Ventricular maps in 804 ADNI subjects: correlations with CSF biomarkers and clinical decline

Impact of amyloid and tau positivity on longitudinal brain atrophy in cognitively normal individuals

BACKGROUND

Individuals on the preclinical Alzheimer's continuum, particularly those with both amyloid and tau positivity (A + T +), display a rapid cognitive decline and elevated disease progression risk. However, limited studies exist on brain atrophy trajectories within this continuum over extended periods.

METHODS

This study involved 367 ADNI participants grouped based on combinations of amyloid and tau statuses determined through cerebrospinal fluid tests. Using longitudinal MRI scans, brain atrophy was determined according to the whole brain, lateral ventricle, and hippocampal volumes and cortical thickness in AD-signature regions. Cognitive performance was evaluated with the Preclinical Alzheimer's Cognitive Composite (PACC). A generalized linear mixed-effects model was used to examine group × time interactions for these measures. In addition, progression risks to mild cognitive impairment (MCI) or dementia were compared among the groups using Cox proportional hazards models.

RESULTS

A total of 367 participants (48 A + T + , 86 A + T - , 63 A - T + , and 170 A - T - ; mean age 73.8 years, mean follow-up 5.1 years, and 47.4% men) were included. For the lateral ventricle and PACC score, the A + T - and A + T + groups demonstrated statistically significantly greater volume expansion and cognitive decline over time than the A - T - group (lateral ventricle: β = 0.757 cm3/year [95% confidence interval 0.463 to 1.050], P < .001 for A + T - , and β = 0.889 cm3/year [0.523 to 1.255], P < .001 for A + T + ; PACC: β =  - 0.19 /year [- 0.36 to - 0.02], P = .029 for A + T - , and β =  - 0.59 /year [- 0.80 to - 0.37], P < .001 for A + T +). Notably, the A + T + group exhibited additional brain atrophy including the whole brain (β =  - 2.782 cm3/year [- 4.060 to - 1.504], P < .001), hippocampus (β =  - 0.057 cm3/year [- 0.085 to - 0.029], P < .001), and AD-signature regions (β =  - 0.02 mm/year [- 0.03 to - 0.01], P < .001). Cox proportional hazards models suggested an increased risk of progressing to MCI or dementia in the A + T + group versus the A - T - group (adjusted hazard ratio = 3.35 [1.76 to 6.39]).

CONCLUSIONS

In cognitively normal individuals, A + T + compounds brain atrophy and cognitive deterioration, amplifying the likelihood of disease progression. Therapeutic interventions targeting A + T + individuals could be pivotal in curbing brain atrophy, cognitive decline, and disease progression.

A comprehensive analysis of APOE genotype effects on human brain structure in the UK Biobank

Alzheimer's disease (AD) risk is increased in carriers of the apolipoprotein E (APOE) ε4 allele and decreased in ε2 allele carriers compared with the ε3ε3 genotype. The aim of this study was to determine whether: the APOE genotype affects brain grey (GM) or white matter (WM) structure; and if differences exist, the age when they become apparent and whether there are differential effects by sex. We used cross-sectional magnetic resonance imaging data from ~43,000 (28,494 after pre-processing) white British cognitively healthy participants (7,446 APOE ε4 carriers) aged 45-80 years from the UK Biobank cohort and investigated image-derived phenotypes (IDPs). We observed no statistically significant effects of APOE genotype on GM structure volumes or median T2* in subcortical structures, a measure related to iron content. The volume of white matter hyperintensities differed significantly between APOE genotype groups with higher volumes in APOE ε4ε4 (effect size 0.14 standard deviations [SD]) and ε3ε4 carriers (effect size 0.04 SD) but no differences in ε2 carriers compared with ε3ε3 carriers. WM integrity measures in the dorsal (mean diffusivity [MD]) and ventral cingulum (MD and intracellular volume fraction), posterior thalamic radiation (MD and isotropic volume fraction) and sagittal stratum (MD) indicated lower integrity in APOE ε4ε4 carriers (effect sizes around 0.2-0.3 SD) and ε3ε4 (effect sizes around 0.05 SD) carriers but no differences in ε2 carriers compared with the APOE ε3ε3 genotype. Effects did not differ between men and women. APOE ε4 homozygotes had lower WM integrity specifically at older ages with a steeper decline of WM integrity from the age of 60 that corresponds to around 5 years greater "brain age". APOE genotype affects various white matters measures, which might be indicative of preclinical AD processes. This hypothesis can be assessed in future when clinical outcomes become available.

Sulfatide Deficiency, an Early Alzheimer's Lipidomic Signature, Causes Brain Ventricular Enlargement in the Absence of Classical Neuropathological Hallmarks

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive memory loss and a decline in activities of daily life. Ventricular enlargement has been associated with worse performance on global cognitive tests and AD. Our previous studies demonstrated that brain sulfatides, myelin-enriched lipids, are dramatically reduced in subjects at the earliest clinically recognizable AD stages via an apolipoprotein E (APOE)-dependent and isoform-specific process. Herein, we provided pre-clinical evidence that sulfatide deficiency is causally associated with brain ventricular enlargement. Specifically, taking advantage of genetic mouse models of global and adult-onset sulfatide deficiency, we demonstrated that sulfatide losses cause ventricular enlargement without significantly affecting hippocampal or whole brain volumes using histological and magnetic resonance imaging approaches. Mild decreases in sulfatide content and mild increases in ventricular areas were also observed in human APOE4 compared to APOE2 knock-in mice. Finally, we provided Western blot and immunofluorescence evidence that aquaporin-4, the most prevalent aquaporin channel in the central nervous system (CNS) that provides fast water transportation and regulates cerebrospinal fluid in the ventricles, is significantly increased under sulfatide-deficient conditions, while other major brain aquaporins (e.g., aquaporin-1) are not altered. In short, we unraveled a novel and causal association between sulfatide deficiency and ventricular enlargement. Finally, we propose putative mechanisms by which sulfatide deficiency may induce ventricular enlargement.

Brain ventricles, CSF and cognition: a narrative review

The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood-cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter-rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta-amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI-dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.

Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease

Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Predicting future cognitive decline with hyperbolic stochastic coding

Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.

Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs

Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site studies (N = 528 subjects for ICV, N = 501 for ventricular segmentation) consisting of older adults with varying degrees of cerebrovascular lesions and atrophy, which pose significant challenges for most segmentation approaches. The models were tested on 238 participants, including subjects with vascular cognitive impairment and high white matter hyperintensity burden. Two of the three test sets came from studies not used in the training dataset. We assessed our algorithms relative to four state-of-the-art ICV extraction methods (MONSTR, BET, Deep Extraction, FreeSurfer, DeepMedic), as well as two ventricular segmentation tools (FreeSurfer, DeepMedic). Our multi-contrast models outperformed other methods across many of the evaluation metrics, with average Dice coefficients of 0.98 and 0.96 for ICV and ventricular segmentation respectively. Both models were also the most time efficient, segmenting the structures in orders of magnitude faster than some of the other available methods. Our networks showed an increased accuracy with the use of a conditional random field (CRF) as a post-processing step. We further validated both segmentation models, highlighting their robustness to images with lower resolution and signal-to-noise ratio, compared to tested techniques. The pipeline and models are available at: https://icvmapp3r.readthedocs.io and https://ventmapp3r.readthedocs.io to enable further investigation of the roles of ICV and ventricles in relation to normal aging and neurodegeneration in large multi-site studies.

Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline

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A completely automated surface-based ventricular morphometry system.

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Generate a whole connected 3D ventricular shape model.

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Test-retest the system in two independent CU subject cohorts.

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Subregional ventricular abnormalities prior to clinically memory decline.

Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images

BACKGROUND

Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches.

OBJECTIVE

A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN.

METHODS

First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog).

RESULTS

We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods.

CONCLUSION

Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.

Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach

INTRODUCTION

Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei.

METHODS

This study introduces thickness and bending angle of the corpus callosum as a putative white matter marker of MCI to AD conversion. The corpus callosum is computationally less demanding to segment automatically compared to more complicated structures and a subject can be processed in a few minutes. We aimed to demonstrate that callosal shape and thickness measures provide a simple, effective, and accurate prognostication tool in ADNI dataset. Using longitudinal datasets, we classified MCI subjects based on conversion to AD assessed via cognitive testing. We evaluated the classification accuracy of callosal shape features in comparison with the existing "gold standard" cortical thickness and subcortical gray matter volume measures.

RESULTS

The callosal thickness measures were less accurate in classifying conversion status by cognitive scores compared to gray matter measures for AD.

CONCLUSIONS

While this paper presented a negative result, this method may be more suitable for a disease of the white matter.

Longitudinal changes in the brain in mild cognitive impairment: a magnetic resonance imaging study using the visual rating method and tensor-based morphometry

Background Brain atrophy is associated with mild cognitive impairment (MCI), and by using volumetric and visual analyzing methods, it is possible to differentiate between individuals with progressive MCI (MCIp) and stable MCI (MCIs). Automated analysis methods detect degenerative changes in the brain earlier and more reliably than visual methods. Purpose To detect and evaluate structural brain changes between and within the MCIs, MCIp, and control groups during a two-year follow-up period. Material and Methods Brain magnetic resonance imaging (MRI) scans of 11 participants with MCIs, 18 participants with MCIp, and 84 controls were analyzed by the visual rating method (VRM) and tensor-based morphometry (TBM). Results At baseline, both VRM and TBM differentiated the whole MCI group (combined MCIs and MCIp) and the MCIp group from the control group, but they did not differentiate the MCIs group from the control group. At follow-up, both methods differentiated the MCIp group from the control group, but minor differences between the MCIs and control groups were only seen by TBM. Neuropsychological tests did not find differences between the MCIs and control groups at follow-up. Neither method revealed relevant signs of brain atrophy progression within or between MCI subgroups during the follow-up time. Conclusion Both methods are equally good in the evaluation of structural brain changes in MCI if the groups are sufficiently large and the disease progresses to AD. Only TBM disclosed minor atrophic changes in the MCIs group compared to controls at follow-up. The results need to be confirmed with a large patient group and longer follow-up time.

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.

Visual rating method and tensor-based morphometry in the diagnosis of mild cognitive impairment and Alzheimer's disease: a comparative magnetic resonance imaging study

BACKGROUND

Atrophy of the medial temporal lobe (MTL) is the main structural magnetic resonance imaging (MRI) finding in the brain of patients with Alzheimer's disease (AD). However, evaluating the degree of atrophy is still demanding.

PURPOSE

The visual rating method (VRM) was compared with multi-template tensor-based morphometry (TBM), in terms of its efficacy in diagnosing of mild cognitive impairment (MCI) and AD.

MATERIAL AND METHODS

Forty-seven patients with MCI, 80 patients with AD and 84 controls were studied.

RESULTS

TBM seems to be more sensitive than VRM at the early stage of dementia in the areas of MTL and ventricles. The methods were equally good in distinguishing controls and the MCI group from the AD group. At the frontal areas TBM was better than VRM in all comparisons.

CONCLUSION

A user-friendly VRM is still useful for the clinical evaluation of MCI patients, but multi-template TBM is more sensitive for diagnosing the early stages of dementia. However, TBM is currently too demanding to use for daily clinical work.

Predicting Alzheimer's disease development: a comparison of cognitive criteria and associated neuroimaging biomarkers

Introduction

The definition of “objective cognitive impairment” in current criteria for mild cognitive impairment (MCI) varies considerably between research groups and clinics. This study aims to compare different methods of defining memory impairment to improve prediction models for the development of Alzheimer’s disease (AD) from baseline to 24 months.

Methods

The sensitivity and specificity of six methods of defining episodic memory impairment (< −1, −1.5 or −2 standard deviations [SD] on one or two memory tests) were compared in 494 non-demented seniors from the Alzheimer’s Disease Neuroimaging Initiative using the area under the curve (AUC) for receiver operating characteristic analysis. The added value of non-memory measures (language and executive function) and biomarkers (hippocampal and white-matter hyperintensity volume, brain parenchymal fraction [BPF], and APOEε4 status) was investigated using logistic regression.

Results

Baseline scores < −1 SD on two memory tests predicted AD with 75.91 % accuracy (AUC = 0.80). Only APOE ε4 status further improved prediction (B = 1.10, SE = 0.45, p = .016). A < −1.5 SD cut-off on one test had 66.60 % accuracy (AUC = 0.77). Prediction was further improved using Trails B/A ratio (B = 0.27, SE = 0.13, p = .033), BPF (B = −15.97, SE = 7.58, p = .035), and APOEε4 status (B = 1.08, SE = 0.45, p = .017). A cut-off of < −2 SD on one memory test (AUC = 0.77, SE = 0.03, 95 % CI 0.72-0.82) had 76.52 % accuracy in predicting AD. Trails B/A ratio (B = 0.31, SE = 0.13, p = .017) and APOE ε4 status (B = 1.07, SE = 0.46, p = .019) improved predictive accuracy.

Conclusions

Episodic memory impairment in MCI should be defined as scores < −1 SD below normative references on at least two measures. Clinicians or researchers who administer a single test should opt for a more stringent cut-off and collect and analyze whole-brain volume. When feasible, ascertaining APOE ε4 status can further improve prediction.

Structural Brain Changes in Early-Onset Alzheimer's Disease Subjects Using the LONI Pipeline Environment

BACKGROUND AND PURPOSE

This study investigates 36 subjects aged 55-65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early-onset (EO) Alzheimer's Disease (EO-AD) using neuroimaging biomarkers.

METHODS

Nine of the subjects had EO-AD, and 27 had EO mild cognitive impairment (EO-MCI). The structural ADNI data were parcellated using BrainParser, and the 15 most discriminating neuroimaging markers between the two cohorts were extracted using the Global Shape Analysis (GSA) Pipeline workflow. Then the Local Shape Analysis (LSA) Pipeline workflow was used to conduct local (per-vertex) post-hoc statistical analyses of the shape differences based on the participants' diagnoses (EO-MCI+EO-AD). Tensor-based Morphometry (TBM) and multivariate regression models were used to identify the significance of the structural brain differences based on the participants' diagnoses.

RESULTS

The significant between-group regional differences using GSA were found in 15 neuroimaging markers. The results of the LSA analysis workflow were based on the subject diagnosis, age, years of education, apolipoprotein E (ε4), Mini-Mental State Examination, visiting times, and logical memory as regressors. All the variables had significant effects on the regional shape measures. Some of these effects survived the false discovery rate (FDR) correction. Similarly, the TBM analysis showed significant effects on the Jacobian displacement vector fields, but these effects were reduced after FDR correction.

CONCLUSIONS

These results may explain some of the differences between EO-AD and EO-MCI, and some of the characteristics of the EO cognitive impairment subjects.

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.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

Mapping ventricular expansion onto cortical gray matter in older adults

Dynamic changes in the brain's lateral ventricles on magnetic resonance imaging are powerful biomarkers of disease progression in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Ventricular measures can represent accumulation of diffuse brain atrophy with very high effect sizes. Despite having no direct role in cognition, ventricular expansion co-occurs with volumetric loss in gray and white matter structures. To better understand relationships between ventricular and cortical changes over time, we related ventricular expansion to atrophy in cognitively relevant cortical gray matter surfaces, which are more challenging to segment. In ADNI participants, percent change in ventricular volumes at 1-year (N = 677) and 2-year (N = 536) intervals was significantly associated with baseline cortical thickness and volume in the full sample controlling for age, sex, and diagnosis, and in MCI separately. Ventricular expansion in MCI was associated with thinner gray matter in frontal, temporal, and parietal regions affected by AD. Ventricular expansion reflects cortical atrophy in early AD, offering a useful biomarker for clinical trials of interventions to slow AD progression.

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

A platelet protein biochip rapidly detects an Alzheimer's disease-specific phenotype

Alzheimer's disease (AD), a multifactorial neurodegenerative condition caused by genetic and environmental factors, is diagnosed using neuropsychological tests and brain imaging; molecular diagnostics are not routinely applied. Studies have identified AD-specific cerebrospinal fluid (CSF) biomarkers but sample collection requires invasive lumbar puncture. To identify AD-modulated proteins in easily accessible blood platelets, which share biochemical signatures with neurons, we compared platelet lysates from 62 AD, 24 amnestic mild cognitive impairment (aMCI), 13 vascular dementia (VaD), and 12 Parkinson's disease (PD) patients with those of 112 matched controls by fluorescence two-dimensional differential gel electrophoresis in independent discovery and verification sets. The optimal sum score of four mass spectrometry (MS)-identified proteins yielded a sensitivity of 94 % and a specificity of 89 % (AUC = 0.969, 95 % CI = 0.944-0.994) to differentiate AD patients from healthy controls. To bridge the gap between bench and bedside, we developed a high-throughput multiplex protein biochip with great potential for routine AD screening. For convenience and speed of application, this array combines loading control-assisted protein quantification of monoamine oxidase B and tropomyosin 1 with protein-based genotyping for single nucleotide polymorphisms (SNPs) in the apolipoprotein E and glutathione S-transferase omega 1 genes. Based on minimally invasive blood drawing, this innovative protein biochip enables identification of AD patients with an accuracy of 92 % in a single analytical step in less than 4 h.

Analysis of sub-anatomic diffusion tensor imaging indices in white matter regions of Alzheimer with MMSE score

In this study, an attempt has been made to find the correlation between diffusion tensor imaging (DTI) indices of white matter (WM) regions and mini mental state examination (MMSE) score of Alzheimer patients. Diffusion weighted images are obtained from the ADNI database. These are preprocessed for eddy current correction and removal of non-brain tissue. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (DA) indices are computed over significant regions (Fornix left, Splenium of corpus callosum left, Splenium of corpus callosum right, bilateral genu of the corpus callosum) affected by Alzheimer disease (AD) pathology. The correlation is computed between diffusion indices of the significant regions and MMSE score using linear fit technique so as to find the relation between clinical parameters and the image features. Binary classification has been employed using support vector machine, decision stumps and simple logistic classifiers on the extracted DTI indices along with MMSE score to classify Alzheimer patients from healthy controls. It is observed that distinct values of DTI indices exist for the range of MMSE score. However, there is no strong correlation (Pearson's correlation coefficient 'r' varies from 0.0383 to -0.1924) between the MMSE score and the diffusion indices over the significant regions. Further, the performance evaluation of classifiers shows 94% accuracy using SVM in differentiating AD and control. In isolation clinical and image features can be used for prescreening and diagnosis of AD but no sub anatomic region correlation exist between these features set. The discussion on the correlation of diffusion indices of WM with MMSE score is presented in this study.

Behavioral and psychological symptoms in Alzheimer's disease

Neuropsychiatric symptoms (NPS) such as depression, apathy, aggression, and psychosis are now recognized as core features of Alzheimer's disease (AD), and there is a general consensus that greater symptom severity is predictive of faster cognitive decline, loss of independence, and even shorter survival. Whether these symptoms result from the same pathogenic processes responsible for cognitive decline or have unique etiologies independent of AD-associated neurodegeneration is unclear. Many structural and metabolic features of the AD brain are associated with individual neuropsychiatric symptoms or symptom clusters. In addition, many genes have been identified and confirmed that are associated with symptom risk in a few cases. However, there are no single genes strongly predictive of individual neuropsychiatric syndromes, while functional and structural brain changes unique to specific symptoms may reflect variability in progression of the same pathological processes. Unfortunately, treatment success for these psychiatric symptoms may be lower when comorbid with AD, underscoring the importance of future research on their pathobiology and treatment. This review summarizes some of the most salient aspects of NPS pathogenesis.

The power of neuroimaging biomarkers for screening frontotemporal dementia

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase the feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N = 93) with cerebrospinal fluid, gray matter (GM) magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89% sensitive and 89% specific) and required a lower minimum sample size (N = 26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach using Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials.

MTA index: a simple 2D-method for assessing atrophy of the medial temporal lobe using clinically available neuroimaging

Background and purpose: Despite a strong correlation to severity of AD pathology, the measurement of medial temporal lobe atrophy (MTA) is not being widely used in daily clinical practice as a criterion in the diagnosis of prodromal and probable AD. This is mainly because the methods available to date are sophisticated and difficult to implement for routine use in most hospitals—volumetric methods—or lack objectivity—visual rating scales. In this pilot study we aim to describe a new, simple and objective method for measuring the rate of MTA in relation to the global atrophy using clinically available neuroimaging and describe the rationale behind this method.

Description: This method consists of calculating a ratio with the area of 3 regions traced manually on one single coronal MRI slide at the level of the interpeduncular fossa: (1) the medial temporal lobe (MTL) region (A); (2) the parenchima within the medial temporal region, that includes the hippocampus and the parahippocampal gyrus—the fimbria taenia and plexus choroideus are excluded—(B); and (3) the body of the ipsilateral lateral ventricle (C). Therefrom we can compute the ratio “Medial Temporal Atrophy index” at both sides as follows: MTAi = (A − B)× 10/C.

Conclusions: The MTAi is a simple 2D-method for measuring the relative extent of atrophy in the MTL in relation to the global brain atrophy. This method can be useful for a more accurate diagnosis of AD in routine clinical practice. Further studies are needed to assess the usefulness of MTAi in the diagnosis of early AD, in tracking the progression of AD and in the differential diagnosis of AD with other dementias.

Ventricular enlargement and its clinical correlates in the imaging cohort from the ADCS MCI donepezil/vitamin E study

We analyzed the baseline and 3-year T1-weighted magnetic resonance imaging data of 110 amnestic mild cognitive impairment (MCI) participants with minimal hippocampal atrophy at baseline from the Alzheimer's Disease Cooperative Study group MCI Donepezil/Vitamin E trial. Forty-six subjects converted to Alzheimer disease (AD) (MCIc), whereas 64 remained stable (MCInc). We used the radial distance technique to examine the differences in lateral ventricle shape and size between MCIc and MCInc and the associations between ventricular enlargement and cognitive decline. MCIc group had significantly larger frontal and right body/occipital horns relative to MCInc at baseline and significantly larger bilateral frontal, body/occipital, and left temporal horns at follow-up. Global cognitive decline measured with AD Assessment scale cognitive subscale and Mini-Mental State Examination and decline in activities of daily living (ADL) were associated with posterior lateral ventricle enlargement. Decline in AD Assessment scale cognitive subscale and ADL were associated with left temporal and decline in Mini-Mental State Examination with right temporal horn enlargement. After correction for baseline hippocampal volume, decline in ADL showed a significant association with right frontal horn enlargement. Executive decline was associated with right frontal and left temporal horn enlargement.

Apolipoprotein E epsilon 4 allele is associated with ventricular expansion rate and surface morphology in dementia and normal aging

The apolipoprotein E epsilon 4 allele (ApoE-ε4) is the strongest known genetic risk factor for late onset Alzheimer's disease. Expansion of the lateral ventricles occurs with normal aging, but dementia accelerates this process. Brain structure and function depend on ApoE genotype not just for Alzheimer's disease patients but also in healthy elderly individuals, and even in asymptomatic young individuals. Therefore, we hypothesized that the ApoE-ε4 allele is associated with altered patterns of longitudinal ventricular expansion, in dementia and normal aging. We tested this hypothesis in a large sample of elderly participants, using a linear discriminant analysis-based approach. Carrying more ApoE-ε4 alleles was associated with faster ventricular expansion bilaterally and with regional patterns of lateral ventricle morphology at 1- and 2-year follow up, after controlling for sex, age, and dementia status. ApoE genotyping is considered critical in clinical trials of Alzheimer's disease. These findings, combined with earlier investigations showing that ApoE is also directly implicated in other conditions, suggest that the selective enrollment of ApoE-ε4 carriers may empower clinical trials of other neurological disorders.

Understanding cognitive deficits in Alzheimer's disease based on neuroimaging findings

Brain amyloid can be measured using positron emission tomography (PET). There are mixed reports regarding whether amyloid measures are correlated with measures of cognition (in particular memory), depending on the cohorts and cognitive domains assessed. In Alzheimer's disease (AD) patients and those at heightened risk for AD, cognitive performance may be related to the level and extent of classical AD pathology (amyloid plaques and neurofibrillary angles), but it is also influenced by neurodegeneration, neurocognitive reserve, and vascular health. We discuss what recent neuroimaging research has discovered about cognitive deficits in AD and offer suggestions for future research.

Surface fluid registration of conformal representation: application to detect disease burden and genetic influence on hippocampus

In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E[element of]4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.

The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Recent advances in imaging Alzheimer's disease

Advances in brain imaging technology in the past five years have contributed greatly to the understanding of Alzheimer's disease (AD). Here, we review recent research related to amyloid imaging, new methods for magnetic resonance imaging analyses, and statistical methods. We also review research that evaluates AD risk factors and brain imaging, in the context of AD prediction and progression. We selected a variety of illustrative studies, describing how they advanced the field and are leading AD research in promising new directions.

Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment

Our aim was to compare the predictive accuracy of 4 different medial temporal lobe measurements for Alzheimer's disease (AD) in subjects with mild cognitive impairment (MCI). Manual hippocampal measurement, automated atlas-based hippocampal measurement, a visual rating scale (MTA-score), and lateral ventricle measurement were compared. Predictive accuracy for AD 2 years after baseline was assessed by receiver operating characteristics analyses with area under the curve as outcome. Annual cognitive decline was assessed by slope analyses up to 5 years after baseline. Correlations with biomarkers in cerebrospinal fluid (CSF) were investigated. Subjects with MCI were selected from the Development of Screening Guidelines and Clinical Criteria for Predementia AD (DESCRIPA) multicenter study (n = 156) and the single-center VU medical center (n = 172). At follow-up, area under the curve was highest for automated atlas-based hippocampal measurement (0.71) and manual hippocampal measurement (0.71), and lower for MTA-score (0.65) and lateral ventricle (0.60). Slope analysis yielded similar results. Hippocampal measurements correlated with CSF total tau and phosphorylated tau, not with beta-amyloid 1-42. MTA-score and lateral ventricle volume correlated with CSF beta-amyloid 1-42. We can conclude that volumetric hippocampal measurements are the best predictors of AD conversion in subjects with MCI.

Cerebrospinal fluid Aβ levels correlate with structural brain changes in Parkinson's disease

ParkWest is a large Norwegian multicenter study of newly diagnosed drug-naïve subjects with Parkinson's disease (PD). Cognitively normal PD subjects (PDCN) and PD subjects with mild cognitive impairment (PDMCI) from this cohort have significant hippocampal atrophy and ventricular enlargement, compared to normal controls. Here, we aimed to investigate whether the same structural changes are associated with cerebrospinal fluid (CSF) levels of amyloid beta (Aβ)38 , Aβ40 , Aβ42 , total tau (t-tau), and phosphorylated tau (p-tau). We performed three-dimensional radial distance analyses of the hippocampi and lateral ventricles using the MRI data from ParkWest subjects who provided CSF at baseline. Our sample consisted of 73 PDCN and 18 PDMCI subjects. We found significant associations between levels of all three CSF Aβ analytes and t-tau and lateral ventricular enlargement in the pooled sample. In the PDCN sample, all three amyloid analytes showed significant associations with the radial distance of the occipital and frontal horns of the lateral ventricles. CSF Aβ38 and Aβ42 showed negative associations, with enlargement in occipital and frontal horns of the lateral ventricles in the pooled sample, and a negative association with the occipital horns in PDMCI. CSF Aβ levels in early PD correlate with ventricular enlargement, previously associated with PD dementia. Therefore, CSF and MRI markers may help identify PD patients at high risk for developing cognitive decline and dementia in the course of their illness. Contrary to Alzheimer's disease, we found no associations between CSF t-tau and p-tau and hippocampal atrophy.

Estimating sample sizes for predementia Alzheimer's trials based on the Alzheimer's Disease Neuroimaging Initiative

This study modeled predementia Alzheimer's disease clinical trials. Longitudinal data from cognitively normal (CN) and mild cognitive impairment (MCI) participants in the Alzheimer's Disease Neuroimaging Initiative were used to calculate sample size requirements for trials using outcome measures, including the Clinical Dementia Rating scale sum of boxes, Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-cognitive subscale with and without delayed recall, and the Rey Auditory Verbal Learning Task. We examined the impact on sample sizes of enrichment for genetic and biomarker criteria, including cerebrospinal fluid protein and neuroimaging analyses. We observed little cognitive decline in the CN population at 36 months, regardless of the enrichment strategy. Nonetheless, in CN subjects, using Rey Auditory Verbal Learning Task total as an outcome at 36 months required the fewest subjects across enrichment strategies, with apolipoprotein E genotype ε4 carrier status requiring the fewest (n = 499 per arm to demonstrate a 25% reduction in disease progression). In MCI, enrichment reduced the required sample sizes for trials, relative to estimates based on all subjects. For MCI, the Clinical Dementia Rating scale sum of boxes consistently required the smallest sample sizes. We conclude that predementia clinical trial conduct in Alzheimer's disease is enhanced by the use of biomarker inclusion criteria.

Voxel and surface-based topography of memory and executive deficits in mild cognitive impairment and Alzheimer's disease

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) are associated with a progressive loss of cognitive abilities. In the present report, we assessed the relationship of memory and executive function with brain structure in a sample of 810 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, including 188 AD, 396 MCI, and 226 healthy older adults (HC). Composite scores of memory (ADNI-Mem) and executive function (ADNI-Exec) were generated by applying modern psychometric theory to item-level data from ADNI's neuropsychological battery. We performed voxel-based morphometry (VBM) and surface-based association (SurfStat) analyses to evaluate relationships of ADNI-Mem and ADNI-Exec with grey matter (GM) density and cortical thickness across the whole brain in the combined sample and within diagnostic groups. We observed strong associations between ADNI-Mem and medial and lateral temporal lobe atrophy. Lower ADNI-Exec scores were associated with advanced GM and cortical atrophy across broadly distributed regions, most impressively in the bilateral parietal and temporal lobes. We also evaluated ADNI-Exec adjusted for ADNI-Mem, and found associations with GM density and cortical thickness primarily in the bilateral parietal, temporal, and frontal lobes. Within-group analyses suggest these associations are strongest in patients with MCI and AD. The present study provides insight into the spatially unbiased associations between brain atrophy and memory and executive function, and underscores the importance of structural brain changes in early cognitive decline.

Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment (MCI), and Alzheimer Disease

Alzheimer disease (AD) is the most common type of dementia worldwide. Hippocampal atrophy and ventricular enlargement have been associated with AD but also with normal aging. We analyzed 1.5-T brain magnetic resonance imaging data from 46 cognitively normal elderly individuals (NC), 33 mild cognitive impairment and 43 AD patients. Hippocampal and ventricular analyses were conducted with 2 novel semiautomated segmentation approaches followed by the radial distance mapping technique. Multiple linear regression was used to assess the effects of age and diagnosis on hippocampal and ventricular volumes and radial distance. In addition, 3-dimensional map correction for multiple comparisons was made with permutation testing. As expected, most significant hippocampal atrophy and ventricular enlargement were seen in the AD versus NC comparison. Mild cognitive impairment patients showed intermediate levels of hippocampal atrophy and ventricular enlargement. Significant effects of age on hippocampal volume and radial distance were seen in the pooled sample and in the NC and AD groups considered separately. Age-associated differences were detected in all hippocampal subfields and in the frontal and body/occipital horn portions of the lateral ventricles. Aging affects both the hippocampus and lateral ventricles independent of AD pathology, and should be included as covariate in all structural, hippocampal, and ventricular analyses when possible.

Sports concussions and aging: a neuroimaging investigation

Recent epidemiological and experimental studies suggest a link between cognitive decline in late adulthood and sports concussions sustained in early adulthood. In order to provide the first in vivo neuroanatomical evidence of this relation, the present study probes the neuroimaging profile of former athletes with concussions in relation to cognition. Former athletes who sustained their last sports concussion >3 decades prior to testing were compared with those with no history of traumatic brain injury. Participants underwent quantitative neuroimaging (optimized voxel-based morphometry [VBM], hippocampal volume, and cortical thickness), proton magnetic resonance spectroscopy ((1)H MRS; medial temporal lobes and prefrontal cortices), and neuropsychological testing, and they were genotyped for APOE polymorphisms. Relative to controls, former athletes with concussions exhibited: 1) Abnormal enlargement of the lateral ventricles, 2) cortical thinning in regions more vulnerable to the aging process, 3) various neurometabolic anomalies found across regions of interest, 4) episodic memory and verbal fluency decline. The cognitive deficits correlated with neuroimaging findings in concussed participants. This study unveiled brain anomalies in otherwise healthy former athletes with concussions and associated those manifestations to the long-term detrimental effects of sports concussion on cognitive function. Findings from this study highlight patterns of decline often associated with abnormal aging.

Brain structure and aging in chronic temporal lobe epilepsy

PURPOSE

To characterize differences in brain structure and their patterns of age-related change in individuals with chronic childhood/adolescent onset temporal lobe epilepsy compared with healthy controls.

METHODS

Subjects included participants with chronic temporal lobe epilepsy (n = 55) of mean childhood/adolescent onset and healthy controls (n = 53), age 14-60 years. Brain magnetic resonance imaging (MRI) studies (1.5 T) were processed using FreeSurfer to obtain measures of lobar thickness, area, and volume as well as volumes of diverse subcortical structures and cerebellum. Group differences were explored followed by cross-sectional lifespan modeling as a function of age.

KEY FINDINGS

Anatomic abnormalities were extensive in participants with chronic temporal lobe epilepsy including distributed subcortical structures (hippocampus, thalamus, caudate, and pallidum), cerebellar gray and white matter, total cerebral gray and white matter; and measures of cortical gray matter thickness, area, or volume in temporal (medial, lateral) and extratemporal lobes (frontal, parietal). Increasing chronologic age was associated with progressive changes in diverse cortical, subcortical, and cerebellar regions for both participants with epilepsy and controls. Age-accelerated changes in epilepsy participants were seen in selected areas (third and lateral ventricles), with largely comparable patterns of age-related change across other regions of interest.

SIGNIFICANCE

Extensive cortical, subcortical, and cerebellar abnormalities are present in participants with mean chronic childhood/adolescent onset temporal lobe epilepsy implicating a significant neurodevelopmental impact on brain structure. With increasing chronologic age, the brain changes occurring in epilepsy appear to proceed in a largely age-appropriate fashion compared to healthy controls, the primary exception being age-accelerated ventricular expansion (lateral and third ventricles). These cumulative structural abnormalities appear to represent a significant anatomic burden for persons with epilepsy, the consequences of which remain to be determined as they progress into elder years.

Emerging imaging tools for use with traumatic brain injury research

This article identifies emerging neuroimaging measures considered by the inter-agency Pediatric Traumatic Brain Injury (TBI) Neuroimaging Workgroup. This article attempts to address some of the potential uses of more advanced forms of imaging in TBI as well as highlight some of the current considerations and unresolved challenges of using them. We summarize emerging elements likely to gain more widespread use in the coming years, because of 1) their utility in diagnosis, prognosis, and understanding the natural course of degeneration or recovery following TBI, and potential for evaluating treatment strategies; 2) the ability of many centers to acquire these data with scanners and equipment that are readily available in existing clinical and research settings; and 3) advances in software that provide more automated, readily available, and cost-effective analysis methods for large scale data image analysis. These include multi-slice CT, volumetric MRI analysis, susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), arterial spin tag labeling (ASL), functional MRI (fMRI), including resting state and connectivity MRI, MR spectroscopy (MRS), and hyperpolarization scanning. However, we also include brief introductions to other specialized forms of advanced imaging that currently do require specialized equipment, for example, single photon emission computed tomography (SPECT), positron emission tomography (PET), encephalography (EEG), and magnetoencephalography (MEG)/magnetic source imaging (MSI). Finally, we identify some of the challenges that users of the emerging imaging CDEs may wish to consider, including quality control, performing multi-site and longitudinal imaging studies, and MR scanning in infants and children.

The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are of great current research interest. While there is no consensus on whether MCIs actually “convert” to AD, this concept is widely applied. Thus, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline brain image, of whether an MCI will convert within a multi-year period following the initial clinical visit. This is not a traditional supervised learning problem since, in ADNI, there are no definitive labeled conversion examples. It is not unsupervised, either, since there are (labeled) ADs and Controls, as well as cognitive scores for MCIs. Prior works have defined MCI subclasses based on whether or not clinical scores significantly change from baseline. There are concerns with these definitions, however, since, e.g., most MCIs (and ADs) do not change from a baseline CDR = 0.5 at any subsequent visit in ADNI, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative definition, wherein an MCI is a converter if any of the patient's brain scans are classified “AD” by a Control-AD classifier. This definition bootstraps design of a second classifier, specifically trained to predict whether or not MCIs will convert. We thus predict whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate that this definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations more consistent with known AD biomarkers (including CSF markers). We also identify key prognostic brain region biomarkers.

Dietary and behavioral interventions protect against age related activation of caspase cascades in the canine brain

Lifestyle interventions such as diet, exercise, and cognitive training represent a quietly emerging revolution in the modern approach to counteracting age-related declines in brain health. Previous studies in our laboratory have shown that long-term dietary supplementation with antioxidants and mitochondrial cofactors (AOX) or behavioral enrichment with social, cognitive, and exercise components (ENR), can effectively improve cognitive performance and reduce brain pathology of aged canines, including oxidative damage and Aβ accumulation. In this study, we build on and extend our previous findings by investigating if the interventions reduce caspase activation and ceramide accumulation in the aged frontal cortex, since caspase activation and ceramide accumulation are common convergence points for oxidative damage and Aβ, among other factors associated with the aged and AD brain. Aged beagles were placed into one of four treatment groups: CON – control environment/control diet, AOX– control environment/antioxidant diet, ENR – enriched environment/control diet, AOX/ENR– enriched environment/antioxidant diet for 2.8 years. Following behavioral testing, brains were removed and frontal cortices were analyzed to monitor levels of active caspase 3, active caspase 9 and their respective cleavage products such as tau and semaphorin7a, and ceramides. Our results show that levels of activated caspase-3 were reduced by ENR and AOX interventions with the largest reduction occurring with combined AOX/ENR group. Further, reductions in caspase-3 correlated with reduced errors in a reversal learning task, which depends on frontal cortex function. In addition, animals treated with an AOX arm showed reduced numbers of cells expressing active caspase 9 or its cleavage product semaphorin 7A, while ENR (but not AOX) reduced ceramide levels. Overall, these data demonstrate that lifestyle interventions curtail activation of pro-degenerative pathways to improve cellular health and are the first to show that lifestyle interventions can regulate caspase pathways in a higher animal model of aging.

Hippocampal and ventricular changes in Parkinson's disease mild cognitive impairment

We analyzed T1-weighted brain magnetic resonance imaging data of 100 cognitively normal elderly controls (NC), 127 cognitively normal Parkinson's disease (PD; PDCN) and 31 PD-associated mild cognitive impairment (PDMCI) subjects from the Norwegian ParkWest study. Using automated segmentation methods, followed by the radial distance technique and multiple linear regression we studied the effect of clinical diagnosis on hippocampal and ventricular radial distance while adjusting for age, education, and scanning site. PDCN subjects had significantly smaller bilateral hippocampal radial distance relative to NC. Nonamnestic PDMCI subjects showed smaller right hippocampal radial distance relative to NC. PDMCI subjects showed significant enlargement of all portions of the lateral ventricles relative to NC and significantly larger bilateral temporal and occipital and left frontal lateral ventricular expansion relative to PDCN subjects. Nonamnestic PDMCI subjects showed significant ventricular enlargement spanning all parts of the lateral ventricle while those with amnestic PDMCI showed changes localized to the left occipital horn. Hippocampal atrophy and lateral ventricular enlargement show promise as structural biomarkers for PD.

Neuroimaging measures as endophenotypes in Alzheimer's disease

Late onset Alzheimer's disease (AD) is moderately to highly heritable. Apolipoprotein E allele ε4 (APOE4) has been replicated consistently as an AD risk factor over many studies, and recently confirmed variants in other genes such as CLU, CR1, and PICALM each increase the lifetime risk of AD. However, much of the heritability of AD remains unexplained. AD is a complex disease that is diagnosed largely through neuropsychological testing, though neuroimaging measures may be more sensitive for detecting the incipient disease stages. Difficulties in early diagnosis and variable environmental contributions to the disease can obscure genetic relationships in traditional case-control genetic studies. Neuroimaging measures may be used as endophenotypes for AD, offering a reliable, objective tool to search for possible genetic risk factors. Imaging measures might also clarify the specific mechanisms by which proposed risk factors influence the brain.

Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study

Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimer's disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.