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

Baseline Brain Volumes Predict Future Brain Atrophy in Mild Cognitive Impairment: A Tensor-based Morphometry Study of the Alzheimer Continuum

OBJECTIVE

Prognostic evaluation of patients with mild cognitive impairment (MCI) is of great importance, and magnetic resonance imaging, as a readily available modality, can play a pivotal role in this field.

METHODS

Using the Alzheimer Disease Neuroimaging Initiative database, we conducted a retrospective longitudinal study of the associations between volumetric brain magnetic resonance imaging and cognitive composite scores in all domains (memory, executive function, language, and visuospatial) with annual whole-brain atrophy based on tensor-based morphometry (TBM) scores among patients with MCI and healthy controls (HCs). The Reliable Change Index was further used to categorize patients into 2 groups including (1) patients with meaningful 1-year reliable cognitive changes [reliable change (RC) group] and (2) patients without (non-RC).

RESULTS

One hundred thirty-seven patients with MCI and 132 HCs were enrolled. The 2 groups showed no significant differences in age, sex, and apolipoprotein E4 expression (P > 0.05). Based on the TBM score, patients with MCI had more significant 1-year brain volume loss than HCs (P < 0.001). After multiple comparison corrections, the 1-year TBM atrophy score was positively correlated with baseline whole brain (P = 0.03), hippocampus (P < 0.0001), entorhinal (P < 0.0001), and middle temporal (P < 0.0001) volumes among MCI patients, indicating that lower volumes in these regions were associated with greater 1-year atrophy rates. Regression analyses showed a positive correlation between baseline and 1-year memory composite scores and annual brain atrophy rate in MCI patients (P = 0.01, 0.04), demonstrating that lower cognitive scores were associated with a greater annual atrophy rate. However, the correlations no longer held significance after correction for multiple comparison (P = 0.05, 0.17). MCI participants with RCs in language composite scores initially had significantly greater brain atrophy than those without (P = 0.03, corrected P = 0.06). However, TBM scores showed no significant differences between RC and non-RC groups for other composite scores (P > 0.05).

CONCLUSIONS

Lower baseline volumes in multiple brain regions of MCI are associated with greater annual brain volume loss based on TBM, suggesting TBM as a potential imaging marker for conventional volumetric studies in MCI. Further research is needed to explore the link between cognitive scores and the application of Reliable Change Index in TBM imaging across the Alzheimer disease spectrum.

The polygenic hazard score mediates the association between plasma neurofilament light chain and brain morphometry in dementia spectrum

INTRODUCTION

Blood-based biomarkers such as plasma neurofilament light chain (pNfL) are crucial biomarkers for Alzheimer's disease (AD). Additionally, neuroimaging techniques such as tensor-based morphometry (TBM), which identify structural changes in the brain, can provide valuable insights into AD pathophysiology. However, the role of genetics in linking the blood based biomarkers and imaging findings has not been well understood. Therefore, we aimed to investigate whether the polygenic hazard score (PHS), affects the association between neurofibrillary tangles and neuritis plaques and brain imaging findings.

METHODS

Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we enrolled all participants for whom a complete dataset of pNfL, PHS, and TBM was available. Using Python, we analyzed the associations between pNfL levels and the TBM data of 567 participants incluidng 152 cognitively normal individuals, 309 participants with mild cognitive impairment (MCI), and 106 patients with AD. We used a mediation analysis to identify the effect of PHS in how pNfL is associated with TBM measures.

RESULTS

We found a negative correlation between the accelerated TBM measure and NfL levels in both the MCI and AD groups. The pNfL concentration predicted both accelerated statistical and anatomical TMB measures in patients with MCI. Furthermore, PHS mediatedthe association between statistical TBM measures and NfL levels in AD patients, to the extent that the significant association between NfL and TBM measures disappeared after accounting for PHS.

CONCLUSION

We showed that although pNfL can predict the cognitiee decline and imaging findings in AD, this effect is mediated by the PHS. Therefore, PHS should be considered when investigating AD biomarkers and their corresponding imaging findings.

A comprehensive investigation of the associations between tensor-based morphometry indices and executive functions, memory, language, and visuospatial abilities in patients in the Alzheimer's disease continuum

OBJECTIVES

Based on the literature, tensor-based morphometry (TBM) parameters were related to neurocognitive functions such as memory, learning, language ability, and executive functions. The present study aims to evaluate the associations between TBM indices with executive functions, memory, language, and visuospatial abilities and the value of TBM in the clinical diagnosis of Alzheimer's disease (AD) among individuals with Alzheimer's disease continuum and mild cognitive impairment (MCI) from Alzheimer's Disease Neuroimaging Initiative (ADNI).

METHODS

The authors used ADNI-memory (ADNI-MEM), ADNI-executive functions (ADNI-EF), ADNI-language (ADNI-LAN), and ADNI-visuospatial (ADNI-VS) composite scores. TBM parameters, including measure 1, which represents average within a statistically defined region-of-interest inside the temporal lobes and measure 2 which indicates average within an anatomically defined region-of-interest including bilateral temporal lobes were utilized in the current study. Statistical analysis was performed using IBM SPSS Statistics version 26, and Pearson's correlation, Bonferroni's correction, and multiple linear regression were utilized for data analysis. P <0.05 was considered statistically significant.

RESULTS

After screening 800 participants, 270 (151 men, 119 women) were selected for a study with TBM scores and cognition-related assessments at 6, 12, and 24 months. Groups included healthy controls (n=53), MCI (n=158), and Alzheimer's Disease (AD) (n=59). TBM indices correlated with cognitive scores in MCI and AD groups but not healthy controls. Changes in TBM indices and cognitive scores were significantly correlated in MCI and AD groups over 24 months. TBM indices were weak predictors of cognitive decline at all time points.

CONCLUSIONS

TBM can help physicians diagnose MCI and AD early. However, TBM could not strongly predict cognitive functions decline at all time points.

Contributions of the ADNI Biostatistics Core

The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.

Growth-Associated Protein 43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.

Functional connectivity favors aberrant visual network c-Fos expression accompanied by cortical synapse loss in a mouse model of Alzheimer's disease

While Alzheimer's disease (AD) has been extensively studied with a focus on cognitive networks, sensory network dysfunction has received comparatively less attention despite compelling evidence of its significance in both Alzheimer's disease patients and mouse models. We recently found that neurons in the primary visual cortex of an AD mouse model expressing human amyloid protein precursor with the Swedish and Indiana mutations (hAPP mutations) exhibit aberrant c-Fos expression and altered synaptic structures at a pre-amyloid plaque stage. However, it is unclear whether aberrant c-Fos expression and synaptic pathology vary across the broader visual network and to what extent c-Fos abnormality in the cortex is inherited through functional connectivity. Using both sexes of 4-6-month AD model mice with hAPP mutations (J20[PDGF-APPSw, Ind]), we found that cortical regions of the visual network show aberrant c-Fos expression and impaired experience-dependent modulation while subcortical regions do not. Interestingly, the average network-wide functional connectivity strength of a brain region in wild type (WT) mice significantly predicts its aberrant c-Fos expression, which in turn correlates with impaired experience-dependent modulation in the AD model. Using in vivo two-photon and ex vivo imaging of presynaptic termini, we observed a subtle yet selective weakening of excitatory cortical synapses in the visual cortex. Intriguingly, the change in the size distribution of cortical boutons in the AD model is downscaled relative to those in WT mice, suggesting that synaptic weakening may reflect an adaptation to aberrant activity. Our observations suggest that cellular and synaptic abnormalities in the AD model represent a maladaptive transformation of the baseline physiological state seen in WT conditions rather than entirely novel and unrelated manifestations.

Effects of risk factors on longitudinal changes in brain structure and function in the progression of AD

INTRODUCTION

Past research on Alzheimer's disease (AD) has focused on biomarkers, cognition, and neuroimaging as primary predictors of its progression, albeit additional ones have recently gained attention. When turning to the prediction of the progression from one stage to another, one could benefit from the joint assessment of imaging-based biomarkers and risk/protective factors.

METHODS

We included 86 studies that fulfilled our inclusion criteria.

RESULTS

Our review summarizes and discusses the results of 30 years of longitudinal research on brain changes assessed with neuroimaging and the risk/protective factors and their effect on AD progression. We group results into four sections: genetic, demographic, cognitive and cardiovascular, and lifestyle factors.

DISCUSSION

Given the complex nature of AD, including risk factors could prove invaluable for a better understanding of AD progression. Some of these risk factors are modifiable and could be targeted by potential future treatments.

Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.

Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis

INTRODUCTION

Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence.

METHODS

A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods.

RESULTS

Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers.

DISCUSSION

On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.

Deep residual-SVD network for brain image registration

Objective. Medical image registration aims to find the deformation field that can align two images in a spatial position. A medical image registration method based on U-Net architecture has been proposed currently. However, U-Net architecture has few training parameters, which leads to weak learning ability, and it ignores the adverse effects of image noise on the registration accuracy. The article aims at addressing the problem of weak network learning ability and the adverse effects of noisy images on registration. Approach. Here we propose a novel unsupervised 3D brain image registration framework, which introduces the residual unit and singular value decomposition (SVD) denoising layer on the U-Net architecture. Residual unit solves the problem of network degradation, that is, registration accuracy becomes saturated and then degrades rapidly with the increase in network depth. SVD denoising layer uses the estimated model order for SVD-based low-rank image reconstruction. we use Akaike information criterion to estimate the appropriate model order, which is used to remove noise components. We use the exponential linear unit (ELU) as the activation function, which is more robust to noise than other peers. Main results. The proposed method is evaluated on the publicly available brain MRI datasets: Mindboggle101 and LPBA40. Experimental results demonstrate our method outperforms several state-of-the-art methods for the metric of Dice Score. The mean number of folding voxels and registration time are comparable to state-of-the-art methods. Significance. This study shows that Deep Residual-SVD Network can improve registration accuracy. This study also demonstrate that the residual unit can enhance the learning ability of the network, the SVD denoising layer can denoise effectively, and the ELU is more robust to noise.

Automated High-Definition MRI Processing Routine Robustly Detects Longitudinal Morphometry Changes in Alzheimer's Disease Patients

Longitudinal MRI studies are of increasing importance to document the time course of neurodegenerative diseases as well as neuroprotective effects of a drug candidate in clinical trials. However, manual longitudinal image assessments are time consuming and conventional assessment routines often deliver unsatisfying study outcomes. Here, we propose a profound analysis pipeline that consists of the following coordinated steps: (1) an automated and highly precise image processing stream including voxel and surface based morphometry using latest highly detailed brain atlases such as the HCP MMP 1.0 atlas with 360 cortical ROIs; (2) a profound statistical assessment using a multiplicative model of annual percent change (APC); and (3) a multiple testing correction adopted from genome-wide association studies that is optimally suited for longitudinal neuroimaging studies. We tested this analysis pipeline with 25 Alzheimer's disease patients against 25 age-matched cognitively normal subjects with a baseline and a 1-year follow-up conventional MRI scan from the ADNI-3 study. Even in this small cohort, we were able to report 22 significant measurements after multiple testing correction from SBM (including cortical volume, area and thickness) complementing only three statistically significant volume changes (left/right hippocampus and left amygdala) found by VBM. A 1-year decrease in brain morphometry coincided with an increasing clinical disability and cognitive decline in patients measured by MMSE, CDR GLOBAL, FAQ TOTAL and NPI TOTAL scores. This work shows that highly precise image assessments, APC computation and an adequate multiple testing correction can produce a significant study outcome even for small study sizes. With this, automated MRI processing is now available and reliable for routine use and clinical trials.

Myelin-Weighted Imaging Presents Reduced Apparent Myelin Water in Patients with Alzheimer’s Disease

The purpose of this study was to investigate myelin loss in both AD and mild cognitive impairment (MCI) patients with a new myelin water mapping technique within reasonable scan time and evaluate the clinical relevance of the apparent myelin water fraction (MWF) values by assessing the relationship between decreases in myelin water and the degree of memory decline or aging. Twenty-nine individuals were assigned to the cognitively normal (CN) elderly group, 32 participants were assigned to the MCI group, and 31 patients were assigned to the AD group. A 3D visualization of the short transverse relaxation time component (ViSTa)-gradient and spin-echo (GraSE) sequence was developed to map apparent MWF. Then, the MWF values were compared between the three participant groups and was evaluated the relationship with the degree of memory loss. The AD group showed a reduced apparent MWF compared to the CN and MCI groups. The largest AUC (area under the curve) value was in the corpus callosum and used to classify the CN and AD groups using the apparent MWF. The ViSTa-GraSE sequence can be a useful tool to map the MWF in a reasonable scan time. Combining the MWF in the corpus callosum with the detection of atrophy in the hippocampus can be valuable for group classification.

Bayesian Fully Convolutional Networks for Brain Image Registration

The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.

Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

Relationship between Brain Tissue Changes and Blood Biomarkers of Cyclophilin A, Heme Oxygenase-1, and Inositol-Requiring Enzyme 1 in Patients with Alzheimer's Disease

Cyclophilin A (CypA), heme oxygenase-1 (HO-1), and inositol-requiring enzyme 1 (IRE1) are believed to be associated with Alzheimer's disease (AD). In this study, we investigated the association between gray matter volume (GMV) changes and blood levels of CypA, HO-1, and IRE1 in cognitively normal (CN) subjects and those with amnestic mild cognitive impairment (aMCI) and AD. Forty-five elderly CN, 34 aMCI, and 39 AD subjects were enrolled in this study. The results of voxel-based multiple regression analysis showed that blood levels of CypA, HO-1, and IRE1 were correlated with GMV on brain magnetic resonance imaging (MRI) in the entire population (p = 0.0005). The three serum protein levels were correlated with GMV of signature AD regions in the population as a whole. CypA values increased with increasing GMV in the occipital gyrus (r = 0.387, p < 0.0001) and posterior cingulate (r = 0.196, p = 0.034). HO-1 values increased with increasing GMV at the uncus (r = 0.307, p = 0.0008), lateral globus pallidus and putamen (r = 0.287, p = 0.002), and hippocampus (r = 0.197, p = 0.034). IRE1 values decreased with increasing GMV at the uncus (r = -0.239, p = 0.010) and lateral globus pallidus and putamen (r = -0.335, p = 0.0002). Associations between the three serum protein levels and regional GMV indicate that the blood levels of these biomarkers may reflect the pathological mechanism of AD in the brain.

The Alzheimer's Association international guidelines for handling of cerebrospinal fluid for routine clinical measurements of amyloid β and tau

The core cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers amyloid beta (Aβ42 and Aβ40), total tau, and phosphorylated tau, have been extensively clinically validated, with very high diagnostic performance for AD, including the early phases of the disease. However, between-center differences in pre-analytical procedures may contribute to variability in measurements across laboratories. To resolve this issue, a workgroup was led by the Alzheimer's Association with experts from both academia and industry. The aim of the group was to develop a simplified and standardized pre-analytical protocol for CSF collection and handling before analysis for routine clinical use, and ultimately to ensure high diagnostic performance and minimize patient misclassification rates. Widespread application of the protocol would help minimize variability in measurements, which would facilitate the implementation of unified cut-off levels across laboratories, and foster the use of CSF biomarkers in AD diagnostics for the benefit of the patients.

Longitudinal neuroimaging biomarkers differ across Alzheimer's disease phenotypes

Alzheimer's disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer's disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer's disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer's disease (18 with typical amnestic Alzheimer's disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer's disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer's disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer's disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer's disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer's disease.

Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.

Is Sleep Disruption a Cause or Consequence of Alzheimer's Disease? Reviewing Its Possible Role as a Biomarker

In recent years, the idea that sleep is critical for cognitive processing has gained strength. Alzheimer's disease (AD) is the most common form of dementia worldwide and presents a high prevalence of sleep disturbances. However, it is difficult to establish causal relations, since a vicious circle emerges between different aspects of the disease. Nowadays, we know that sleep is crucial to consolidate memory and to remove the excess of beta-amyloid and hyperphosphorilated tau accumulated in AD patients' brains. In this review, we discuss how sleep disturbances often precede in years some pathological traits, as well as cognitive decline, in AD. We describe the relevance of sleep to memory consolidation, focusing on changes in sleep patterns in AD in contrast to normal aging. We also analyze whether sleep alterations could be useful biomarkers to predict the risk of developing AD and we compile some sleep-related proposed biomarkers. The relevance of the analysis of the sleep microstructure is highlighted to detect specific oscillatory patterns that could be useful as AD biomarkers.

Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change

Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.

Brain Morphological Dynamics of Procrastination: The Crucial Role of the Self-Control, Emotional, and Episodic Prospection Network

Globally, about 17% individuals are suffering from the maladaptive procrastination until now, which impacts individual's financial status, mental health, and even public policy. However, the comprehensive understanding of neuroanatomical understructure of procrastination still remains gap. 688 participants including 3 independent samples were recruited for this study. Brain morphological dynamics referred to the idiosyncrasies of both brain size and brain shape. Multilinear regression analysis was utilized to delineate brain morphological dynamics of procrastination in Sample 1. In the Sample 2, cross-validation was yielded. Finally, prediction models of machine learning were conducted in Sample 3. Procrastination had a significantly positive correlation with the gray matter volume (GMV) in the left insula, anterior cingulate gyrus (ACC), and parahippocampal gyrus (PHC) but was negatively correlated with GMV of dorsolateral prefrontal cortex (dlPFC) and gray matter density of ACC. Furthermore, procrastination was positively correlated to the cortical thickness and cortical complexity of bilateral orbital frontal cortex (OFC). In Sample 2, all the results were cross-validated highly. Predication analysis demonstrated that these brain morphological dynamic can predict procrastination with high accuracy. This study ascertained the brain morphological dynamics involving in self-control, emotion, and episodic prospection brain network for procrastination, which advanced promising aspects of the biomarkers for it.

Spatial patterns of progressive brain volume loss after moderate-severe traumatic brain injury

Traumatic brain injury leads to significant loss of brain volume, which continues into the chronic stage. This can be sensitively measured using volumetric analysis of MRI. Here we: (i) investigated longitudinal patterns of brain atrophy; (ii) tested whether atrophy is greatest in sulcal cortical regions; and (iii) showed how atrophy could be used to power intervention trials aimed at slowing neurodegeneration. In 61 patients with moderate-severe traumatic brain injury (mean age = 41.55 years ± 12.77) and 32 healthy controls (mean age = 34.22 years ± 10.29), cross-sectional and longitudinal (1-year follow-up) brain structure was assessed using voxel-based morphometry on T₁-weighted scans. Longitudinal brain volume changes were characterized using a novel neuroimaging analysis pipeline that generates a Jacobian determinant metric, reflecting spatial warping between baseline and follow-up scans. Jacobian determinant values were summarized regionally and compared with clinical and neuropsychological measures. Patients with traumatic brain injury showed lower grey and white matter volume in multiple brain regions compared to controls at baseline. Atrophy over 1 year was pronounced following traumatic brain injury. Patients with traumatic brain injury lost a mean (± standard deviation) of 1.55% ± 2.19 of grey matter volume per year, 1.49% ± 2.20 of white matter volume or 1.51% ± 1.60 of whole brain volume. Healthy controls lost 0.55% ± 1.13 of grey matter volume and gained 0.26% ± 1.11 of white matter volume; equating to a 0.22% ± 0.83 reduction in whole brain volume. Atrophy was greatest in white matter, where the majority (84%) of regions were affected. This effect was independent of and substantially greater than that of ageing. Increased atrophy was also seen in cortical sulci compared to gyri. There was no relationship between atrophy and time since injury or age at baseline. Atrophy rates were related to memory performance at the end of the follow-up period, as well as to changes in memory performance, prior to multiple comparison correction. In conclusion, traumatic brain injury results in progressive loss of brain tissue volume, which continues for many years post-injury. Atrophy is most prominent in the white matter, but is also more pronounced in cortical sulci compared to gyri. These findings suggest the Jacobian determinant provides a method of quantifying brain atrophy following a traumatic brain injury and is informative in determining the long-term neurodegenerative effects after injury. Power calculations indicate that Jacobian determinant images are an efficient surrogate marker in clinical trials of neuroprotective therapeutics.

Brain biomarkers and cognition across adulthood

Understanding the associations between brain biomarkers (BMs) and cognition across age is of paramount importance. Five hundred and sixty-two participants (19-80 years old, 16 mean years of education) were studied. Data from structural T1, diffusion tensor imaging, fluid-attenuated inversion recovery, and resting-state functional magnetic resonance imaging scans combined with a neuropsychological evaluation were used. More specifically, the measures of cortical, entorhinal, and parahippocampal thickness, hippocampal and striatal volume, default-mode network and fronto-parietal control network, fractional anisotropy (FA), and white matter hyperintensity (WMH) were assessed. z-Scores for three cognitive domains measuring episodic memory, executive function, and speed of processing were computed. Multiple linear regressions and interaction effects between each of the BMs and age on cognition were examined. Adjustments were made for age, sex, education, intracranial volume, and then, further, for general cognition and motion. BMs were significantly associated with cognition. Across the adult lifespan, slow speed was associated with low striatal volume, low FA, and high WMH burden. Poor executive function was associated with low FA, while poor memory was associated with high WMH burden. After adjustments, results were significant for the associations: speed-FA and WMH, memory-entorhinal thickness. There was also a significant interaction between hippocampal volume and age in memory. In age-stratified analyses, the most significant associations for the young group occurred between FA and executive function, WMH, and memory, while for the old group, between entorhinal thickness and speed, and WMH and speed, executive function. Unique sets of BMs can explain variation in specific cognitive domains across adulthood. Such results provide essential information about the neurobiology of aging.

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.

A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers

Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment or cure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causation in the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number of quantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers. The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however, not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-β and tau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans, in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonly used diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in a representative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, we suggest directions in which research could move in order to advance our understanding of this complex disease, including repeat biomarker measurements, standardization and increased sample sizes.

Morphological Biomarker Differentiating MCI Converters from Nonconverters: Longitudinal Evidence Based on Hemispheric Asymmetry

Identifying subjects with mild cognitive impairment (MCI) who may probably progress to Alzheimer's disease (AD) is important for better understanding the disease mechanisms and facilitating early treatments. In addition to the direct volumetric and thickness measurement based on high-resolution magnetic resonance imaging (MRI), hemispheric asymmetry could be a potential index to detect morphological variations in MCI patients with a high risk of conversion to AD. The present study collected a set of longitudinal MRI data from 53 MCI converters and nonconverters and investigated the asymmetry differences between groups. Asymmetry variation was observed in the medial temporal lobe, especially in the entorhinal cortex, between converters and nonconverters 3 years before the former developed AD. The proposed asymmetry analysis was observed to be sensitive to detect morphological changes between groups as compared to the methods of voxel-based morphometry (VBM) and thickness measurement. Hemispheric asymmetry in specific brain regions as a neuroimaging biomarker can provide helpful information for prediction of MCI conversion.

Does left ventricular hypertrophy affect cognition and brain structural integrity in type 2 diabetes? Study design and rationale of the Diabetes and Dementia (D2) study

BACKGROUND

Cognitive impairment is common in type 2 diabetes mellitus, and there is a strong association between type 2 diabetes and Alzheimer's disease. However, we do not know which type 2 diabetes patients will dement or which biomarkers predict cognitive decline. Left ventricular hypertrophy (LVH) is potentially such a marker. LVH is highly prevalent in type 2 diabetes and is a strong, independent predictor of cardiovascular events. To date, no studies have investigated the association between LVH and cognitive decline in type 2 diabetes. The Diabetes and Dementia (D2) study is designed to establish whether patients with type 2 diabetes and LVH have increased rates of brain atrophy and cognitive decline.

METHODS

The D2 study is a single centre, observational, longitudinal case control study that will follow 168 adult patients aged >50 years with type 2 diabetes: 50% with LVH (case) and 50% without LVH (control). It will assess change in cardiovascular risk, brain imaging and neuropsychological testing between two time-points, baseline (0 months) and 24 months. The primary outcome is brain volume change at 24 months. The co-primary outcome is the presence of cognitive decline at 24 months. The secondary outcome is change in left ventricular mass associated with brain atrophy and cognitive decline at 24 months.

DISCUSSION

The D2 study will test the hypothesis that patients with type 2 diabetes and LVH will exhibit greater brain atrophy than those without LVH. An understanding of whether LVH contributes to cognitive decline, and in which patients, will allow us to identify patients at particular risk.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ( ACTRN12616000546459 ), date registered, 28/04/2016.

Brain structural changes following adaptive cognitive training assessed by Tensor-Based Morphometry (TBM)

Tensor-Based Morphometry (TBM) allows the automatic mapping of brain changes across time building 3D deformation maps. This technique has been applied for tracking brain degeneration in Alzheimer's and other neurodegenerative diseases with high sensitivity and reliability. Here we applied TBM to quantify changes in brain structure after completing a challenging adaptive cognitive training program based on the n-back task. Twenty-six young women completed twenty-four training sessions across twelve weeks and they showed, on average, large cognitive improvements. High-resolution MRI scans were obtained before and after training. The computed longitudinal deformation maps were analyzed for answering three questions: (a) Are there differential brain structural changes in the training group as compared with a matched control group? (b) Are these changes related to performance differences in the training program? (c) Are standardized changes in a set of psychological factors (fluid and crystallized intelligence, working memory, and attention control) measured before and after training, related to structural changes in the brain? Results showed (a) greater structural changes for the training group in the temporal lobe, (b) a negative correlation between these changes and performance across training sessions (the greater the structural change, the lower the cognitive performance improvements), and (c) negligible effects regarding the psychological factors measured before and after training.

Mapping the Progression of Atrophy in Early- and Late-Onset Alzheimer's Disease

The term early-onset Alzheimer's disease (EOAD) identifies patients who meet criteria for AD, but show onset of symptoms before the age of 65. We map progression of gray matter atrophy in EOAD patients compared to late-onset AD (LOAD). T1-weighted MRI scans were obtained at diagnosis and one-year follow-up from 15 EOAD, 10 LOAD, and 38 age-matched controls. Voxel-based and tensor-based morphometry were used, respectively, to assess the baseline and progression of atrophy. At baseline, EOAD patients already showed a widespread atrophy in temporal, parietal, occipital, and frontal cortices. After one year, EOAD had atrophy progression in medial temporal and medial parietal cortices. At baseline, LOAD patients showed atrophy in the medial temporal regions only, and, after one year, an extensive pattern of atrophy progression in the same neocortical cortices of EOAD. Although atrophy mainly involved different lateral neocortical or medial temporal hubs at baseline, it eventually progressed along the same brain default-network regions in both groups. The cortical region showing a significant progression in both groups was the medial precuneus/posterior cingulate.

Following the Spreading of Brain Structural Changes in Alzheimer's Disease: A Longitudinal, Multimodal MRI Study

BACKGROUND

Longitudinal MRI studies in Alzheimer's disease (AD) are one of the most reliable way to track brain changes along the course of the disease.

OBJECTIVE

To investigate the evolution of grey matter (GM) atrophy and white matter (WM) damage in AD patients, and to assess the relationships of MRI changes with baseline clinical and cognitive variables and their evolution over time.

METHODS

Clinical, neuropsychological, and MRI assessments (T1-weighted and diffusion tensor [DT]-MRI) were obtained from 14 patients with AD at baseline and after a 16 ± 3 month period. Lumbar puncture was obtained at study entry. At baseline, AD patients were compared to 37 controls. GM atrophy progression was assessed with tensor-based morphometry and GM volumes of interest, and WM damage progression using tract-based spatial statistics and tractography.

RESULTS

At baseline, patients showed cortical atrophy in the medial temporal and parietal regions and a widespread pattern of WM damage involving the corpus callosum, cingulum, and temporo-occipital, parietal, and frontal WM tracts. During follow up, AD patients showed total GM atrophy, while total WM volume did not change. GM tissue loss was found in frontal, temporal, and parietal regions. In addition, AD patients showed a progression of WM microstructural damage to the corpus callosum, cingulum, fronto-parietal and temporo-occipital connections bilaterally. Patients with higher baseline cerebrospinal fluid total tau showed greater WM integrity loss at follow up. GM and WM changes over time did not correlate with each other nor with cognitive evolution.

CONCLUSION

In AD, GM atrophy and WM tract damage are likely to progress, at least partially, independently. This study suggests that a multimodal imaging approach, which includes both T1-weighted and DT MR imaging, may provide additional markers to monitor disease progression.

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

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.

Biochemical and Radiological Markers of Alzheimer's Disease Progression

Alzheimer's disease (AD) is a neurodegenerative, inevitably progressive disease with a rate of cognitive, functional, and behavioral decline that varies highly from patient to patient. Although several clinical predictors of AD progression have been identified, to our mind in clinical practice there is a lack of a reliable biomarker that enables one to stratify the risk of deterioration. Identification of biomarkers that allow the monitoring of AD progression could change the way physicians and caregivers make treatment decisions. This review summarizes the results of studies on potential biochemical and radiological markers related to AD progression.

Cerebral atrophy in mild cognitive impairment: A systematic review with meta-analysis

INTRODUCTION

Although mild cognitive impairment (MCI) diagnosis is mainly based on cognitive assessment, reliable estimates of structural changes in specific brain regions, that could be contrasted against normal brain aging and inform diagnosis, are lacking. This study aimed to systematically review the literature reporting on MCI-related brain changes.

METHODS

The MEDLINE database was searched for studies investigating longitudinal structural changes in MCI. Studies with compatible data were included in the meta-analyses. A qualitative review was conducted for studies excluded from meta-analyses.

RESULTS

The analyses revealed a 2.2-fold higher volume loss in the hippocampus, 1.8-fold in the whole brain, and 1.5-fold in the entorhinal cortex in MCI participants.

DISCUSSION

Although the medial temporal lobe is likely to be more vulnerable to MCI pathology, atrophy in this brain area represents a relatively small proportion of whole brain loss, suggesting that future investigations are needed to identify the source of unaccounted volume loss in MCI.

Are transversal MR images sufficient to distinguish persons with mild cognitive impairment from healthy controls?

RATIONALE AND OBJECTIVES

Mild cognitive impairment (MCI) is associated with an increased risk of developing dementia. This study aims to determine whether current standard magnetic resonance imaging (MRI) is providing markers that can distinguish between subjects with amnestic MCI (aMCI), nonamnestic MCI (naMCI), and healthy controls (HCs).

MATERIALS AND METHODS

A subset of 126 MCI subjects and 126 age-, gender-, and education-appropriate HCs (mean age, 70.9 years) were recruited from 4157 participants in the longitudinal community-based Heinz Nixdorf Recall Study. The burden of white matter hyperintensities (WMHs), cerebral microbleeds, and brain atrophy was evaluated on transversal MR images from a single 1.5-T MR scanner by two blinded neuroradiologists. Logistic regression and receiver-operating characteristic analysis were used for statistical analysis.

RESULTS

Occipital WMH burden was significantly increased in aMCI, but not in naMCI relative to HCs (P = .01). The combined MCI group showed brain atrophy relative to HCs (P = .01) pronounced at caudate nuclei (P = .01) and temporal horn level (P = .004) of aMCI patients and increased at the frontal and occipital horns of naMCI patients compared to either aMCI or HCs. Microbleeds were equally distributed in the MCI and control group, but more frequent in aMCI (22 of 84) compared to naMCI subjects (3 of 23).

CONCLUSIONS

In his cohort, increased occipital WMHs and cortical and subcortical brain atrophies at temporal horn and caudate nuclei level distinguished aMCI from naMCI subjects and controls. Volumetric indices appear of interest and should be assessed under reproducible conditions to gain diagnostic accuracy.

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.

Amyloid-β sequester proteins as blood-based biomarkers of cognitive decline

Introduction

There are no blood-based biomarkers for cognitive decline in aging, or mild cognitive impairment (MCI) and Alzheimer's disease (AD). Cumulative evidence suggests that apolipoproteins, complement system, and transthyretin are involved in AD pathogenesis by sequestration of amyloid β. However, there is no clinical study to assess the utility of “sequester proteins” in risk assessment and/or diagnosis of MCI and AD.

Methods

Serum levels of sequester proteins and their clinical potential in cognitive decline assessment were analyzed by longitudinal and cross-sectional studies using independent cohorts and were confirmed by a prospective study.

Results

A combination of apolipoprotein A1, complement C3, and transthyretin achieved an area under the curve of 0.89 (sensitivity 91% and specificity 80%) in MCI versus healthy controls and also discriminated individuals with mild cognitive decline from healthy controls.

Discussion

A set of sequester proteins could be blood-based biomarkers for assessment of early stages of cognitive decline.

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.

A consensus in Korea regarding a protocol to reduce preanalytical sources of variability in the measurement of the cerebrospinal fluid biomarkers of Alzheimer's disease

Cerebrospinal fluid (CSF) can provide vital informative about pathological processes occurring in the brain. In particular, the CSF concentrations of Aβ42, tTau, and pTau181 are useful for the early diagnosis of Alzheimer's disease (AD). However, many studies have demonstrated that confounding factors related to the preanalytical processing of CSF can seriously influence measurements of these AD biomarkers. It is therefore important to develop a standardized protocol for the acquisition and handling of CSF, particularly with regard to the types of tube used for collection and storage, the proper aliquot volume, blood contamination, and the number of tube transfers and freeze-thaw cycles, because these aspects of the procedure have been shown to affect AD biomarker measurements. A survey of the impact of several individual preanalytical procedures on the measurement of AD biomarkers in CSF was conducted for this review article, and the implications of the differences among them are discussed. Furthermore, following a review of the procedures used in Korean and international biomarker laboratories, a consensus was reached among a cooperative Korean multicenter research group regarding a standardized protocol for the analysis of AD biomarkers in CSF. All efforts were made to be stringent regarding the controversial issues associated with this protocol, thus minimizing the confounding influence of various factors on current investigations using established AD biomarkers and on future studies using novel biomarkers of AD and other neurodegenerative disorders.

Frontal and striatal alterations associated with psychopathic traits in adolescents

Neuroimaging research has demonstrated a range of structural deficits in adults with psychopathy, but little is known about structural correlates of psychopathic tendencies in adolescents. Here we examined structural magnetic resonance imaging (sMRI) data obtained from 14-year-old adolescents (n=108) using tensor-based morphometry (TBM) to isolate global and localized differences in brain tissue volumes associated with psychopathic traits in this otherwise healthy developmental population. We found that greater levels of psychopathic traits were correlated with increased brain tissue volumes in the left putamen, left ansa peduncularis, right superiomedial prefrontal cortex, left inferior frontal cortex, right orbitofrontal cortex, and right medial temporal regions and reduced brain tissues volumes in the right middle frontal cortex, left superior parietal lobule, and left inferior parietal lobule. Post hoc analyses of parcellated regional volumes also showed putamen enlargements to correlate with increased psychopathic traits. Consistent with earlier studies, findings suggest poor decision-making and emotional dysregulation associated with psychopathy may be due, in part, to structural anomalies in frontal and temporal regions whereas striatal structural variations may contribute to sensation-seeking and reward-driven behavior in psychopathic individuals. Future studies will help clarify how disturbances in brain maturational processes might lead to the developmental trajectory from psychopathic tendencies in adolescents to adult psychopathy.

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.

Using structural and diffusion magnetic resonance imaging to differentiate the dementias

Dementia is one of the major causes of personal, societal and financial dependence in older people and in today's ageing society there is a pressing need for early and accurate markers of cognitive decline. There are several subtypes of dementia but the four most common are Alzheimer's disease, Lewy body dementia, vascular dementia and frontotemporal dementia. These disorders can only be diagnosed at autopsy, and ante-mortem assessments of "probable dementia (e.g. of Alzheimer type)" are traditionally driven by clinical symptoms of cognitive or behavioural deficits. However, owing to the overlapping nature of symptoms and age of onset, a significant proportion of dementia cases remain incorrectly diagnosed. Misdiagnosis can have an extensive impact, both at the level of the individual, who may not be offered the appropriate treatment, and on a wider scale, by influencing the entry of patients into relevant clinical trials. Magnetic resonance imaging (MRI) may help to improve diagnosis by providing non-invasive and detailed disease-specific markers of cognitive decline. MRI-derived measurements of grey and white matter structural integrity are potential surrogate markers of disease progression, and may also provide valuable diagnostic information. This review summarises the latest evidence on the use of structural and diffusion MRI in differentiating between the four major dementia subtypes.

Regional gray matter atrophy in relapsing remitting multiple sclerosis: baseline analysis of multi-center data

Regional gray matter (GM) atrophy in multiple sclerosis (MS) at disease onset and its temporal variation can provide objective information regarding disease evolution. An automated pipeline for estimating atrophy of various GM structures was developed using tensor based morphometry (TBM) and implemented on a multi-center sub-cohort of 1008 relapsing remitting MS (RRMS) patients enrolled in a Phase 3 clinical trial. Four hundred age and gender matched healthy controls were used for comparison. Using the analysis of covariance, atrophy differences between MS patients and healthy controls were assessed on a voxel-by-voxel analysis. Regional GM atrophy was observed in a number of deep GM structures that included thalamus, caudate nucleus, putamen, and cortical GM regions. General linear regression analysis was performed to analyze the effects of age, gender, and scanner field strength, and imaging sequence on the regional atrophy. Correlations between regional GM volumes and expanded disability status scale (EDSS) scores, disease duration (DD), T2 lesion load (T2 LL), T1 lesion load (T1 LL), and normalized cerebrospinal fluid (nCSF) were analyzed using Pearson׳s correlation coefficient. Thalamic atrophy observed in MS patients compared to healthy controls remained consistent within subgroups based on gender and scanner field strength. Weak correlations between thalamic volume and EDSS (r=-0.133; p<0.001) and DD (r=-0.098; p=0.003) were observed. Of all the structures, thalamic volume moderately correlated with T2 LL (r=-0.492; P-value<0.001), T1 LL (r=-0.473; P-value<0.001) and nCSF (r=-0.367; P-value<0.001).

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.