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1.  Antemortem Differential Diagnosis of Dementia Pathology using Structural MRI: Differential-STAND 
NeuroImage  2010;55(2):522-531.
The common neurodegenerative pathologies underlying dementia are Alzheimer’s disease (AD), Lewy body disease (LBD) and Frontotemporal lobar degeneration (FTLD). Our aim was to identify patterns of atrophy unique to each of these diseases using antemortem structural-MRI scans of pathologically-confirmed dementia cases and build an MRI-based differential diagnosis system. Our approach of creating atrophy maps using structural-MRI and applying them for classification of new incoming patients is labeled Differential-STAND (Differential-diagnosis based on STructural Abnormality in NeuroDegeneration). Pathologically-confirmed subjects with a single dementing pathologic diagnosis who had an MRI at the time of clinical diagnosis of dementia were identified: 48 AD, 20 LBD, 47 FTLD-TDP (pathology-confirmed FTLD with TDP-43). Gray matter density in 91 regions-of-interest was measured in each subject and adjusted for head-size and age using a database of 120 cognitively normal elderly. The atrophy patterns in each dementia type when compared to pathologically-confirmed controls mirrored known disease-specific anatomic patterns: AD-temporoparietal association cortices and medial temporal lobe; FTLD-TDP-frontal and temporal lobes and LBD-bilateral amygdalae, dorsal midbrain and inferior temporal lobes. Differential-STAND based classification of each case was done based on a mixture model generated using bisecting k-means clustering of the information from the MRI scans. Leave-one-out classification showed reasonable performance compared to the autopsy gold-standard and clinical diagnosis: AD (sensitivity:90.7%; specificity:84 %), LBD (sensitivity:78.6%; specificity:98.8%) and FTLD-TDP (sensitivity:84.4%; specificity:93.8%). The proposed approach establishes a direct a priori relationship between specific topographic patterns on MRI and “gold standard” of pathology which can then be used to predict underlying dementia pathology in new incoming patients.
PMCID: PMC3039279  PMID: 21195775
MRI; Alzheimer’s disease; Lewy body disease; Frontotemporal lobar degeneration
2.  Time-to-event Voxel Based Techniques to Assess Regional Atrophy Associated with MCI Risk of Progression to AD 
NeuroImage  2010;54(2):985-991.
When using imaging to predict time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD), time-to-event statistical methods account for varying lengths of follow-up times among subjects whereas two-sample t-tests in voxel-based morphometry (VBM) do not. Our objectives were to apply a time-to-event voxel-based analytic method to identify regions on MRI where atrophy is associated with significantly increased risk of future progression to AD in subjects with MCI and to compare it to traditional voxel-level patterns obtained by applying two-sample methods. We also compared the power required to detect an association using time-to-event methods versus two-sample approaches.
Subjects with MCI at baseline were followed prospectively. The event of interest was clinical diagnosis of AD. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on rank-transformed voxel-level gray matter density (GMD) estimates.
The greatest risk of progression to AD was associated with atrophy of the medial temporal lobes. Patients ranked at the 25th percentile of GMD in these regions had more than a doubling of risk of progression to AD at a given time-point compared to patients at the 75th percentile. Power calculations showed the time-to-event approach to be more efficient than the traditional two-sample approach.
We present a new voxel-based analytic method that incorporates time-to-event statistical methods. In the context of a progressive disease like AD, time-to-event VBM seems more appropriate and powerful than traditional two-sample methods.
PMCID: PMC2997139  PMID: 20832487
Alzheimer Disease; mild cognitive impairment; magnetic resonance imaging; Cox proportional hazards model
3.  Antemortem MRI based Structural Abnormality Index (STAND)-Scores Correlate with Postmortem Braak Neurofibrillary Tangle Stage 
NeuroImage  2008;42(2):559-567.
The clinical diagnosis of Alzheimer Disease (AD) does not exactly match the pathological findings at autopsy in every subject. Therefore, in-vivo imaging measures, such as Magnetic Resonance Imaging (MRI) that measure anatomical variations in each brain due to atrophy, would be clinically useful independent supplementary measures of pathology. We have developed an algorithm that extracts atrophy information from individual patient’s 3D MRI scans and assigns a STructural Abnormality iNDex (STAND)-score to the scan based on the degree of atrophy in comparison to patterns extracted from a large library of clinically well characterized AD and CN (cognitively normal) subject’s MRI scans. STAND-scores can be adjusted for demographics to give adjusted-STAND (aSTAND)-scores which are typically > 0 for subjects with abnormal brains. Since histopathological findings are considered to represent the “ground truth”, our objective was to assess the sensitivity of aSTAND-scores to pathological AD staging. This was done by comparing antemortem MRI based aSTAND-scores with post mortem grading of disease severity in 101 subjects who had both antemortem MRI and postmortem Braak neurofibrillary tangle (NFT) staging. We found a rank correlation of 0.62 (p<0.0001) between Braak NFT stage and aSTAND-scores. The results show that optimally extracted information from MRI scans such as STAND-scores accurately capture disease severity and can be used as an independent approximate surrogate marker for in-vivo pathological staging as well as for early identification of AD in individual subjects.
PMCID: PMC3097053  PMID: 18572417
Alzheimer Disease; neurofibrillary tangles; amnestic mild cognitive impairment; Braak NFT stage; magnetic resonance imaging
4.  Alzheimer's Disease Diagnosis in Individual Subjects using Structural MR Images: Validation Studies 
NeuroImage  2007;39(3):1186-1197.
To develop and validate a tool for Alzheimer's disease (AD) diagnosis in individual subjects using support vector machine (SVM) based classification of structural MR (sMR) images.
Libraries of sMR scans of clinically well characterized subjects can be harnessed for the purpose of diagnosing new incoming subjects.
190 patients with probable AD were age- and gender-matched with 190 cognitively normal (CN) subjects. Three different classification models were implemented: Model I uses tissue densities obtained from sMR scans to give STructural Abnormality iNDex (STAND)-score; and Models II and III use tissue densities as well as covariates (demographics and Apolipoprotein E genotype) to give adjusted-STAND (aSTAND)-score. Data from 140 AD and 140 CN were used for training. The SVM parameter optimization and training was done by four-fold cross validation. The remaining independent sample of 50 AD and 50 CN were used to obtain a minimally biased estimate of the generalization error of the algorithm.
The CV accuracy of Model II and Model III aSTAND-scores was 88.5% and 89.3% respectively and the developed models generalized well on the independent test datasets. Anatomic patterns best differentiating the groups were consistent with the known distribution of neurofibrillary AD pathology.
This paper presents preliminary evidence that application of SVM-based classification of an individual sMR scan relative to a library of scans can provide useful information in individual subjects for diagnosis of AD. Including demographic and genetic information in the classification algorithm slightly improves diagnostic accuracy.
PMCID: PMC2390889  PMID: 18054253
support vector machines; classification; diagnosis; Alzheimer's
5.  Rates of brain atrophy over time in autopsy proven frontotemporal dementia and Alzheimer disease 
NeuroImage  2007;39(3):1034-1040.
Rates of brain loss have been shown to accelerate over time in early Alzheimer’s disease (AD); however the trajectory of change in frontotemporal lobar degeneration with ubiquitin immunoreactive-changes (FTLD-U) is unknown. This study compared the progression of atrophy over multiple MRI in subjects with autopsy confirmed AD and FTLD-U. Nine subjects with autopsy confirmed FTLD-U, and nine with autopsy confirmed AD, were identified that had three or more serial MRI. The boundary-shift integral was used to calculate change over time in whole brain and ventricular volume. A hierarchical regression model was used to estimate the slope of volume change in AD and FTLD-U over time and to estimate differences in the slopes across the subject groups. Whole brain volume loss did not deviate from a linear rate over time in both AD and FTLD-U subjects, although this may be due to limited sample size. The FTLD-U subjects had a faster rate (23ml/yr) than the AD subjects (10ml/yr). The rate of ventricular expansion accelerated over time. At the point when each subject had a Clinical Dementia Rating Sum-of-Boxes score of 6 the annual rate was 7ml/yr in FTLD-U and 5ml/yr in AD. These rates of change increased by an estimated 1.66ml/yr in FTLD-U, and 0.44ml/yr in AD, although these estimates were not significantly different between the two groups. The trajectory of brain and ventricular changes were similar in AD and FTLD-U suggesting that it is independent of pathology, although subjects with FTLD-U show a more rapidly progressive decline.
PMCID: PMC2268641  PMID: 17988893

Results 1-5 (5)