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Background: The cerebrospinal fluid (CSF) biomarkers amyloid-β 1-42 (Aβ42), total and phosphorylated tau (t-tau, p-tau) are increasingly used to assist in the clinical diagnosis of Alzheimer's disease (AD). However, CSF biomarker levels can be affected by confounding factors., Objective: To investigate the association of white matter hyperintensities (WMHs) present in the brain with AD CSF biomarker levels., Methods: We included CSF biomarker and magnetic resonance imaging (MRI) data of 172 subjects (52 controls, 72 mild cognitive impairment (MCI), and 48 AD patients) from 9 European Memory Clinics. A computer aided detection system for standardized automated segmentation of WMHs was used on MRI scans to determine WMH volumes. Association of WMH volume with AD CSF biomarkers was determined using linear regression analysis., Results: A small, negative association of CSF Aβ42, but not p-tau and t-tau, levels with WMH volume was observed in the AD (r2 = 0.084, p = 0.046), but not the MCI and control groups, which was slightly increased when including the distance of WMHs to the ventricles in the analysis (r2 = 0.105, p = 0.025). Three global patterns of WMH distribution, either with 1) a low, 2) a peak close to the ventricles, or 3) a high, broadly-distributed WMH volume could be observed in brains of subjects in each diagnostic group., Conclusion: Despite an association of WMH volume with CSF Aβ42 levels in AD patients, the occurrence of WMHs is not accompanied by excess release of cellular proteins in the CSF, suggesting that WMHs are no major confounder for AD CSF biomarker assessment.

Cerebrospinal fluid (CSF) biomarkers may support the diagnosis of Alzheimer's disease (AD). We studied if the diagnostic power of AD CSF biomarker concentrations, i.e., Aβ42, total tau (t-tau), and phosphorylated tau (p-tau), is affected by differences in lateral ventricular volume (VV), using CSF biomarker data and magnetic resonance imaging (MRI) scans of 730 subjects, from 13 European Memory Clinics. We developed a Matlab-algorithm for standardized automated segmentation analysis of T1 weighted MRI scans in SPM8 for determining VV, and computed its ratio with total intracranial volume (TIV) as proxy for total CSF volume. The diagnostic power of CSF biomarkers (and their combination), either corrected for VV/TIV ratio or not, was determined by ROC analysis. CSF Aβ42 levels inversely correlated to VV/TIV in the whole study population (Aβ42: r = -0.28; p < 0.0001). For CSF t-tau and p-tau, this association only reached statistical significance in the combined MCI and AD group (t-tau: r = -0.15; p-tau: r = -0.13; both p < 0.01). Correction for differences in VV/TIV improved the differentiation of AD versus controls based on CSF Aβ42 alone (AUC: 0.75 versus 0.81) or in combination with t-tau (AUC: 0.81 versus 0.91). In conclusion, differences in VV may be an important confounder in interpreting CSF Aβ42 levels.

Background: The Disease State Index (DSI) is a method which interprets data originating from multiple different sources, assisting the clinician in the diagnosis and follow-up of dementia diseases., Objective: We compared the differences in accuracy in differentiating stable mild cognitive impairment (S-MCI) and progressive MCI (P-MCI) obtained from different data combinations using the DSI., Methods: We investigated 212 cases with S-MCI and 165 cases with P-MCI from the Alzheimer's Disease Neuroimaging Initiative cohort. Data from neuropsychological tests, cerebrospinal fluid, apolipoprotein E (APOE) genotype, magnetic resonance imaging (MRI) and positron emission tomography (PET) were included., Results: The combination of all parameters gave the highest accuracy (accuracy 0.70, sensitivity 0.71, specificity 0.68). In the different categories, neuropsychological tests (0.65, 0.65, 0.65) and hippocampal volumetry (0.66, 0.66, 0.66) achieved the highest accuracy., Conclusion: In addition to neuropsychological testing, MRI is recommended to be included for differentiating S-MCI from P-MCI. APOE genotype, CSF and PET may provide some additional information.

Purpose: To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI and CSF biomarkers., Methods: Altogether 391 MCI cases (158 AD converters) were selected from the ADNI cohort. All the cases had baseline cognitive tests, MRI and/or CSF levels of Aβ1-42 and Tau. Using baseline data, the status of MCI patients (AD or MCI) three years later was predicted using current diagnostic research guidelines and the PredictAD software tool designed for supporting clinical diagnostics. The data used were 1) clinical criteria for episodic memory loss of the hippocampal type, 2) visual MTA, 3) positive CSF markers, 4) their combinations, and 5) when the PredictAD tool was applied, automatically computed MRI measures were used instead of the visual MTA results. The accuracies of diagnosis were evaluated with the diagnosis made 3 years later., Results: The PredictAD tool achieved the overall accuracy of 72% (sensitivity 73%, specificity 71%) in predicting the AD diagnosis. The corresponding number for a clinician's prediction with the assistance of the PredictAD tool was 71% (sensitivity 75%, specificity 68%). Diagnosis with the PredictAD tool was significantly better than diagnosis by biomarkers alone or the combinations of clinical diagnosis of hippocampal pattern for the memory loss and biomarkers (p≤0.037)., Conclusion: With the assistance of PredictAD tool, the clinician can predict AD conversion more accurately than the current diagnostic criteria.

Background: Disease state index and disease state fingerprint represent a novel tool which collates data information from different sources, helping the clinician in the diagnosis and follow-up of dementia diseases. It has been demonstrated that it is applicable in the diagnosis of Alzheimer's disease (AD)., Objective: We applied this novel tool to classify frontotemporal dementia (FTD) cases in comparison with controls, AD, and mild cognitive impairment (MCI) subjects., Methods: Thirty seven patients with FTD, 35 patients with AD, 26 control subjects, and 64 subjects with MCI were included in the study. The disease state index encompassed data from cognitive performance assessed by Mini-Mental State Examination, cerebrospinal fluid biomarkers, MRI volumetric and morphometric parameters as well as APOE genotype., Results: We applied the Disease State Index for comparisons at the group level. The data showed that FTD patients could be differentiated with a high accuracy, sensitivity, and specificity from controls (0.84, 0.84, 0.83) and from MCI (0.79, 0.78, 0.80). However, the correct accuracy was lower in the FTD versus AD comparison (0.69, 0.70, 0.71). In addition, we demonstrated the use of Disease State Fingerprint by comparing one particular FTD case with control, AD, and MCI population data., Conclusion: The results suggest that the Disease State Fingerprint and the underlying Disease State Index are particularly useful in differentiating between normal status and disease in patients with dementia, but it may also help to distinguish between the two dementia diseases, FTD and AD.

Introduction: The aim of this study was to determine whether years of schooling influences regional cortical thicknesses and volumes in Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy age-matched controls., Methods: Using an automated image analysis pipeline, 33 regional cortical thickness and 15 regional volumes measures from MRI images were determined in 121 subjects with MCI, 121 patients with AD, and 113 controls from AddNeuroMed study. Correlations with years of schooling were determined and more highly and less highly educated subjects compared, controlling for intracranial volume, age, gender, country of origin, cognitive status, and multiple testing., Results: After controlling for confounding factors and multiple testing, in the control group, subjects with more education had larger regional cortical thickness in transverse temporal cortex, insula, and isthmus of cingulate cortex than subjects with less education. However, in the AD group, the subjects with more education had smaller regional cortical thickness in temporal gyrus, inferior and superior parietal gyri, and lateral occipital cortex than the subjects with less education. No significant difference was found in the MCI group., Conclusion: Education may increase regional cortical thickness in healthy controls, leading to increased brain reserve, as well as helping AD patients to cope better with the effects of brain atrophy by increasing cognitive reserve.

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., (Copyright © 2009 Elsevier Inc. All rights reserved.)

To study the ability of neuropsychological tests, manual MRI hippocampal volume measures, regional volume and cortical thickness measures to identify subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy age-matched controls. Neuropsychological tests, manual hippocampal volume, automated regional volume and regional cortical thickness measures were performed in 120 AD patients, 120 MCI subjects, and 111 controls. The regional cortical thickness and volumes in MCI subjects were significantly decreased in limbic/paralimbic areas and temporal lobe compared to controls. Atrophy was much more extensive in the AD patients compared to MCI subjects and controls. The combination of neuropsychological tests and volumes revealed the highest accuracy (82% AD vs. MCI; 94% AD vs. control; 83% MCI vs. control). Adding regional cortical thicknesses into the discriminate analysis did not improve accuracy. We conclude that regional cortical thickness and volume measures provide a panoramic view of brain atrophy in AD and MCI subjects. A combination of neuropsychological tests and regional volumes are important when discriminating AD from healthy controls and MCI., (Copyright © 2009 Elsevier Inc. All rights reserved.)

We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD., (Copyright © 2010 Elsevier Inc. All rights reserved.)

We determined predictors of conversion to Alzheimer's disease (AD) from mild cognitive impairment (MCI) with automated magnetic resonance imaging (MRI) regional cortical volume and thickness measures. One hundred amnestic MCI subjects, 118 AD patients, and 94 age-matched healthy controls were selected from AddNeuroMed study. Twenty-four regional cortical volumes and 34 cortical thicknesses were measured with automated image processing software at baseline. Twenty-one subjects converted from MCI to AD determined with the cognitive tests at baseline and 1 year later. The hippocampus, amygdala, and caudate volumes were significantly smaller in progressive MCI subjects than in controls and stable MCI subjects. The cortical volumes achieved higher predictive accuracy than did cognitive tests or cortical thickness. Combining the volumes, thicknesses, and cognitive tests did not improve the accuracy. The volume of amygdala and caudate were independent variables in predicting conversion from MCI to AD. We conclude that regional cortical volume measures are more powerful than those common cognitive tests we used in identifying AD patients at the very earliest stage of the disease., (2010 Elsevier Inc. All rights reserved.)

The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer's disease (AD), but its effect on brain volumes is controversial. We explored the effect of the ε4 allele on regional cortical thickness and volume measurements using an automated pipeline in 111 subjects with mild cognitive impairment (MCI), 115 AD patients, and 107 age-matched healthy controls. The clinical data were used as covariates in the thickness and volume comparisons. The ε4 carriers had significantly smaller volume than non-carriers in caudate (p=0.028) in controls; in amygdala and caudate in the MCI group (p