The goal of this work was to assess statistical power to detect treatment effects in Alzheimer’s disease (AD) clinical trials using magnetic resonance imaging (MRI)–derived brain biomarkers. We used unbiased tensor-based morphometry (TBM) to analyze n = 5,738 scans, from Alzheimer’s Disease Neuroimaging Initiative 2 participants scanned with both accelerated and nonaccelerated T1-weighted MRI at 3T. The study cohort included 198 healthy controls, 111 participants with significant memory complaint, 182 with early mild cognitive impairment (EMCI) and 177 late mild cognitive impairment (LMCI), and 155 AD patients, scanned at screening and 3, 6, 12, and 24 months. The statistical power to track brain change in TBM-based imaging biomarkers depends on the interscan interval, disease stage, and methods used to extract numerical summaries. To achieve reasonable sample size estimates for potential clinical trials, the minimal scan interval was 6 months for LMCI and AD and 12 months for EMCI. TBM-based imaging biomarkers were not sensitive to MRI scan acceleration, which gave results comparable with nonaccelerated sequences. ApoE status and baseline amyloid-beta positron emission tomography data improved statistical power. Among healthy, EMCI, and LMCI participants, sample size requirements were significantly lower in the amyloid+/ApoE4+ group than for the amyloid−/ApoE4− group. ApoE4 strongly predicted atrophy rates across brain regions most affected by AD, but the remaining 9 of the top 10 AD risk genes offered no added predictive value in this cohort.
Alzheimer’s disease; Mild cognitive impairment; Imaging biomarker; Longitudinal; Enrichment; ApoE; Amyloid
To describe a model-based reconstruction strategy for routine magnetic resonance imaging (MRI) that accounts for gradient nonlinearity (GNL) during rather than after transformation to the image domain, and demonstrate that this approach reduces the spatial resolution loss that occurs during strictly image-domain GNL-correction.
After reviewing conventional GNL-correction methods, we propose a generic signal model for GNL-affected MRI acquisitions, discuss how it incorporates into contemporary image reconstruction platforms, and describe efficient non-uniform fast Fourier transform (NUFFT)-based computational routines for these. The impact of GNL-correction on spatial resolution by the conventional and proposed approaches is investigated on phantom data acquired at varying offsets from gradient isocenter, as well as on fully-sampled and (retrospectively) undersampled in vivo acquisitions.
Phantom results demonstrate that resolution loss that occurs during GNL-correction is significantly less for the proposed strategy than for the standard approach at distances >10 cm from isocenter with a 35 cm FOV gradient coil. The in vivo results suggest that the proposed strategy better preserves fine anatomical detail than retrospective GNL-correction while offering comparable geometric correction.
Accounting for GNL during image reconstruction allows geometric distortion to be corrected with less spatial resolution loss than is typically observed with the conventional image domain correction strategy.
Model-based Reconstruction; Gradient Nonlinearity; Nonuniform Fast Fourier Transform
ADNI is now in its 10th year. The primary objective of the MRI core of ADNI has been to improve methods for clinical trials in Alzheimer’s disease and related disorders.
We review the contributions of the MRI core from present and past cycles of ADNI (ADNI 1, GO and 2). We also review plans for the future – ADNI 3.
Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials.
Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in Alzheimer’s disease and will continue to do so in the future.
Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer’s disease (AD), such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.
diffusion tensor imaging; fractional anisotropy; Alzheimer’s disease; voxel-based analysis; support vector machines
Our understanding of network breakdown in Alzheimer’s disease (AD) is likely to be enhanced through advanced mathematical descriptors. Here, we applied spectral graph theory to provide novel metrics of structural connectivity based on 3-Tesla diffusion weighted images in 42 AD patients and 50 healthy controls. We reconstructed connectivity networks using whole-brain tractography and examined, for the first time here, cortical disconnection based on the graph energy and spectrum. We further assessed supporting metrics - link density and nodal strength - to better interpret our results. Metrics were analyzed in relation to the well-known APOE-4 genetic risk factor for late-onset AD. The number of disconnected cortical regions increased with the number of copies of the APOE-4 risk gene in people with AD. Each additional copy of the APOE-4 risk gene may lead to more dysfunctional networks with weakened or abnormal connections, providing evidence for the previously hypothesized “disconnection syndrome”.
graph spectrum; energy; Alzheimer’s disease; APOE-4; disconnection syndrome
Stable MR acquisition is essential for reliable measurement of brain atrophy in longitudinal studies. One attractive recent advance in MRI is to speed up acquisition using parallel imaging (e.g. reducing volumetric T1-weighted acquisition scan times from around 9 to 5 minutes). In some studies, a decision to change to an accelerated acquisition may have been deliberately taken, while in others repeat scans may occasionally be accidentally acquired with an accelerated acquisition. In ADNI, non-accelerated and accelerated scans were acquired in the same scanning session on each individual. We investigated the impact on brain atrophy as measured by k-means normalised boundary shift integral (KN-BSI) and deformation-based morphometry when changing from non-accelerated to accelerated MRI acquisitions over a 12-month interval using scans of 422 subjects from ADNI. KN-BSIs were calculated using both a non-accelerated baseline scan and non-accelerated 12-month scans (i.e. consistent acquisition), and a non-accelerated baseline scan and an accelerated 12-month scan (i.e. changed acquisition). Fluid-based non-rigid registration was also performed on those scans to estimate the brain atrophy rate. We found that the effect on KN-BSI and fluid-based non-rigid registration depended on the scanner manufacturer. For KN-BSI, in Philips and Siemens scanners, the change had very little impact on the measured atrophy rate (increase of 0.051% in Philips and -0.035% in Siemens from consistent acquisition to changed acquisition), whereas, in GE, the change caused a mean reduction of 0.65% in the brain atrophy rate. This is likely due to the difference in tissue contrast between grey matter and cerebrospinal fluid in the non-accelerated and accelerated scans in GE, which uses IR-FSPGR instead of MP-RAGE. For fluid-based non-rigid registration, the change caused a mean increase of 0.29% in the brain atrophy rate in the changed acquisition compared to consistent acquisition in Philips, whereas in GE and Siemens, the change had less impact on the mean atrophy rate (increase of 0.18% in GE and 0.049% in Siemens). Moving from non-accelerated baseline scans to accelerated scans for follow-up may have surprisingly little effect on computed atrophy rates depending on the exact sequence details and the scanner manufacturer; even accidentally inconsistent scans of this nature may still be useful.
Boundary shift integral; accelerated acquisition; non-accelerated acquisition; brain atrophy; Alzheimer's disease
Alzheimer’s disease (AD) is characterized by cortical atrophy and disrupted anatomical connectivity, and leads to abnormal interactions between neural systems. Diffusion weighted imaging (DWI) and graph theory can be used to evaluate major brain networks, and detect signs of a breakdown in network connectivity. In a longitudinal study using both DWI and standard MRI, we assessed baseline white matter connectivity patterns in 30 subjects with mild cognitive impairment (MCI; mean age: 71.8+/−7.5 yrs; 18M/12F) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using both standard MRI-based cortical parcellations and whole-brain tractography, we computed baseline connectivity maps from which we calculated global “small-world” architecture measures, including mean clustering coefficient (MCC) and characteristic path length (CPL). We evaluated whether these baseline network measures predicted future volumetric brain atrophy in MCI subjects, who are at risk for developing AD, as determined by 3D Jacobian “expansion factor maps” between baseline and 6-month follow-up anatomical scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.
Graph theory; brain networks; white matter; DTI; tractography; ADNI; TBM; small worldness; connectivity
Brain connectivity is progressively disrupted in Alzheimer’s disease (AD). Here we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain’s fiber networks from diffusion-weighted MRI scans of 200 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of DTI scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of MCI or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model--combining genotype, educational level, and clinical diagnosis.
Brain connectivity; neuroimaging genetics; HARDI tractography; seemingly unrelated regression (SUR); APOE4; multivariate analysis
Characterizing brain changes in Alzheimer’s disease (AD) is important for patient prognosis, and for assessing brain deterioration in clinical trials. In this diffusion tensor imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment (MCI), and 37 AD patients. After clustering tractography using an ROI atlas, we used a shortest path graph search through each bundle’s fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects as well as MD differences between CTL and late MCI subjects. MD and FA were also associated with widely used clinical scores (MMSE). As an MDP is a compact, low-dimensional representation of white matter organization, we tested the utility of DTI measures along these MDPs as features for support vector machine (SVM) based classification of AD.
ADNI; tractography; DTI; fiber tract modeling; white matter; connectivity; SVM; classification
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) recently implemented accelerated T1-weighted structural imaging to reduce scan times. Faster scans may reduce study costs and patient attrition by accommodating people who cannot tolerate long scan sessions. However, little is known about how scan acceleration affects the power to detect longitudinal brain change. Using tensor-based morphometry (TBM), no significant difference was detected in numerical summaries of atrophy rates from accelerated and nonaccelerated scans in subgroups of patients with Alzheimer’s disease, early or late mild cognitive impairment, or healthy controls over a 6 and 12-month scan interval. Whole-brain, voxel-wise mapping analyses revealed some apparent regional differences in 6-month atrophy rates when comparing all subjects irrespective of diagnosis (n=345). No such whole brain difference was detected for the 12-month scan interval (n=156). Effect sizes for structural brain changes were not detectably different in accelerated versus nonaccelerated data. Scan acceleration may influence brain measures, but has minimal effects on TBM-derived atrophy measures, at least over the 6- and 12-month intervals examined here.
Alzheimer’s disease; MRI; scan acceleration; longitudinal; tensor-based morphometry; neuroimaging; biomarker; drug trial enrichment
Several common genetic variants influence cholesterol levels, which play a key role in overall health. Myelin synthesis and maintenance are highly sensitive to cholesterol concentrations, and abnormal cholesterol levels increase the risk for various brain diseases, including Alzheimer's disease (AD). We report significant associations between higher serum cholesterol (CHOL) levels and high-density lipoproteins (HDL) and higher fractional anisotropy in 403 young adults (23.8±2.4 years) scanned with diffusion imaging and anatomical MRI at 4 Tesla. By fitting a multi-locus genetic model within white matter areas associated with CHOL, we found that a set of 18 cholesterol-related SNPs implicated in AD risk predicted FA. We focused on the SNP with the largest individual effects - CETP (rs5882) – and found that increased G-allele dosage was associated with higher FA and lower radial and mean diffusivities in voxel-wise analyses of the whole brain. A follow-up analysis detected WM associations with rs5882 in the opposite direction in 78 older individuals (74.3±7.3 years). Cholesterol levels may influence WM integrity, and cholesterol-related genes may exert age-dependent effects.
brain structure; DTI; imaging genetics; cholesterol; development; aging
Cardiovascular magnetic resonance (CMR) phase contrast imaging has undergone a wide range of changes with the development and availability of improved calibration procedures, visualization tools, and analysis methods. This article provides a comprehensive review of the current state-of-the-art in CMR phase contrast imaging methodology, clinical applications including summaries of past clinical performance, and emerging research and clinical applications that utilize today’s latest technology.
Electronic supplementary material
The online version of this article (doi:10.1186/s12968-015-0172-7) contains supplementary material, which is available to authorized users.
CMR flow imaging; Phase contrast; Valvular disease; Congenital defects
Our primary objective was to compare the performance of unaccelerated vs. accelerated structural MRI for measuring disease progression using serial scans in Alzheimer’s disease (AD).
We identified cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD subjects from all available Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects with usable pairs of accelerated and unaccelerated scans. There were a total of 696 subjects with baseline and 3 month scans, 628 subjects with baseline and 6 month scans and 464 subjects with baseline and 12 month scans available. We employed the Symmetric Diffeomorphic Image Normalization method (SyN) for normalization of the serial scans to obtain Tensor Based Morphometry (TBM) maps which indicate the structural changes between pairs of scans. We computed a TBM-SyN summary score of annualized structural changes over 31 regions of interest (ROIs) that are characteristically affected in AD. TBM-SyN scores were computed using accelerated and unaccelerated scan pairs and compared in terms of agreement, group-wise discrimination, and sample size estimates for a hypothetical therapeutic trial.
We observed a number of systematic differences between TBM-SyN scores computed from accelerated and unaccelerated pairs of scans. TBM-SyN scores computed from accelerated scans tended to have overall higher estimated values than those from unaccelerated scans. However, the performance of accelerated scans was comparable to unaccelerated scans in terms of discrimination between clinical groups and sample sizes required in each clinical group for a therapeutic trial. We also found that the quality of both accelerated vs. unaccelerated scans were similar.
Accelerated scanning protocols reduce scan time considerably. Their group-wise discrimination and sample size estimates were comparable to those obtained with unaccelerated scans. The two protocols did not produce interchangeable TBM-SyN estimates, so it is arguably important to use either accelerated pairs of scans or unaccelerated pairs of scans throughout the study duration.
To present preliminary, in vivo temperature measurements during MRI of a pig implanted with a deep brain stimulation (DBS) system.
Materials and Methods
DBS system (Medtronic Inc., Minneapolis, MN) was implanted in the brain of an anesthetized pig. 3.0T MRI was performed with a T/R head coil using the low-SAR GRE EPI and IR-prepped GRE sequences (SAR: 0.42 W/kg and 0.39 W/kg, respectively), and the high-SAR 4-echo RF spin echo (SAR: 2.9 W/kg). Fluoroptic thermometry was used to directly measure RF-related heating at the DBS electrodes, and at the implantable pulse generator (IPG). For reference the measurements were repeated in the same pig at 1.5T and, at both field strengths, in a phantom.
At 3.0T, the maximal temperature elevations at DBS electrodes were 0.46 °C and 2.3 °C, for the low- and high-SAR sequences, respectively. No heating was observed on the implanted IPG during any of the measurements. Measurements of in-vivo heating differed from those obtained in the phantom.
The 3.0T MRI using GRE EPI and IR-prepped GRE sequences resulted in local temperature elevations at DBS electrodes of no more than 0.46°C. Although no extrapolation should be made to human exams and much further study will be needed, these preliminary data are encouraging for the future use 3.0T MRI in patients with DBS.
MRI; fMRI; medical device safety; DBS; Deep Brain Stimulation; 3.0T
The ADNI 3D T1-weighted MRI acquisitions provide a rich dataset for developing and testing analysis techniques for extracting structural endpoints. To promote greater rigor in analysis and meaningful comparison of different algorithms, the ADNI MRI Core has created standardized analysis sets of data comprising scans that met minimum quality control requirements. We encourage researchers to test and report their techniques against these data. Standard analysis sets of volumetric scans from ADNI-1 have been created, comprising: screening visits, 1 year completers (subjects who all have screening, 6 and 12 month scans), two year annual completers (screening, 1, and 2 year scans), two year completers (screening, 6 months, 1 year, 18 months (MCI only) and 2 years) and complete visits (screening, 6 months, 1 year, 18 months (MCI only), 2, and 3 year (normal and MCI only) scans). As the ADNI-GO/ADNI-2 data becomes available, updated standard analysis sets will be posted regularly.
To evaluate the effects of recent advances in MRI RF coil and parallel imaging technology on brain volume measurement consistency.
Materials and Methods
103 whole-brain MRI volumes were acquired at a clinical 3T MRI, equipped with a 12- and 32-channel head coil, using the T1-weighted protocol as employed in the Alzheimer’s Disease Neuroimaging Initiative study with parallel imaging accelerations ranging from 1 to 5. An experienced reader performed qualitative ratings of the images. For quantitative analysis, differences in composite width (CW, a measure of image similarity) and boundary shift integral (BSI, a measure of whole-brain atrophy) were calculated.
Intra- and inter-session comparisons of CW and BSI measures from scans with equal acceleration demonstrated excellent scan-rescan accuracy, even at the highest acceleration applied. Pairs-of-scans acquired with different accelerations exhibited poor scan-rescan consistency only when differences in the acceleration factor were maximized. A change in the coil hardware between compared scans was found to bias the BSI measure.
The most important findings are that the accelerated acquisitions appear to be compatible with the assessment of high-quality quantitative information and that for highest scan-rescan accuracy in serial scans the acquisition protocol should be kept as consistent as possible over time.
Magnetic resonance imaging (MRI); brain; measurement consistency
To characterize the shape of the trajectories of Alzheimer’s Disease (AD) biomarkers as a function of MMSE.
Longitudinal registries from the Mayo Clinic and the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Two different samples (n=343 and n=598) were created that spanned the cognitive spectrum from normal to AD dementia. Subgroup analyses were performed in members of both cohorts (n=243 and n=328) who were amyloid positive at baseline.
Main Outcome Measures
The shape of biomarker trajectories as a function of MMSE, adjusted for age, was modeled and described as baseline (cross-sectional) and within-subject longitudinal effects. Biomarkers evaluated were cerebro spinal fluid (CSF) Aβ42 and tau; amyloid and fluoro deoxyglucose position emission tomography (PET) imaging, and structural magnetic resonance imaging (MRI).
Baseline biomarker values generally worsened (i.e., non-zero slope) with lower baseline MMSE. Baseline hippocampal volume, amyloid PET and FDG PET values plateaued (i.e., non-linear slope) with lower MMSE in one or more analyses. Longitudinally, within-subject rates of biomarker change were associated with worsening MMSE. Non-constant within-subject rates (deceleration) of biomarker change were found in only one model.
Biomarker trajectory shapes by MMSE were complex and were affected by interactions with age and APOE status. Non-linearity was found in several baseline effects models. Non-constant within-subject rates of biomarker change were found in only one model, likely due to limited within-subject longitudinal follow up. Creating reliable models that describe the full trajectories of AD biomarkers will require significant additional longitudinal data in individual participants.
Alzheimer’s disease biomarkers; Magnetic Resonance Imaging; cerebro spinal fluid; amyloid PET imaging; FDG PET imaging
Diffusion tensor imaging (DTI) is sensitive to the directionally- constrained flow of water, which diffuses preferentially along axons. Tractography programs may be used to infer matrices of connectivity (anatomical networks) between pairs of brain regions. Little is known about how these computed connectivity measures depend on the scans’ spatial and angular resolutions. To determine this, we scanned 8 young adults with DTI at 2.5 and 3 mm resolutions, and an additional subject at 4 resolutions between 2–4 mm. We computed 70×70 connectivity matrices, using whole-brain tractography to measure fiber density between all pairs of 70 cortical and subcortical regions. Spatial and angular resolution affected the computed connectivity for narrower tracts (internal capsule and cerebellum), but also for the corticospinal tract. Data resolution affected the apparent role of some key structures in cortical anatomic networks. Care is needed when comparing network data across studies, and interpreting apparent disagreements among findings.
Connectivity; diffusion imaging; tractography; networks; MRI; brain
Deep brain stimulation (DBS) is an established neurosurgical technique used to treat a variety of neurological disorders, including Parkinson disease, essential tremor, dystonia, epilepsy, depression, and obsessive-compulsive disorder. This study reports on the use of intraoperative MR imaging during DBS surgery to evaluate acute hemorrhage, intracranial air, brain shift, and accuracy of lead placement.
During a 46-month period, 143 patients underwent 152 DBS surgeries including 289 lead placements utilizing intraoperative 1.5-T MR imaging. Imaging was supervised by an MR imaging physicist to maintain the specific absorption rate below the required level of 0.1 W/kg and always included T1 magnetization-prepared rapid gradient echo and T2* gradient echo sequences with selected use of T2 fluid attenuated inversion recovery (FLAIR) and T2 fast spin echo (FSE). Retrospective review of the intraoperative MR imaging examinations was performed to quantify the amount of hemorrhage and the amount of air introduced during the DBS surgery.
Intraoperative MR imaging revealed 5 subdural hematomas, 3 subarachnoid hemorrhages, and 1 intra-parenchymal hemorrhage in 9 of the 143 patients. Only 1 patient experiencing a subarachnoid hemorrhage developed clinically apparent symptoms, which included transient severe headache and mild confusion. Brain shift due to intracranial air was identified in 144 separate instances.
Intraoperative MR imaging can be safely performed and may assist in demonstrating acute changes involving intracranial hemorrhage and air during DBS surgery. These findings are rarely clinically significant and typically resolve prior to follow-up imaging. Selective use of T2 FLAIR and T2 FSE imaging can confirm the presence of hemorrhage or air and preclude the need for CT examinations.
deep brain stimulation; intraoperative MR imaging; Parkinson disease; intracranial hemorrhage; functional neurosurgery
The application of sparsity-driven reconstruction methods to MRI to date has largely focused on situations where high-contrast features (e.g., gadolinium-enhanced vessels) are of primary interest. In clinical practice, however, low contrast features such as subtle lesions are often of equal or greater interest. Using an American College of Radiology (ACR) MR quality assurance phantom and test, we describe a novel framework for systematically and automatically evaluating the low-contrast object detectability (LCOD) performance of different undersampled image reconstruction methods. This platform is used to evaluate three such methods, two based on classic Tikhonov regularization and one sparsity-driven method based on ℓ1-norm minimization (which is commonly used in Compressive Sensing applications), across a wide range of sampling rates and parameterizations. Both the automated evaluation system and a manual evaluation of anatomical images with numerically-generated low contrast inserts demonstrate that sparse reconstructions exhibit superior LCOD performance compared to both Tikhonov-regularized reconstructions. The implications of this result, and potential applications of both the described LCOD platform and generalizations of it are then discussed.
Contrast; Detectability; Image Quality; Sparsity; Compressive Sensing
The promise of Alzheimer’s disease (AD) biomarkers has led to their incorporation in new diagnostic criteria and in therapeutic trials; however, significant barriers exist to widespread use. Chief among these is the lack of internationally accepted standards for quantitative metrics. Hippocampal volumetry is the most widely studied quantitative magnetic resonance imaging (MRI) measure in AD and thus represents the most rational target for an initial effort at standardization.
Methods and Results
The authors of this position paper propose a path toward this goal. The steps include: 1) Establish and empower an oversight board to manage and assess the effort, 2) Adopt the standardized definition of anatomic hippocampal boundaries on MRI arising from the EADC-ADNI hippocampal harmonization effort as a Reference Standard, 3) Establish a scientifically appropriate, publicly available Reference Standard Dataset based on manual delineation of the hippocampus in an appropriate sample of subjects (ADNI), and 4) Define minimum technical and prognostic performance metrics for validation of new measurement techniques using the Reference Standard Dataset as a benchmark.
Although manual delineation of the hippocampus is the best available reference standard, practical application of hippocampal volumetry will require automated methods. Our intent is to establish a mechanism for credentialing automated software applications to achieve internationally recognized accuracy and prognostic performance standards that lead to the systematic evaluation and then widespread acceptance and use of hippocampal volumetry. The standardization and assay validation process outlined for hippocampal volumetry is envisioned as a template that could be applied to other imaging biomarkers.
Alzheimer’s disease; biomarkers; Magnetic resonance imaging; hippocampus; biomarker standards
Functions of the ADNI MRI core fall into three categories: (1) those of the central MRI core lab at Mayo Clinic, Rochester, Minnesota, needed to generate high quality MRI data in all subjects at each time point; (2) those of the funded ADNI MRI core imaging analysis groups responsible for analyzing the MRI data, and (3) the joint function of the entire MRI core in designing and problem solving MR image acquisition, pre-processing and analyses methods. The primary objective of ADNI was and continues to be improving methods for clinical trials in Alzheimer's disease. Our approach to the present (“ADNI-GO”) and future (“ADNI-2”, if funded) MRI protocol will be to maintain MRI methodological consistency in previously enrolled “ADNI-1” subjects who are followed longitudinally in ADNI-GO and ADNI-2. We will modernize and expand the MRI protocol for all newly enrolled ADNI-GO and ADNI-2 subjects. All newly enrolled subjects will be scanned at 3T with a core set of three sequence types: 3D T1-weighted volume, FLAIR, and a long TE gradient echo volumetric acquisition for micro hemorrhage detection. In addition to this core ADNI-GO and ADNI-2 protocol, we will perform vendor specific pilot sub-studies of arterial spin labeling perfusion, resting state functional connectivity and diffusion tensor imaging. One each of these sequences will be added to the core protocol on systems from each MRI vendor. These experimental sub-studies are designed to demonstrate the feasibility of acquiring useful data in a multi-center (but single vendor) setting for these three emerging MRI applications.
The objective of this study was to investigate how a measure of educational and occupational attainment, a component of cognitive reserve, modifies the relationship between biomarkers of pathology and cognition in Alzheimer's disease. The biomarkers evaluated quantified neurodegeneration via atrophy on magnetic resonance images, neuronal injury via cerebral spinal fluid t-tau, brain amyloid-β load via cerebral spinal fluid amyloid-β1–42 and vascular disease via white matter hyperintensities on T2/proton density magnetic resonance images. We included 109 cognitively normal subjects, 192 amnestic patients with mild cognitive impairment and 98 patients with Alzheimer's disease, from the Alzheimer's Disease Neuroimaging Initiative study, who had undergone baseline lumbar puncture and magnetic resonance imaging. We combined patients with mild cognitive impairment and Alzheimer's disease in a group labelled ‘cognitively impaired’ subjects. Structural Abnormality Index scores, which reflect the degree of Alzheimer's disease-like anatomic features on magnetic resonance images, were computed for each subject. We assessed Alzheimer's Disease Assessment Scale (cognitive behaviour section) and mini-mental state examination scores as measures of general cognition and Auditory–Verbal Learning Test delayed recall, Boston naming and Trails B scores as measures of specific domains in both groups of subjects. The number of errors on the American National Adult Reading Test was used as a measure of environmental enrichment provided by educational and occupational attainment, a component of cognitive reserve. We found that in cognitively normal subjects, none of the biomarkers correlated with the measures of cognition, whereas American National Adult Reading Test scores were significantly correlated with Boston naming and mini-mental state examination results. In cognitively impaired subjects, the American National Adult Reading Test and all biomarkers of neuronal pathology and amyloid load were independently correlated with all cognitive measures. Exceptions to this general conclusion were absence of correlation between cerebral spinal fluid amyloid-β1–42 and Boston naming and Trails B. In contrast, white matter hyperintensities were only correlated with Boston naming and Trails B results in the cognitively impaired. When all subjects were included in a flexible ordinal regression model that allowed for non-linear effects and interactions, we found that the American National Adult Reading Test had an independent additive association such that better performance was associated with better cognitive performance across the biomarker distribution. Our main conclusions included: (i) that in cognitively normal subjects, the variability in cognitive performance is explained partly by the American National Adult Reading Test and not by biomarkers of Alzheimer's disease pathology; (ii) in cognitively impaired subjects, the American National Adult Reading Test, biomarkers of neuronal pathology (structural magnetic resonance imaging and cerebral spinal fluid t-tau) and amyloid load (cerebral spinal fluid amyloid-β1–42) all independently explain variability in general cognitive performance; and (iii) that the association between cognition and the American National Adult Reading Test was found to be additive rather than to interact with biomarkers of Alzheimer's disease pathology.
Alzheimer's disease; mild cognitive impairment; CSF biomarkers; MRI; cognitive reserve
A key question in designing MRI-based clinical trials is how the main magnetic field strength of the scanner affects the power to detect disease effects. In 110 subjects scanned longitudinally at both 3.0 and 1.5 T, including 24 patients with Alzheimer's Disease (AD) [74.8 ± 9.2 years, MMSE: 22.6 ± 2.0 at baseline], 51 individuals with mild cognitive impairment (MCI) [74.1 ± 8.0 years, MMSE: 26.6 ± 2.0], and 35 controls [75.9 ± 4.6 years, MMSE: 29.3 ± 0.8], we assessed whether higher-field MR imaging offers higher or lower power to detect longitudinal changes in the brain, using tensor-based morphometry (TBM) to reveal the location of progressive atrophy. As expected, at both field strengths, progressive atrophy was widespread in AD and more spatially restricted in MCI. Power analysis revealed that, to detect a 25% slowing of atrophy (with 80% power), 37 AD and 108 MCI subjects would be needed at 1.5 T versus 49 AD and 166 MCI subjects at 3 T; however, the increased power at 1.5 T was not statistically significant (α = 0.05) either for TBM, or for SIENA, a related method for computing volume loss rates. Analysis of cumulative distribution functions and false discovery rates showed that, at both field strengths, temporal lobe atrophy rates were correlated with interval decline in Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog), mini-mental status exam (MMSE), and Clinical Dementia Rating sum-of-boxes (CDR-SB) scores. Overall, 1.5 and 3 T scans did not significantly differ in their power to detect neurodegenerative changes over a year.
Alzheimer's disease; tensor-based morphometry; MRI; field strength
Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimer’s disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7 ± 7.2 years; MMSE: 23.3 ± 1.8, at baseline), 254 amnestic MCI subjects (75.0 ± 7.2 years; 27.0 ± 1.8), and 157 healthy elderly subjects (75.9 ± 5.1 years; 29.1 ± 1.0), as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients. Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at α = 0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-of-boxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.