MRI measures of temporoparietal regions show differential rates of atrophy during prodromal AD

Visual Functions Are Associated with Biomarker Changes in Alzheimer's Disease

Background

While various biomarkers of Alzheimer's disease (AD) have been associated with general cognitive function, their association to visual-perceptive function across the AD spectrum warrant more attention due to its significant impact on quality of life. Thus, this study explores how AD biomarkers are associated with decline in this cognitive domain.

Objective

To explore associations between various fluid and imaging biomarkers and visual-based cognitive assessments in participants across the AD spectrum.

Methods

Data from participants (N = 1,460) in the Alzheimer's Disease Neuroimaging Initiative were analyzed, including fluid and imaging biomarkers. Along with the Mini-Mental State Examination (MMSE), three specific visual-based cognitive tests were investigated: Trail Making Test (TMT) A and TMT B, and the Boston Naming Test (BNT). Locally estimated scatterplot smoothing curves and Pearson correlation coefficients were used to examine associations.

Results

MMSE showed the strongest correlations with most biomarkers, followed by TMT-B. The p-tau181/Aβ1-42 ratio, along with the volume of the hippocampus and entorhinal cortex, had the strongest associations among the biomarkers.

Conclusions

Several biomarkers are associated with visual processing across the disease spectrum, emphasizing their potential in assessing disease severity and contributing to progression models of visual function and cognition.

Structural connectivity of cytoarchitectonically distinct human left temporal pole subregions: a diffusion MRI tractography study

The temporal pole (TP) is considered one of the major paralimbic cortical regions, and is involved in a variety of functions such as sensory perception, emotion, semantic processing, and social cognition. Based on differences in cytoarchitecture, the TP can be further subdivided into smaller regions (dorsal, ventrolateral and ventromedial), each forming key nodes of distinct functional networks. However, the brain structural connectivity profile of TP subregions is not fully clarified. Using diffusion MRI data in a set of 31 healthy subjects, we aimed to elucidate the comprehensive structural connectivity of three cytoarchitectonically distinct TP subregions. Diffusion tensor imaging (DTI) analysis suggested that major association fiber pathways such as the inferior longitudinal, middle longitudinal, arcuate, and uncinate fasciculi provide structural connectivity to the TP. Further analysis suggested partially overlapping yet still distinct structural connectivity patterns across the TP subregions. Specifically, the dorsal subregion is strongly connected with wide areas in the parietal lobe, the ventrolateral subregion with areas including constituents of the default-semantic network, and the ventromedial subregion with limbic and paralimbic areas. Our results suggest the involvement of the TP in a set of extensive but distinct networks of cortical regions, consistent with its functional roles.

Decreased Brain Structural Network Connectivity in Patients with Mild Cognitive Impairment: A Novel Fractal Dimension Analysis

Mild cognitive impairment (MCI) is widely regarded to be the intermediate stage to Alzheimer's disease. Cerebral morphological alteration in cortical subregions can provide an accurate predictor for early recognition of MCI. Thirty patients with MCI and thirty healthy control subjects participated in this study. The Desikan-Killiany cortical atlas was applied to segment participants' cerebral cortex into 68 subregions. A complexity measure termed fractal dimension (FD) was applied to assess morphological changes in cortical subregions of participants. The MCI group revealed significantly decreased FD values in the bilateral temporal lobes, right parietal lobe including the medial temporal, fusiform, para hippocampal, and also the orbitofrontal lobes. We further proposed a novel FD-based brain structural network to compare network parameters, including intra- and inter-lobular connectivity between groups. The control group had five modules, and the MCI group had six modules in their brain networks. The MCI group demonstrated shrinkage of modular sizes with fewer components integrated, and significantly decreased global modularity in the brain network. The MCI group had lower intra- and inter-lobular connectivity in all lobes. Between cerebral lobes, the MCI patients may maintain nodal connections between both hemispheres to reduce connectivity loss in the lateral hemispheres. The method and results presented in this study could be a suitable tool for early detection of MCI.

Medial Temporal Lobe Atrophy in Predementia Alzheimer's Disease: A Longitudinal Multi-Site Study Comparing Staging and A/T/N in a Clinical Research Cohort

BACKGROUND

Atrophy of the medial temporal lobe (MTL) is a biological characteristic of Alzheimer's disease (AD) and can be measured by segmentation of magnetic resonance images (MRI).

OBJECTIVE

To assess the clinical utility of automated volumetry in a cognitively well-defined and biomarker-classified multi-center longitudinal predementia cohort.

METHODS

We used Automatic Segmentation of Hippocampal Subfields (ASHS) to determine MTL morphometry from MRI. We harmonized scanner effects using the recently developed longitudinal ComBat. Subjects were classified according to the A/T/N system, and as normal controls (NC), subjective cognitive decline (SCD), or mild cognitive impairment (MCI). Positive or negative values of A, T, and N were determined by cerebrospinal fluid measurements of the Aβ42/40 ratio, phosphorylated and total tau. From 406 included subjects, longitudinal data was available for 206 subjects by stage, and 212 subjects by A/T/N.

RESULTS

Compared to A-/T-/N- at baseline, the entorhinal cortex, anterior and posterior hippocampus were smaller in A+/T+orN+. Compared to NC A- at baseline, these subregions were also smaller in MCI A+. Longitudinally, SCD A+ and MCI A+, and A+/T-/N- and A+/T+orN+, had significantly greater atrophy compared to controls in both anterior and posterior hippocampus. In the entorhinal and parahippocampal cortices, longitudinal atrophy was observed only in MCI A+ compared to NC A-, and in A+/T-/N- and A+/T+orN+ compared to A-/T-/N-.

CONCLUSION

We found MTL neurodegeneration largely consistent with existing models, suggesting that harmonized MRI volumetry may be used under conditions that are common in clinical multi-center cohorts.

Assessing brain and biological aging trajectories associated with Alzheimer’s disease

The development of effective treatments to prevent and slow Alzheimer’s disease (AD) pathogenesis is needed in order to tackle the steady increase in the global prevalence of AD. This challenge is complicated by the need to identify key health shifts that precede the onset of AD and cognitive decline as these represent windows of opportunity for intervening and preventing disease. Such shifts may be captured through the measurement of biomarkers that reflect the health of the individual, in particular those that reflect brain age and biological age. Brain age biomarkers provide a composite view of the health of the brain based on neuroanatomical analyses, while biological age biomarkers, which encompass the epigenetic clock, provide a measurement of the overall health state of an individual based on DNA methylation analysis. Acceleration of brain and biological ages is associated with changes in cognitive function, as well as neuropathological markers of AD. In this mini-review, we discuss brain age and biological age research in the context of cognitive decline and AD. While more research is needed, studies show that brain and biological aging trajectories are variable across individuals and that such trajectories are non-linear at older ages. Longitudinal monitoring of these biomarkers may be valuable for enabling earlier identification of divergent pathological trajectories toward AD and providing insight into points for intervention.

Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion

Progressive brain atrophy is a key neuropathological hallmark of Alzheimer's disease (AD) dementia. However, atrophy patterns along the progression of AD dementia are diffuse and variable and are often missed by univariate methods. Consequently, identifying the major regional atrophy patterns underlying AD dementia progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy patterns are predictive of AD dementia progression, while holding all other atrophy changes constant using a total sample of 334 subjects. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD dementia versus those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding different regions of interest (ROIs) from the model's testing set's input. We also validated this approach by occluding ROIs based on Braak's staging scheme. We found that the hippocampus, fusiform, and inferior temporal gyri were the strongest predictors of AD dementia progression, in agreement with established staging models. We also found that occlusion of limbic ROIs defined according to Braak stage III had the largest impact on the performance of the model. Our predictive model reveals the major regional patterns of atrophy predictive of AD dementia progression. These results highlight the potential for early diagnosis and stratification of individuals with prodromal AD dementia based on patterns of cortical atrophy, prior to interventional clinical trials.

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.

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.

Unraveling Early Signs of Navigational Impairment in APPswe/PS1dE9 Mice Using Morris Water Maze

Mild behavioral deficits, which are part of normal aging, can be early indicators of an impending Alzheimer's disease. Using the APPswe/PS1dE9 (APP/PS1) mouse model of Alzheimer's disease, we utilized the Morris water maze spatial learning paradigm to systematically evaluate mild behavioral deficits that occur during the early stages of disease pathogenesis. Conventional behavioral analysis using this model indicates that spatial memory is intact at 2 months of age. In this study, we used an alternative method to analyze the behavior of mice, aiming to gain a better understanding of the nature of cognitive deficits by focusing on the unsuccessful trials during water maze learning rather than on the successful ones. APP/PS1 mice displayed a higher number of unsuccessful trials during the initial days of training, unlike their wild-type counterparts. However, with repeated trial and error, learning in APP/PS1 reached levels comparable to that of the wild-type mice during the later days of training. Individual APP/PS1 mice preferred a non-cognitive search strategy called circling, which led to abrupt learning transitions and an increased number of unsuccessful trials. These findings indicate the significance of subtle intermediate readouts as early indicators of conditions such as Alzheimer's disease.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

The effect of white matter hyperintensities on regional brain volumes and white matter microstructure, a population-based study in HUNT

Even though age-related white matter hyperintensities (WMH) begin to emerge in middle age, their effect on brain micro- and macrostructure in this age group is not fully elucidated. We have examined how presence of WMH and load of WMH affect regional brain volumes and microstructure in a validated, representative general population sample of 873 individuals between 50 and 66 years. Presence of WMH was determined as Fazakas grade ≥1. WMH load was WMH volume from manual tracing of WMHs divided on intracranial volume. The impact of age appropriate WMH (Fazakas grade 1) on the brain was also investigated. Major novel findings were that even the age appropriate WMH group had widespread macro- and microstructural changes in gray and white matter, showing that the mere presence of WMH, not just WMH load is an important clinical indicator of brain health. With increasing WMH load, structural changes spread centrifugally. Further, we found three major patterns of FA and MD changes related to increasing WMH load, demonstrating a heterogeneous effect on white matter microstructure, where distinct patterns were found in the proximity of the lesions, in deep white matter and in white matter near the cortex. This study also raises several questions about the onset of WMH related pathology, in particular, whether some of the aberrant brain structural and microstructural findings are present before the emergence of WMH. We also found, similar to other studies, that WMH risk factors had low explanatory power for WMH, making it unclear which factors lead to WMH.

Memory for familiar locations: The impact of age, education and cognitive efficiency on two neuropsychological allocentric tasks

This research aims to reconsider and support the use of spatial tasks based on familiar geographical information in the neuropsychological assessment of topographical (dis)orientation. Performance on two spatial tasks based on familiar information -l andmark positioning on a map and map of Italy - were compared in two studies assessing allocentric orientation among young and healthy elderly with different levels of education (Study 1) and elderly with and without probable cognitive impairment (Study 2). Results from Study 1 showed that the map of Italy task was affected by education, while the landmark positioning on a map was not. Results of Study 2 showed that both tasks were sensitive to different levels of cognitive functioning in a sample of community-dwelling seniors. Overall, spatial tasks based on mental representation of the hometown environment may be an important supplement in the assessment of allocentric topographical disorientation, discriminating typical from atypical aging.

Analysis of conversion of Alzheimer’s disease using a multi-state Markov model

With rapid aging of world population, Alzheimer’s disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer’s disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer’s disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer’s Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer’s disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

BACKGROUND

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL).

METHODS

We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients.

RESULTS

Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei.

CONCLUSION

The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

Monitoring disease progression in mild cognitive impairment: Associations between atrophy patterns, cognition, APOE and amyloid

BACKGROUND

A disease severity index (SI) for Alzheimer's disease (AD) has been proposed that summarizes MRI-derived structural measures into a single score using multivariate data analysis.

OBJECTIVES

To longitudinally evaluate the use of the SI to monitor disease progression and predict future progression to AD in mild cognitive impairment (MCI). Further, to investigate the association between longitudinal change in the SI and cognitive impairment, Apolipoprotein E (APOE) genotype as well as the levels of cerebrospinal fluid amyloid-beta 1-42 (Aβ) peptide.

METHODS

The dataset included 195 AD, 145 MCI and 228 control subjects with annual follow-up for three years, where 70 MCI subjects progressed to AD (MCI-p). For each subject the SI was generated at baseline and follow-ups using 55 regional cortical thickness and subcortical volumes measures that extracted by the FreeSurfer longitudinal stream.

RESULTS

MCI-p subjects had a faster increase of the SI over time (p < 0.001). A higher SI at baseline in MCI-p was related to progression to AD at earlier follow-ups (p < 0.001) and worse cognitive impairment (p < 0.001). AD-like MCI patients with the APOE ε4 allele and abnormal Aβ levels had a faster increase of the SI, independently (p = 0.003 and p = 0.004).

CONCLUSIONS

Longitudinal changes in the SI reflect structural brain changes and can identify MCI patients at risk of progression to AD. Disease-related brain structural changes are influenced independently by APOE genotype and amyloid pathology. The SI has the potential to be used as a sensitive tool to predict future dementia, monitor disease progression as well as an outcome measure for clinical trials.

The cerebellum shrinks faster than normal ageing in Alzheimer's disease but not in mild cognitive impairment

BACKGROUND

While acceleration in age-related cerebral atrophy has been well documented in Alzheimer's disease, the cerebellar contributions to this effect have not been thoroughly investigated.

OBJECTIVE

This study investigated cerebellar volume and atrophy rate using magnetic resonance imaging in individuals with normal cognition (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD).

METHODS

Two hundred twenty-nine CN, 398 MCI and 191 AD participants of stage I ADNI database with screening scans were evaluated for cerebellar volume. Of those, 758 individuals with two or more follow-up scans were categorized into stable, converted, and reverted CN, MCI and AD and evaluated for cerebellar atrophy rate.

RESULTS

Cerebellar volume was 2.5% larger in CN than in those with AD but there were no differences between CN and MCI and MCI and AD in cross-sectional analysis. Similarly, the atrophy rate was 49% larger in AD and 64% larger in MCI who converted to AD but no difference was detected between CN and MCI. There were no association between education and APOEe4 and cerebellar volume or cerebellar atrophy across the diagnostic groups.

CONCLUSION

Cerebellar atrophy contributes to Alzheimer's clinical progression but mostly at the late stage of the disease. However, even in the late stage shrinkage rate is less than the average of the shrinkage in the cerebrum and is not associated with AD moderators. This suggests that cerebellar involvement is secondary to cerebral involvement and can be due to network connection spread regardless of the primary pathology. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. Hum Brain Mapp 38:3141-3150, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. Hum Brain Mapp 38:3141-3150, 2017. © 2017 Wiley Periodicals, Inc.

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.

Structural MRI and Amyloid PET Imaging for Prediction of Conversion to Alzheimer's Disease in Patients with Mild Cognitive Impairment: A Meta-Analysis

OBJECTIVE

The aim of this study was to explore the prognostic values of biomarkers of neurodegeneration as measured by magnetic resonance imaging (MRI) and amyloid burden as measured by amyloid positron emission tomography (PET) in predicting conversion to Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI).

METHODS

PubMed and EMBASE databases were searched for structural MRI or amyloid PET imaging studies published between January 2000 and July 2014 that reported conversion to AD in patients with MCI. Means and standard deviations or individual numbers of biomarkers with positive or negative status at baseline and corresponding numbers of patients who had progressed to AD at follow-up were retrieved from each study. The effect size of each biomarker was expressed as Hedges's g.

RESULTS

Twenty-four MRI studies and 8 amyloid PET imaging studies were retrieved. 674 of the 1741 participants (39%) developed AD. The effect size for predicting conversion to AD was 0.770 [95% confidence interval (CI) 0.607-0.934] for across MRI and 1.316 (95% CI 0.920-1.412) for amyloid PET imaging (p<0.001). The effect size was 1.256 (95% CI 0.902-1.609) for entorhinal cortex volume from MRI.

CONCLUSION

Our study suggests that volumetric MRI measurement may be useful for the early detection of AD.

Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer Disease: Do Regions Outside the Hippocampus Matter?

BACKGROUND AND PURPOSE

Alzheimer disease is a prevalent neurodegenerative disease. Computer assessment of brain atrophy patterns can help predict conversion to Alzheimer disease. Our aim was to assess the prognostic efficacy of individual-versus-combined regional volumetrics in 2 commercially available brain volumetric software packages for predicting conversion of patients with mild cognitive impairment to Alzheimer disease.

MATERIALS AND METHODS

Data were obtained through the Alzheimer's Disease Neuroimaging Initiative. One hundred ninety-two subjects (mean age, 74.8 years; 39% female) diagnosed with mild cognitive impairment at baseline were studied. All had T1-weighted MR imaging sequences at baseline and 3-year clinical follow-up. Analysis was performed with NeuroQuant and Neuroreader. Receiver operating characteristic curves assessing the prognostic efficacy of each software package were generated by using a univariable approach using individual regional brain volumes and 2 multivariable approaches (multiple regression and random forest), combining multiple volumes.

RESULTS

On univariable analysis of 11 NeuroQuant and 11 Neuroreader regional volumes, hippocampal volume had the highest area under the curve for both software packages (0.69, NeuroQuant; 0.68, Neuroreader) and was not significantly different (P > .05) between packages. Multivariable analysis did not increase the area under the curve for either package (0.63, logistic regression; 0.60, random forest NeuroQuant; 0.65, logistic regression; 0.62, random forest Neuroreader).

CONCLUSIONS

Of the multiple regional volume measures available in FDA-cleared brain volumetric software packages, hippocampal volume remains the best single predictor of conversion of mild cognitive impairment to Alzheimer disease at 3-year follow-up. Combining volumetrics did not add additional prognostic efficacy. Therefore, future prognostic studies in mild cognitive impairment, combining such tools with demographic and other biomarker measures, are justified in using hippocampal volume as the only volumetric biomarker.

Chronotropic Response and Cognitive Function in a Cohort at Risk for Alzheimer's Disease

The objective of this study was to examine the association of chronotropic response (CR) and heart rate (HR) recovery- two indices of cardiovascular function within the context of a graded exercise test- with cognitive performance in a cognitively healthy, late-middle-aged cohort at risk for Alzheimer's disease (AD). Ninety participants (age = 63.52±5.86 years; 65.6% female) from the Wisconsin Registry for Alzheimer's Prevention participated in this study. They underwent graded exercise testing and a comprehensive neuropsychological assessment that assessed the following four cognitive domains: Immediate Memory, Verbal & Learning Memory, Working Memory, and Speed & Flexibility. Regression analyses, adjusted for age, sex, and education, were used to examine the association between CR, HR recovery, and cognition. We found significant associations between CR and cognitive performance in the domains of Immediate Memory, Verbal Learning & Memory, and Speed & Flexibility. In contrast, HR recovery was not significantly associated with cognitive function. The association between CR and cognition persisted even after controlling for HR recovery. Together, these findings indicatethat, in a cognitively normal, late-middle-aged cohort, CR is a stronger correlate of cognitive performance than HR recovery. Overall, this study reinforces the idea that cardiovascular health plays an important role in cognitive function, specifically in a cohort at risk for AD; and that interventions that promote vascular health may be a viable pathway to preventing or slowing cognitive decline due to AD.

The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease

In our aging society, diseases in the elderly come more and more into focus. An important issue in research is Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) with their causes, diagnosis, treatment, and disease prediction. We applied the Brain Age Gap Estimation (BrainAGE) method to examine the impact of the Apolipoprotein E (APOE) genotype on structural brain aging, utilizing longitudinal magnetic resonance image (MRI) data of 405 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We tested for differences in neuroanatomical aging between carrier and non-carrier of APOE ε4 within the diagnostic groups and for longitudinal changes in individual brain aging during about three years follow-up. We further examined whether a combination of BrainAGE and APOE status could improve prediction accuracy of conversion to AD in MCI patients. The influence of the APOE status on conversion from MCI to AD was analyzed within all allelic subgroups as well as for ε4 carriers and non-carriers. The BrainAGE scores differed significantly between normal controls, stable MCI (sMCI) and progressive MCI (pMCI) as well as AD patients. Differences in BrainAGE changing rates over time were observed for APOE ε4 carrier status as well as in the pMCI and AD groups. At baseline and during follow-up, BrainAGE scores correlated significantly with neuropsychological test scores in APOE ε4 carriers and non-carriers, especially in pMCI and AD patients. Prediction of conversion was most accurate using the BrainAGE score as compared to neuropsychological test scores, even when the patient’s APOE status was unknown. For assessing the individual risk of coming down with AD as well as predicting conversion from MCI to AD, the BrainAGE method proves to be a useful and accurate tool even if the information of the patient’s APOE status is missing.

Distinct Pattern of Gray Matter Atrophy in Mild Alzheimer's Disease Impacts on Cognitive Outcomes of Noninvasive Brain Stimulation

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is a promising tool to study and modulate brain plasticity.

OBJECTIVE

Our aim was to investigate the effects of rTMS on cognitive functions in patients with mild cognitive impairment and Alzheimer's disease (MCI/AD) and assess the effect of gray matter (GM) atrophy on stimulation outcomes.

METHODS

Twenty MCI/AD patients participated in the proof-of-concept controlled study. Each patient received three sessions of 10 Hz rTMS of the right inferior frontal gyrus (IFG), the right superior temporal gyrus (STG), and the vertex (VTX, a control stimulation site) in a randomized order. Cognitive functions were tested prior to and immediately after each session. The GM volumetric data of patients were: 1) compared to healthy controls (HC) using source-based morphometry; 2) correlated with rTMS-induced cognitive improvement.

RESULTS

The effect of the stimulated site on the difference in cognitive scores was statistically significant for the Word part of the Stroop test (ST-W, p = 0.012, linear mixed models). As compared to the VTX stimulation, patients significantly improved after both IFG and STG stimulation in this cognitive measure. MCI/AD patients had significant GM atrophy in characteristic brain regions as compared to HC (p = 0.029, Bonferroni corrected). The amount of atrophy correlated with the change in ST-W scores after rTMS of the STG.

CONCLUSION

rTMS enhanced cognitive functions in MCI/AD patients. We demonstrated for the first time that distinct pattern of GM atrophy in MCI/AD diminishes the cognitive effects induced by rTMS of the temporal neocortex.

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.

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

OBJECTIVE

Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease (AD) is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance images.

METHODS

We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection, and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion.

RESULTS

Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79-81% for the prediction of MCI-to-AD conversion within three years in tenfold cross validations. The classification AUC further increases to 84-92% when age and cognitive measures are combined with the proposed grading biomarker.

CONCLUSION

The obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space, the removal of the normal aging effect, selection of discriminative voxels, the calculation of the grading biomarker using AD and normal control groups, and the integration of sparse representation technique and the combination of cognitive measures.

SIGNIFICANCE

The evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion.

Glucose Dysregulation Interacts With APOE-∊4 to Potentiate Temporoparietal Cortical Thinning

We examined the interactive effects of apolipoprotein ∊4 (APOE-∊4), a risk factor for Alzheimer's disease (AD), and diabetes risk on cortical thickness among 107 healthy elderly participants; in particular, participants included 27 APOE-∊4+ and 80 APOE-∊4- controls using T1-weighted structural magnetic resonance imaging. Regions of interests included select frontal, temporal, and parietal cortical regions. Among APOE-∊4, glucose abnormalities independently predicted reduced cortical thickness among temporoparietal regions but failed to predict changes for noncarriers. However, among noncarriers, age independently predicted reduced cortical thickness among temporoparietal and frontal regions. Diabetes risk is particularly important for the integrity of cortical gray matter in APOE-∊4 and demonstrates a pattern of thinning that is expected in preclinical AD. However, in the absence of this genetic factor, age confers risk for reduced cortical thickness among regions of expected compromise. This study supports aggressive management of cerebrovascular factors and earlier preclinical detection of AD among individuals presenting with genetic and metabolic risks.

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.

CNS biomarkers: Potential from a regulatory perspective: Case study - Focus in low hippocampus volume as a biomarker measured by MRI

Our objectives are to describe the procedure for qualification advice and opinion from EU regulators on the use of novel methodologies in drug development, the key stakeholders involved and the evidence requirements for qualification opinion. We present a case study of the request from the Coalition Against Major Disease (CAMD) Consortium of the Critical Path (C-Path) Institute for EU regulators׳ qualification opinion on the use of low hippocampal volume as a biomarker for population enrichment in clinical trials of novel drugs in Alzheimer׳s disease (AD). We discuss the main concerns from the regulators, data analysis requests and guidance during the qualification. EU regulators concluded that low hippocampal volume, measured by vMRI and considered as a dichotomized variable (low volume or not), appears to help enriching recruitment into clinical trials aimed at studying drugs that potentially slow the progression of the pre-dementia stage of AD. The biomarker qualification procedure is a dynamic process in which pharmaceutical companies and research consortia can submit further data to update the qualifications and improve the predictive value of the biomarkers.

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.

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects

Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.

Hippocampus atrophy and the longitudinal course of late-life depression

OBJECTIVES

Smaller hippocampal volumes are observed in depression but it remains unclear how antidepressant response and persistent depression relate to changes in hippocampal volume. We examined the longitudinal relationship between hippocampal atrophy and course of late-life depression.

SETTING

Academic medical center.

PARTICIPANTS

Depressed and never-depressed cognitively intact subjects age 60 years or older.

MEASUREMENTS

Depression severity was measured every three months with the Montgomery-Asberg Depression Rating Scale (MADRS). Participants also completed cranial 1.5-T magnetic resonance imaging every 2 years. We compared 2-year change in hippocampal volume based on remission status, then in expanded analyses examined how hippocampal volumes predicted MADRS score.

RESULTS

In analyses of 92 depressed and 70 never-depressed subjects, over 2 years the cohort whose depression never remitted exhibited greater hippocampal atrophy than the never-depressed cohort. In expanded analyses of a broader sample of 152 depressed elders, depression severity was significantly predicted by a hippocampus × time interaction where smaller hippocampus volumes over time were associated with greater depression severity.

CONCLUSIONS

Hippocampal atrophy is associated with greater and persistent depression severity. Neuropathological studies are needed to determine if this atrophy is related to the toxic effects of persistent depression or related to underlying Alzheimer disease.

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.

Advances in longitudinal studies of amnestic mild cognitive impairment and Alzheimer's disease based on multi-modal MRI techniques

Amnestic mild cognitive impairment (aMCI) is a prodromal stage of Alzheimer's disease (AD), and 75%-80% of aMCI patients finally develop AD. So, early identification of patients with aMCI or AD is of great significance for prevention and intervention. According to cross-sectional studies, it is known that the hippocampus, posterior cingulate cortex, and corpus callosum are key areas in studies based on structural MRI (sMRI), functional MRI (fMRI), and diffusion tensor imaging (DTI) respectively. Recently, longitudinal studies using each MRI modality have demonstrated that the neuroimaging abnormalities generally involve the posterior brain regions at the very beginning and then gradually affect the anterior areas during the progression of aMCI to AD. However, it is not known whether follow-up studies based on multi-modal neuroimaging techniques (e.g., sMRI, fMRI, and DTI) can help build effective MRI models that can be directly applied to the screening and diagnosis of aMCI and AD. Thus, in the future, large-scale multi-center follow-up studies are urgently needed, not only to build an MRI diagnostic model that can be used on a single person, but also to evaluate the variability and stability of the model in the general population. In this review, we present longitudinal studies using each MRI modality separately, and then discuss the future directions in this field.

BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease

Alzheimer’s disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07–1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.

Clinical use of brain volumetry

Magnetic resonance imaging (MRI)-based brain volumetry is increasingly being used in the clinical setting to assess brain volume changes from structural MR images in a range of neurologic conditions. Measures of brain volumes have been shown to be valid biomarkers of the clinical state and progression by offering high reliability and robust inferences on the underlying disease-related mechanisms. This review critically examines the different scenarios of the application of MRI-based brain volumetry in neurology: 1) supporting disease diagnosis, 2) understanding mechanisms and tracking clinical progression of disease, and 3) monitoring treatment effect. These aspects will be discussed in a wide range of neurologic conditions, with particular emphasis on Alzheimer's disease and multiple sclerosis.

Preventing Alzheimer's disease-related gray matter atrophy by B-vitamin treatment

Is it possible to prevent atrophy of key brain regions related to cognitive decline and Alzheimer's disease (AD)? One approach is to modify nongenetic risk factors, for instance by lowering elevated plasma homocysteine using B vitamins. In an initial, randomized controlled study on elderly subjects with increased dementia risk (mild cognitive impairment according to 2004 Petersen criteria), we showed that high-dose B-vitamin treatment (folic acid 0.8 mg, vitamin B6 20 mg, vitamin B12 0.5 mg) slowed shrinkage of the whole brain volume over 2 y. Here, we go further by demonstrating that B-vitamin treatment reduces, by as much as seven fold, the cerebral atrophy in those gray matter (GM) regions specifically vulnerable to the AD process, including the medial temporal lobe. In the placebo group, higher homocysteine levels at baseline are associated with faster GM atrophy, but this deleterious effect is largely prevented by B-vitamin treatment. We additionally show that the beneficial effect of B vitamins is confined to participants with high homocysteine (above the median, 11 µmol/L) and that, in these participants, a causal Bayesian network analysis indicates the following chain of events: B vitamins lower homocysteine, which directly leads to a decrease in GM atrophy, thereby slowing cognitive decline. Our results show that B-vitamin supplementation can slow the atrophy of specific brain regions that are a key component of the AD process and that are associated with cognitive decline. Further B-vitamin supplementation trials focusing on elderly subjets with high homocysteine levels are warranted to see if progression to dementia can be prevented.

[Early diagnosis of Alzheimer's disease]

INTRODUCTION

Diagnosis of Alzheimer's disease (AD) remains difficult to establish, and can only be considered as certain thanks to anatomopathological evidence, or genetic mutations. Current diagnostic criteria rely on innovative imaging and biological tools, in order to detect pathological cues from very early stages, and with best sensibility and sensitivity.

STATE OF ART

Advances in neuro-imaging enabled the development of different tools to help establishing the diagnosis, such as cerebral atrophy assessment on magnetic resonance imaging (MRI), and cerebral metabolism study on positron emission tomography (PET). Besides, the increasing use of in vivo biological markers, combined to clinical criteria, enables to discriminate patients from healthy controls at even earlier stages. This includes studies on tau and beta-amyloid proteins concentrations in the cerebrosinal fluid, and amyloid-specific radioligands uptake. Familial forms of Alzheimer represent a great model for studying early or even pre-symptomatic AD, as genetic analyses constitute a diagnosis of certainty, even though they usually evolve earlier and faster.

PERSPECTIVES, CONCLUSION

Diagnostic tools are more and more numerous and performant. According to patients' clinical heterogeneity, it appears essential to associate different method to investigate, in order to make a diagnosis as early and as reliable as possible.

Surgery and brain atrophy in cognitively normal elderly subjects and subjects diagnosed with mild cognitive impairment

BACKGROUND

Structural magnetic resonance imaging is used to longitudinally monitor the progression of Alzheimer disease from its presymptomatic to symptomatic phases. Using magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative, we tested the hypothesis that surgery would impact brain parameters associated with progression of dementia.

METHODS

Brain images from the neuroimaging initiative database were used to study normal volunteer subjects and patients with mild cognitive impairment for the age group 55 to 90 inclusive. We compared changes in regional brain anatomy for three visits that defined two intervisit intervals for a surgical cohort (n = 41) and a propensity matched nonsurgical control cohort (n = 123). The first interval for the surgical cohort contained the surgical date. Regional brain volumes were determined with Freesurfer and quantitatively described with J-image software (University of California at San Francisco, San Francisco, California). Statistical analysis used Repeated Measures ANCOVA (SPSS, v.18.0; Chicago, IL).

RESULTS

We found that surgical patients, during the first follow-up interval (5-9 months), but not subsequently, had increased rates of atrophy for cortical gray matter and hippocampus, and lateral ventricle enlargement, as compared with nonsurgical controls. A composite score of five cognitive tests during this interval showed reduced performance for surgical patients with mild cognitive impairment.

CONCLUSIONS

Elderly subjects after surgery experienced an increased rate of brain atrophy during the initial evaluation interval, a time associated with enhanced risk for postoperative cognitive dysfunction. Although there was no difference in atrophy rate by diagnosis, subjects with mild cognitive impairment suffered greater subsequent cognitive effects.

Relationship between CSF biomarkers of Alzheimer's disease and rates of regional cortical thinning in ADNI data

Previously it was reported that Alzheimer's disease (AD) patients have reduced amyloid (Aβ 1-42) and elevated total tau (t-tau) and phosphorylated tau (p-tau 181p) in the cerebro-spinal fluid (CSF), suggesting that these same measures could be used to detect early AD pathology in healthy elderly (CN) and mild cognitive impairment (MCI). In this study, we tested the hypothesis that there would be an association among rates of regional brain atrophy, the CSF biomarkers Aβ 1-42, t-tau, and p-tau 181p and ApoE ε4 status, and that the pattern of this association would be diagnosis specific. Our findings primarily showed that lower CSF Aβ 1-42 and higher tau concentrations were associated with increased rates of regional brain tissue loss and the patterns varied across the clinical groups. Taken together, these findings demonstrate that CSF biomarker concentrations are associated with the characteristic patterns of structural brain changes in CN and MCI that resemble to a large extent the pathology seen in AD. Therefore, the finding of faster progression of brain atrophy in the presence of lower Aβ 1-42 levels and higher p-tau levels supports the hypothesis that CSF Aβ 1-42 and tau are measures of early AD pathology. Moreover, the relationship among CSF biomarkers, ApoE ε4 status, and brain atrophy rates are regionally varying, supporting the view that the genetic predisposition of the brain to amyloid and tau mediated pathology is regional and disease stage specific.

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

Alliance for aging research AD biomarkers work group: structural MRI

Biomarkers of Alzheimer's disease (AD) are increasingly important. All modern AD therapeutic trials employ AD biomarkers in some capacity. In addition, AD biomarkers are an essential component of recently updated diagnostic criteria for AD from the National Institute on Aging--Alzheimer's Association. Biomarkers serve as proxies for specific pathophysiological features of disease. The 5 most well established AD biomarkers include both brain imaging and cerebrospinal fluid (CSF) measures--cerebrospinal fluid Abeta and tau, amyloid positron emission tomography (PET), fluorodeoxyglucose (FDG) positron emission tomography, and structural magnetic resonance imaging (MRI). This article reviews evidence supporting the position that MRI is a biomarker of neurodegenerative atrophy. Topics covered include methods of extracting quantitative and semiquantitative information from structural MRI; imaging-autopsy correlation; and evidence supporting diagnostic and prognostic value of MRI measures. Finally, the place of MRI in a hypothetical model of temporal ordering of AD biomarkers is reviewed.

Hippocampal subregions are differentially affected in the progression to Alzheimer's disease

Atrophy within the hippocampus (HP) as measured by magnetic resonance imaging (MRI) is a promising biomarker for the progression to Alzheimer's disease (AD). Subregions of the HP along the longitudinal axis have been found to demonstrate unique function, as well as undergo differential changes in the progression to AD. Little is known of relationships between such HP subregions and other potential biomarkers, such as neuropsychological (NP), genetic, and cerebral spinal fluid (CSF) beta amyloid and tau measures. The purpose of this study was to subdivide the hippocampus to determine how the head, body, and tail were affected in normal control, mild cognitively impaired, and AD subjects, and investigate relationships with HP subregions and other potential biomarkers. MRI scans of 120 participants of the Alzheimer's Disease Neuroimaging Initiative were processed using FreeSurfer, and the HP was subdivided using 3D Slicer. Each subregion was compared among groups, and correlations were used to determine relationships with NP, genetic, and CSF measures. Results suggest that HP subregions are undergoing differential atrophy in AD, and demonstrate unique relationships with NP and CSF data. Discriminant function analyses revealed that these regions, when combined with NP and CSF measures, were able to classify by diagnostic group, and classify MCI subjects who would and would not progress to AD within 12 months.

Consistent multi-time-point brain atrophy estimation from the boundary shift integral

Brain atrophy measurement is increasingly important in studies of neurodegenerative diseases such as Alzheimer's disease (AD), with particular relevance to trials of potential disease-modifying drugs. Automated registration-based methods such as the boundary shift integral (BSI) have been developed to provide more precise measures of change from a pair of serial MR scans. However, when a method treats one image of the pair (typically the baseline) as the reference to which the other is compared, this systematic asymmetry risks introducing bias into the measurement. Recent concern about potential biases in longitudinal studies has led to several suggestions to use symmetric image registration, though some of these methods are limited to two time-points per subject. Therapeutic trials and natural history studies increasingly involve several serial scans, it would therefore be useful to have a method that can consistently estimate brain atrophy over multiple time-points. Here, we use the log-Euclidean concept of a within-subject average to develop affine registration and differential bias correction methods suitable for any number of time-points, yielding a longitudinally consistent multi-time-point BSI technique. Baseline, 12-month and 24-month MR scans of healthy controls, subjects with mild cognitive impairment and AD patients from the Alzheimer's Disease Neuroimaging Initiative are used for testing the bias in processing scans with different amounts of atrophy. Four tests are used to assess bias in brain volume loss from BSI: (a) inverse consistency with respect to ordering of pairs of scans 12 months apart; (b) transitivity consistency over three time-points; (c) randomly ordered back-to-back scans, expected to show no consistent change over subjects; and (d) linear regression of the atrophy rates calculated from the baseline and 12-month scans and the baseline and 24-month scans, where any additive bias should be indicated by a non-zero intercept. Results indicate that the traditional BSI processing pipeline does not exhibit significant bias due to its use of windowed sinc interpolation, but with linear interpolation and asymmetric registration, bias can be pronounced. Either improved interpolation or symmetric registration alone can greatly reduce this bias, and our proposed method combining both aspects shows no significant bias in any of the four experiments.

A pilot study of quantitative MRI measurements of ventricular volume and cortical atrophy for the differential diagnosis of normal pressure hydrocephalus

Current radiologic diagnosis of normal pressure hydrocephalus (NPH) requires a subjective judgment of whether lateral ventricular enlargement is disproportionate to cerebral atrophy based on visual inspection of brain images. We investigated whether quantitative measurements of lateral ventricular volume and total cortical thickness (a correlate of cerebral atrophy) could be used to more objectively distinguish NPH from normal controls (NC), Alzheimer's (AD), and Parkinson's disease (PD). Volumetric MRIs were obtained prospectively from patients with NPH (n = 5), PD (n = 5), and NC (5). Additional NC (n = 5) and AD patients (n = 10) from the ADNI cohort were examined. Although mean ventricular volume was significantly greater in the NPH group than all others, the range of values overlapped those of the AD group. Individuals with NPH could be better distinguished when ventricular volume and total cortical thickness were considered in combination. This pilot study suggests that volumetric MRI measurements hold promise for improving NPH differential diagnosis.

Association of ApoE and LRP mRNA levels with dementia and AD neuropathology

Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. Despite the considerable converging evidence that implicates ApoE and LRP in the pathogenesis of AD, the precise mechanism by which ApoE and LRP modulate the risk for AD remains elusive. Moreover, studies investigating expression of ApoE and LRP in AD brain have reported variable and contradictory results. To overcome these inconsistencies, we studied the mRNA expression of ApoE and LRP in the postmortem brain of persons who died at different stages of dementia and AD-associated neuropathology relative to controls by quantitative polymerase chain reaction (qPCR) and Western blotting analyses. Clinical dementia rating scores were used as a measure of dementia severity, whereas, Braak neuropathological staging and neuritic plaque density were used as indexes of the neuropathological progression of AD. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Aβ) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.

Minimal atrophy of the entorhinal cortex and hippocampus: progression of cognitive impairment

BACKGROUND

In Alzheimer's disease, neurodegenerative atrophy progresses from the entorhinal cortex (ERC) to the hippocampus (HP), limbic system and neocortex. The significance of very mild atrophy of the ERC and HP on MRI scans among elderly subjects is unknown.

METHODS

A validated visual rating system on coronal MRI scans was used to identify no atrophy of the HP or ERC (HP(0); ERC(0)), or minimal atrophy of the HP or ERC (HP(ma); ERC(ma)), among 414 participants. Subjects fell into the following groups: (1) ERC(0)/HP(0), (2) ERC(ma)/HP(0), (3) ERC(0)/HP(ma), and (4) ERC(ma)/HP(ma). HP volume was independently measured using volumetric methods.

RESULTS

In comparison to ERC(0)/HP(0) subjects, those with ERC(0)/HP(ma) had impairment on 1 memory test, ERC(ma)/HP(0) subjects had impairment on 2 memory tests and the Mini Mental State Examination (MMSE), while ERC(ma)/HP(ma) subjects had impairment on 3 memory tests, the MMSE and Clinical Dementia Rating. Progression rates of cognitive and functional impairment were significantly greater among subjects with ERC(ma).

CONCLUSION

Minimal atrophy of the ERC results in greater impairment than minimal atrophy of the HP, and the combination is additive when measured by cognitive and functional tests. Rates of progression to greater impairment were higher among ERC(ma) subjects.

Explicit and implicit memory in late-life depression

OBJECTIVE

Late-life depression has been associated with memory loss and is frequently assumed to be a risk factor for continued cognitive decline. This study examined cognition in patients with late-life depression with a focus on the assessment of the extent and type of memory loss among elderly depressed patients.

METHODS

Two-year cross-sectional study of elderly depressed (N = 112) and nondepressed (N = 138) individuals at or older than 60 years in an urban area surrounding a major medical center in southern California. Participants had little to moderate stroke risk. Volunteers were screened with the Hamilton Depression Rating Scale and the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) Axis I Disorders. Patients were diagnosed for major depression by a geriatric psychiatrist using DSM-IV criteria. Volunteers completed neuropsychological testing, a standard battery of laboratory tests, and a neurologic and psychiatric evaluation to rule out a medical burden that might contribute to depression or early dementia.

RESULTS

Depressed patients showed deficits in attention and processing, executive function, and immediate explicit recall. Implicit learning and episodic recall of the testing procedure, semantic and phonetic fluency, and retention of newly acquired verbal material after a delay period were comparable with controls.

CONCLUSION

Moderately depressed patients demonstrate a pattern of cognitive deficits suggestive of mild frontal dysfunction during recall tasks. Their retention of material over a delay period and their intact language skills indicate medial hippocampal function similar to controls. Subcortically mediated implicit memory is also at normal levels. These findings support current efforts to identify pathways of frontal and/or striatal compromise during depressive episodes.

Spatial patterns of brain amyloid-beta burden and atrophy rate associations in mild cognitive impairment

Amyloid-β accumulation in the brain is thought to be one of the earliest events in Alzheimer's disease, possibly leading to synaptic dysfunction, neurodegeneration and cognitive/functional decline. The earliest detectable changes seen with neuroimaging appear to be amyloid-β accumulation detected by (11)C-labelled Pittsburgh compound B positron emission tomography imaging. However, some individuals tolerate high brain amyloid-β loads without developing symptoms, while others progressively decline, suggesting that events in the brain downstream from amyloid-β deposition, such as regional brain atrophy rates, play an important role. The main purpose of this study was to understand the relationship between the regional distributions of increased amyloid-β and the regional distribution of increased brain atrophy rates in patients with mild cognitive impairment. To simultaneously capture the spatial distributions of amyloid-β and brain atrophy rates, we employed the statistical concept of parallel independent component analysis, an effective method for joint analysis of multimodal imaging data. Parallel independent component analysis identified significant relationships between two patterns of amyloid-β deposition and atrophy rates: (i) increased amyloid-β burden in the left precuneus/cuneus and medial-temporal regions was associated with increased brain atrophy rates in the left medial-temporal and parietal regions; and (ii) in contrast, increased amyloid-β burden in bilateral precuneus/cuneus and parietal regions was associated with increased brain atrophy rates in the right medial temporal regions. The spatial distribution of increased amyloid-β and the associated spatial distribution of increased brain atrophy rates embrace a characteristic pattern of brain structures known for a high vulnerability to Alzheimer's disease pathology, encouraging for the use of (11)C-labelled Pittsburgh compound B positron emission tomography measures as early indicators of Alzheimer's disease. These results may begin to shed light on the mechanisms by which amyloid-β deposition leads to neurodegeneration and cognitive decline and the development of a more specific Alzheimer's disease-specific imaging signature for diagnosis and use of this knowledge in the development of new anti-therapies for Alzheimer's disease.