Algorithms, atrophy and Alzheimer's disease: cautionary tales for clinical trials

Association of lamina cribrosa thickness and hippocampal volume in Alzheimer's disease patients

BACKGROUND

 Alzheimer's disease (AD) is the most common cause of dementia and affects a large portion of the elderly population worldwide.

OBJECTIVE

 To analyze the relationship between lamina cribrosa thickness (LCT) and hippocampal volume in patients with AD and mild cognitive impairment (MCI).

METHODS

 The sample in the present study consisted of 20 recently diagnosed MCI patients, 20 recently diagnosed AD patients, and 20 matched healthy volunteers. Every patient underwent magnetic resonance imaging (MRI) scans. The VolBrain software (open-access platform for MRI brain analysis) was used to calculate the hippocampal volume. Optical coherence tomography was performed to measure the LCT. Analysis of variance and Pearson chi-squared tests were employed to assess the results.

RESULTS

 The lowest total hippocampal volume (p < 0.05) was in the AD group, which was 6.14 ± 0.66 mm3, while in the control group, it was 7.7 ± 9.65 mm3, and 6.69 ± 0.46 mm3 in the MCI group. In comparison to the rest of the groups, in the AD group, the LCT was the thinnest (202.17 ± 16.35 µm). As per the results of the study population as a whole, low hippocampal volume causes low LCT, which shows an important relationship (r: 0.41; p < 0.05).

CONCLUSION

 The current findings present evidence of the relationship between hippocampal volume and LCT in patients with AD and MCI.

Longitudinal amyloid and tau PET imaging in Alzheimer's disease: A systematic review of methodologies and factors affecting quantification

Deposition of amyloid and tau pathology can be quantified in vivo using positron emission tomography (PET). Accurate longitudinal measurements of accumulation from these images are critical for characterizing the start and spread of the disease. However, these measurements are challenging; precision and accuracy can be affected substantially by various sources of errors and variability. This review, supported by a systematic search of the literature, summarizes the current design and methodologies of longitudinal PET studies. Intrinsic, biological causes of variability of the Alzheimer's disease (AD) protein load over time are then detailed. Technical factors contributing to longitudinal PET measurement uncertainty are highlighted, followed by suggestions for mitigating these factors, including possible techniques that leverage shared information between serial scans. Controlling for intrinsic variability and reducing measurement uncertainty in longitudinal PET pipelines will provide more accurate and precise markers of disease evolution, improve clinical trial design, and aid therapy response monitoring.

Limitations of clinical trial sample size estimate by subtraction of two measurements

In planning randomized clinical trials (RCTs) for diseases such as Alzheimer's disease (AD), researchers frequently rely on the use of existing data obtained from only two time points to estimate sample size via the subtraction of baseline from follow-up measurements in each subject. However, the inadequacy of this method has not been reported. The aim of this study is to discuss the limitation of sample size estimation based on the subtraction of available data from only two time points for RCTs. Mathematical equations are derived to demonstrate the condition under which the obtained data pairs with variable time intervals could be used to adequately estimate sample size. The MRI-based hippocampal volume measurements from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Monte Carlo simulations (MCS) were used to illustrate the existing bias and variability of estimates. MCS results support the theoretically derived condition under which the subtraction approach may work. MCS also show the systematically under- or over-estimated sample sizes by up to 32.27 % bias. Not used properly, such subtraction approach outputs the same sample size regardless of trial durations partly due to the way measurement errors are handled. Estimating sample size by subtracting two measurements should be treated with caution. Such estimates can be biased, the magnitude of which depends on the planned RCT duration. To estimate sample sizes, we recommend using more than two measurements and more comprehensive approaches such as linear mixed effect models.

Ten years of image analysis and machine learning competitions in dementia

Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in clinical practice and clinical trials, seven grand challenges have been organized in the last decade: MIRIAD (2012), Alzheimer's Disease Big Data DREAM (2014), CADDementia (2014), Machine Learning Challenge (2014), MCI Neuroimaging (2017), TADPOLE (2017), and the Predictive Analytics Competition (2019). Based on two challenge evaluation frameworks, we analyzed how these grand challenges are complementing each other regarding research questions, datasets, validation approaches, results and impact. The seven grand challenges addressed questions related to screening, clinical status estimation, prediction and monitoring in (pre-clinical) dementia. There was little overlap in clinical questions, tasks and performance metrics. Whereas this aids providing insight on a broad range of questions, it also limits the validation of results across challenges. The validation process itself was mostly comparable between challenges, using similar methods for ensuring objective comparison, uncertainty estimation and statistical testing. In general, winning algorithms performed rigorous data pre-processing and combined a wide range of input features. Despite high state-of-the-art performances, most of the methods evaluated by the challenges are not clinically used. To increase impact, future challenges could pay more attention to statistical analysis of which factors (i.e., features, models) relate to higher performance, to clinical questions beyond Alzheimer's disease, and to using testing data beyond the Alzheimer's Disease Neuroimaging Initiative. Grand challenges would be an ideal venue for assessing the generalizability of algorithm performance to unseen data of other cohorts. Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared. Given the potential and lessons learned in the past ten years, we are excited by the prospects of grand challenges in machine learning and neuroimaging for the next ten years and beyond.

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.

Safety and efficacy of losartan for the reduction of brain atrophy in clinically diagnosed Alzheimer's disease (the RADAR trial): a double-blind, randomised, placebo-controlled, phase 2 trial

Background

Drugs modifying angiotensin II signalling could reduce Alzheimer's disease pathology, thus decreasing the rate of disease progression. We investigated whether the angiotensin II receptor antagonist losartan, compared with placebo, could reduce brain volume loss, as a measure of disease progression, in clinically diagnosed mild-to-moderate Alzheimer's disease.

Methods

In this double-blind, multicentre, randomised controlled trial, eligible patients aged 55 years or older, previously untreated with angiotensin II drugs and diagnosed (National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association criteria) with mild-to-moderate Alzheimer's disease, and who had capacity to consent, were recruited from 23 UK National Health Service hospital trusts. After undergoing a 4-week, open-label phase of active treatment then washout, participants were randomly assigned (1:1) oral over-encapsulated preparations of either 100 mg losartan (after an initial two-dose titration stage) or matched placebo daily for 12 months. Randomisation, minimised by age and baseline medial temporal lobe atrophy score, was undertaken online or via pin-access service by telephone. Participants, their study companions, and study personnel were masked to group assignment. The primary outcome, analysed by the intention-to-treat principle (ie, participants analysed in the group to which they were randomised, without imputation for missing data), was change in whole brain volume between baseline and 12 months, measured using volumetric MRI and determined by boundary shift interval (BSI) analysis. The trial is registered with the International Standard Randomised Controlled Trial Register (ISRCTN93682878) and the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT 2012–003641–15), and is completed.

Findings

Between July 22, 2014, and May 17, 2018, 261 participants entered the open-label phase. 211 were randomly assigned losartan (n=105) or placebo (n=106). Of 197 (93%) participants who completed the study, 171 (81%) had complete primary outcome data. The mean brain volume (BSI) reduction was 19·1 mL (SD 10·3) in the losartan group and 20·0 mL (10·8) in the placebo group. The difference in total volume reduction between groups was –2·29 mL (95% CI –6·46 to 0·89; p=0·14). The number of adverse events was low (22 in the losartan group and 20 in the placebo group) with no differences between treatment groups. There was one treatment-related death per treatment group.

Interpretation

12 months of treatment with losartan was well tolerated but was not effective in reducing the rate of brain atrophy in individuals with clinically diagnosed mild-to-moderate Alzheimer's disease. Further research is needed to assess the potential therapeutic benefit from earlier treatment in patients with milder cognitive impairment or from longer treatment periods.

Funding

Efficacy and Mechanism Evaluation Programme (UK Medical Research Council and National Institute for Health Research).

Short and long-term effects of single and multiple sessions of electroconvulsive therapy on brain gray matter volumes

BACKGROUND

Electroconvulsive therapy (ECT) has been shown to induce broadly distributed cortical and subcortical volume increases, more prominently in the amygdala and the hippocampus. Structural changes after one ECT session and in the long-term have been understudied.

OBJECTIVE

The aim of this study was to describe short-term and long-term volume changes induced in cortical and subcortical regions by ECT.

METHODS

Structural brain data were acquired from depressed patients before and 2 h after their first ECT session, 7-14 days after the end of the ECT series and at 6 months follow up (N = 34). Healthy, age and gender matched volunteers were scanned according to the same schedule (N = 18) and patients affected by atrial fibrillation were scanned 1-2 h before and after undergoing electrical cardioversion (N = 16). Images were parcelled using FreeSurfer and estimates of cortical gray matter volume and subcortical volume changes were obtained using Quarc.

RESULTS

Volume increase was observable in most of gray matter regions after 2 h from the first ECT session, with significant results in brain stem, bilateral hippocampi, right putamen and left thalamus, temporal and occipital regions in the right hemisphere. At the end of treatment series, widespread significant volume changes were observed. After six months, the right amygdala volume was still significantly increased. No significant changes were observed in the comparison groups.

CONCLUSIONS

Volume increases in gray matter areas can be detected 2 h after a single ECT session. Further studies are warranted to explore the underlying molecular mechanisms.

Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study

Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.

Dietary Melatonin Therapy Alleviates the Lamina Cribrosa Damages in Patients with Mild Cognitive Impairments: A Double-Blinded, Randomized Controlled Study

BACKGROUND Alzheimer's disease (AD) is a degenerative disease that is characterized by massive neuron devastations in the hippocampus and cortex. Mild cognitive impairment (MCI) is the transitory stage between normality and AD dementia. This study aimed to investigate the melatonin induced effects on the lamina cribrosa thickness (LCT) of patients with MCI. MATERIAL AND METHODS The LCT data of patients with MCI were compared to LCT data of healthy controls. Subsequently, all MCI patients were randomly assigned into an experimental group (with melatonin treatment) or a placebo group (without any melatonin treatment). RESULTS The LCT of MCI patients decreased significantly compared with healthy controls. The univariate analysis showed that the lower the Mini Mental State Examination (MMSE) score (P=0.038; 95% CI: 0.876, -0.209), the smaller hippocampus volume (P=0.001; 95% CI: -1.594, -2.911), and the upregulated level of cerebrospinal fluid (CSF) T-tau (P=0.036; 95% CI: 2.546, -0.271) were associated significantly with the thinner LCT in MCI patients. There were 40 patients in the experimental group and 39 patients in the placebo group. The mean age of the experimental group was not significantly different from the placebo group (66.3±8.8 versus 66.5±8.3; P>0.05). The LCT and hippocampus volume of the melatonin treated group were significantly larger compared with the placebo group (P<0.001). On the other hand, the CSF T-tau level of the melatonin treated group was significantly lower compared with the untreated group (P<0.001). CONCLUSIONS LCT assessment might allow early diagnosis of MCI. Dietary melatonin therapy could provide an effective medication for MCI patients with LCT alterations.

Longitudinal Neuroimaging Hippocampal Markers for Diagnosing Alzheimer's Disease

Hippocampal atrophy measures from magnetic resonance imaging (MRI) are powerful tools for monitoring Alzheimer's disease (AD) progression. In this paper, we introduce a longitudinal image analysis framework based on robust registration and simultaneous hippocampal segmentation and longitudinal marker classification of brain MRI of an arbitrary number of time points. The framework comprises two innovative parts: a longitudinal segmentation and a longitudinal classification step. The results show that both steps of the longitudinal pipeline improved the reliability and the accuracy of the discrimination between clinical groups. We introduce a novel approach to the joint segmentation of the hippocampus across multiple time points; this approach is based on graph cuts of longitudinal MRI scans with constraints on hippocampal atrophy and supported by atlases. Furthermore, we use linear mixed effect (LME) modeling for differential diagnosis between clinical groups. The classifiers are trained from the average residue between the longitudinal marker of the subjects and the LME model. In our experiments, we analyzed MRI-derived longitudinal hippocampal markers from two publicly available datasets (Alzheimer's Disease Neuroimaging Initiative, ADNI and Minimal Interval Resonance Imaging in Alzheimer's Disease, MIRIAD). In test/retest reliability experiments, the proposed method yielded lower volume errors and significantly higher dice overlaps than the cross-sectional approach (volume errors: 1.55% vs 0.8%; dice overlaps: 0.945 vs 0.975). To diagnose AD, the discrimination ability of our proposal gave an area under the receiver operating characteristic (ROC) curve (AUC) [Formula: see text] 0.947 for the control vs AD, AUC [Formula: see text] 0.720 for mild cognitive impairment (MCI) vs AD, and AUC [Formula: see text] 0.805 for the control vs MCI.

Fast predictive simple geodesic regression

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.

A Comparison of Partial Volume Correction Techniques for Measuring Change in Serial Amyloid PET SUVR

Longitudinal PET studies in aging and Alzheimer's disease populations rely on accurate and precise measurements of change over time from serial PET scans. Various methods for partial volume correction (PVC) are commonly applied to such studies, but existing comparisons and validations of these PVC methods have focused on cross-sectional measurements. Rate of change measurements inherently have smaller magnitudes than cross-sectional measurements, so levels of noise amplification due to PVC must be smaller, and it is necessary to re-evaluate methods in this context. Here we compare the relative precision in longitudinal measurements from serial amyloid PET scans when using geometric transfer matrix (GTM) PVC versus the traditional two-compartment (Meltzer-style), three-compartment (Müller-Gärtner-style), and no-PVC approaches. We used two independent implementations of standardized uptake value ratio (SUVR) measurement and PVC (one in-house pipeline based on SPM12 and ANTs, and one using FreeSurfer 6.0). For each approach, we also tested longitudinal-specific variants. Overall, we found that measurements using GTM PVC had significantly worse relative precision (unexplained within-subject variability ≈4-8%) than those using two-compartment, three-compartment, or no PVC (≈2-4%). Longitudinally-stabilized approaches did not improve these properties. This data suggests that GTM PVC methods may be less suitable than traditional approaches when measuring within-person change over time in longitudinal amyloid PET.

Secondary prevention of Alzheimer's dementia: neuroimaging contributions

BACKGROUND

In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations.

MAIN BODY

Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation.

CONCLUSION

We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.

When change is the only constant: The promise of longitudinal neuroimaging in understanding social anxiety disorder

Longitudinal studies offer a unique window into developmental change. Yet, most of what we know about the pathophysiology of psychiatric disorders is based on cross-sectional work. Here, we highlight the importance of adopting a longitudinal approach in order to make progress towards identifying the neurobiological mechanisms of social anxiety disorder (SAD). Using examples, we illustrate how longitudinal data can uniquely inform SAD etiology and timing of interventions. The brain's inherently adaptive quality requires that we model risk correlates of disorders as dynamic in their expression. Developmental theories regarding timing of environmental events, cascading effects and (mal)adaptations of the developing brain will be crucial components of comprehensive, integrative models of SAD. We close by discussing analytical considerations when working with longitudinal, developmental data.

A 16-Week Aerobic Exercise Intervention Does Not Affect Hippocampal Volume and Cortical Thickness in Mild to Moderate Alzheimer's Disease

Introduction: Brain imaging studies in healthy elderly subjects suggest a positive effect of aerobic exercise on both brain structure and function, while the effects of aerobic exercise in Alzheimer’s Disease (AD) has been scarcely investigated.

Methods: In a single-blinded randomized MRI study, we assessed the effects of an aerobic exercise intervention on brain volume as measured by magnetic resonance imaging (MRI) and its correlation to cognitive functioning in patients with AD. The study was a sub-study of a larger randomized controlled trial (ADEX study). Forty-one patients were assigned to a control or exercise group. The exercise group performed 60-min of aerobic exercise three times per week for 16 weeks. All participants underwent whole-brain MRI at 3 Tesla and cognitive assessment at baseline and after 16 weeks. Attendance and intensity were monitored providing a total exercise load. Changes in regional brain volumes and cortical thickness were analyzed using Freesurfer software.

Results: There was no effect of the type of intervention on MRI-derived brain volumes. In the entire group with and without training, Exercise load showed a positive correlation with changes in volume in the hippocampus, as well as frontal cortical thickness. Volume changes in frontal cortical thickness correlated with changes in measures of mental speed and attention and exercise load in the exercise group.

Conclusion: We did not find evidence to support an effect of 16 weeks of aerobic exercise on brain volume changes in patients with AD. Longer intervention periods may be needed to affect brain structure as measured with volumetric MRI.

Clinical Trial registration:ClinicalTrials.gov Identifier: NCT01681602, registered September 10th, 2012 (Retrospectively registered).

Sample Size Estimation for Alzheimer's Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging

Background: Little is known about the sample sizes required for clinical trials of Alzheimer’s disease (AD)-modifying treatments using atrophy measures from serial brain magnetic resonance imaging (MRI) in the Japanese population.

Objective: The primary objective of the present study was to estimate how large a sample size would be needed for future clinical trials for AD-modifying treatments in Japan using atrophy measures of the brain as a surrogate biomarker.

Methods: Sample sizes were estimated from the rates of change of the whole brain and hippocampus by the k-means normalized boundary shift integral (KN-BSI) and cognitive measures using the data of 537 Japanese Alzheimer’s Neuroimaging Initiative (J-ADNI) participants with a linear mixed-effects model. We also examined the potential use of ApoE status as a trial enrichment strategy.

Results: The hippocampal atrophy rate required smaller sample sizes than cognitive measures of AD and mild cognitive impairment (MCI). Inclusion of ApoE status reduced sample sizes for AD and MCI patients in the atrophy measures.

Conclusion: These results show the potential use of longitudinal hippocampal atrophy measurement using automated image analysis as a progression biomarker and ApoE status as a trial enrichment strategy in a clinical trial of AD-modifying treatment in Japanese people.

The Rationale and Design of the Reducing Pathology in Alzheimer's Disease through Angiotensin TaRgeting (RADAR) Trial

BACKGROUND

Anti-hypertensives that modify the renin angiotensin system may reduce Alzheimer's disease (AD) pathology and reduce the rate of disease progression.

OBJECTIVE

To conduct a phase II, two arm, double-blind, placebo-controlled, randomized trial of losartan to test the efficacy of Reducing pathology in Alzheimer's Disease through Angiotensin TaRgeting (RADAR).

METHODS

Men and women aged at least 55 years with mild-to-moderate AD will be randomly allocated 100 mg encapsulated generic losartan or placebo once daily for 12 months after successful completion of a 2-week open-label phase and 2-week placebo washout to establish drug tolerability. 228 participants will provide at least 182 subjects with final assessments to provide 84% power to detect a 25% difference in atrophy rate (therapeutic benefit) change over 12 months at an alpha level of 0.05. We will use intention-to-treat analysis, estimating between-group differences in outcomes derived from appropriate (linear or logistic) multivariable regression models adjusting for minimization variables.

RESULTS

The primary outcome will be rate of whole brain atrophy as a surrogate measure of disease progression. Secondary outcomes will include changes to 1) white matter hyperintensity volume and cerebral blood flow; 2) performance on a standard series of assessments of memory, cognitive function, activities of daily living, and quality of life. Major assessments (for all outcomes) and relevant safety monitoring of blood pressure and bloods will be at baseline and 12 months. Additional cognitive assessment will also be conducted at 6 months along with safety blood pressure and blood monitoring. Monitoring of blood pressure, bloods, and self-reported side effects will occur during the open-label phase and during the majority of the post-randomization dispensing visits.

CONCLUSION

This study will identify whether losartan is efficacious in the treatment of AD and whether definitive Phase III trials are warranted.

Mid-space-independent deformable image registration

Aligning images in a mid-space is a common approach to ensuring that deformable image registration is symmetric - that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the mathematical definition of the mid-space. In particular, the set of possible solutions is typically restricted by the constraints that are enforced on the transformations to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed as an approach to mid-space image registration. In this work, we show that when the atlas is aligned to each image in the native image space, the data term of implicit-atlas-based deformable registration is inherently independent of the mid-space. In addition, we show that the regularization term can be reformulated independently of the mid-space as well. We derive a new symmetric cost function that only depends on the transformation morphing the images to each other, rather than to the atlas. This eliminates the need for anti-drift constraints, thereby expanding the space of allowable deformations. We provide an implementation scheme for the proposed framework, and validate it through diffeomorphic registration experiments on brain magnetic resonance images.

The Global ECT-MRI Research Collaboration (GEMRIC): Establishing a multi-site investigation of the neural mechanisms underlying response to electroconvulsive therapy

Major depression, currently the world's primary cause of disability, leads to profound personal suffering and increased risk of suicide. Unfortunately, the success of antidepressant treatment varies amongst individuals and can take weeks to months in those who respond. Electroconvulsive therapy (ECT), generally prescribed for the most severely depressed and when standard treatments fail, produces a more rapid response and remains the most effective intervention for severe depression. Exploring the neurobiological effects of ECT is thus an ideal approach to better understand the mechanisms of successful therapeutic response. Though several recent neuroimaging studies show structural and functional changes associated with ECT, not all brain changes associate with clinical outcome. Larger studies that can address individual differences in clinical and treatment parameters may better target biological factors relating to or predictive of ECT-related therapeutic response. We have thus formed the Global ECT-MRI Research Collaboration (GEMRIC) that aims to combine longitudinal neuroimaging as well as clinical, behavioral and other physiological data across multiple independent sites. Here, we summarize the ECT sample characteristics from currently participating sites, and the common data-repository and standardized image analysis pipeline developed for this initiative. This includes data harmonization across sites and MRI platforms, and a method for obtaining unbiased estimates of structural change based on longitudinal measurements with serial MRI scans. The optimized analysis pipeline, together with the large and heterogeneous combined GEMRIC dataset, will provide new opportunities to elucidate the mechanisms of ECT response and the factors mediating and predictive of clinical outcomes, which may ultimately lead to more effective personalized treatment approaches.

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A global collaboration for longitudinal neuroimaging of ECT was established.

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A secure data portal with individual-patient level data.

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The feasibility of a standardized image analysis pipeline is demonstrated.

APOE ε4 status is associated with white matter hyperintensities volume accumulation rate independent of AD diagnosis

To assess the relationship between carriage of APOE ε4 allele and evolution of white matter hyperintensities (WMHs) volume, we longitudinally studied 339 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort with diagnoses ranging from normal controls to probable Alzheimer's disease (AD). A purpose-built longitudinal automatic method was used to segment WMH using constraints derived from an atlas-based model selection applied to a time-averaged image. Linear mixed models were used to evaluate the differences in rate of change across diagnosis and genetic groups. After adjustment for covariates (age, sex, and total intracranial volume), homozygous APOE ε4ε4 subjects had a significantly higher rate of WMH accumulation (22.5% per year 95% CI [14.4, 31.2] for a standardized population having typical values of covariates) compared with the heterozygous (ε4ε3) subjects (10.0% per year [6.7, 13.4]) and homozygous ε3ε3 (6.6% per year [4.1, 9.3]) subjects. Rates of accumulation increased with diagnostic severity; controls accumulated 5.8% per year 95% CI: [2.2, 9.6] for the standardized population, early mild cognitive impairment 6.6% per year [3.9, 9.4], late mild cognitive impairment 12.5% per year [8.2, 17.0] and AD subjects 14.7% per year [6.0, 24.0]. Following adjustment for APOE status, these differences became nonstatistically significant suggesting that APOE ε4 genotype is the major driver of accumulation of WMH volume rather than diagnosis of AD.

Tracking the development of agrammatic aphasia: A tensor-based morphometry study

Agrammatic aphasia can be observed in neurodegenerative disorders and has been traditionally linked with damage to Broca's area, although there have been disagreements concerning whether damage to Broca's area is necessary or sufficient for the development of agrammatism. We aimed to investigate the neuroanatomical correlates of the emergence of agrammatic aphasia utilizing a unique cohort of patients with primary progressive apraxia of speech (PPAOS) that did not have agrammatism at baseline but developed agrammatic aphasia over time. Twenty PPAOS patients were recruited and underwent detailed speech/language assessments and 3T MRI at two visits, approximately two years apart. None of the patients showed evidence of agrammatism in writing or speech at baseline. Eight patients developed aphasia at follow-up (progressors) and 12 did not (non-progressors). Tensor-based morphometry utilizing symmetric normalization (SyN) was used to assess patterns of grey matter atrophy and voxel-based morphometry was used to assess patterns of grey matter loss at baseline. The progressors were younger at onset and more likely to show distorted sound substitutions or additions compared to non-progressors. Both groups showed change over time in premotor and motor cortices, posterior frontal lobe, basal ganglia, thalamus and midbrain, but the progressors showed greater rates of atrophy in left pars triangularis, thalamus and putamen compared to non-progressors. The progressors also showed greater grey matter loss in pars triangularis and putamen at baseline. This cohort provided a unique opportunity to assess the anatomical changes that accompany the development of agrammatic aphasia. The results suggest that damage to a network of regions including Broca's area, thalamus and basal ganglia are responsible for the development of agrammatic aphasia in PPAOS. Clinical and neuroimaging abnormalities were also present before the onset of agrammatism that could help improve prognosis in these subjects.

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

INTRODUCTION

Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders.

METHODS

We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3.

RESULTS

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.

DISCUSSION

Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets

In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.

Neuroimaging of hippocampal atrophy in early recognition of Alzheimer's disease--a critical appraisal after two decades of research

As a characteristic feature of Alzheimer's disease (AD) hippocampal atrophy (HA) can be demonstrated in the majority of patients by using neuroimaging techniques in particular magnetic resonance imaging (MRI). Hippocampal atrophy is associated with declarative memory deficits and can also be associated with changes of adjacent medial temporal substructures such as the parahippocampal gyrus or the the entorhinal cortex. Similar findings are present in patients with mild cognitive impairment (MCI) albeit to a lesser extent. While these finding facilitate the diagnostic process in patients with clinical suspicious AD, the metric properties of hippocampal atrophy for delineating healthy aging from MCI and mild AD still appear to be rather limited; as such it is not sufficient to establish the diagnosis of AD (and even more so of MCI). This limitation partly refers to methodological issues and partly to the fact that hippocampal tissue integrity is subject to various pathogenetic influences other than AD. Moreover,the effects of hippocampal atrophy on the behavioral level (e.g. cognitive deficits) are modulated by the individual's cognitive reserve. From a clinical standpoint these observations are in line with the hypothesis that the onset and course of AD is influenced by a number of peristatic factors which are partly conceptualized in the concepts of brain and/or cognitive reserve. These complex interactions have to be considered when using the presence of hippocampal atrophy in the routine diagnostic procedure of AD.

MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials

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.

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated “direct” measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24 months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: − 1.4% to − 2.2% (AD) and − 0.35% to − 0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: − 1.5% to − 7.0% (AD) and − 0.4% to − 1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12 month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

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We compared numerous brain atrophy measurement techniques using multiple metrics.

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Each participant produced measures on the exact same dataset, blinded to disease.

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A central statistical analysis using linear mixed effect models was performed.

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Head to head comparisons for each region were performed using sample size estimates.

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Brain and ventricle measures were more consistent across groups than for hippocampi.

Regional flux analysis for discovering and quantifying anatomical changes: An application to the brain morphometry in Alzheimer's disease

In this study we introduce the regional flux analysis, a novel approach to deformation based morphometry based on the Helmholtz decomposition of deformations parameterized by stationary velocity fields. We use the scalar pressure map associated to the irrotational component of the deformation to discover the critical regions of volume change. These regions are used to consistently quantify the associated measure of volume change by the probabilistic integration of the flux of the longitudinal deformations across the boundaries. The presented framework unifies voxel-based and regional approaches, and robustly describes the volume changes at both group-wise and subject-specific level as a spatial process governed by consistently defined regions. Our experiments on the large cohorts of the ADNI dataset show that the regional flux analysis is a powerful and flexible instrument for the study of Alzheimer's disease in a wide range of scenarios: cross-sectional deformation based morphometry, longitudinal discovery and quantification of group-wise volume changes, and statistically powered and robust quantification of hippocampal and ventricular atrophy.

Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials.

METHODS

We searched for ADNI publications using established methods.

RESULTS

ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis.

DISCUSSION

ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials.

Sample size calculations for clinical trials targeting tauopathies: a new potential disease target

Disease-modifying therapies are being developed to target tau pathology, and should, therefore, be tested in primary tauopathies. We propose that progressive apraxia of speech should be considered one such target group. In this study, we investigate potential neuroimaging and clinical outcome measures for progressive apraxia of speech and determine sample size estimates for clinical trials. We prospectively recruited 24 patients with progressive apraxia of speech who underwent two serial MRI with an interval of approximately 2 years. Detailed speech and language assessments included the Apraxia of Speech Rating Scale and Motor Speech Disorders severity scale. Rates of ventricular expansion and rates of whole brain, striatal and midbrain atrophy were calculated. Atrophy rates across 38 cortical regions were also calculated and the regions that best differentiated patients from controls were selected. Sample size estimates required to power placebo-controlled treatment trials were calculated. The smallest sample size estimates were obtained with rates of atrophy of the precentral gyrus and supplementary motor area, with both measures requiring less than 50 subjects per arm to detect a 25% treatment effect with 80% power. These measures outperformed the other regional and global MRI measures and the clinical scales. Regional rates of cortical atrophy, therefore, provide the best outcome measures in progressive apraxia of speech. The small sample size estimates demonstrate feasibility for including progressive apraxia of speech in future clinical treatment trials targeting tau.

Accelerated vs. unaccelerated serial MRI based TBM-SyN measurements for clinical trials in Alzheimer's disease

OBJECTIVE

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Empowering imaging biomarkers of Alzheimer's disease

In a previous report, we proposed a method for combining multiple markers of atrophy caused by Alzheimer's disease into a single atrophy score that is more powerful than any one feature. We applied the method to expansion rates of the lateral ventricles, achieving the most powerful ventricular atrophy measure to date. Here, we expand our method's application to tensor-based morphometry measures. We also combine the volumetric tensor-based morphometry measures with previously computed ventricular surface measures into a combined atrophy score. We show that our atrophy scores are longitudinally unbiased with the intercept bias estimated at 2 orders of magnitude below the mean atrophy of control subjects at 1 year. Both approaches yield the most powerful biomarker of atrophy not only for ventricular measures but also for all published unbiased imaging measures to date. A 2-year trial using our measures requires only 31 (22, 43) Alzheimer's disease subjects or 56 (44, 64) subjects with mild cognitive impairment to detect 25% slowing in atrophy with 80% power and 95% confidence.

Longitudinal assessment of global and regional rate of grey matter atrophy in 1,172 healthy older adults: modulation by sex and age

To characterize the neuroanatomical changes in healthy older adults is important to differentiate pathological from normal brain structural aging. The present study investigated the annualized rate of GM atrophy in a large sample of older participants, focusing on the hippocampus, and searching for modulation by age and sex. In this 4-year longitudinal community cohort study, we used a VBM analysis to estimate the annualized rate of GM loss, at both the global and regional levels, in 1,172 healthy older adults (65–82 years) scanned at 1.5T. The global annualized rate of GM was −4.0 cm³/year (−0.83%/year). The highest rates of regional GM loss were found in the frontal and parietal cortices, middle occipital gyri, temporal cortex and hippocampus. The rate of GM atrophy was higher in women (−4.7 cm³/year, −0.91%/year) than men (−3.3 cm³/year, −0.65%/year). The global annualized rate of GM atrophy remained constant throughout the age range of the cohort, in both sexes. This pattern was replicated at the regional level, with the exception of the hippocampi, which showed a rate of GM atrophy that accelerated with age (2.8%/year per year of age) similarly for men and women. The present study reports a global and regional description of the annualized rate of grey matter loss and its evolution after the age of 65. Our results suggest greater anatomical vulnerability of women in late life and highlight a specific vulnerability of the hippocampus to the aging processes after 65 years of age.

Multi-modal robust inverse-consistent linear registration

Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for cross-modal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss-Newton update and show that it is equivalent to the efficient second-order minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images.

A new fast accurate nonlinear medical image registration program including surface preserving regularization

Recently inexpensive graphical processing units (GPUs) have become established as a viable alternative to traditional CPUs for many medical image processing applications. GPUs offer the potential of very significant improvements in performance at low cost and with low power consumption. One way in which GPU programs differ from traditional CPU programs is that increasingly elaborate calculations per voxel may not impact of the overall processing time because memory accesses can dominate execution time. This paper presents a new GPU based elastic image registration program named Ezys. The Ezys image registration algorithm belongs to the wide class of diffeomorphic demons but uses surface preserving image smoothing and regularization filters designed for a GPU that would be computationally expensive on a CPU. We describe the methods used in Ezys and present results from two important neuroscience applications. Firstly inter-subject registration for transfer of anatomical labels and secondly longitudinal intra-subject registration to quantify atrophy in individual subjects. Both experiments showed that Ezys registration compares favorably with other popular elastic image registration programs. We believe Ezys is a useful tool for neuroscience and other applications, and also demonstrates the value of developing of novel image processing filters specifically designed for GPUs.

Avoiding symmetry-breaking spatial non-uniformity in deformable image registration via a quasi-volume-preserving constraint

The choice of a reference image typically influences the results of deformable image registration, thereby making it asymmetric. This is a consequence of a spatially non-uniform weighting in the cost function integral that leads to general registration inaccuracy. The inhomogeneous integral measure--which is the local volume change in the transformation, thus varying through the course of the registration--causes image regions to contribute differently to the objective function. More importantly, the optimization algorithm is allowed to minimize the cost function by manipulating the volume change, instead of aligning the images. The approaches that restore symmetry to deformable registration successfully achieve inverse-consistency, but do not eliminate the regional bias that is the source of the error. In this work, we address the root of the problem: the non-uniformity of the cost function integral. We introduce a new quasi-volume-preserving constraint that allows for volume change only in areas with well-matching image intensities, and show that such a constraint puts a bound on the error arising from spatial non-uniformity. We demonstrate the advantages of adding the proposed constraint to standard (asymmetric and symmetrized) demons and diffeomorphic demons algorithms through experiments on synthetic images, and real X-ray and 2D/3D brain MRI data. Specifically, the results show that our approach leads to image alignment with more accurate matching of manually defined neuroanatomical structures, better tradeoff between image intensity matching and registration-induced distortion, improved native symmetry, and lower susceptibility to local optima. In summary, the inclusion of this space- and time-varying constraint leads to better image registration along every dimension that we have measured it.

Imago Mundi, Imago AD, Imago ADNI

Since the launch in 2003 of the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the USA, ever growing, similarly oriented consortia have been organized and assembled around the world. The various accomplishments of ADNI have contributed substantially to a better understanding of the underlying physiopathology of aging and Alzheimer's disease (AD). These accomplishments are basically predicated in the trinity of multimodality, standardization and sharing. This multimodality approach can now better identify those subjects with AD-specific traits that are more likely to present cognitive decline in the near future and that might represent the best candidates for smaller but more efficient therapeutic trials - trials that, through gained and shared knowledge, can be more focused on a specific target or a specific stage of the disease process. In summary, data generated from ADNI have helped elucidate some of the pathophysiological mechanisms underpinning aging and AD pathology, while contributing to the international effort in setting the groundwork for biomarker discovery and establishing standards for early diagnosis of AD.

Global image registration using a symmetric block-matching approach

Most medical image registration algorithms suffer from a directionality bias that has been shown to largely impact subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of nonlinear registration, but little work has been done for global registration. We propose a symmetric approach based on a block-matching technique and least-trimmed square regression. The proposed method is suitable for multimodal registration and is robust to outliers in the input images. The symmetric framework is compared with the original asymmetric block-matching technique and is shown to outperform it in terms of accuracy and robustness. The methodology presented in this article has been made available to the community as part of the NiftyReg open-source package.

The Association between Gray Matter Volume and Reading Proficiency: A Longitudinal Study of Beginning Readers

Neural systems involved in the processing of written language have been identified by a number of functional imaging studies. Structural changes in cortical anatomy that occur in the course of literacy acquisition, however, remain largely unknown. Here, we follow elementary school children over their first 2 years of formal reading instruction and use tensor-based morphometry to relate reading proficiency to cortical volume at baseline and follow-up measurement as well as to intraindividual longitudinal volume development between the two measurement time points. A positive relationship was found between baseline gray matter volume in the left superior temporal gyrus and subsequent changes in reading proficiency. Furthermore, a negative relationship was found between reading proficiency at the second measurement time point and intraindividual cortical volume development in the inferior parietal lobule and the precentral and postcentral gyri of the left hemisphere. These results are interpreted as evidence that reading acquisition is associated with preexisting structural differences as well as with experience-dependent structural changes involving dendritic and synaptic pruning.

Temporal and spatial evolution of grey matter atrophy in primary progressive multiple sclerosis

Grey matter (GM) atrophy occurs early in primary progressive MS (PPMS), but it is unknown whether its progression involves different brain regions at different rates, as is seen in other neurodegenerative diseases. We aimed to investigate the temporal and regional evolution of GM volume loss over 5 years and its relationship with disability progression in early PPMS.

We studied 36 patients with PPMS within five years from onset and 19 age and gender-matched healthy controls with clinical and imaging assessments at study entry and yearly for 3 years and then at 5 years. Patients were scored on the expanded disability status scale (EDSS) and MS Functional Composite (MSFC) at each time-point. An unbiased longitudinal voxel-based morphometry approach, based on high-dimensional spatial alignment within-subject, was applied to the serial imaging data. The rate of local (voxel-wise) volume change per year was compared between groups and its relationship with clinical outcomes was assessed.

Patients deteriorated significantly during the five years follow-up. Patients showed a greater decline of GM volume (p < 0.05, FWE-corrected) bilaterally in the cingulate cortex, thalamus, putamen, precentral gyrus, insula and cerebellum when compared to healthy controls over five years, although the rate of volume loss varied across the brain, and was the fastest in the cingulate cortex. Significant (p < 0.05, FWE-corrected) volume loss was detected in the left insula, left precuneus, and right cingulate cortex in patients at three years, as compared to baseline, whilst the bilateral putamen and the left superior temporal gyrus showed volume loss at five years. In patients, there was a relationship between a higher rate of volume loss in the bilateral cingulate cortex and greater clinical disability, as measured by the MSFC, at five years (Pearson's r = 0.49, p = 0.003).

Longitudinal VBM demonstrated that the progression of GM atrophy in PPMS occurs at different rates in different regions across the brain. The involvement of the cingulate cortex occurs early in the disease course, continues at a steady rate throughout the follow-up period and is associated with patient outcome. These findings provide new insights into the characteristics of GM atrophy across the brain in MS, and have potential consequences for the selection of brain atrophy as an outcome measure in neuroprotective clinical trials.

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Longitudinal VBM and TBM can be used in longitudinal studies in multiple sclerosis.

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GM loss is a dynamic process in primary progressive multiple sclerosis.

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The highest rate of GM reduction is seen in the cingulate gyri.

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GM atrophy may be used as an outcome measure for neuroprotective clinical trials.

On the estimation and correction of bias in local atrophy estimations using example atrophy simulations

Brain atrophy is considered an important marker of disease progression in many chronic neuro-degenerative diseases such as multiple sclerosis (MS). A great deal of attention is being paid toward developing tools that manipulate magnetic resonance (MR) images for obtaining an accurate estimate of atrophy. Nevertheless, artifacts in MR images, inaccuracies of intermediate steps and inadequacies of the mathematical model representing the physical brain volume change, make it rather difficult to obtain a precise and unbiased estimate. This work revolves around the nature and magnitude of bias in atrophy estimations as well as a potential way of correcting them. First, we demonstrate that for different atrophy estimation methods, bias estimates exhibit varying relations to the expected atrophy and these bias estimates are of the order of the expected atrophies for standard algorithms, stressing the need for bias correction procedures. Next, a framework for estimating uncertainty in longitudinal brain atrophy by means of constructing confidence intervals is developed. Errors arising from MRI artifacts and bias in estimations are learned from example atrophy simulations and anatomies. Results are discussed for three popular non-rigid registration approaches with the help of simulated localized brain atrophy in real MR images.

MIRIAD--Public release of a multiple time point Alzheimer's MR imaging dataset

The Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset is a series of longitudinal volumetric T1 MRI scans of 46 mild-moderate Alzheimer's subjects and 23 controls. It consists of 708 scans conducted by the same radiographer with the same scanner and sequences at intervals of 2, 6, 14, 26, 38 and 52 weeks, 18 and 24 months from baseline, with accompanying information on gender, age and Mini Mental State Examination (MMSE) scores. Details of the cohort and imaging results have been described in peer-reviewed publications, and the data are here made publicly available as a common resource for researchers to develop, validate and compare techniques, particularly for measurement of longitudinal volume change in serially acquired MR.

Symmetric diffeomorphic modeling of longitudinal structural MRI

This technology report describes the longitudinal registration approach that we intend to incorporate into SPM12. It essentially describes a group-wise intra-subject modeling framework, which combines diffeomorphic and rigid-body registration, incorporating a correction for the intensity inhomogeneity artifact usually seen in MRI data. Emphasis is placed on achieving internal consistency and accounting for many of the mathematical subtleties that most implementations overlook. The implementation was evaluated using examples from the OASIS Longitudinal MRI Data in Non-demented and Demented Older Adults.

Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features

We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80-in two-fold nested cross-validation-is 104 MCI subjects (95% CI: [94,139]) for a 1-year drug trial, and 75AD subjects [64,102]. This compares favorably with other MRI analysis methods. The standard "statistical ROI" approach applied to the same ventricular surfaces requires 165 MCI or 94AD subjects. At 2 years, the best LDA measure needs only 67 MCI and 52AD subjects, versus 119 MCI and 80AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.

Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials

Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.

A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease

Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer's disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. Unfortunately, morphological heterogeneity in AD can prevent accurate demarcation of the hippocampus. Recent developments in automated volumetry commonly use multi-template fusion driven by expert manual labels, enabling highly accurate and reproducible segmentation in disease and healthy subjects. However, there are several protocols to define the hippocampus anatomically in vivo, and the method used to generate atlases may impact automatic accuracy and sensitivity - particularly in pathologically heterogeneous samples. Here we report a fully automated segmentation technique that provides a robust platform to directly evaluate both technical and biomarker performance in AD among anatomically unique labeling protocols. For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) baseline and 24-month dataset. We based these atlas libraries on the protocols of (Haller et al., 1997; Killiany et al., 1993; Malykhin et al., 2007; Pantel et al., 2000; Pruessner et al., 2000), and a single operator performed all manual tracings to generate de facto "ground truth" labels. All methods distinguished between normal elders, mild cognitive impairment (MCI), and AD in the expected directions, and showed comparable correlations with measures of episodic memory performance. Only more inclusive protocols distinguished between stable MCI and MCI-to-AD converters, and had slightly better associations with episodic memory. Moreover, we demonstrate that protocols including more posterior anatomy and dorsal white matter compartments furnish the best voxel-overlap accuracies (Dice Similarity Coefficient=0.87-0.89), compared to expert manual tracings, and achieve the smallest sample sizes required to power clinical trials in MCI and AD. The greatest distribution of errors was localized to the caudal hippocampus and the alveus-fimbria compartment when these regions were excluded. The definition of the medial body did not significantly alter accuracy among more comprehensive protocols. Voxel-overlap accuracies between automatic and manual labels were lower for the more pathologically heterogeneous Sunnybrook study in comparison to the ADNI-1 sample. Finally, accuracy among protocols appears to significantly differ the most in AD subjects compared to MCI and normal elders. Together, these results suggest that selection of a candidate protocol for fully automatic multi-template based segmentation in AD can influence both segmentation accuracy when compared to expert manual labels and performance as a biomarker in MCI and AD.

Enrichment and stratification for predementia Alzheimer disease clinical trials

The tau and amyloid pathobiological processes underlying Alzheimer disease (AD) progresses slowly over periods of decades before clinical manifestation as mild cognitive impairment (MCI), then more rapidly to dementia, and eventually to end-stage organ failure. The failure of clinical trials of candidate disease modifying therapies to slow disease progression in patients already diagnosed with early AD has led to increased interest in exploring the possibility of early intervention and prevention trials, targeting MCI and cognitively healthy (HC) populations. Here, we stratify MCI individuals based on cerebrospinal fluid (CSF) biomarkers and structural atrophy risk factors for the disease. We also stratify HC individuals into risk groups on the basis of CSF biomarkers for the two hallmark AD pathologies. Results show that the broad category of MCI can be decomposed into subsets of individuals with significantly different average regional atrophy rates. By thus selectively identifying individuals, combinations of these biomarkers and risk factors could enable significant reductions in sample size requirements for clinical trials of investigational AD-modifying therapies, and provide stratification mechanisms to more finely assess response to therapy. Power is sufficiently high that detecting efficacy in MCI cohorts should not be a limiting factor in AD therapeutics research. In contrast, we show that sample size estimates for clinical trials aimed at the preclinical stage of the disorder (HCs with evidence of AD pathology) are prohibitively large. Longer natural history studies are needed to inform design of trials aimed at the presymptomatic stage.

Rates of decline in Alzheimer disease decrease with age

Age is the strongest risk factor for sporadic Alzheimer disease (AD), yet the effects of age on rates of clinical decline and brain atrophy in AD have been largely unexplored. Here, we examined longitudinal rates of change as a function of baseline age for measures of clinical decline and structural MRI-based regional brain atrophy, in cohorts of AD, mild cognitive impairment (MCI), and cognitively healthy (HC) individuals aged 65 to 90 years (total n = 723). The effect of age was modeled using mixed effects linear regression. There was pronounced reduction in rates of clinical decline and atrophy with age for AD and MCI individuals, whereas HCs showed increased rates of clinical decline and atrophy with age. This resulted in convergence in rates of change for HCs and patients with advancing age for several measures. Baseline cerebrospinal fluid densities of AD-relevant proteins, Aβ_1–42, tau, and phospho-tau_181p (ptau), showed a similar pattern of convergence with advanced age across cohorts, particularly for ptau. In contrast, baseline clinical measures did not differ by age, indicating uniformity of clinical severity at baseline. These results imply that the phenotypic expression of AD is relatively mild in individuals older than approximately 85 years, and this may affect the ability to distinguish AD from normal aging in the very old. Our findings show that inclusion of older individuals in clinical trials will substantially reduce the power to detect disease-modifying therapeutic effects, leading to dramatic increases in required clinical trial sample sizes with age of study sample.

Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play

Alzheimer disease (AD) is one of, if not the most, feared diseases associated with aging. The prevalence of AD increases exponentially with age after 60 years. Increasing life expectancy coupled with the absence of any approved disease-modifying therapies at present position AD as a dominant public health problem. Major advances have occurred in the development of disease biomarkers for AD in the past 2 decades. At present, the most well-developed AD biomarkers are the cerebrospinal fluid analytes amyloid-β 42 and tau and the brain imaging measures amyloid positron emission tomography (PET), fluorodeoxyglucose PET, and magnetic resonance imaging. CSF and imaging biomarkers are incorporated into revised diagnostic guidelines for AD, which have recently been updated for the first time since their original formulation in 1984. Results of recent studies suggest the possibility of an ordered evolution of AD biomarker abnormalities that can be used to stage the typical 20-30-year course of the disease. When compared with biomarkers in other areas of medicine, however, the absence of standardized quantitative metrics for AD imaging biomarkers constitutes a major deficiency. Failure to move toward a standardized system of quantitative metrics has substantially limited potential diagnostic usefulness of imaging in AD. This presents an important opportunity that, if widely embraced, could greatly expand the application of imaging to improve clinical diagnosis and the quality and efficiency of clinical trials.