Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI)

Scales as outcome measures for Alzheimer's disease

The assessment of patient outcomes in clinical trials of new therapeutics for Alzheimer's disease (AD) continues to evolve. In addition to assessing drugs for symptomatic relief, an increasing number of trials are focusing on potential disease-modifying agents. Moreover, participants with AD are being studied earlier in their course of disease. As a result, the limitations of current outcome measures have become more apparent, as has the need for better instruments. In recognition of the need to review and possibly revise current assessment measures, the Alzheimer's Association, in cooperation with industry leaders and academic investigators, convened a Research Roundtable meeting devoted to scales as outcome measures for AD clinical trials. The meeting included a discussion of methodological issues in the use of scales in AD clinical trials, including cross-cultural issues. Specific topics related to the use of cognitive, functional, global, and neuropsychiatric scales were also presented. Speakers also addressed academic and industry initiatives for pooling data from untreated and placebo-treated patients in clinical trials. A number of regulatory topics were also discussed with agency representatives. Panel discussions highlighted areas of controversy, in an effort to gain consensus on various topics.

Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

We used a new method we developed for automated hippocampal segmentation, called the auto context model, to analyze brain MRI scans of 400 subjects from the Alzheimer's disease neuroimaging initiative. After training the classifier on 21 hand-labeled expert segmentations, we created binary maps of the hippocampus for three age- and sex-matched groups: 100 subjects with Alzheimer's disease (AD), 200 with mild cognitive impairment (MCI) and 100 elderly controls (mean age: 75.84; SD: 6.64). Hippocampal traces were converted to parametric surface meshes and a radial atrophy mapping technique was used to compute average surface models and local statistics of atrophy. Surface-based statistical maps visualized links between regional atrophy and diagnosis (MCI versus controls: P = 0.008; MCI versus AD: P = 0.001), mini-mental state exam (MMSE) scores, and global and sum-of-boxes clinical dementia rating scores (CDR; all P < 0.0001, corrected). Right but not left hippocampal atrophy was associated with geriatric depression scores (P = 0.004, corrected); hippocampal atrophy was not associated with subsequent decline in MMSE and CDR scores, educational level, ApoE genotype, systolic or diastolic blood pressure measures, or homocysteine. We gradually reduced sample sizes and used false discovery rate curves to examine the method's power to detect associations with diagnosis and cognition in smaller samples. Forty subjects were sufficient to discriminate AD from normal and correlate atrophy with CDR scores; 104, 200, and 304 subjects, respectively, were required to correlate MMSE with atrophy, to distinguish MCI from normal, and MCI from AD.

Targeting Abeta and tau in Alzheimer's disease, an early interim report

The amyloid beta (Abeta) and tau proteins, which misfold, aggregate, and accumulate in the Alzheimer's disease (AD) brain, are implicated as central factors in a complex neurodegenerative cascade. Studies of mutations that cause early onset AD and promote Abeta accumulation in the brain strongly support the notion that inhibiting Abeta aggregation will prevent AD. Similarly, genetic studies of frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17 MAPT) showing that mutations in the MAPT gene encoding tau lead to abnormal tau accumulation and neurodegeneration. Such genetic studies clearly show that tau dysfunction and aggregation can be central to neurodegeneration, however, most likely in a secondary fashion in relation to AD. Additional pathologic, biochemical, and modeling studies further support the concept that Abeta and tau are prime targets for disease modifying therapies in AD. Treatment strategies aimed at preventing the aggregation and accumulation of Abeta, tau, or both proteins should therefore be theoretically possible, assuming that treatment can be initiated before either irreversible damage is present or downstream, self-sustaining, pathological cascades have been initiated. Herein, we will review recent advances and also potential setbacks with respect to the myriad of therapeutic strategies that are designed to slow down, prevent, or clear the accumulation of either "pathological" Abeta or tau. We will also discuss the need for thoughtful prioritization with respect to clinical development of the preclinically validated modifiers of Abeta and tau pathology. The current number of candidate therapies targeting Abeta is becoming so large that a triage process is clearly needed to insure that resources are invested in a way such that the best candidates for disease modifying therapy are rapidly moved toward clinical trials. Finally, we will discuss the challenges for an appropriate "triage" after potential disease modifying therapies targeting tau and Abeta have entered clinical trials.

Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer's disease

Background

With the exception of APOE ε4 allele, the common genetic risk factors for sporadic Alzheimer's Disease (AD) are unknown.

Methods and Findings

We completed a genome-wide association study on 381 participants in the ADNI (Alzheimer's Disease Neuroimaging Initiative) study. Samples were genotyped using the Illumina Human610-Quad BeadChip. 516,645 unique Single Nucleotide Polymorphisms (SNPs) were included in the analysis following quality control measures. The genotype data and raw genetic data are freely available for download (LONI, http://www.loni.ucla.edu/ADNI/Data/). Two analyses were completed: a standard case-control analysis, and a novel approach using hippocampal atrophy measured on MRI as an objectively defined, quantitative phenotype. A General Linear Model was applied to identify SNPs for which there was an interaction between the genotype and diagnosis on the quantitative trait. The case-control analysis identified APOE and a new risk gene, TOMM40 (translocase of outer mitochondrial membrane 40), at a genome-wide significance level of≤10⁻⁶ (10⁻¹¹ for a haplotype). TOMM40 risk alleles were approximately twice as frequent in AD subjects as controls. The quantitative trait analysis identified 21 genes or chromosomal areas with at least one SNP with a p-value≤10⁻⁶, which can be considered potential “new” candidate loci to explore in the etiology of sporadic AD. These candidates included EFNA5, CAND1, MAGI2, ARSB, and PRUNE2, genes involved in the regulation of protein degradation, apoptosis, neuronal loss and neurodevelopment. Thus, we identified common genetic variants associated with the increased risk of developing AD in the ADNI cohort, and present publicly available genome-wide data. Supportive evidence based on case-control studies and biological plausibility by gene annotation is provided. Currently no available sample with both imaging and genetic data is available for replication.

Conclusions

Using hippocampal atrophy as a quantitative phenotype in a genome-wide scan, we have identified candidate risk genes for sporadic Alzheimer's disease that merit further investigation.

Parallel imaging: is GRAPPA a useful acquisition tool for MR imaging intended for volumetric brain analysis?

Background

The work presented here investigates parallel imaging applied to T1-weighted high resolution imaging for use in longitudinal volumetric clinical studies involving Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) patients. This was in an effort to shorten acquisition times to minimise the risk of motion artefacts caused by patient discomfort and disorientation. The principle question is, "Can parallel imaging be used to acquire images at 1.5 T of sufficient quality to allow volumetric analysis of patient brains?"

Methods

Optimisation studies were performed on a young healthy volunteer and the selected protocol (including the use of two different parallel imaging acceleration factors) was then tested on a cohort of 15 elderly volunteers including MCI and AD patients. In addition to automatic brain segmentation, hippocampus volumes were manually outlined and measured in all patients. The 15 patients were scanned on a second occasion approximately one week later using the same protocol and evaluated in the same manner to test repeatability of measurement using images acquired with the GRAPPA parallel imaging technique applied to the MPRAGE sequence.

Results

Intraclass correlation tests show that almost perfect agreement between repeated measurements of both segmented brain parenchyma fraction and regional measurement of hippocampi. The protocol is suitable for both global and regional volumetric measurement dementia patients.

Conclusion

In summary, these results indicate that parallel imaging can be used without detrimental effect to brain tissue segmentation and volumetric measurement and should be considered for both clinical and research studies where longitudinal measurements of brain tissue volumes are of interest.

The Italian Brain Normative Archive of structural MR scans: norms for medial temporal atrophy and white matter lesions

BACKGROUND AND AIMS

To describe the clinical and neuropsychological features of a large group of cognitively intact persons subjected to brain high-resolution magnetic resonance (MR), to compare them with the general population, and to set norms for medial temporal atrophy and white matter lesions.

METHODS

Participants in the Italian Brain Normative Archive (IBNA) study were 483 consecutive volunteers undergoing MR for reasons unrelated to cognition (migraine or headache, visual and balance or auditory disturbances, paresthesias, and others) and showing no brain damage. Manual tracing of hippocampal and amygdalar volumes and visual rating of white matter lesions were made. The whole study group was stratified by age (</=60 and 60+ yrs) and by the reason for MR prescription.

RESULTS

In the whole group, mean age and education were 52.4+/-13.7 and 9.8+/-4.2 years, respectively, and the prevalence of women was 63%. Clinical, neuropsychological and morphometric features were similar in the stratified subgroups. Neuropsychological features were those expected for age and education based on Italian normative values. Hippocampal and amygdalar volumes were not associated with age, except for the right amygdala (B -0.159, 95% CI -0.28 to -0.03, p=0.016).

CONCLUSIONS

Persons in the IBNA study had clinical and neuropsychological features consistent with that of the general population. Their brain morphometric features may be used as normative references for patients with suspected neurodegenerative disorders.

High-throughput, fully automated volumetry for prediction of MMSE and CDR decline in mild cognitive impairment

Medial temporal lobe (MTL) atrophy is associated with increased risk for conversion to Alzheimer disease, but manual tracing techniques and even semiautomated techniques for volumetric assessment are not practical in the clinical setting. In addition, most studies that examined MTL atrophy in Alzheimer disease have focused only on the hippocampus. It is unknown the extent to which volumes of amygdala and temporal horn of the lateral ventricle predict subsequent clinical decline. This study examined whether measures of hippocampus, amygdala, and temporal horn volume predict clinical decline over the following 6-month period in patients with mild cognitive impairment (MCI). Fully automated volume measurements were performed in 269 MCI patients. Baseline volumes of the hippocampus, amygdala, and temporal horn were evaluated as predictors of change in Mini-mental State Examination and Clinical Dementia Rating Sum of Boxes over a 6-month interval. Fully automated measurements of baseline hippocampus and amygdala volumes correlated with baseline delayed recall scores. Patients with smaller baseline volumes of the hippocampus and amygdala or larger baseline volumes of the temporal horn had more rapid subsequent clinical decline on Mini-mental State Examination and Clinical Dementia Rating Sum of Boxes. Fully automated and rapid measurement of segmental MTL volumes may help clinicians predict clinical decline in MCI patients.

Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline

The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols (Rex et al., 2003). It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides three layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available online (http://Pipeline.loni.ucla.edu).

Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls

We aimed to improve on the single-atlas ventricular segmentation method of (Carmichael, O.T., Thompson, P.M., Dutton, R.A., Lu, A., Lee, S.E., Lee, J.Y., Kuller, L.H., Lopez, O.L., Aizenstein, H.J., Meltzer, C.C., Liu, Y., Toga, A.W., Becker, J.T., 2006. Mapping ventricular changes related to dementia and mild cognitive impairment in a large community-based cohort. IEEE ISBI. 315-318) by using multi-atlas segmentation, which has been shown to lead to more accurate segmentations (Chou, Y., Leporé, N., de Zubicaray, G., Carmichael, O., Becker, J., Toga, A., Thompson, P., 2008. Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease, NeuroImage 40(2): 615-630); with this method, we calculated minimal numbers of subjects needed to detect correlations between clinical scores and ventricular maps. We also assessed correlations between emerging CSF biomarkers of Alzheimer's disease pathology and localizable deficits in the brain, in 80 AD, 80 mild cognitive impairment (MCI), and 80 healthy controls from the Alzheimer's Disease Neuroimaging Initiative. Six expertly segmented images and their embedded parametric mesh surfaces were fluidly registered to each brain; segmentations were averaged within subjects to reduce errors. Surface-based statistical maps revealed powerful correlations between surface morphology and 4 variables: (1) diagnosis, (2) depression severity, (3) cognitive function at baseline, and (4) future cognitive decline over the following year. Cognitive function was assessed using the mini-mental state exam (MMSE), global and sum-of-boxes clinical dementia rating (CDR) scores, at baseline and 1-year follow-up. Lower CSF Abeta(1-42) protein levels, a biomarker of AD pathology assessed in 138 of the 240 subjects, were correlated with lateral ventricular expansion. Using false discovery rate (FDR) methods, 40 and 120 subjects, respectively, were needed to discriminate AD and MCI from normal groups. 120 subjects were required to detect correlations between ventricular enlargement and MMSE, global CDR, sum-of-boxes CDR and clinical depression scores. Ventricular expansion maps correlate with pathological and cognitive measures in AD, and may be useful in future imaging-based clinical trials.

Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset

Structural and functional brain images are playing an important role in helping us understand the changes associated with neurological disorders such as Alzheimer's disease (AD). Recent efforts have now started investigating their utility for diagnosis purposes. This line of research has shown promising results where methods from machine learning (such as Support Vector Machines) have been used to identify AD-related patterns from images, for use in diagnosing new individual subjects. In this paper, we propose a new framework for AD classification which makes use of the Linear Program (LP) boosting with novel additional regularization based on spatial "smoothness" in 3D image coordinate spaces. The algorithm formalizes the expectation that since the examples for training the classifier are images, the voxels eventually selected for specifying the decision boundary must constitute spatially contiguous chunks, i.e., "regions" must be preferred over isolated voxels. This prior belief turns out to be useful for significantly reducing the space of possible classifiers and leads to substantial benefits in generalization. In our method, the requirement of spatial contiguity (of selected discriminating voxels) is incorporated within the optimization framework directly. Other methods have made use of similar biases as a pre- or post-processing step, however, our model incorporates this emphasis on spatial smoothness directly into the learning step. We report on extensive evaluations of our algorithm on MR and FDG-PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and discuss the relationship of the classification output with the clinical and cognitive biomarker data available within ADNI.

Regional shape abnormalities in mild cognitive impairment and Alzheimer's disease

Magnetic resonance (MR) based shape analysis provides an opportunity to detect regional specificity of volumetric changes that may distinguish mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy elderly controls (CON), and predict future conversion to AD. We assessed the surface deformation of seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, body and temporal horn of the lateral ventricles) in 383 MRI volumes, based on data shared through the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI), to identify regionally-specific shape abnormalities in MCI and AD. Large deformation diffeomorphic metric mapping (LDDMM) was used to generate the shapes of seven structures based on template shapes injected into segmented subcortical volumes. LDDMM then constructed the surface deformation maps encoding the local shape variation of each subject relative to the template. Hierarchical models were developed to detect differences in local shape in MCI and AD relative to CON. Our findings revealed that surface inward-deformation in MCI and AD is most prominent in the anterior hippocampal segment and the basolateral complex of the amygdala. Most pronounced surface outward-deformation in MCI and AD occurs in the lateral ventricles. Mild surface inward-deformation in MCI and AD occurs in the anterior-lateral and ventral-lateral aspects of the thalamus, with no evidence of regionally-specific deformation in the putamen or globus pallidus. Although the locations of the shape abnormalities in MCI and AD are primarily within the mesial temporal region, analyses support distinct components of correlated shape variation that may help predict future MCI conversion.

Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve

Brain atrophy measured by magnetic resonance structural imaging has been proposed as a surrogate marker for the early diagnosis of Alzheimer's disease. Studies on large samples are still required to determine its practical interest at the individual level, especially with regards to the capacity of anatomical magnetic resonance imaging to disentangle the confounding role of the cognitive reserve in the early diagnosis of Alzheimer's disease. One hundred and thirty healthy controls, 122 subjects with mild cognitive impairment of the amnestic type and 130 Alzheimer's disease patients were included from the ADNI database and followed up for 24 months. After 24 months, 72 amnestic mild cognitive impairment had converted to Alzheimer's disease (referred to as progressive mild cognitive impairment, as opposed to stable mild cognitive impairment). For each subject, cortical thickness was measured on the baseline magnetic resonance imaging volume. The resulting cortical thickness map was parcellated into 22 regions and a normalized thickness index was computed using the subset of regions (right medial temporal, left lateral temporal, right posterior cingulate) that optimally distinguished stable mild cognitive impairment from progressive mild cognitive impairment. We tested the ability of baseline normalized thickness index to predict evolution from amnestic mild cognitive impairment to Alzheimer's disease and compared it to the predictive values of the main cognitive scores at baseline. In addition, we studied the relationship between the normalized thickness index, the education level and the timeline of conversion to Alzheimer's disease. Normalized thickness index at baseline differed significantly among all the four diagnosis groups (P < 0.001) and correctly distinguished Alzheimer's disease patients from healthy controls with an 85% cross-validated accuracy. Normalized thickness index also correctly predicted evolution to Alzheimer's disease for 76% of amnestic mild cognitive impairment subjects after cross-validation, thus showing an advantage over cognitive scores (range 63–72%). Moreover, progressive mild cognitive impairment subjects, who converted later than 1 year after baseline, showed a significantly higher education level than those who converted earlier than 1 year after baseline. Using a normalized thickness index-based criterion may help with early diagnosis of Alzheimer's disease at the individual level, especially for highly educated subjects, up to 24 months before clinical criteria for Alzheimer's disease diagnosis are met.

Differential memory test sensitivity for diagnosing amnestic mild cognitive impairment and predicting conversion to Alzheimer's disease

Episodic memory is the first and most severely affected cognitive domain in Alzheimer's disease (AD), and it is also the key early marker in prodromal stages including amnestic mild cognitive impairment (MCI). The relative ability of memory tests to discriminate between MCI and normal aging has not been well characterized. We compared the classification value of widely used verbal memory tests in distinguishing healthy older adults (n = 51) from those with MCI (n = 38). Univariate logistic regression indicated that the total learning score from the California Verbal Learning Test-II (CVLT-II) ranked highest in terms of distinguishing MCI from normal aging (sensitivity = 90.2; specificity = 84.2). Inclusion of the delayed recall condition of a story memory task (i.e., WMS-III Logical Memory, Story A) enhanced the overall accuracy of classification (sensitivity = 92.2; specificity = 94.7). Combining Logical Memory recognition and CVLT-II long delay best predicted progression from MCI to AD over a 4-year period (accurate classification = 87.5%). Learning across multiple trials may provide the most sensitive index for initial diagnosis of MCI, but inclusion of additional variables may enhance overall accuracy and may represent the optimal strategy for identifying individuals most likely to progress to dementia.

Alzheimer's disease neuroimaging initiative: a one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition

Tensor-based morphometry can recover three-dimensional longitudinal brain changes over time by nonlinearly registering baseline to follow-up MRI scans of the same subject. Here, we compared the anatomical distribution of longitudinal brain structural changes, over 12 months, using a subset of the ADNI dataset consisting of 20 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with mild cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map) was created using an unbiased registration technique, and spatially normalized to a geometrically-centered average image based on healthy controls. Voxelwise statistical analyses revealed regional differences in atrophy rates, and these differences were correlated with clinical measures and biomarkers. Consistent with prior studies, we detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental state exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning memory scores. In AD, temporal atrophy rates were correlated with several biomarker indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid 1-42 (ABeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.

Maximizing the Alzheimer's Disease Neuroimaging Initiative II

The Alzheimer's Disease Neuroimaging Initiative is the largest public-private partnership on brain research underway at the National Institutes of Health. This 6-year study tracks cognitive and brain changes in normal subjects, those with mild cognitive impairment, and individuals with Alzheimer's disease. It was designed to provide better tools for performing effective clinical trials, and is slated to run until 2010. While data are being generated and analyzed, researchers involved in the study are developing an extension, i.e., the Alzheimer's Disease Neuroimaging Initiative II. The Foundation for the National Institutes of Health and the Alzheimer's Association convened a meeting to review the progress, evaluate future directions, and obtain the United States Food and Drug Administration's perspective on how the Alzheimer's Disease Neuroimaging Initiative could affect the drug-approval process.

Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects

OBJECTIVE

Develop a cerebrospinal fluid biomarker signature for mild Alzheimer's disease (AD) in Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects.

METHODS

Amyloid-beta 1 to 42 peptide (A beta(1-42)), total tau (t-tau), and tau phosphorylated at the threonine 181 were measured in (1) cerebrospinal fluid (CSF) samples obtained during baseline evaluation of 100 mild AD, 196 mild cognitive impairment, and 114 elderly cognitively normal (NC) subjects in ADNI; and (2) independent 56 autopsy-confirmed AD cases and 52 age-matched elderly NCs using a multiplex immunoassay. Detection of an AD CSF profile for t-tau and A beta(1-42) in ADNI subjects was achieved using receiver operating characteristic cut points and logistic regression models derived from the autopsy-confirmed CSF data.

RESULTS

CSF A beta(1-42) was the most sensitive biomarker for AD in the autopsy cohort of CSF samples: receiver operating characteristic area under the curve of 0.913 and sensitivity for AD detection of 96.4%. In the ADNI cohort, a logistic regression model for A beta(1-42), t-tau, and APO epsilon 4 allele count provided the best assessment delineation of mild AD. An AD-like baseline CSF profile for t-tau/A beta(1-42) was detected in 33 of 37 ADNI mild cognitive impairment subjects who converted to probable AD during the first year of the study.

INTERPRETATION

The CSF biomarker signature of AD defined by A beta(1-42) and t-tau in the autopsy-confirmed AD cohort and confirmed in the cohort followed in ADNI for 12 months detects mild AD in a large, multisite, prospective clinical investigation, and this signature appears to predict conversion from mild cognitive impairment to AD.

Analytic methods for factors, dimensions and endpoints in clinical trials for Alzheimer's disease

Alzheimer's disease (AD) is a complex disease process, so finding a single biomarker to track in clinical trials has proven difficult. This paper describes and contrasts statistical methods that might be used with biomarkers in clinical trials for AD, highlighting their differences, limitations and interpretations. The first method is traditional regression, within which one dependent variable, the Best Empirically Supported Indicator (BESI), must be identified. In this approach one biomarker (e.g., the ratio of tau to Abeta42 from CSF) is the indicator for an individual's disease status, and change in that status. The second approach is an exploratory factor analysis (EFA) to consolidate a multitude of candidate dependent variables into a sample-dependent, mathematically-optimized smaller set of 'factors'. The third method is latent variable (LV) modeling of multiple indicators of an entity (e.g., "disease burden"). The LV approach can yield a complex 'dependent variable', the Best Measurement Model Indicator (BMMI). A measurement model represents an entity that several dependent variables reflect or measure, and so can include many 'dependent variables', and estimate their relative contributions to the underlying entity. The selection of a single BESI is an artifact of regression that limits the investigator's ability to utilize all relevant variables representing the entity of interest. EFA results in sample-specific combination of biomarkers that might not generalize to a new sample - and fit of the EFA results cannot be tested. Latent variable methods can be useful to construct powerful, efficient statistical models that optimally combine diverse biomarkers into a single, multidimensional dependent variable that can generalize across samples when they are theory-driven and not sample-dependent. This paper shows that EFA can work to uncover underlying structure, but that it does not always yield solutions that 'fit' the data. It is not recommended as a method to build BMMIs, which will be useful in establishing diagnostic criteria, creating and evaluating benchmarks, and monitoring progression in clinical trials.

In vivo mapping of incremental cortical atrophy from incipient to overt Alzheimer's disease

Progressive brain atrophy is believed to be the Alzheimer's disease (AD) marker with the greatest evidence for validity. Mapping the topography of cortical atrophy throughout the stages of severity may allow the neural networks affected to be identified. Twenty healthy elderly persons (OH, MMSE 29.1 +/- 1.0), 11 patients with incipient AD (iAD, 26.5 +/- 2.0), 15 with mild AD (miAD, 23.5 +/- 2.2), and 15 with moderate AD (moAD, 16.5 +/- 2.0) underwent 3D magnetic resonance. Cortical pattern matching analysis was performed and maps of percent differences in gray matter distribution were computed between the following groups: iAD versus OH, miAD versus iAD, and moAD versus miAD. Compared to OH, iAD patients exhibited a mean cortical gray matter loss of 9-20% in areas encompassing the polysynaptic hippocampal pathway (posterior cingulate/retrosplenial and medial temporal cortex) and subgenual/orbitofrontal cortices, and a less widespread loss of 5-11% in other neocortical areas. Compared to iAD, miAD featured widespread mean gray matter loss of 14-19% in areas encompassing the direct hippocampal pathway (temporal pole, temporoparietal association cortex, and dorsal prefrontal cortex), sensorimotor, and visual cortex, with a less marked loss (7-9%) in the polysynaptic pathway areas. Compared to miAD, only atrophy in the primary sensorimotor cortex was still relatively marked in moAD, with a mean gray matter loss of 10-11%; the loss in other regions was generally below 10%. These findings suggest that the polysynaptic hippocampal pathway is affected in iAD, the direct pathway and sensorimotor and visual networks are affected in moAD, and the sensorimotor network is affected in moAD.

Computational anatomy with the SPM software

An overview of computational procedures for examining neuroanatomical variability is presented. The review focuses on approaches that can be applied using the SPM software package, beginning by explaining briefly how statistical parametric mapping is usually applied to functional imaging data. The review then proceeds to discuss volumetry, with an emphasis on voxel-based morphometry, and the pre-processing steps involved using the SPM software. Most volumetric studies involve univariate approaches, with a correction for some global measure, such as total brain volume. In contrast, the overall form of the brain may be more accurately modeled using multivariate approaches. Such models of anatomical variability may prove accurate enough for computer assisted diagnoses.

K-Bayes reconstruction for perfusion MRI. I: concepts and application

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

Biomarkers for Parkinson's [corrected] disease: tools to assess Parkinson's disease onset and progression

Reliable and well-validated biomarkers for PD to identify individuals "at risk" before motor symptoms, accurately diagnose individuals at the threshold of clinical PD, and monitor PD progression throughout its course would dramatically accelerate research into both PD cause and therapeutics. Biomarkers offer the potential to provide a window onto disease mechanism, potentially generating therapeutic targets for disease. In particular, biomarkers enable investigation of the premotor period of PD before typical symptoms are manifest, but while degeneration has already begun. Given the multiple genetic causes for PD already identified, the marked variability in the loss of dopaminergic markers measured by imaging at motor symptom onset and the clear heterogeneity of clinical symptoms in PD onset and clinical progression, it is likely many biomarkers with a focus ranging from clinical symptoms to PD pathobiology to molecular genetic mechanisms will be necessary to fully map PD risk and progression. Biomarkers are also critical in new drug development for PD, both in early validation studies to assess drug dosing and to determine drug penetrance into the brain, and in later efficacy studies to complement PD clinical outcomes. During the past two decades, much progress has been made in identifying and assessing PD biomarkers, but as yet, no fully validated biomarker for PD is currently available. Nonetheless, there is increasing evidence that molecular genetics, focused -omic (proteomic, metabolomic, and transcriptomic) assessment of blood and cerebrospinal fluid, and advanced in vivo brain imaging will provide critical clues to assist in the diagnosis and medical management of PD patients.

Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment

PURPOSE

To use structural magnetic resonance (MR) images to identify a pattern of regional atrophy characteristic of mild Alzheimer disease (AD) and to investigate whether presence of this pattern prospectively can aid prediction of 1-year clinical decline and increased structural loss in mild cognitive impairment (MCI).

MATERIALS AND METHODS

The study was conducted with institutional review board approval and compliance with HIPAA regulations. Written informed consent was obtained from each participant. High-throughput volumetric segmentation and cortical surface reconstruction methods were applied to MR images from 84 subjects with mild AD, 175 with MCI, and 139 healthy control (HC) subjects. Stepwise linear discriminant analysis was used to identify regions that best can aid discrimination of HC subjects from subjects with AD. A classifier trained on data from HC subjects and those with AD was applied to data from subjects with MCI to determine whether presence of phenotypic AD atrophy at baseline was predictive of clinical decline and structural loss.

RESULTS

Atrophy in mesial and lateral temporal, isthmus cingulate, and orbitofrontal areas aided discrimination of HC subjects from subjects with AD, with fully cross-validated sensitivity of 83% and specificity of 93%. Subjects with MCI who had phenotypic AD atrophy showed significantly greater 1-year clinical decline and structural loss than those who did not and were more likely to have progression to probable AD (annual progression rate of 29% for subjects with MCI who had AD atrophy vs 8% for those who did not).

CONCLUSION

Semiautomated, individually specific quantitative MR imaging methods can be used to identify a pattern of regional atrophy in MCI that is predictive of clinical decline. Such information may aid in prediction of patient prognosis and increase the efficiency of clinical trials.

Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer's Disease Neuroimaging Initiative (ADNI)

In mostly small single-center studies, Alzheimer's disease (AD) is associated with characteristic and progressive reductions in fluorodeoxyglucose positron emission tomography (PET) measurements of the regional cerebral metabolic rate for glucose (CMRgl). The AD Neuroimaging Initiative (ADNI) is acquiring FDG PET, volumetric magnetic resonance imaging, and other biomarker measurements in a large longitudinal multi-center study of initially mildly affected probable AD (pAD) patients, amnestic mild cognitive impairment (aMCI) patients, who are at increased AD risk, and cognitively normal controls (NC), and we are responsible for analyzing the PET images using statistical parametric mapping (SPM). Here we compare baseline CMRgl measurements from 74 pAD patients and 142 aMCI patients to those from 82 NC, we correlate CMRgl with categorical and continuous measures of clinical disease severity, and we compare apolipoprotein E (APOE) varepsilon4 carriers to non-carriers in each of these subject groups. In comparison with NC, the pAD and aMCI groups each had significantly lower CMRgl bilaterally in posterior cingulate, precuneus, parietotemporal and frontal cortex. Similar reductions were observed when categories of disease severity or lower Mini-Mental State Exam (MMSE) scores were correlated with lower CMRgl. However, when analyses were restricted to the pAD patients, lower MMSE scores were significantly correlated with lower left frontal and temporal CMRgl. These findings from a large, multi-site study support previous single-site findings, supports the characteristic pattern of baseline CMRgl reductions in AD and aMCI patients, as well as preferential anterior CMRgl reductions after the onset of AD dementia.

Monitoring the amyloid beta-peptide in vivo--caveat emptor

As a wave of 'disease modifying' (DM) therapies for Alzheimer's disease (AD) progresses towards the later stages of clinical development, an evaluation of our ability to measure relevant pharmacodynamic effects of such therapies is warranted. Reducing accumulation of amyloid beta (Abeta)-peptide in the brain parenchyma is the primary objective of most current DM approaches. Although a number of methods are available to measure Abeta in blood, cerebrospinal fluid (CSF) and the cerebrum, putative DM-induced changes in the levels of the peptides may not be fully captured, and the reasons for any such changes are not fully understood. Additional candidate biofluid (tau and isoprostanes) and imaging (MRI, FDG-PET) measures may provide alternative supporting evidence of drug activity and subsequent clinical efficacy in patient populations.

Fully-automated white matter hyperintensity detection with anatomical prior knowledge and without FLAIR

This paper presents a method for detection of cerebral white matter hyperintensities (WMH) based on run-time PD-, T1-, and T2-weighted structural magnetic resonance (MR) images of the brain along with labeled training examples. Unlike most prior approaches, the method is able to reliably detect WMHs in elderly brains in the absence of fluid-attenuated (FLAIR) images. Its success is due to the learning of probabilistic models of WMH spatial distribution and neighborhood dependencies from ground-truth examples of FLAIR-based WMH detections. These models are combined with a probabilistic model of the PD, T1, and T2 intensities of WMHs in a Markov Random Field (MRF) framework that provides the machinery for inferring the positions of WMHs in novel test images. The method is shown to accurately detect WMHs in a set of 114 elderly subjects from an academic dementia clinic. Experiments show that standard off-the-shelf MRF training and inference methods provide robust results, and that increasing the complexity of neighborhood dependency models does not necessarily help performance. The method is also shown to perform well when training and test data are drawn from distinct scanners and subject pools.

Advances in neuroimaging of traumatic brain injury and posttraumatic stress disorder

Improved diagnosis and treatment of traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are needed for our military and veterans, their families, and society at large. Advances in brain imaging offer important biomarkers of structural, functional, and metabolic information concerning the brain. This article reviews the application of various imaging techniques to the clinical problems of TBI and PTSD. For TBI, we focus on findings and advances in neuroimaging that hold promise for better detection, characterization, and monitoring of objective brain changes in symptomatic patients with combat-related, closed-head brain injuries not readily apparent by standard computed tomography or conventional magnetic resonance imaging techniques.

Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

As one of the earliest structures to degenerate in Alzheimer's disease (AD), the hippocampus is the target of many studies of factors that influence rates of brain degeneration in the elderly. In one of the largest brain mapping studies to date, we mapped the 3D profile of hippocampal degeneration over time in 490 subjects scanned twice with brain MRI over a 1-year interval (980 scans). We examined baseline and 1-year follow-up scans of 97 AD subjects (49 males/48 females), 148 healthy control subjects (75 males/73 females), and 245 subjects with mild cognitive impairment (MCI; 160 males/85 females). We used our previously validated automated segmentation method, based on AdaBoost, to create 3D hippocampal surface models in all 980 scans. Hippocampal volume loss rates increased with worsening diagnosis (normal=0.66%/year; MCI=3.12%/year; AD=5.59%/year), and correlated with both baseline and interval changes in Mini-Mental State Examination (MMSE) scores and global and sum-of-boxes Clinical Dementia Rating scale (CDR) scores. Surface-based statistical maps visualized a selective profile of ongoing atrophy in all three diagnostic groups. Healthy controls carrying the ApoE4 gene atrophied faster than non-carriers, while more educated controls atrophied more slowly; converters from MCI to AD showed faster atrophy than non-converters. Hippocampal loss rates can be rapidly mapped, and they track cognitive decline closely enough to be used as surrogate markers of Alzheimer's disease in drug trials. They also reveal genetically greater atrophy in cognitively intact subjects.

Biomarkers in geriatric psychiatry: searching for the holy grail?

PURPOSE OF REVIEW

Biomarkers in clinical medicine are used to detect or diagnose specific illnesses, predict disease progression, and predict the response to treatment. As the proportion of adults over 65 years of age rises, there is a growing need to detect neurodegenerative disease at an earlier stage with the goal of improving treatment for highly prevalent illnesses such as late-life depression and dementia.

RECENT FINDINGS

The search for biomarkers of late-life mental disorders includes the exploration of structural neuroimaging, functional neuroimaging, genomics, proteomics, noninvasive neurophysiology, cerebrospinal fluid, and plasma analysis. Novel structural and functional neuroimaging techniques that have recently been developed show promise as biomarkers of both late-life depression and specific dementia syndromes. The fields of proteomics and genomics are advancing our ability to identify genes and aberrant proteins that detect preclinical dementia. As depression is often a harbinger of dementia in late life, recent studies are beginning to elucidate the relationship between different types of late-life depression and the subsequent emergence of dementia.

SUMMARY

Biomarker research in late-life mental disorders is progressing at a rapid pace. The application of current biomarkers to clinical practice may be on the horizon with further research that refines their sensitivity and specificity.

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 Alzheimer's 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.

The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimer's disease. Magnetic resonance imaging (MRI), (18F)-fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquired at multiple time points. All data will be cross-linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications that guided protocol development. A major effort was devoted to evaluating 3D T(1)-weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced-scale clinical trial. The protocol selected for the ADNI study includes: back-to-back 3D magnetization prepared rapid gradient echo (MP-RAGE) scans; B(1)-calibration scans when applicable; and an axial proton density-T(2) dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom-based monitoring of all scanners could be used as a model for other multisite trials.

Optimizing phase II of drug development for disease-modifying compounds

Phase II proof of concept (POC) (IIa) and dose-finding (IIb) studies represent major challenges in drug development. Prolonged development times delay effective therapies from reaching patients in need and adversely affect industry goals of decreasing time to market. Biomarkers including magnetic resonance imaging, cerebrospinal fluid tau and amyloid beta, and amyloid positron emission tomography have been considered as alternative outcomes to clinical measures. None of these is yet validated. Population enrichment is another possible solution to POC studies. More rapid progression to prespecified milestones can be achieved by enriching the population with risk factors. Conclusions based on enriched populations must be extrapolated with caution. Clinical measures with greater sensitivity than standard trial instruments might represent another strategy applicable to POC studies. Adaptive dose-response designs are being considered as a means of shortening phase IIb studies and creating a seamless interface with phase III. None of these strategies have been validated in a successful drug development program; all have some promise for reforming phase II and answering the central question of "how much information is sufficient to proceed to phase III without excessive risk for failure?"

The pilot European Alzheimer's Disease Neuroimaging Initiative of the European Alzheimer's Disease Consortium

BACKGROUND

In North America, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has established a platform to track the brain changes of Alzheimer's disease. A pilot study has been carried out in Europe to test the feasibility of the adoption of the ADNI platform (pilot E-ADNI).

METHODS

Seven academic sites of the European Alzheimer's Disease Consortium (EADC) enrolled 19 patients with mild cognitive impairment (MCI), 22 with AD, and 18 older healthy persons by using the ADNI clinical and neuropsychological battery. ADNI compliant magnetic resonance imaging (MRI) scans, cerebrospinal fluid, and blood samples were shipped to central repositories. Medial temporal atrophy (MTA) and white matter hyperintensities (WMH) were assessed by a single rater by using visual rating scales.

RESULTS

Recruitment rate was 3.5 subjects per month per site. The cognitive, behavioral, and neuropsychological features of the European subjects were very similar to their U.S. counterparts. Three-dimensional T1-weighted MRI sequences were successfully performed on all subjects, and cerebrospinal fluid samples were obtained from 77%, 68%, and 83% of AD patients, MCI patients, and controls, respectively. Mean MTA score showed a significant increase from controls (left, right: 0.4, 0.3) to MCI patients (0.9, 0.8) to AD patients (2.3, 2.0), whereas mean WMH score did not differ among the three diagnostic groups (between 0.7 and 0.9). The distribution of both MRI markers was comparable to matched US-ADNI subjects.

CONCLUSIONS

Academic EADC centers can adopt the ADNI platform to enroll MCI and AD patients and older controls with global cognitive and structural imaging features remarkably similar to those of the US-ADNI.

Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and normal subjects

In one of the largest brain MRI studies to date, we used tensor-based morphometry (TBM) to create 3D maps of structural atrophy in 676 subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy elderly controls, scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using inverse-consistent 3D non-linear elastic image registration, we warped 676 individual brain MRI volumes to a population mean geometric template. Jacobian determinant maps were created, revealing the 3D profile of local volumetric expansion and compression. We compared the anatomical distribution of atrophy in 165 AD patients (age: 75.6+/-7.6 years), 330 MCI subjects (74.8+/-7.5), and 181 controls (75.9+/-5.1). Brain atrophy in selected regions-of-interest was correlated with clinical measurements--the sum-of-boxes clinical dementia rating (CDR-SB), mini-mental state examination (MMSE), and the logical memory test scores - at voxel level followed by correction for multiple comparisons. Baseline temporal lobe atrophy correlated with current cognitive performance, future cognitive decline, and conversion from MCI to AD over the following year; it predicted future decline even in healthy subjects. Over half of the AD and MCI subjects carried the ApoE4 (apolipoprotein E4) gene, which increases risk for AD; they showed greater hippocampal and temporal lobe deficits than non-carriers. ApoE2 gene carriers--1/6 of the normal group--showed reduced ventricular expansion, suggesting a protective effect. As an automated image analysis technique, TBM reveals 3D correlations between neuroimaging markers, genes, and future clinical changes, and is highly efficient for large-scale MRI studies.

Maximizing the potential of plasma amyloid-beta as a diagnostic biomarker for Alzheimer's disease

Amyloid plaques are composed primarily of amyloid-beta (Abeta) peptides derived from proteolytic cleavage of amyloid precursor protein (APP) and are considered to play a pivotal role in Alzheimer's disease (AD) pathogenesis. Presently, AD is diagnosed after the onset of clinical manifestations. With the arrival of novel therapeutic agents for treatment of AD, there is an urgent need for biomarkers to detect early stages of AD. Measurement of plasma Abeta has been suggested as an inexpensive and non-invasive tool to diagnose AD and to monitor Abeta modifying therapies. However, the majority of cross-sectional studies on plasma Abeta levels in humans have not shown differences between individuals with AD compared to controls. Similarly, cross-sectional studies of mouse plasma Abeta have yielded inconsistent trends in different mouse models. However, longitudinal studies appear to be more promising in humans. Recently, efforts to modify plasma Abeta levels using modulators have shown some promise. In this review, we will summarize the present data on plasma Abeta in humans and mouse models of AD. We will discuss the potential of modulators of Abeta levels in plasma, including antibodies and insulin, and the challenges associated with measuring plasma Abeta. Modulators of plasma Abeta may provide an important tool to optimize plasma Abeta levels and may improve the diagnostic potential of this approach.

Provenance in neuroimaging

Provenance, the description of the history of a set of data, has grown more important with the proliferation of research consortia-related efforts in neuroimaging. Knowledge about the origin and history of an image is crucial for establishing data and results quality; detailed information about how it was processed, including the specific software routines and operating systems that were used, is necessary for proper interpretation, high fidelity replication and re-use. We have drafted a mechanism for describing provenance in a simple and easy to use environment, alleviating the burden of documentation from the user while still providing a rich description of an image's provenance. This combination of ease of use and highly descriptive metadata should greatly facilitate the collection of provenance and subsequent sharing of data.

Current and future uses of neuroimaging for cognitively impaired patients

Technological advances have led to greater use of both structural and functional brain imaging to assist with the diagnosis of dementia for the increasing numbers of people with cognitive decline as they age. In current clinical practice, structural imaging (CT or MRI) is used to identify space-occupying lesions and stroke. Functional methods, such as PET scanning of glucose metabolism, could be used to differentiate Alzheimer's disease from frontotemporal dementia, which helps to guide clinicians in symptomatic treatment strategies. New neuroimaging methods that are currently being developed can measure specific neurotransmitter systems, amyloid plaque and tau tangle concentrations, and neuronal integrity and connectivity. Successful co-development of neuroimaging surrogate markers and preventive treatments might eventually lead to so-called brain-check scans for determining risk of cognitive decline, so that physicians can administer disease-modifying medications, vaccines, or other interventions to avoid future cognitive losses and to delay onset of disease.

3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

Imaging of amyloid beta in Alzheimer's disease with 18F-BAY94-9172, a novel PET tracer: proof of mechanism

BACKGROUND

Amyloid-beta (Abeta) plaque formation is a hallmark of Alzheimer's disease (AD) and precedes the onset of dementia. Abeta imaging should allow earlier diagnosis, but clinical application is hindered by the short decay half-life of current Abeta-specific ligands. (18)F-BAY94-9172 is an Abeta ligand that, due to the half-life of (18)F, is suitable for clinical use. We thus studied the effectiveness of this ligand in identifying patients with AD.

METHODS

15 patients with mild AD, 15 healthy elderly controls, and five individuals with frontotemporal lobar degeneration (FTLD) were studied. (18)F-BAY94-9172 binding was quantified by use of the standardised uptake value ratio (SUVR), which was calculated for the neocortex by use of the cerebellum as reference region. SUVR images were visually rated as normal or AD.

FINDINGS

(18)F-BAY94-9172 binding matched the reported post-mortem distribution of Abeta plaques. All AD patients showed widespread neocortical binding, which was greater in the precuneus/posterior cingulate and frontal cortex than in the lateral temporal and parietal cortex. There was relative sparing of sensorimotor, occipital, and medial temporal cortex. Healthy controls and FTLD patients showed only white-matter binding, although three controls and one FTLD patient had mild uptake in frontal and precuneus cortex. At 90-120 min after injection, higher neocortical SUVR was observed in AD patients (2.0 [SD 0.3]) than in healthy controls (1.3 [SD 0.2]; p<0.0001) or FTLD patients (1.2 [SD 0.2]; p=0.009). Visual interpretation was 100% sensitive and 90% specific for detection of AD.

INTERPRETATION

(18)F-BAY94-9172 PET discriminates between AD and FTLD or healthy controls and might facilitate integration of Abeta imaging into clinical practice.

Mapping progressive brain structural changes in early Alzheimer's disease and mild cognitive impairment

Alzheimer's disease (AD), the most common neurodegenerative disorder of the elderly, ranks third in health care cost after heart disease and cancer. Given the disproportionate aging of the population in all developed countries, the socio-economic impact of AD will continue to rise. Mild cognitive impairment (MCI), a transitional state between normal aging and dementia, carries a four- to sixfold increased risk of future diagnosis of dementia. As complete drug-induced reversal of AD symptoms seems unlikely, researchers are now focusing on the earliest stages of AD where a therapeutic intervention is likely to realize the greatest impact. Recently neuroimaging has received significant scientific consideration as a promising in vivo disease-tracking modality that can also provide potential surrogate biomarkers for therapeutic trials. While several volumetric techniques laid the foundation of the neuroimaging research in AD and MCI, more precise computational anatomy techniques have recently become available. This new technology detects and visualizes discrete changes in cortical and hippocampal integrity and tracks the spread of AD pathology throughout the living brain. Related methods can visualize regionally specific correlations between brain atrophy and important proxy measures of disease such as neuropsychological tests, age of onset or factors that may influence disease progression. We describe extensively validated cortical and hippocampal mapping techniques that are sensitive to clinically relevant changes even in the single individual, and can identify group differences in epidemiological studies or clinical treatment trials. We give an overview of some recent neuroimaging advances in AD and MCI and discuss strengths and weaknesses of the various analytic approaches.

An automated procedure for the assessment of white matter hyperintensities by multispectral (T1, T2, PD) MRI and an evaluation of its between-centre reproducibility based on two large community databases

INTRODUCTION

An automated procedure for the detection, quantification, localization and statistical mapping of white matter hyperintensities (WMH) on T2-weighted magnetic resonance (MR) images is presented and validated based on the results of a between-centre reproducibility study.

METHODS

The first step is the identification of white matter (WM) tissue using a multispectral (T1, T2, PD) segmentation. In a second step, WMH are identified within the WM tissue by segmenting T2 images, isolating two different classes of WMH voxels - low- and high-contrast WMH voxels, respectively. The reliability of the whole procedure was assessed by applying it to the analysis of two large MR imaging databases (n = 650 and n= 710, respectively) of healthy elderly subjects matched for demographic characteristics.

RESULTS

Average overall WMH load and spatial distribution were found to be similar in the two samples, (1.81 and 1.79% of the WM volume, respectively). White matter hyperintensity load was found to be significantly associated with both age and high blood pressure, with similar effects in both samples. With specific reference to the 650 subject cohort, we also found that WMH load provided by this automated procedure was significantly associated with visual grading of the severity of WMH, as assessed by a trained neurologist.

CONCLUSION

The results show that this method is sensitive, well correlated with semi-quantitative visual rating and highly reproducible.

Alzheimer 100 – highlights in the history of Alzheimer research

Alzheimer disease, a progressive neurodegenerative disorder of hitherto unknown etiology leading progressively to severe incapacity and death, has become the pandemic of the 21(st) century. On World Alzheimer Day, September 21, 2006, the 100(th) anniversary of the first description of the clinical and histological findings in this disorder by A. Alzheimer, was celebrated. This retrospective review of the most important events and advances in Alzheimer research presents its early history in which only clinical and histologic signs of this peculiar disease were described. Electron microscopy, quantitative morphology and modern biochemistry emerging in the second half of the 20(th) century opened a new era in dementia research with description of the ultrastructure and biochemistry of senile plaques and neurofibrillary tangles, the major disease markers of AD. Advances in the development of clinical, neuropathological, and neuroimaging criteria, modern instruments and algorithms in the diagnosis of the disorder followed, enabling long-term studies and more exact diagnosis of AD and related disorders. Landmark studies were the development of operational criteria for the post mortem diagnosis of AD based on semiquantitative assessment and developmental patterns of its major markers. Basic research gave insight into the molecular genetics and pathophysiology of AD, and, based on the biochemical findings, new pharmacological treatment options were opened. Recently, biological and other surrogate, in particular functional neuroimaging, markers allow an early detection of presymptomatic stages of AD, their risk factors and progression which, in the future, might be prevented or at least slowed by new therapeutic approaches. Since the etiology of AD is hitherto unknown, causative therapies are still not available. The paper discusses future research needs and challenges for developing new diagnostic strategies for early and accurate detection of neurodegenerative processes leading to dementia, better epidemiologic and gender data as well as more insights into the pathogenic cascade of AD and other dementing disorders which will depend on international networks and close cooperation between clinicians, neuroscientists, caregivers, public health institutions, and individual sponsors.

Mild cognitive impairment should be considered for DSM-V

Mild cognitive impairment is a topic of great activity from both clinical and research perspectives. It represents a transitional state between the cognitive changes of aging and the earliest clinical manifestations of dementia. We present a case for its inclusion in the Diagnostic and Statistical Manual of Mental Disorders (5th ed; DSM-V) based on clinical, outcome, epidemiological, neuroimaging, and pathophysiological data. The strongest case for inclusion can be made for the amnestic subtype, which is likely a clinical precursor of Alzheimer's disease. Arguments are presented as to why mild cognitive impairment can be considered as an entity distinct from normal aging and from clinically probable Alzheimer's disease and why it deserves consideration as a separate construct. In many respects, mild cognitive impairment fulfills criteria for inclusion more adequately than many other conditions currently codified in DSM-IV. Future research directions to help clarify some of the remaining uncertainties are proposed.