件, 分析从ADNI数据库的818份磁共振图像中得到的脑部解剖学特征。首先对这些特征使用线性回归模型来估计正常衰老引起的萎缩因素, 并将其从特征中去除;随后采用极限学习机作为分类器, 使用处理后的特征来诊断阿尔兹海默病和轻度认知障碍。在实验过程中, 通过十折交叉验证来测试该方法的诊断准确率、敏感度、特异度和曲线下面积。通过100次实验求平均的方式计算得出, 该方法诊断阿尔兹海默病的准确率达到87.62%, 曲线下面积达到94.25%;诊断轻度认知障碍的准确率达到73.38%, 敏感度达到83.88%, 其中年龄矫正能有效提高轻度认知障碍诊断的准确率。实验结果表明, 该方法能有效诊断阿尔兹海默病和轻度认知障碍。     

Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features

The aim of this study is to look for differential effects in white matter (WM) of bipolar disorder (BD) and Alzheimer's disease (AD) patients. We proceed by investigating the feasibility of discriminating between BD and AD patients, and from healthy controls (HC), using multivariate data analysis based on diffusion tensor imaging (DTI) data features. Specifically, support vector machine (SVM) classifiers were trained and tested on fractional anisotropy (FA). Voxel sites are selected as features for classification if their Pearson's correlation between FA values at voxel site across subjects and the indicative variable specifying the subject class is above the threshold set by a percentile of its empirical distribution. To avoid double dipping, selection was performed only on training data in a leave one out cross-validation study. Classification results show that FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in AD vs. HC, BD vs. HC, and AD vs. BD leave-one-out cross-validation studies. The localization of the discriminant voxel sites on a probabilistic tractography atlas shows effects on seven major WM tracts in each hemisphere and two commissural tracts.     

Multiple DTI index analysis in normal aging, amnestic MCI and AD. Relationship with neuropsychological performance

White matter (WM) damage has been reported in Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) in diffusion tensor imaging (DTI) studies. It is, however, unknown how the investigation of multiple tensor indexes in the same patients, can differentiate them from normal aging or relate to patients cognition. Forty-six individuals (15 healthy, 16 a-MCI and 15 AD) were included. Voxel-based tract based spatial-statistics (TBSS) was used to obtain whole-brain maps of main WM bundles for fractional anisotropy (FA), radial diffusivity (DR), axial diffusivity (DA) and mean diffusivity (MD). FA reductions were evidenced among AD patients with posterior predominance. A-MCI patients displayed reduced mean FA in these critical regions, compared to healthy elders. MD increases were widespread in both groups of patients. Interestingly, a-MCI patients exhibited DR increases in overlapping areas of FA shrinkages in AD, whereas DA increases were only observed in AD. Gray matter atrophy explained most DTI differences, except those regarding MD in both groups as well as DR increases in posterior associative pathways among a-MCI cases. FA values were the only DTI measure significantly related to memory performance among patients. Present findings suggest that most DTI-derived changes in AD and a-MCI are largely secondary to gray matter atrophy. Notably however, specific DR signal increases in posterior parts of the inferior fronto-occipital and longitudinal fasciculi may reflect early WM compromise in preclinical dementia, which is independent of atrophy. Finally, global measures of integrity, particularly orientation coherence (FA) of diffusion, appear to be more closely related to the cognitive profile of our patients than indexes reflecting water movement parallel (DA) and perpendicular (DR) to the primary diffusion direction.     

Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer's disease

This study aimed to investigate the atrophy of the posterior cingulate cortex (PCC) and medical temporal lobe (MTL) structures (i.e., the entorhinal cortex (ERC) and hippocampus) and the regional disruption of the cingulum bundle in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. The relationships between atrophy of these structures and regional cingulum disruption were also explored. Three-dimensional MRI and diffusion tensor imaging were applied to 19 MCI, 19 probable AD patients, and 18 normal controls (NC). Fractional anisotropy (FA) values were obtained from three different regions of the cingulum. Both MCI and AD patients showed decreased PCC volumes compared with NC. ERC atrophy was also significant in AD and MCI, while hippocampus atrophy was significant only in AD. MCI patients showed a significant FA decrease in the parahippocampal cingulum (PH-C), whereas AD patients had lower FA values in the posterior cingulate cingulum (PC-C) and PH-C, as compared with NC. However, the middle cingulate cingulum (MC-C) showed no significant FA differences between groups. Moreover, the volumes of MTL structures were significantly correlated with PH-C and PC-C FA values. In terms of PCC functional deficit in MCI or early AD, our results support both the direct effect of PCC atrophy itself and the indirect effect of cingulum fiber degeneration secondary to MTL atrophy.     

Hippocampal formation glucose metabolism and volume losses in MCI and AD.

We used MRI volume sampling with coregistered and atrophy corrected FDG-PET scans to test three hypotheses: 1) hippocampal formation measures are superior to temporal neocortical measures in the discrimination of normal (NL) and mild cognitive impairment (MCI); 2) neocortical measures are most useful in the separation of Alzheimer disease (AD) from NL or MCI; 3) measures of PET glucose metabolism (MRglu) have greater diagnostic sensitivity than MRI volume. Three groups of age, education, and gender matched NL, MCI, and AD subjects were studied. The results supported the hypotheses: 1) entorhinal cortex MRglu and hippocampal volume were most accurate in classifying NL and MCI; 2) both imaging modalities identified the temporal neocortex as best separating MCI and AD, whereas widespread changes accurately classified NL and AD; 3) In most between group comparisons regional MRglu measures were diagnostically superior to volume measures. These cross-sectional data show that in MCI hippocampal formation changes exist without significant neocortical changes. Neocortical changes best characterize AD. In both MCI and AD, metabolism reductions exceed volume losses.     

Imaging markers of mild cognitive impairment: multivariate analysis of CBF SPECT

This study aimed to investigate cross-sectional and longitudinal changes of regional cerebral blood flow (rCBF) in preclinical dementia using single photon emission computed tomography (SPECT). SPECT and cognitive function were investigated in 39 mild cognitive impairment (MCI) subjects and 20 age-matched controls. All subjects were followed longitudinally 19 months on average, 16 MCI subjects progressed to Alzheimer's disease (AD), who were retrospectively defined as progressive mild cognitive impairment (PMCI) at baseline and 23 MCI subjects remained stable and were defined as stable mild cognitive impairment (SMCI) at baseline. SPECT was performed both at the initial investigation and at follow-up. Image data were analyzed using multivariate analysis, SPM and volume of interest (VOI)-based analysis. Significant covariate patterns were derived, which differentiate among PMCI, SMCI and controls at baseline as well as describe the longitudinal progression of PMCI. The combined SPECT and neuropsychology increased the diagnostic accuracy of PMCI at baseline. SPECT and neuropsychological testing can be used objectively for both baseline diagnosis and to monitor changes in brain function during very early AD.     

White matter diffusion alterations in normal women at risk of Alzheimer's disease

Increased white matter mean diffusivity and decreased fractional anisotropy (FA) has been observed in subjects diagnosed with mild cognitive impairment (MCI) and Alzheimer's disease (AD). We sought to determine whether similar alterations of white matter occur in normal individuals at risk of AD. Diffusion tensor images were acquired in 42 cognitively normal right-handed women with both a family history of dementia and at least one apolipoprotein E4 allele. These were compared with images from 23 normal women without either AD risk factor. Group analyses were performed using tract-based spatial statistics. Reduced FA was observed in the fronto-occipital and inferior temporal fasciculi (particularly posteriorly), the splenium of the corpus callosum, subcallosal white matter and the cingulum bundle. These findings demonstrate that specific white matter pathways are altered in normal women at increased risk of AD years before the expected onset of cognitive symptoms.     

Combined 99mTc-ECD SPECT and neuropsychological studies in MCI for the assessment of conversion to AD

Identifying pre-clinical Alzheimer's disease (AD) in subjects with mild cognitive impairment (MCI) is a major issue in clinical diagnosis. Establishing a combination of predictive markers from different fields of research might help in increasing the diagnostic accuracy. Aim of this study was to evaluate the potential role of 99mTc-ECD single photon emission computed tomography (SPECT) and memory scores in predicting conversion to AD in MCI subjects. Thirty-one MCI subjects underwent a clinical and neuropsychological examination, and a regional cerebral blood flow (rCBF) SPECT scan at baseline. Subjects had been followed periodically through 2 years in order to monitor the progression of cognitive symptoms. Canonical variate analysis of principal components was able to separate all subjects who converted to AD from those who remained stable, the former being characterized by a specific hypometabolic pattern, involving the parietal and temporal lobes, precuneus, and posterior cingulate cortex. Canonical correlation analysis of combined baseline memory deficits and rCBF SPECT images identified pre-clinical AD with a sensitivity and specificity of 77.8%. The pattern of hypoperfusion 99mTc-ECD SPECT and the severity of memory deficits predict the risk of progression to probable AD dementia in MCI subjects.     

Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance

The current study tested the accuracy of primary MRI and cerebrospinal fluid (CSF) biomarker candidates and neuropsychological tests for predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia. In a cross-validation paradigm, predictor models were estimated in the training set of AD (N = 81) and elderly control subjects (N = 101). A combination of CSF t-tau/Aβ(1-4) ratio and MRI biomarkers or neuropsychological tests (free recall and trail making test B (TMT-B)) showed the best statistical fit in the AD vs. HC comparison, reaching a classification accuracy of up to 64% when applied to the prediction of MCI conversion (3.3-year observation interval, mean = 2.3 years). However, several single-predictor models showed a predictive accuracy of MCI conversion comparable to that of any multipredictor model. The best single predictors were right entorhinal cortex (prediction accuracy = 68.5% (95% CI (59.5, 77.4))) and TMT-B test (prediction accuracy 64.6% (95% CI (55.5, 73.4%))). In conclusion, short-term conversion to AD is predicted by single marker models to a comparable degree as by multimarker models in amnestic MCI subjects.     

Abnormal integrity of corticocortical tracts in mild cognitive impairment: a diffusion tensor imaging study

Mild cognitive impairment (MCI) has been defined as a transitional state between normal aging and Alzheimer disease. Diffusion tensor imaging (DTI) can estimate the microstructural integrity of white matter tracts in MCI. We evaluated the microstructural changes in the white matter of MCI patients with DTI. We recruited 11 patients with MCI who met the working criteria of MCI and 11 elderly normal controls. The mean diffusivity (MD) and fractional anisotropy (FA) were measured in 26 regions of the brain with the regions of interest (ROIs) method. In the MCI patients, FA values were significantly decreased in the hippocampus, the posterior limb of the internal capsule, the splenium of corpus callosum, and in the superior and inferior longitudinal fasciculus compared to the control group. MD values were significantly increased in the hippocampus, the anterior and posterior limbs of the internal capsules, the splenium of the corpus callosum, the right frontal lobe, and in the superior and the inferior longitudinal fasciculus. Microstructural changes of several corticocortical tracts associated with cognition were identified in patients with MCI. FA and MD values of DTI may be used as novel biomarkers for the evaluation of neurodegenerative disorders.     

Comparison of Regional Gray Matter Atrophy,White Matter Alteration,and Glucose Metabolism as a Predictor of the Conversion to Alzheimer's Disease in Mild Cognitive Impairment

We compared the predictive ability of the various neuroimaging tools and determined the most cost-effective, non-invasive Alzheimer''s disease (AD) prediction model in mild cognitive impairment (MCI) individuals. Thirty-two MCI subjects were evaluated at baseline with [¹⁸F]-fluorodeoxyglucose positron emission tomography (FDG-PET), MRI, diffusion tensor imaging (DTI), and neuropsychological tests, and then followed up for 2 yr. After a follow up period, 12 MCI subjects converted to AD (MCIc) and 20 did not (MCInc). Of the voxel-based statistical comparisons of baseline neuroimaging data, the MCIc showed reduced cerebral glucose metabolism (CMgl) in the temporo-parietal, posterior cingulate, precuneus, and frontal regions, and gray matter (GM) density in multiple cortical areas including the frontal, temporal and parietal regions compared to the MCInc, whereas regional fractional anisotropy derived from DTI were not significantly different between the two groups. The MCIc also had lower Mini-Mental State Examination (MMSE) score than the MCInc. Through a series of model selection steps, the MMSE combined with CMgl model was selected as a final model (classification accuracy 93.8%). In conclusion, the combination of MMSE with regional CMgl measurement based on FDG-PET is probably the most efficient, non-invasive method to predict AD in MCI individuals after a two-year follow-up period.

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相似文献   

基于多模态典型相关特征表达的阿尔兹海默病诊断

对阿尔兹海默(AD)疾病进程的建模研究,有利于在其早期阶段--轻度认知障碍(MCI)进行更准确的诊断。不仅利用多模态影像数据,还分析模态间特征关系,用于增强与AD/MCI相关的特征表达能力。首先,基于典型相关分析融合不同模态间多个感兴趣区域并生成多模态关系特征表达;其次,基于稀疏最小二乘回归损失函数,以此获得稳定且有识别力的相关性表达特征;最后,使用交叉验证方法将随机选择的训练样本用于支持向量机分类模型,再对测试集受试者进行疾病阶段诊断。实验基于Alzheimer′s Disease Neuroimaging Initiative(ADNI)公共数据库的805位受试者,包括AD,MCI和正常受试者(NC)。此方法对于AD vs NC,MCI vs NC和p-MCI(进行性轻度认知障碍)vs s-MCI(稳定性轻度认知障碍)等3种不同类型数据,诊断结果分别为92.01%,74.83%和70.27%。与其他算法相比,分类准确率都有明显提高。表明所提出的方法能够有效应用于多模态数据对阿尔兹海默病的诊断分析研究。     

Mild cognitive impairment as predictor for Alzheimer's disease in clinical practice: effect of age and diagnostic criteria

BACKGROUND: We investigated whether the predictive accuracy of mild cognitive impairment (MCI) for Alzheimer-type dementia (AD) in a clinical setting is dependent on age and the definition of MCI used. METHOD: Non-demented subjects older than 40 (n=320) who attended a memory clinic of a university hospital were reassessed 5 years later for the presence of AD. MCI was diagnosed according to the criteria of amnestic MCI, mild functional impairment (MFI), ageing-associated cognitive decline (AACD), and age-associated memory impairment (AAMI). The main outcome measure was the area under the curve (AUC) of a receiver operating characteristic (ROC) curve. Analyses were conducted on the entire sample and on subgroups of subjects aged 40-54, 55-69 and 70-85 years. RESULTS: A diagnosis of AD at follow-up was made in 58 subjects. Four of them were in the 40-54 age group, 29 in the 55-69 age group and 25 in the 70-85 age group. The diagnostic accuracy in the entire sample was low to moderately high with AUCs ranging from 0.56 (AACD) to 0.75 (amnestic MCI). A good predictive accuracy with an AUC >0.80 was only observed in subjects aged 70-85 using the criteria of amnestic MCI (AUC=0.84). CONCLUSIONS: The predictive accuracy of MCI for AD is dependent on age and the definition of MCI used. The predictive accuracy is good only for amnestic MCI in subjects 70-85 years. As subjects with prodromal AD are often younger than 70, the usefulness of MCI as predictor of AD in clinical practice is limited.