Clifford R. Jack

Molecular targeting of Alzheimer's amyloid plaques for contrast-enhanced magnetic resonance imaging.

Smart molecular probes for both diagnostic and therapeutic purposes are expected to provide significant advances in clinical medicine and biomedical research. We describe such a probe that targets beta-amyloid plaques of Alzheimers disease and is detectable by magnetic resonance imaging (MRI) because of contrast imparted by gadolinium labeling. Three properties essential for contrast enhancement of beta-amyloid plaques on MRI exist in this smart molecular probe, putrescine-gadolinium-amyloid-beta peptide: (1) transport across the blood-brain barrier following intravenous injection conferred by the polyamine moiety, (2) binding to plaques with molecular specificity by putrescine-amyloid-beta, and (3) magnetic resonance imaging contrast by gadolinium. MRI was performed on ex vivo tissue specimens at 7 T at a spatial resolution approximating plaque size (62.5 microm(3)), in order to prove the concept that the probe, when administered intravenously, can selectively enhance plaques. The plaque-to-background tissue contrast-to-noise ratio, which was precisely correlated with histologically stained plaques, was enhanced more than nine-fold in regions of cortex and hippocampus following intravenous administration of this probe in AD transgenic mice. Continuing engineering efforts to improve spatial resolution are underway in MRI, which may enable in vivo imaging at the resolution of individual plaques with this or similar contrast probes. This could enable early diagnosis and also provide a direct measure of the efficacy of anti-amyloid therapies currently being developed.

Validation of a Fully Automated 3D Hippocampal Segmentation Method Using Subjects with Alzheimer's Disease, Mild Cognitive Impairment, and Elderly Controls

We introduce a new method for brain MRI segmentation, called the auto context model (ACM), to segment the hippocampus automatically in 3D T1-weighted structural brain MRI scans of subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI). In a training phase, our algorithm used 21 hand-labeled segmentations to learn a classification rule for hippocampal versus non-hippocampal regions using a modified AdaBoost method, based on approximately 18,000 features (image intensity, position, image curvatures, image gradients, tissue classification maps of gray/white matter and CSF, and mean, standard deviation, and Haar filters of size 1x1x1 to 7x7x7). We linearly registered all brains to a standard template to devise a basic shape prior to capture the global shape of the hippocampus, defined as the pointwise summation of all the training masks. We also included curvature, gradient, mean, standard deviation, and Haar filters of the shape prior and the tissue classified images as features. During each iteration of ACM - our extension of AdaBoost - the Bayesian posterior distribution of the labeling was fed back in as an input, along with its neighborhood features as new features for AdaBoost to use. In validation studies, we compared our results with hand-labeled segmentations by two experts. Using a leave-one-out approach and standard overlap and distance error metrics, our automated segmentations agreed well with human raters; any differences were comparable to differences between trained human raters. Our error metrics compare favorably with those previously reported for other automated hippocampal segmentations, suggesting the utility of the approach for large-scale studies.

Comparison of different methodological implementations of voxel-based morphometry in neurodegenerative disease.

Voxel-based morphometry (VBM) is a popular method for probing inter-group differences in brain morphology. Variation in the detailed implementation of the algorithm, however, will affect the apparent results of VBM analyses and in turn the inferences drawn about the anatomic expression of specific disease states. We qualitatively assessed group comparisons of 43 normal elderly control subjects and 51 patients with probable Alzheimers disease, using five different VBM variations. Based on the known pathologic expression of the disease, we evaluated the biological plausibility of each. The use of a custom template and custom tissue class prior probability images (priors) produced inter-group comparison maps with greater biological plausibility than the use of the Montreal Neurological Institute (MNI) template and priors. We present a method for initializing the normalization to a custom template, and conclude that, when incorporated into the VBM processing chain, it yields the most biologically plausible inter-group differences of the five methods presented.

MRI-based hippocampal volumetrics: Data acquisition, normal ranges, and optimal protocol

The process of producing magnetic resonance (MR) volume measurements can be divided into considerations of acquisition and postprocessing of the MR data. With careful attention to both of these, precise and reproducible measurements can be achieved. A statistical description of hippocampal measurements in normal volunteers must be available for comparison if volumetrics are employed either for clinical or research purposes. A wide range in normal hippocampal volume is present in the studies of normal young adults that have been reported to date. This variability is most probably due to interinstitutional differences in hippocampal boundary criteria, and in the software employed for counting pixels in a defined region of interest (ROI). Because the numeric output from the volume measurement procedure is highly technique-dependent, the statistical description of normal should be determined or calibrated at each institution wishing to use these techniques.

Interpreting scan data acquired from multiple scanners: A study with Alzheimer's disease

Large, multi-site studies utilizing MRI-derived measures from multiple scanners present an opportunity to advance research by pooling data. On the other hand, it remains unclear whether or not the potential confound introduced by different scanners and upgrades will devalue the integrity of any results. Although there are studies of scanner differences for the purpose of calibration and quality control, the current literature is devoid of studies that describe the analysis of multi-scanner data with regard to the interaction of scanner(s) with effects of interest. We investigated a data-set of 136 subjects, 62 patients with mild to moderate Alzheimers disease and 74 cognitively normal elderly controls, with MRI scans from one center that were acquired over 10 years with 6 different scanners and multiple upgrades over time. We used a whole-brain voxel-wise analysis to evaluate the effect of scanner, effect of disease, and the interaction of scanner and disease for the 6 different scanners. The effect of disease in patients showed the expected significant reduction of grey matter in the medial temporal lobe. Scanner differences were substantially less than the group differences and only significant in the thalamus. There was no significant interaction of scanner with disease group. We describe the rationale for concluding that our results were not confounded by scanner differences. Similar analyses in other multi-scanner data-sets could be used to justify the pooling of data when needed, such as in studies of rare disorders or in multi-center designs.

Genome-Wide Analysis Reveals Novel Genes Influencing Temporal Lobe Structure with Relevance to Neurodegeneration in Alzheimer’s Disease

In a genome-wide association study of structural brain degeneration, we mapped the 3D profile of temporal lobe volume differences in 742 brain MRI scans of Alzheimers disease patients, mildly impaired, and healthy elderly subjects. After searching 546,314 genomic markers, 2 single nucleotide polymorphisms (SNPs) were associated with bilateral temporal lobe volume (P<5 x 10(-7)). One SNP, rs10845840, is located in the GRIN2B gene which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit. This protein - involved in learning and memory, and excitotoxic cell death - has age-dependent prevalence in the synapse and is already a therapeutic target in Alzheimers disease. Risk alleles for lower temporal lobe volume at this SNP were significantly over-represented in AD and MCI subjects vs. controls (odds ratio=1.273; P=0.039) and were associated with mini-mental state exam scores (MMSE; t=-2.114; P=0.035) demonstrating a negative effect on global cognitive function. Voxelwise maps of genetic association of this SNP with regional brain volumes, revealed intense temporal lobe effects (FDR correction at q=0.05; critical P=0.0257). This study uses large-scale brain mapping for gene discovery with implications for Alzheimers disease.

Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: Tissue-specific intensity normalization and parameter selection

We describe an improved method of measuring brain atrophy rates from serial MRI for multi-site imaging studies of Alzheimers disease (AD). The method (referred to as KN-BSI) improves an existing brain atrophy measurement technique-the boundary shift integral (classic-BSI), by performing tissue-specific intensity normalization and parameter selection. We applied KN-BSI to measure brain atrophy rates of 200 normal and 141 AD subjects using baseline and 1-year MRI scans downloaded from the Alzheimers Disease Neuroimaging Initiative database. Baseline and repeat images were reviewed as pairs by expert raters and given quality scores. Including all image pairs, regardless of quality score, mean KN-BSI atrophy rates were 0.09% higher (95% CI 0.03% to 0.16%, p=0.007) than classic-BSI rates in controls and 0.07% higher (-0.01% to 0.16%, p=0.07) higher in ADs. The SD of the KN-BSI rates was 22% lower (15% to 29%, p<0.001) in controls and 13% lower (6% to 20%, p=0.001) in ADs, compared to classic-BSI. Using these results, the estimated sample size (needed per treatment arm) for a hypothetical trial of a treatment for AD (80% power, 5% significance to detect a 25% reduction in atrophy rate) would be reduced from 120 to 81 (a 32% reduction, 95% CI=18% to 45%, p<0.001) when using KN-BSI instead of classic-BSI. We concluded that KN-BSI offers more robust brain atrophy measurement than classic-BSI and substantially reduces sample sizes needed in clinical trials.

Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics

Tract-Based Spatial Statistics (TBSS) is a popular software pipeline to coregister sets of diffusion tensor Fractional Anisotropy (FA) images for performing voxel-wise comparisons. It is primarily defined by its skeleton projection step intended to reduce effects of local misregistration. A white matter “skeleton” is computed by morphological thinning of the inter-subject mean FA, and then all voxels are projected to the nearest location on this skeleton. Here we investigate several enhancements to the TBSS pipeline based on recent advances in registration for other modalities, principally based on groupwise registration with the ANTS-SyN algorithm. We validate these enhancements using simulation experiments with synthetically-modified images. When used with these enhancements, we discover that TBSSs skeleton projection step actually reduces algorithm accuracy, as the improved registration leaves fewer errors to warrant correction, and the effects of this projections compromises become stronger than those of its benefits. In our experiments, our proposed pipeline without skeleton projection is more sensitive for detecting true changes and has greater specificity in resisting false positives from misregistration. We also present comparative results of the proposed and traditional methods, both with and without the skeleton projection step, on three real-life datasets: two comparing differing populations of Alzheimers disease patients to matched controls, and one comparing progressive supranuclear palsy patients to matched controls. The proposed pipeline produces more plausible results according to each diseases pathophysiology.

Voxelwise gene-wide association study (vGeneWAS): Multivariate gene-based association testing in 731 elderly subjects

Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56±6.82SD years; 430 males) from the Alzheimers Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.

Antemortem Differential Diagnosis of Dementia Pathology using Structural MRI: Differential-STAND

The common neurodegenerative pathologies underlying dementia are Alzheimers 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.