Temporoparietal MR imaging measures of atrophy in subjects with mild cognitive impairment that predict subsequent diagnosis of Alzheimer disease

Impaired Glymphatic Flow on Diffusion Tensor MRI as a Marker of Neurodegeneration in Alzheimer's Disease: Correlation with Gray Matter Volume Loss and Cognitive Decline Independent of Cerebral Amyloid Deposition

Background

Impaired glymphatic flow on the Alzheimer's disease (AD) spectrum may be evaluated using diffusion tensor image analysis along the perivascular space (DTI-ALPS).

Objective

We aimed to validate impaired glymphatic flow and explore its association with gray matter volume, cognitive status, and cerebral amyloid deposition on the AD spectrum.

Methods

80 participants (mean age, 76.9±8.5 years; 57 women) with AD (n = 65) and cognitively normal (CN) (n = 15) who underwent 3T brain MRI including DTI and/or amyloid PET were included. After adjusting for age, sex, apolipoprotein E status, and burden of white matter hyperintensities, the ALPS-index was compared according to the AD spectrum. The association between the ALPS-index and gray matter volume, cognitive status, and quantitative amyloid from PET was assessed.

Results

The ALPS-index in the AD was significantly lower (mean, 1.476; 95% CI, 1.395-1.556) than in the CN (1.784;1.615-1.952; p = 0.026). Volumes of the entorhinal cortex, hippocampus, temporal pole, and primary motor cortex showed significant associations with the ALPS-index (all, p < 0.05). There was a positive correlation between the ALPS-index and MMSE score (partial r = 0.435; p < 0.001), but there was no significant correlation between the ALPS-index and amyloid SUVRs (all, p > 0.05).

Conclusions

Decreased glymphatic flow measured by DTI-ALPS in AD may serve as a marker of neurodegeneration correlating with structural atrophy and cognitive decline.

X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study

Introduction

Alzheimer's disease (AD) is a complex neurodegenerative disease with high heritability. Compared to autosomes, a higher proportion of disorder-associated genes on X chromosome are expressed in the brain. However, only a few studies focused on the identification of the susceptibility loci for AD on X chromosome.

Methods

Using the data from the Alzheimer's Disease Neuroimaging Initiative Study, we conducted an X chromosome-wide association study between 16 AD quantitative biomarkers and 19,692 single nucleotide polymorphisms (SNPs) based on both the cross-sectional and longitudinal studies.

Results

We identified 15 SNPs statistically significantly associated with different quantitative biomarkers of the AD. For the cross-sectional study, six SNPs (rs5927116, rs4596772, rs5929538, rs2213488, rs5920524, and rs5945306) are located in or near to six genes DMD, TBX22, LOC101928437, TENM1, SPANXN1, and ZFP92, which have been reported to be associated with schizophrenia or neuropsychiatric diseases in literature. For the longitudinal study, four SNPs (rs4829868, rs5931111, rs6540385, and rs763320) are included in or near to two genes RAC1P4 and AFF2, which have been demonstrated to be associated with brain development or intellectual disability in literature, while the functional annotations of other five novel SNPs (rs12157031, rs428303, rs5953487, rs10284107, and rs5955016) have not been found.

Discussion

15 SNPs were found statistically significantly associated with the quantitative biomarkers of the AD. Follow-up study in molecular genetics is needed to verify whether they are indeed related to AD. The findings in this article expand our understanding of the role of the X chromosome in exploring disease susceptibility, introduce new insights into the molecular genetics behind the AD, and may provide a mechanistic clue to further AD-related studies.

Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer's disease and related dementia

BACKGROUND

Alzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable.

METHODS

We developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed.

RESULTS

The estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of A[Formula: see text].

CONCLUSION

By leveraging the available clinical diagnosis timing information, our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.

TRIAL REGISTRATION

clinicaltrials.gov, NCT01926249. Registered on 16 August 2013.

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.

Investigation of Underlying Association Between Whole Brain Regions and Alzheimer’s Disease: A Research Based on an Artificial Intelligence Model

Alzheimer’s disease (AD) is the most common form of dementia, causing progressive cognitive decline. Radiomic features obtained from structural magnetic resonance imaging (sMRI) have shown a great potential in predicting this disease. However, radiomic features based on the whole brain segmented regions have not been explored yet. In our study, we collected sMRI data that include 80 patients with AD and 80 healthy controls (HCs). For each patient, the T1 weighted image (T1WI) images were segmented into 106 subregions, and radiomic features were extracted from each subregion. Then, we analyzed the radiomic features of specific brain subregions that were most related to AD. Based on the selective radiomic features from specific brain subregions, we built an integrated model using the best machine learning algorithms, and the diagnostic accuracy was evaluated. The subregions most relevant to AD included the hippocampus, the inferior parietal lobe, the precuneus, and the lateral occipital gyrus. These subregions exhibited several important radiomic features that include shape, gray level size zone matrix (GLSZM), and gray level dependence matrix (GLDM), among others. Based on the comparison among different algorithms, we constructed the best model using the Logistic regression (LR) algorithm, which reached an accuracy of 0.962. Conclusively, we constructed an excellent model based on radiomic features from several specific AD-related subregions, which could give a potential biomarker for predicting AD.

Features of the superficial white matter as biomarkers for the detection of Alzheimer's disease and mild cognitive impairment: A diffusion tensor imaging study

BACKGROUND

With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI).

OBJECTIVES

In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM).

PARTICIPANTS

For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD.

MEASURE

ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software.

RESULTS

The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups.

CONCLUSIONS

Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.

Role of Retinoid X Receptors (RXRs) and dietary vitamin A in Alzheimer's disease: Evidence from clinicopathological and preclinical studies

BACKGROUND

Vitamin A (VitA), via its active metabolite retinoic acid (RA), is critical for the maintenance of memory function with advancing age. Although its role in Alzheimer's disease (AD) is not well understood, data suggest that impaired brain VitA signaling is associated with the accumulation of β-amyloid peptides (Aβ), and could thus contribute to the onset of AD.

METHODS

We evaluated the protective action of a six-month-long dietary VitA-supplementation (20 IU/g), starting at 8 months of age, on the memory and the neuropathology of the 3xTg-AD mouse model of AD (n = 11-14/group; including 4-6 females and 7-8 males). We also measured protein levels of Retinoic Acid Receptor β (RARβ) and Retinoid X Receptor γ (RXRγ) in homogenates from the inferior parietal cortex of 60 participants of the Religious Orders study (ROS) divided in three groups: no cognitive impairment (NCI) (n = 20), mild cognitive impairment (MCI) (n = 20) and AD (n = 20).

RESULTS

The VitA-enriched diet preserved spatial memory of 3xTg-AD mice in the Y maze. VitA-supplementation affected hippocampal RXR expression in an opposite way according to sex by tending to increase in males and decrease in females their mRNA expression. VitA-enriched diet also reduced the amount of hippocampal Aβ₄₀ and Aβ₄₂, as well as the phosphorylation of tau protein at sites Ser396/Ser404 (PHF-1) in males. VitA-supplementation had no effect on tau phosphorylation in females but worsened their hippocampal Aβ load. However, the expression of Rxr-β in the hippocampus was negatively correlated with the amount of both soluble and insoluble Aβ in both males and females. Western immunoblotting in the human cortical samples of the ROS study did not reveal differences in RARβ levels. However, it evidenced a switch from a 60-kDa-RXRγ to a 55-kDa-RXRγ in AD, correlating with ante mortem cognitive decline and the accumulation of neuritic plaques in the brain cortex.

CONCLUSION

Our data suggest that (i) an altered expression of RXRs receptors is a contributor to β-amyloid pathology in both humans and 3xTg-AD mice, (ii) a chronic exposure of 3xTg-AD mice to a VitA-enriched diet may be protective in males, but not in females.

Productive Social Engagement as a Vehicle to Promote Activity and Neuro-Cognitive Health in Later Adulthood

OBJECTIVE

We have witnessed two key findings that shift our understanding of human brain aging in new directions. First, we learned that the adult brain remains plastic beyond childhood development, generating new neurons in response to activity and new experiences, particularly in regions that integrate memories in social contexts. The second emerging finding is the importance of physical activity and social engagement to cognitive aging. I integrate these and other empirical findings with our understanding of brain development over the life span and the later-life developmental need to give back to younger generations to posit the importance of maintaining our "social" brain through retirement and into later life when activity remains beneficial to brain health.

CONCLUSIONS

Opportunities for improved cognitive and brain health that can be brought to scale need to capitalize on aging adults' need to remain socially relevant and on community infrastructures so that those with lower neighborhood access to activity can safely engage. Evidence is summarized here from one such community-based model of social engagement through school-based, volunteer service, entitled Experience Corps®. This program seeks to increase daily physical, cognitive, and social activity to promote cognitive and mental health.

Relationship between Cortical Thickness and EEG Alterations during Sleep in the Alzheimer's Disease

Recent evidence showed that EEG activity alterations that occur during sleep are associated with structural, age-related, changes in healthy aging brains, and predict age-related decline in memory performance. Alzheimer's disease (AD) patients show specific EEG alterations during sleep associated with cognitive decline, including reduced sleep spindles during NREM sleep and EEG slowing during REM sleep. We investigated the relationship between these EEG sleep alterations and brain structure changes in a study of 23 AD patients who underwent polysomnographic recording of their undisturbed sleep and 1.5T MRI scans. Cortical thickness measures were correlated with EEG power in the sigma band during NREM sleep and with delta- and beta-power during REM sleep. Thinning in the right precuneus correlated with all the EEG indexes considered in this study. Frontal-central NREM sigma power showed an inverse correlation with thinning of the left entorhinal cortex. Increased delta activity at the frontopolar and temporal regions was significantly associated with atrophy in some temporal, parietal, and frontal cortices, and with mean thickness of the right hemisphere. Our findings revealed an association between sleep EEG alterations and the changes to AD patients' brain structures. Findings also highlight possible compensatory processes involving the sources of frontal-central sleep spindles.

Combination of automated brain volumetry on MRI and quantitative tau deposition on THK-5351 PET to support diagnosis of Alzheimer's disease

Imaging biomarkers support the diagnosis of Alzheimer’s disease (AD). We aimed to determine whether combining automated brain volumetry on MRI and quantitative measurement of tau deposition on [18F] THK-5351 PET can aid discrimination of AD spectrum. From a prospective database in an IRB-approved multicenter study (NCT02656498), 113 subjects (32 healthy control, 55 mild cognitive impairment, and 26 Alzheimer disease) with baseline structural MRI and [18F] THK-5351 PET were included. Cortical volumes were quantified from FDA-approved software for automated volumetric MRI analysis (NeuroQuant). Standardized uptake value ratio (SUVR) was calculated from tau PET images for 6 composite FreeSurfer-derived regions-of-interests approximating in vivo Braak stage (Braak ROIs). On volumetric MRI analysis, stepwise logistic regression analyses identified the cingulate isthmus and inferior parietal lobule as significant regions in discriminating AD from HC and MCI. The combined model incorporating automated volumes of selected brain regions on MRI (cingulate isthmus, inferior parietal lobule, hippocampus) and SUVRs of Braak ROIs on [18F] THK-5351 PET showed higher performance than SUVRs of Braak ROIs on [18F] THK-5351 PET in discriminating AD from HC (0.98 vs 0.88, P = 0.033) but not in discriminating AD from MCI (0.85 vs 0.79, P = 0.178). The combined model showed comparable performance to automated volumes of selected brain regions on MRI in discriminating AD from HC (0.98 vs 0.94, P = 0.094) and MCI (0.85 vs 0.78; P = 0.065).

MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction

BACKGROUND

Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans.

RESULTS

We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 [Formula: see text] loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average [Formula: see text] loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921.

CONCLUSIONS

Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans.

Fronto-temporal cortical atrophy in 'nyaope' combination heroin and cannabis use disorder

Sub-Saharan Africa is one of the top three regions with the highest rates of opioid-related premature mortality. Nyaope is the street name for what is believed to be a drug cocktail in South Africa although recent research suggests that it is predominantly heroin. Nyaope powder is most commonly smoked together with cannabis, a drug-use pattern unique to the region. Due to the increasing burden of this drug in low-income communities and the absence of human structural neuroimaging data of combination heroin and cannabis use disorder, we initiated an important cohort study in order to identify neuroanatomical sequelae. Twenty-eight male nyaope users and thirty healthy, matched controls were recruited from drug rehabilitation centers and the community, respectively. T1-weighted MRI images were obtained using a 3 T General Electric Discovery and cortical thickness was examined and compared. Nyaope users displayed extensive grey matter atrophy in the right hemispheric medial orbitofrontal, rostral middle frontal, superior temporal, superior frontal, and supramarginal gyri (two-sided t-test, p < 0.05, corrected for multiple comparisons). Our findings indicate cortical abnormality in nyaope users in regions involved in impulse control, decision making, social- and self-perception, and working memory. Importantly, affected brain regions show large overlap with the pattern of cortical abnormalities shown in heroin use disorder.

Dual-task performance is associated with brain MRI Morphometry in individuals with mild cognitive impairment

BACKGROUND AND PURPOSE

Cognitive impairment is a critical health problem in the elderly population. Research has shown that patients with mild cognitive impairment (MCI) may develop dementia in later years. Therefore, early identification of MCI could allow for interventions to help delay the progression of this devastating disease. Our objective in this study was to detect the early presence of MCI in elderly patients via neuroimaging and dual-task performance.

METHODS

Brain MRI scans from 21 older adult volunteers, including cognitively healthy adults (HA, n = 9, age = 68-79 years) and mild cognitively impaired (MCI, n = 12, age = 66-92 years) were analyzed using automatic segmentation techniques. Regional volume, surface area, and thickness measures were correlated with simultaneous performance of motor and cognitive tasks (dual-task) within a novel upper-extremity function (UEF) test, using multivariate analysis of variance models.

RESULTS

We found significant associations of dual-task performance with volume of five cortical brain regions (P ≤ .048) and thickness of 13 regions (P ≤ .043) within the frontal, temporal, and parietal lobes. There was a significant interaction effect of cognitive group on dual-task score for the inferior temporal gyrus volume (P ≤ .034), and the inferior parietal lobule, inferior temporal gyrus, and middle temporal gyrus average thickness (P ≤ .037).

CONCLUSIONS

This study highlighted the potential of dual-tasking and MRI morphometric changes as a simple and accurate tool for early detection of cognitive impairment among community-dwelling older adults. The strong interaction effects of cognitive group on UEF dual-task score suggest higher association between atrophy of these brain structures and compromised dual-task performance among the MCI group.

Predicting Alzheimer's conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers

Patients with mild cognitive impairment (MCI) have a high risk for conversion to Alzheimer's disease (AD). Early diagnose of AD in MCI subjects could help to slow or halt the disease progression. Selecting a set of relevant markers from multimodal data to predict conversion from MCI to probable AD has become a challenging task. The aim of this paper is to quantify the impact of longitudinal predictive models with single- or multisource data for predicting MCI-to-AD conversion and identifying a very small subset of features that are highly predictive of conversion. We developed predictive models of MCI-to-AD progression that combine magnetic resonance imaging (MRI)-based markers (cortical thickness and volume of subcortical structures) with neuropsychological tests. These models were built with longitudinal data and validated using baseline values. By using a linear mixed effects approach, we modeled the longitudinal trajectories of the markers. A set of longitudinal features potentially discriminating between MCI subjects who convert to dementia and those who remain stable over a period of 3 years was obtained. Classifier were trained using the marginal longitudinal trajectory residues from the selected features. Our best models predicted conversion with 77% accuracy at baseline (AUC = 0.855, 84% sensitivity, 70% specificity). As more visits were available, longitudinal predictive models improved their predictions with 84% accuracy (AUC = 0.912, 83% sensitivity, 84% specificity). The proposed approach was developed, trained and evaluated using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with a total of 2491 visits from 610 subjects.

Longitudinal survival analysis and two-group comparison for predicting the progression of mild cognitive impairment to Alzheimer's disease

BACKGROUND

Longitudinal studies using structural magnetic resonance imaging (MRI) and neuropsychological measurements (NMs) allow a noninvasive means of following the subtle anatomical changes occurring during the evolution of AD.

NEW METHOD

This paper compared two approaches for the construction of longitudinal predictive models: a) two-group comparison between converter and nonconverter MCI subjects and b) longitudinal survival analysis. Predictive models combined MRI-based markers with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effects modeling to capture the longitudinal trajectories of the markers. The two-group comparison approaches used linear discriminant analysis and the survival analysis used risk ratios obtained from the extended Cox model and logistic regression.

RESULTS

The proposed approaches were developed and evaluated using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with a total of 1330 visits from 321 subjects. With both approaches, a very small number of features were selected. These markers are easily interpretable, generating robust, verifiable and reliable predictive models. Our best models predicted conversion with 78% accuracy at baseline (AUC = 0.860, 79% sensitivity, 76% specificity). As more visits were made, longitudinal predictive models improved their predictions with 85% accuracy (AUC = 0.944, 86% sensitivity, 85% specificity).

COMPARISON WITH EXISTING METHOD

Unlike the recently published models, there was also an improvement in the prediction accuracy of the conversion to AD when considering the longitudinal trajectory of the patients.

CONCLUSIONS

The survival-based predictive models showed a better balance between sensitivity and specificity with respect to the models based on the two-group comparison approach.

Clinical Use of Integrated Positron Emission Tomography-Magnetic Resonance Imaging for Dementia Patients

Combining magnetic resonance imaging (MRI) with 2-deoxy-2-F-fluoro-D-glucose positron emission tomography (FDG-PET) data improve the imaging accuracy for detection of Alzheimer disease and related dementias. Integrated FDG-PET-MRI is a recent technical innovation that allows both imaging modalities to be obtained simultaneously from individual patients with cognitive impairment. This report describes the practical benefits and challenges of using integrated FDG-PET-MRI to support the clinical diagnosis of various dementias. Over the past 7 years, we have performed integrated FDG-PET-MRI on >1500 patients with possible cognitive impairment or dementia. The FDG-PET and MRI protocols are the same as current conventions, but are obtained simultaneously over 25 minutes. An additional Dixon MRI sequence with superimposed bone atlas is used to calculate PET attenuation correction. A single radiologist interprets all imaging data and generates 1 report. The most common positive finding is concordant temporoparietal volume loss and FDG hypometabolism that suggests increased risk for underlying Alzheimer disease. Lobar-specific atrophy and FDG hypometabolism patterns that may be subtle, asymmetric, and focal also are more easily recognized using combined FDG-PET and MRI, thereby improving detection of other neurodegeneration conditions such as primary progressive aphasias and frontotemporal degeneration. Integrated PET-MRI has many practical benefits to individual patients, referrers, and interpreting radiologists. The integrated PET-MRI system requires several modifications to standard imaging center workflows, and requires training individual radiologists to interpret both modalities in conjunction. Reading MRI and FDG-PET together increases imaging diagnostic yield for individual patients; however, both modalities have limitations in specificity.

Differentiating Between Healthy Control Participants and Those with Mild Cognitive Impairment Using Volumetric MRI Data

OBJECTIVE

To determine whether volumetric measures of the hippocampus, entorhinal cortex, and other cortical measures can differentiate between cognitively normal individuals and subjects with mild cognitive impairment (MCI).

METHOD

Magnetic resonance imaging (MRI) data from 46 cognitively normal subjects and 50 subjects with MCI as part of the Boston University Alzheimer's Disease Center research registry and the Alzheimer's Disease Neuroimaging Initiative were used in this cross-sectional study. Cortical, subcortical, and hippocampal subfield volumes were generated from each subject's MRI data using FreeSurfer v6.0. Nominal logistic regression models containing these variables were used to identify subjects as control or MCI.

RESULTS

A model containing regions of interest (superior temporal cortex, caudal anterior cingulate, pars opercularis, subiculum, precentral cortex, caudal middle frontal cortex, rostral middle frontal cortex, pars orbitalis, middle temporal cortex, insula, banks of the superior temporal sulcus, parasubiculum, paracentral lobule) fit the data best (R2 = .7310, whole model test chi-square = 97.16, p < .0001).

CONCLUSIONS

MRI data correctly classified most subjects using measures of selected medial temporal lobe structures in combination with those from other cortical areas, yielding an overall classification accuracy of 93.75%. These findings support the notion that, while volumes of medial temporal lobe regions differ between cognitively normal and MCI subjects, differences that can be used to distinguish between these two populations are present elsewhere in the brain.

The usefulness of visual rating of posterior atrophy in predicting rapid cognitive decline in Alzheimer disease: A preliminary study

BACKGROUND

Approximately 10% to 30% of Alzheimer disease (AD) patients progress rapidly in severity and become more dependent on caregivers. Although several studies have investigated whether imaging biomarkers such as medial temporal atrophy (MTA) and posterior atrophy (PA) are useful for predicting the rapid progression of AD, their results have been inconsistent.

OBJECTIVE

The study aims to investigate the association of visually rated MTA and PA with rapid disease progression in AD.

METHODS

This was a retrospective cohort study of 159 AD patients who were initially diagnosed with mild AD and were followed for 1 year to determine whether they progressed rapidly (a decrease of three points or more on the Mini-Mental State Examination over 1 year). We used 5-point and 4-point visual rating scales to assess MTA and PA, respectively. MTA and PA scores for each patient were dichotomized as normal (without atrophy) or abnormal (atrophy). We performed a logistic regression analysis to determine the odds ratios (ORs) of MTA and PA for rapid disease progression with adjustment for covariates.

RESULTS

Within the study population, 47 (29.6%) patients progressed rapidly. Visual assessment of the magnetic resonance imaging (MRI) scans revealed that 112 patients (70.4%) showed MTA, whereas 80 patients (50.3%) showed PA. The ORs with 95% confidence intervals for MTA and PA were 1.825 (0.819-4.070) and 2.844 (1.378-5.835), respectively. The association of visually assessed PA, but not MTA, with rapid progression was significant after adjustment for covariates.

CONCLUSION

In patients with mild AD, visual assessment of PA exhibits independent predictive value for rapid disease progression.

Polygenic hazard score, amyloid deposition and Alzheimer's neurodegeneration

Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-β protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.

Discriminating Alzheimer's disease progression using a new hippocampal marker from T1-weighted MRI: The local surface roughness

Hippocampal atrophy is one of the main hallmarks of Alzheimer's disease (AD). However, there is still controversy about whether this sign is a robust finding during the early stages of the disease, such as in mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Considering this background, we proposed a new marker for assessing hippocampal atrophy: the local surface roughness (LSR). We tested this marker in a sample of 307 subjects (normal control (NC) = 70, SCD = 87, MCI = 137, AD = 13). In addition, 97 patients with MCI were followed-up over a 3-year period and classified as stable MCI (sMCI) (n = 61) or progressive MCI (pMCI) (n = 36). We did not find significant differences using traditional markers, such as normalized hippocampal volumes (NHV), between the NC and SCD groups or between the sMCI and pMCI groups. However, with LSR we found significant differences between the sMCI and pMCI groups and a better ability to discriminate between NC and SCD. The classification accuracy of the LSR for NC and SCD was 68.2%, while NHV had a 57.2% accuracy. In addition, the classification accuracy of the LSR for sMCI and pMCI was 74.3%, and NHV had a 68.3% accuracy. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on hippocampal markers and conversion times. The LSR marker showed better prediction of conversion to AD than NHV. These results suggest the relevance of considering the LSR as a new hippocampal marker for the AD continuum.

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning

Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.

Continuous measurement of object location memory is sensitive to effects of age and mild cognitive impairment and related to medial temporal lobe volume

Introduction

We present findings of a novel and ecologically relevant associative memory test, the Object Location Touchscreen Test (OLTT), which was posited as sensitive to early medial temporal lobe compromise associated with mild cognitive impairment (MCI).

Methods

A total of 114 participants, including healthy young and older controls and patients with MCI, completed the OLTT and standard neuropsychological testing. The OLTT required participants to recall the location of objects under free and cued recall conditions, with accuracy evaluated using distance measures (i.e., a continuous error score), and a standard recognition format. Correlations between performance and volumetric data were evaluated from a subset of 77 participants.

Results

Significant age effects were dwarfed by MCI effects across all test conditions. OLTT Cued Recall was strongly and specifically related to the volume of disease-relevant medial temporal lobe regions, generally more than traditional memory tests.

Discussion

The OLTT may be sensitive to early structural compromise in regions affected by Alzheimer's disease.

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Evaluated age and mild cognitive impairment effects using ecologically relevant object location (OL) task.

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Performance evaluated using both continuous and dichotomous measures of accuracy.

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Greater decline in OL memory with mild cognitive impairment than with “healthy” aging.

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Performance, especially continuous measure, reflected medial temporal integrity.

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Novel OL memory task may be sensitive to early structural compromise.

Entorhinal Cortex: Antemortem Cortical Thickness and Postmortem Neurofibrillary Tangles and Amyloid Pathology

BACKGROUND AND PURPOSE

The entorhinal cortex, a critical gateway between the neocortex and hippocampus, is one of the earliest regions affected by Alzheimer disease-associated neurofibrillary tangle pathology. Although our prior work has automatically delineated an MR imaging-based measure of the entorhinal cortex, whether antemortem entorhinal cortex thickness is associated with postmortem tangle burden within the entorhinal cortex is still unknown. Our objective was to evaluate the relationship between antemortem MRI measures of entorhinal cortex thickness and postmortem neuropathological measures.

MATERIALS AND METHODS

We evaluated 50 participants from the Rush Memory and Aging Project with antemortem structural T1-weighted MR imaging and postmortem neuropathologic assessments. Here, we focused on thickness within the entorhinal cortex as anatomically defined by our previously developed MR imaging parcellation system (Desikan-Killiany Atlas in FreeSurfer). Using linear regression, we evaluated the association between entorhinal cortex thickness and tangles and amyloid-β load within the entorhinal cortex and medial temporal and neocortical regions.

RESULTS

We found a significant relationship between antemortem entorhinal cortex thickness and entorhinal cortex (P = .006) and medial temporal lobe tangles (P = .002); we found no relationship between entorhinal cortex thickness and entorhinal cortex (P = .09) and medial temporal lobe amyloid-β (P = .09). We also found a significant association between entorhinal cortex thickness and cortical tangles (P = .003) and amyloid-β (P = .01). We found no relationship between parahippocampal gyrus thickness and entorhinal cortex (P = .31) and medial temporal lobe tangles (P = .051).

CONCLUSIONS

Our findings indicate that entorhinal cortex-associated in vivo cortical thinning may represent a marker of postmortem medial temporal and neocortical Alzheimer disease pathology.

Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala

Structural magnetic resonance imaging data are frequently analysed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this article, we introduce a novel technique to study shape asymmetries of neuroanatomical structures across subcortical and cortical brain regions. We demonstrate that neurodegeneration of subcortical structures in Alzheimer's disease is not symmetric. The hippocampus shows a significant increase in asymmetry longitudinally and both hippocampus and amygdala show a significantly higher asymmetry cross-sectionally concurrent with disease severity above and beyond an ageing effect. Our results further suggest that the asymmetry in these structures is undirectional and that primarily the anterior hippocampus becomes asymmetric. Based on longitudinal asymmetry measures we subsequently study the progression from mild cognitive impairment to dementia, demonstrating that shape asymmetry in hippocampus, amygdala, caudate and cortex is predictive of disease onset. The same analyses on scalar volume measurements did not produce any significant results, indicating that shape asymmetries, potentially induced by morphometric changes in subnuclei, rather than size asymmetries are associated with disease progression and can yield a powerful imaging biomarker for the early presymptomatic classification and prediction of Alzheimer's disease. Because literature has focused on contralateral volume differences, subcortical disease lateralization may have been overlooked thus far.

Structural Neuroimaging Genetics Interactions in Alzheimer's Disease

This article investigates late-onset cognitive impairment using neuroimaging and genetics biomarkers for Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Eight-hundred and eight ADNI subjects were identified and divided into three groups: 200 subjects with Alzheimer's disease (AD), 383 subjects with mild cognitive impairment (MCI), and 225 asymptomatic normal controls (NC). Their structural magnetic resonance imaging (MRI) data were parcellated using BrainParser, and the 80 most important neuroimaging biomarkers were extracted using the global shape analysis Pipeline workflow. Using Plink via the Pipeline environment, we obtained 80 SNPs highly-associated with the imaging biomarkers. In the AD cohort, rs2137962 was significantly associated bilaterally with changes in the hippocampi and the parahippocampal gyri, and rs1498853, rs288503, and rs288496 were associated with the left and right hippocampi, the right parahippocampal gyrus, and the left inferior temporal gyrus. In the MCI cohort, rs17028008 and rs17027976 were significantly associated with the right caudate and right fusiform gyrus, rs2075650 (TOMM40) was associated with the right caudate, and rs1334496 and rs4829605 were significantly associated with the right inferior temporal gyrus. In the NC cohort, Chromosome 15 [rs734854 (STOML1), rs11072463 (PML), rs4886844 (PML), and rs1052242 (PML)] was significantly associated with both hippocampi and both insular cortices, and rs4899412 (RGS6) was significantly associated with the caudate. We observed significant correlations between genetic and neuroimaging phenotypes in the 808 ADNI subjects. These results suggest that differences between AD, MCI, and NC cohorts may be examined by using powerful joint models of morphometric, imaging and genotypic data.

Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features

Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection step, we first employed sparse linear regression with stability selection, for the selection of discriminative features in the iterative combinations of MRI and network measures. Subsequently the top K features of available combinations were selected as optimal features for classification. To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification. The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively. Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30. The present results suggest that meaningful predictors composed of MRI and network measures may offer the possibility for early detection of progression from MCI to AD.

Blood glucose levels and cortical thinning in cognitively normal, middle-aged adults

Type II diabetes mellitus (DM) increases risk for cognitive decline and is associated with brain atrophy in older demented and non-demented individuals. We investigated (1) the cross-sectional association between fasting blood glucose level and cortical thickness in a sample of largely middle-aged, cognitively normal adults, and (2) whether these associations were modified by genes associated with both lipid processing and dementia. To explore possible modifications by genetic status, we investigated the interaction between blood glucose levels and the apolipoprotein E (APOE) ε4 allele and the translocase of the outer mitochondrial membrane (TOMM) 40 '523 genotype on cortical thickness. Cortical thickness measures were based on mean thickness in a subset of a priori-selected brain regions hypothesized to be vulnerable to atrophy in Alzheimer's disease (AD) (i.e., 'AD vulnerable regions'). Participants included 233 cognitively normal subjects in the BIOCARD study who had a measure of fasting blood glucose and cortical thickness measures, quantified by magnetic resonance imaging (MRI) scans. After adjustment for age, sex, race, education, depression, and medical conditions, higher blood glucose was associated with thinner parahippocampal gyri (B=-0.002; 95% CI -0.004, -0.0004) and temporal pole (B=-0.002; 95% CI -0.004, -0.0001), as well as reduced average thickness over AD vulnerable regions (B=-0.001; 95% CI -0.002, -0.0001). There was no evidence for greater cortical thinning in ε4 carriers of the APOE gene or in APOE ε3/3 individuals carrying the TOMM40 VL/VL genotypes. When individuals with glucose levels in the diabetic range (≥126mg/dL), were excluded from the analysis, the associations between glucose levels and cortical thickness were no longer significant. These findings suggest that glucose levels in the diabetic range are associated with reduced cortical thickness in AD vulnerable regions as early as middle age.

HLA-A2 Alleles Mediate Alzheimer's Disease by Altering Hippocampal Volume

HLA-A is a locus of the major histocompatibility complex situated on chromosome 6p21.3. HLA-A has been shown to be associated with susceptibility to Alzheimer's disease (AD). In this study, we firstly investigated the association of gene variants in HLA-A and brain structures on MRI in a large sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to explore the effects of HLA-A on AD pathogenesis. We selected the hippocampus, parahippocampus, posterior cingulate, precuneus, middle temporal, entorhinal cortex, and amygdala as regions of interest (ROIs). In hybrid population analysis, our results showed a marginally significant association between rs9260168 and the atrophy of the left parahippocampus (P = 0.007, Pc = 0.054), rs3823342 and the atrophy of the left parahippocampus (P = 0.014, Pc = 0.054), rs76475517, which only exists in Caucasians with HLA-A23 or HLA-A24 alleles, and the atrophy of the right amygdala (P = 0.010, Pc = 0.085) at baseline. In particular, the haplotype (TGACAAGG), as a surrogate marker of HLA-A2, was founded to be positively associated with the atrophy of the right hippocampus (P = 0.047) at baseline. Furthermore, we detected the above four associations in mild cognitive impairment (MCI) subpopulation analysis. Our study provided preliminary evidences supporting HLA-A2 in Caucasians contribute to the risk of AD by modulating the alteration of hippocampal volume and HLA-A gene variants appear to play a role in altering AD-related brain structures on MRI.

BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE

The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer's disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.

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.

Precuneus and Cingulate Cortex Atrophy and Hypometabolism in Patients with Alzheimer's Disease and Mild Cognitive Impairment: MRI and (18)F-FDG PET Quantitative Analysis Using FreeSurfer

Objective. The objective of this study was to compare glucose metabolism and atrophy, in the precuneus and cingulate cortex, in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI), using FreeSurfer. Methods. 47 individuals (17 patients with AD, 17 patients with amnestic MCI, and 13 healthy controls (HC)) were included. MRI and PET images using ¹⁸F-FDG (mean injected dose of 185 MBq) were acquired and analyzed using FreeSurfer to define regions of interest in the hippocampus, amygdala, precuneus, and anterior and posterior cingulate cortex. Regional volumes were generated. PET images were registered to the T1-weighted MRI images and regional uptake normalized by cerebellum uptake (SUVr) was measured. Results. Mean posterior cingulate volume was reduced in MCI and AD. SUVr were different between the three groups: mean precuneus SUVr was 1.02 for AD, 1.09 for MCI, and 1.26 for controls (p < 0.05); mean posterior cingulate SUVr was 0.96, 1.06, and 1.22 for AD, MCI, and controls, respectively (p < 0.05). Conclusion. We found graduated hypometabolism in the posterior cingulate cortex and the precuneus in prodromal AD (MCI) and AD, whereas atrophy was not significant. This suggests that the use of ¹⁸F-FDG in these two regions could be a neurodegenerative biomarker.

Impact of the Baltimore Experience Corps Trial on cortical and hippocampal volumes

INTRODUCTION

There is a substantial interest in identifying interventions that can protect and buffer older adults from atrophy in the cortex and particularly, the hippocampus, a region important to memory. We report the 2-year effects of a randomized controlled trial of an intergenerational social health promotion program on older men's and women's brain volumes.

METHODS

The Brain Health Study simultaneously enrolled, evaluated, and randomized 111 men and women (58 interventions; 53 controls) within the Baltimore Experience Corps Trial to evaluate the intervention impact on biomarkers of brain health at baseline and annual follow-ups during the 2-year trial exposure.

RESULTS

Intention-to-treat analyses on cortical and hippocampal volumes for full and sex-stratified samples revealed program-specific increases in volumes that reached significance in men only (P's ≤ .04). Although men in the control arm exhibited age-related declines for 2 years, men in the Experience Corps arm showed a 0.7% to 1.6% increase in brain volumes. Women also exhibited modest intervention-specific gains of 0.3% to 0.54% by the second year of exposure that contrasted with declines of about 1% among women in the control group.

DISCUSSION

These findings showed that purposeful activity embedded within a social health promotion program halted and, in men, reversed declines in brain volume in regions vulnerable to dementia.

CLINICAL TRIAL REGISTRATION

NCT0038.

Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease

BACKGROUND

Regulatory qualification of a biomarker for a defined context of use provides scientifically robust assurances to sponsors and regulators that accelerate appropriate adoption of biomarkers into drug development.

METHODS

The Coalition Against Major Diseases submitted a dossier to the Scientific Advice Working Party of the European Medicines Agency requesting a qualification opinion on the use of hippocampal volume as a biomarker for enriching clinical trials in subjects with mild cognitive impairment, incorporating a scientific rationale, a literature review and a de novo analysis of Alzheimer's Disease Neuroimaging Initiative data.

RESULTS

The literature review and de novo analysis were consistent with the proposed context of use, and the Committee for Medicinal Products for Human Use released an opinion in November 2011.

CONCLUSIONS

We summarize the scientific rationale and the data that supported the first qualification of an imaging biomarker by the European Medicines Agency.

Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment

OBJECTIVE

This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.

METHODS

Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.

RESULTS

We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).

CONCLUSION

We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

Medial temporal cortices in ex vivo magnetic resonance imaging

This review focuses on the ex vivo magnetic resonance imaging (MRI) modeling of medial temporal cortices and associated structures, the entorhinal verrucae and the perforant pathway. Typical in vivo MRI has limited resolution due to constraints on scan times and does not show laminae in the medial temporal lobe. Recent studies using ex vivo MRI have demonstrated lamina in the entorhinal, perirhinal, and hippocampal cortices. These studies have enabled probabilistic brain mapping that is based on the ex vivo MRI contrast, validated to histology, and subsequently mapped onto an in vivo spherically warped surface model. Probabilistic maps are applicable to other in vivo studies.

Event time analysis of longitudinal neuroimage data

This paper presents a method for the statistical analysis of the associations between longitudinal neuroimaging measurements, e.g., of cortical thickness, and the timing of a clinical event of interest, e.g., disease onset. The proposed approach consists of two steps, the first of which employs a linear mixed effects (LME) model to capture temporal variation in serial imaging data. The second step utilizes the extended Cox regression model to examine the relationship between time-dependent imaging measurements and the timing of the event of interest. We demonstrate the proposed method both for the univariate analysis of image-derived biomarkers, e.g., the volume of a structure of interest, and the exploratory mass-univariate analysis of measurements contained in maps, such as cortical thickness and gray matter density. The mass-univariate method employs a recently developed spatial extension of the LME model. We applied our method to analyze structural measurements computed using FreeSurfer, a widely used brain Magnetic Resonance Image (MRI) analysis software package. We provide a quantitative and objective empirical evaluation of the statistical performance of the proposed method on longitudinal data from subjects suffering from Mild Cognitive Impairment (MCI) at baseline.

Genetic variation of oxidative phosphorylation genes in stroke and Alzheimer's disease

Previous research implicates alterations in oxidative phosphorylation (OXPHOS) in the development of Alzheimer's disease (AD). We sought to test whether genetic variants within OXPHOS genes increase the risk of AD. We first used gene-set enrichment analysis to identify associations, and then applied a previously replicated stroke genetic risk score to determine if OXPHOS genetic overlap exists between stroke and AD. Gene-set enrichment analysis identified associations between variation in OXPHOS genes and AD versus control status (p = 0.012). Conversion from cognitively normal controls to mild cognitive impairment was also associated with the OXPHOS gene-set (p = 0.045). Subset analyses demonstrated association for complex I genes (p < 0.05), but not for complexes II-V. Among neuroimaging measures, hippocampal volume and entorhinal cortex thickness were associated with OXPHOS genes (all p < 0.025). The stroke genetic risk score demonstrated association with clinical status, baseline and longitudinal imaging measures (p < 0.05). OXPHOS genetic variation influences clinical status and neuroimaging intermediates of AD. OXPHOS genetic variants associated with stroke are also linked to AD progression. Further studies are needed to explore functional consequences of these OXPHOS variants.

An expanded role for neuroimaging in the evaluation of memory impairment

SUMMARY

Alzheimer disease affects millions of people worldwide. The neuropathologic process underlying this disease begins years, if not decades, before the onset of memory decline. Recent advances in neuroimaging suggest that it is now possible to detect Alzheimer-associated neuropathologic changes well before dementia onset. Here, we evaluate the role of recently developed in vivo biomarkers in the clinical evaluation of Alzheimer disease. We discuss how assessment strategies might incorporate neuroimaging markers to better inform patients, families, and clinicians when memory impairment prompts a search for diagnosis and management options.

Rate of conversion from prodromal Alzheimer's disease to Alzheimer's dementia: a systematic review of the literature

Background

The purpose of this study was to summarize published estimates for conversion from mild cognitive impairment or amnestic mild cognitive impairment to Alzheimer's dementia. We carried out a systematic review of English language publications to identify cohort studies published since January 2006 that reported the risk or rate of conversion.

Summary

Thirty-two cohort studies were identified, of which 14 reported annualized conversion rates (ACRs). Conversions over 1 year ranged from 10.2 to 33.6% (5 studies, median: 19.0%), and over 2 years from 9.8 to 36.3% (7 studies, median: 18.6%). ACRs ranged from 7.5 to 16.5% (7 studies, median: 11.0%) per person-year for studies recruiting from clinics, and from 5.4 to 11.5% (7 studies, median: 7.1%) for community samples.

Key Message

Extensive variation was observed in conversion rates due to the population sampled, diagnostic criteria, and duration, and because many studies did not account for loss to follow-up.

Comparison of hippocampal volume measured using magnetic resonance imaging in Alzheimer's disease, vascular dementia, mild cognitive impairment and pseudodementia

OBJECTIVE

To examine the relationship between different types of dementia and hippocampal volume.

METHODS

Hippocampal volume was measured by magnetic resonance imaging in patients with Alzheimer's disease (n = 22), vascular dementia (n = 14), mild cognitive impairment (n = 12) or pseudodementia (n = 16), and in healthy control subjects (n = 11). The Mini Mental State Examination was used to stratify subjects according to cognitive function.

RESULTS

Hippocampal volume was reduced by 42% in Alzheimer's disease, 21% in vascular dementia, 15% in mild cognitive impairment and 13% in pseudodementia compared with controls. The severity of dementia increased in line with decreasing hippocampal volume.

CONCLUSIONS

Measurement of hippocampal volume may facilitate differentiation between dementia subtypes. There was a relationship between reduced hippocampal volume and the degree of cognitive impairment.

Evaluation of automatic measurement of the intracranial volume based on quantitative MR imaging

BACKGROUND AND PURPOSE

Brain size is commonly described in relation to ICV, whereby accurate assessment of this quantity is fundamental. Recently, an optimized MR sequence (QRAPMASTER) was developed for simultaneous quantification of T1, T2, and proton density. ICV can be measured automatically within minutes from QRAPMASTER outputs and a dedicated software, SyMRI. Automatic estimations of ICV were evaluated against the manual segmentation.

MATERIALS AND METHODS

In 19 healthy subjects, manual segmentation of ICV was performed by 2 neuroradiologists (Obs1, Obs2) by using QBrain software and conventional T2-weighted images. The automatic segmentation from the QRAPMASTER output was performed by using SyMRI. Manual corrections of the automatic segmentation were performed (corrected-automatic) by Obs1 and Obs2, who were blinded from each other. Finally, the repeatability of the automatic method was evaluated in 6 additional healthy subjects, each having 6 repeated QRAPMASTER scans. The time required to measure ICV was recorded.

RESULTS

No significant difference was found between reference and automatic (and corrected-automatic) ICV (P > .25). The mean difference between the reference and automatic measurement was -4.84 ± 19.57 mL (or 0.31 ± 1.35%). Mean differences between the reference and the corrected-automatic measurements were -0.47 ± 17.95 mL (-0.01 ± 1.24%) and -1.26 ± 17.68 mL (-0.06 ± 1.22%) for Obs1 and Obs2, respectively. The repeatability errors of the automatic and the corrected-automatic method were <1%. The automatic method required 1 minute 11 seconds (SD = 12 seconds) of processing. Adding manual corrections required another 1 minute 32 seconds (SD = 38 seconds).

CONCLUSIONS

Automatic and corrected-automatic quantification of ICV showed good agreement with the reference method. SyMRI software provided a fast and reproducible measure of ICV.