Structural magnetic resonance imaging in established and prodromal Alzheimer disease: a review

N-acetyl-aspartate and Myo-inositol as Markers of White Matter Microstructural Organization in Mild Cognitive Impairment: Evidence from a DTI-1H-MRS Pilot Study

We implemented a multimodal approach to examine associations between structural and neurochemical changes that could signify neurodegenerative processes related to mild cognitive impairment (MCI). Fifty-nine older adults (60-85 years; 22 MCI) underwent whole-brain structural 3T MRI (T1W, T2W, DTI) and proton magnetic resonance spectroscopy (¹H-MRS). The regions of interest (ROIs) for ¹H-MRS measurements were the dorsal posterior cingulate cortex, left hippocampal cortex, left medial temporal cortex, left primary sensorimotor cortex, and right dorsolateral prefrontal cortex. The findings revealed that subjects in the MCI group showed moderate to strong positive associations between the total N-acetylaspartate to total creatine and the total N-acetylaspartate to myo-inositol ratios in the hippocampus and dorsal posterior cingulate cortex and fractional anisotropy (FA) of WM tracts crossing these regions-specifically, the left temporal tapetum, right corona radiata, and right posterior cingulate gyri. In addition, negative associations between the myo-inositol to total creatine ratio and FA of the left temporal tapetum and right posterior cingulate gyri were observed. These observations suggest that the biochemical integrity of the hippocampus and cingulate cortex is associated with a microstructural organization of ipsilateral WM tracts originating in the hippocampus. Specifically, elevated myo-inositol might be an underlying mechanism for decreased connectivity between the hippocampus and the prefrontal/cingulate cortex in MCI.

Brain atrophy pattern in patients with mild cognitive impairment: MRI study

We evaluated the accuracy of the quantitative and semiquantitative analysis in detecting regional atrophy patterns and differentiating mild cognitive impairment patients who remain stable (aMCI-S) from patients who develop Alzheimer’s disease (aMCI-AD) at clinical follow-up. Baseline magnetic resonance imaging was used for quantitative and semiquantitative analysis using visual rating scales. Visual rating scores were related to gray matter thicknesses or volume measures of some structures belonging to the same brain regions. Receiver operating characteristic (ROC) analysis was performed to assess measures’ accuracy in differentiating aMCI-S from aMCI-AD. Comparing aMCI-S and aMCI-AD patients, significant differences were found for specific rating scales, for cortical thickness belonging to the middle temporal lobe (MTL), anterior temporal (AT), and fronto-insular (FI) regions, for gray matter volumes belonging to MTL and AT regions. ROC curve analysis showed that middle temporal atrophy, AT, and FI visual scales showed better diagnostic accuracy than quantitative measures also when thickness measures were combined with hippocampal volumes. Semiquantitative evaluation, performed by trained observers, is a fast and reliable tool in differentiating, at the early stage of disease, aMCI patients that remain stable from those patients that may progress to AD since visual rating scales may be informative both about early hippocampal volume loss and cortical thickness reduction.

The Causal Role of Lipoxidative Damage in Mitochondrial Bioenergetic Dysfunction Linked to Alzheimer's Disease Pathology

Current shreds of evidence point to the entorhinal cortex (EC) as the origin of the Alzheimer’s disease (AD) pathology in the cerebrum. Compared with other cortical areas, the neurons from this brain region possess an inherent selective vulnerability derived from particular oxidative stress conditions that favor increased mitochondrial molecular damage with early bioenergetic involvement. This alteration of energy metabolism is the starting point for subsequent changes in a multitude of cell mechanisms, leading to neuronal dysfunction and, ultimately, cell death. These events are induced by changes that come with age, creating the substrate for the alteration of several neuronal pathways that will evolve toward neurodegeneration and, consequently, the development of AD pathology. In this context, the present review will focus on description of the biological mechanisms that confer vulnerability specifically to neurons of the entorhinal cortex, the changes induced by the aging process in this brain region, and the alterations at the mitochondrial level as the earliest mechanism for the development of AD pathology. Current findings allow us to propose the existence of an altered allostatic mechanism at the entorhinal cortex whose core is made up of mitochondrial oxidative stress, lipid metabolism, and energy production, and which, in a positive loop, evolves to neurodegeneration, laying the basis for the onset and progression of AD pathology.

Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique to derive such measures from non-surface-based volumetric tissue maps. ANTs provides an open-source method for estimating cortical thickness, derived by applying DiReCT to an atlas-based segmentation. In this paper, we propose DL+DiReCT, a method using high-quality deep learning-based neuroanatomy segmentations followed by DiReCT, yielding accurate and reliable cortical thickness measures in a short time. We evaluate the methods on two independent datasets and compare the results against surface-based measures from FreeSurfer. Good correlation of DL+DiReCT with FreeSurfer was observed (r = .887) for global mean cortical thickness compared to ANTs versus FreeSurfer (r = .608). Experiments suggest that both DiReCT-based methods had higher sensitivity to changes in cortical thickness than Freesurfer. However, while ANTs showed low scan-rescan robustness, DL+DiReCT showed similar robustness to Freesurfer. Effect-sizes for group-wise differences of healthy controls compared to individuals with dementia were highest with the deep learning-based segmentation. DL+DiReCT is a promising combination of a deep learning-based method with a traditional registration technique to detect subtle changes in cortical thickness.

Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease

This study examines the atrophy patterns in the entorhinal and transentorhinal cortices of subjects that converted from normal cognition to mild cognitive impairment. The regions were manually segmented from 3T MRI, then corrected for variability in boundary definition over time using an automated approach called longitudinal diffeomorphometry. Cortical thickness was calculated by deforming the gray matter-white matter boundary surface to the pial surface using an approach called normal geodesic flow. The surface was parcellated based on four atlases using large deformation diffeomorphic metric mapping. Average cortical thickness was calculated for (1) manually-defined entorhinal cortex, and (2) manually-defined transentorhinal cortex. Group-wise difference analysis was applied to determine where atrophy occurred, and change point analysis was applied to determine when atrophy started to occur. The results showed that by the time a diagnosis of mild cognitive impairment is made, the transentorhinal cortex and entorhinal cortex was up to 0.6 mm thinner than a control with normal cognition. A change point in atrophy rate was detected in the transentorhinal cortex 9–14 years prior to a diagnosis of mild cognitive impairment, and in the entorhinal cortex 8–11 years prior. The findings are consistent with autopsy findings that demonstrate neuronal changes in the transentorhinal cortex before the entorhinal cortex.

Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores

As a hot topic in brain disease prognosis, predicting clinical measures of subjects based on brain magnetic resonance imaging (MRI) data helps to assess the stage of pathology and predict future development of the disease. Due to incomplete clinical labels/scores, previous learning-based studies often simply discard subjects without ground-truth scores. This would result in limited training data for learning reliable and robust models. Also, existing methods focus only on using hand-crafted features (e.g., image intensity or tissue volume) of MRI data, and these features may not be well coordinated with prediction models. In this paper, we propose a weakly supervised densely connected neural network (wiseDNN) for brain disease prognosis using baseline MRI data and incomplete clinical scores. Specifically, we first extract multiscale image patches (located by anatomical landmarks) from MRI to capture local-to-global structural information of images, and then develop a weakly supervised densely connected network for task-oriented extraction of imaging features and joint prediction of multiple clinical measures. A weighted loss function is further employed to make full use of all available subjects (even those without ground-truth scores at certain time-points) for network training. The experimental results on 1469 subjects from both ADNI-1 and ADNI-2 datasets demonstrate that our proposed method can efficiently predict future clinical measures of subjects.

Insight in frontotemporal dementia and progressive supranuclear palsy

INTRODUCTION

Progressive supranuclear palsy (PSP) and behavioural variant frontotemporal dementia - (bv-FTD) share common neuropsychological features except for online monitoring awareness. Therefore, the aim of our study is to explore if this assessment could be used in standard clinical practice.

MATERIALS AND METHODS

We retrospectively analyse 93 subjects (27 FTD, 25 PSP, 42 healthy controls). Neuropsychological and instrumental examinations were performed for each patient.

RESULTS

FTD patients made fewer self-corrections than PSP patients despite a similar number of total errors. We also performed ROC curves: the area under the curve (AUC) is 0.79. A model for a logistic regression was also developed: the only significant predictor is the number of self-corrections (p = 0.004 β = 1244).

DISCUSSION AND CONCLUSIONS

In conclusion, our findings show online awareness is more compromised in FTD patients than in PSP patients. This difference could be useful for making a differential diagnosis between the two diseases: for each extra point in number of self-corrections the probability of suffering from PSP increases by about three and a half times (OR 3.47).

Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment

In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets.

Gray Matter Densities in Limbic Areas and APOE4 Independently Predict Cognitive Decline in Normal Brain Aging

Cross-sectional magnetic resonance imaging (MRI) studies reported significant associations between gray matter (GM) density changes in various limbic and neocortical areas and worst cognitive performances in elderly controls. Longitudinal studies in this field remain scarce and led to conflicting data. We report a clinico-radiological investigation of 380 cognitively preserved individuals who undergo neuropsychological assessment at baseline and after 18 months. All cases were assessed using a continuous cognitive score taking into account the global evolution of neuropsychological performances. The vast majority of Mini Mental State Examination (MMSE) 29 and 30 cases showed equal or worst performance at follow-up due to a ceiling effect. GM densities, white matter hyperintensities and arterial spin labeling (ASL) values were assessed in the hippocampus, amygdala, mesial temporal and parietal cortex at inclusion using 3 Tesla MRI Scans. Florbetapir positron emission tomography (PET) amyloid was available in a representative subsample of 64 cases. Regional amyloid uptake ratios (SUVr), mean cortical SUVr values (mcSUVr) and corresponding z-scores were calculated. Linear regression models were built to explore the association between the continuous cognitive score and imaging variables. The presence of an APOE-ε4 allele was negatively related to the continuous cognitive score. Among the areas studied, significant associations were found between GM densities in the hippocampus and amygdala but not mesial temporal and parietal areas and continuous cognitive score. Neither ASL values, Fazekas score nor mean and regional PET amyloid load was related to the cognitive score. In multivariate models, the presence of APOE-ε4 allele and GM densities in the hippocampus and amygdala were independently associated with worst cognitive evolution at follow-up. Our data support the idea that early GM damage in the hippocampus and amygdala occur long before the emergence of the very first signs of cognitive failure in brain aging.

Comparison of different MRI-based morphometric estimates for defining neurodegeneration across the Alzheimer's disease continuum

Background

Several neurodegeneration (N) metrics using structural MRI are used for the purpose of Alzheimer's disease (AD)-related staging, including hippocampal volume, global atrophy, and an “AD signature” composite consisting of thickness or volumetric estimates derived from regions impacted early in AD. This study sought to determine if less user-intensive estimates of global atrophy and hippocampal volume were equivalent to a thickness-based AD signature from FreeSurfer for defining N across the AD continuum (i.e., individuals who are amyloid-positive (A+)).

Methods

Cognitively unimpaired (CU) late middle-aged and older adults, as well as A+ mild cognitive impairment (MCI) and A+ AD dementia individuals, with available CSF and structural MRI scan <1.5 years apart, were selected for the study (n = 325, mean age = 62). First, in a subsample of A+ AD dementia and matched biomarker-negative (i.e., A- and tau tangle pathology (T)-) CU controls (n = 40), we examined ROC characteristics and identified N cut-offs using Youden's J for neurofilament light chain protein (NfL) and each of three MRI-based measures: a thickness-based AD signature from FreeSurfer, hippocampal volume (using FIRST), and a simple estimate of global atrophy (the ratio of intracranial CSF segmented volume to brain tissue volume, using SPM12). Based on the results from the ROC analyses, we then examined the concordance between NfL N positivity and N positivity for each MRI-based metric using Cohen's Kappa in the remaining subsample of 285 individuals. Finally, in the full sample (n = 325), we examined the relationship between the four measures of N and group membership across the AD continuum using Kruskal-Wallis tests and Cliff's deltas.

Results

The three MRI-based metrics and CSF NfL similarly discriminated between the A-T- CU (n = 20) and A+ AD (n = 20) groups (AUCs ≥0.885; ps < 0.001). Using the cut-off values derived from the ROCs to define N positivity, there was weak concordance between NfL and all three MRI-derived metrics of N in the subsample of 285 individuals (Cohen's Kappas ≤0.429). Finally, the three MRI-based measures of N and CSF NfL showed similar associations with AD continuum group (i.e., Kruskal-Wallis ps < 0.001), with relatively larger effect sizes noted when comparing the A-T- CU to the A+ MCI (Cliff's deltas ≥0.741) and A+ AD groups (Cliff's deltas ≥0.810) than to the A+T- CU (Cliff's deltas = 0.112–0.298) and A + T+ CU groups (Cliff's deltas = 0.212–0.731).

Conclusions

These findings suggest that the three MRI-based morphometric estimates and CSF NfL similarly differentiate individuals across the AD continuum on N status. In many applications, a simple estimate of global atrophy may be preferred as an MRI marker of N across the AD continuum given its methodological robustness and ease of calculation when compared to hippocampal volume or a cortical thickness AD signature.

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Hippocampus, global atrophy and AD signature had strong diagnostic accuracy in AD.

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There was weak concordance between NfL and all three MRI-derived metrics.

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NfL and the three MRI-derived metrics performed similarly across the AD spectrum.

Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis

In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic resonance imaging (MRI) have attracted increasing attention since these two tasks are highly correlated. Most of existing joint learning approaches require hand-crafted feature representations for MR images. Since hand-crafted features of MRI and classification/regression models may not coordinate well with each other, conventional methods may lead to sub-optimal learning performance. Also, demographic information (e.g., age, gender, and education) of subjects may also be related to brain status, and thus can help improve the diagnostic performance. However, conventional joint learning methods seldom incorporate such demographic information into the learning models. To this end, we propose a deep multi-task multi-channel learning (DM 2L) framework for simultaneous brain disease classification and clinical score regression, using MRI data and demographic information of subjects. Specifically, we first identify the discriminative anatomical landmarks from MR images in a data-driven manner, and then extract multiple image patches around these detected landmarks. We then propose a deep multi-task multi-channel convolutional neural network for joint classification and regression. Our DM 2L framework can not only automatically learn discriminative features for MR images, but also explicitly incorporate the demographic information of subjects into the learning process. We evaluate the proposed method on four large multi-center cohorts with 1984 subjects, and the experimental results demonstrate that DM 2L is superior to several state-of-the-art joint learning methods in both the tasks of disease classification and clinical score regression.

Effect of sleep quality on amnestic mild cognitive impairment vulnerable brain regions in cognitively normal elderly individuals

STUDY OBJECTIVES

This study aims to evaluate the extent to which sleep quality impacts amnestic mild cognitive impairment (aMCI)-related brain regions in a cognitively normal cohort of individuals.

METHODS

Seventy-four participants were rigorously evaluated using a battery of cognitive tests and a detailed clinical assessment to verify normal cognitive status. We then screened for sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and depressive symptoms using the Geriatric Depression Scale (GDS). Five subjects were excluded due to mild depression. Overall 38 individuals with mean age 70.7 ± 7 were classified as poor sleepers and 31 with mean age of 69.6 ± 6 years as normal sleepers. Structural MRI and Freesurfer brain parcellation were used to measure aMCI-related brain regions.

RESULTS

Relative to normal sleepers, poor sleepers exhibited significant reductions in cortical and subcortical volumes bilaterally in the hippocampi, as well as in the superior parietal lobules and left amygdala. The effects were strongest in the left superior parietal lobule (p < .015), followed by the hippocampi. Diffuse patterns of cortical thinning were observed in the frontal lobes, but significant effects were concentrated in the right mesial frontal cortex. Lower sleep duration was most correlated with cortical volume and thickness reductions among all subjects.

CONCLUSIONS

Atrophy related to poor sleep quality impacted a number of regions implicated in aMCI and Alzheimer's disease (AD). As such, interventions targeted towards improving sleep quality amongst the elderly may prove an effective tool for modulating the course of aMCI and AD.

Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment

This study examines the atrophy rates of subjects with mild cognitive impairment (MCI) compared to controls in four regions within the medial temporal lobe: the transentorhinal cortex (TEC), entorhinal cortex (ERC), hippocampus, and amygdala. These regions were manually segmented and then corrected for undesirable longitudinal variability via Large Deformation Diffeomorphic Metric Mapping (LDDMM) based longitudinal diffeomorphometry. Diffeomorphometry techniques were used to compare thickness measurements in the TEC with the ERC. There were more significant changes in thickness atrophy rate in the TEC than medial regions of the entorhinal cortex. Volume measures were also calculated for all four regions. Classifiers were constructed using linear discriminant analysis to demonstrate that average thickness and atrophy rate of TEC together was the most discriminating measure compared to the thickness and volume measures in the areas examined, in differentiating MCI from controls. These findings are consistent with autopsy findings demonstrating that initial neuronal changes are found in TEC before spreading more medially in the ERC and to other regions in the medial temporal lobe. These findings suggest that the TEC thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease.

Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest defined by experts. Also, because of possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this paper, we propose a landmark-based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer's disease. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer's disease neuroimaging initiative (ADNI-1), ADNI-2, and the minimal interval resonance imaging in alzheimer's disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of disease classification and MR image retrieval.

Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks

Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer's disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.

Radiological-Pathological Correlation in Alzheimer's Disease: Systematic Review of Antemortem Magnetic Resonance Imaging Findings

BACKGROUND

The standard method of ascertaining Alzheimer's disease (AD) remains postmortem assessment of amyloid plaques and neurofibrillary degeneration. Vascular pathology, Lewy bodies, TDP-43, and hippocampal sclerosis are frequent comorbidities. There is therefore a need for biomarkers that can assess these etiologies and provide a diagnosis in vivo.

OBJECTIVE

We conducted a systematic review of published radiological-pathological correlation studies to determine the relationship between antemortem magnetic resonance imaging (MRI) and neuropathological findings in AD.

METHODS

We explored PubMed in June-July 2015 using "Alzheimer's disease" and combinations of radiological and pathological terms. After exclusion following screening and full-text assessment of the 552 extracted manuscripts, three others were added from their reference list. In the end, we report results based on 27 articles.

RESULTS

Independently of normal age-related brain atrophy, AD pathology is associated with whole-brain and hippocampal atrophy and ventricular expansion as observed on T1-weighted images. Moreover, cerebral amyloid angiopathy and cortical microinfarcts are also related to brain volume loss in AD. Hippocampal sclerosis and TDP-43 are associated with hippocampal and medial temporal lobe atrophy, respectively. Brain volume loss correlates more strongly with tangles than with any other pathological finding. White matter hyperintensities observed on proton density, T2-weighted and FLAIR images are strongly related to vascular pathologies, but are also associated with other histological changes such as gliosis or demyelination.

CONCLUSION

Cerebral atrophy and white matter changes in the living brain reflect underlying neuropathology and may be detectable using antemortem MRI. In vivo MRI may therefore be an avenue for AD pathological staging.

Changes in Blood Factors and Ultrasound Findings in Mild Cognitive Impairment and Dementia

The present study aimed to assess the changes in blood factors and ultrasound measures of atherosclerosis burden patient with mild cognitive impairment (MCI) and dementia. Peripheral blood samples and ultrasonography findings were obtained for 53 enrolled participants. Flow cytometry was used to evaluate levels of activated platelets and platelet-leukocyte aggregates (PLAs). The number of platelets expressing p-selectin was correlated with intima media thickness (IMT) and plaque number in both the MCI and dementia groups. The number of platelets expressing p-selectin glycoprotein ligand (PSGL) was strongly correlated with IMT in patients with MCI, whereas the number of platelets expressing PGSL was correlated with plaque number rather than IMT in patients with dementia. PLAs was associated with both IMT and plaque number in patients with MCI but not in those with dementia. Our findings demonstrate that alterations in IMT and plaque number are associated with an increased risk of cognitive decline as well as conversion from MCI to dementia and that blood factor analysis may aid to detect the severity of cognitive decline.

Landmark-based deep multi-instance learning for brain disease diagnosis

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches.

[Perspectives of the diagnosis of Alzheimer's disease using optic coherent tomography]

There is a constant search for new possibilities of the diagnosis of neurodegenerative diseases that cause dementia. The problem is important because of the growing prevalence of dementia, 60-80% of which are caused by Alzheimer's disease (AD). Over the last years, changes in the retina are thought to be a marker neurodegeneration. Assessment of these changes is performed using a method of optical coherent tomography (OCT) that allows taking cross-sectional imaging of a tissue. OCT is considered a potential biomarker of the early stage of AD. Identification of the relationship of the changes in retina and the optic nerve with cognitive impairment open new possibilities for the diagnosis of AD.

Hierarchical Subcortical Sub-Regional Shape Network Analysis in Alzheimer's Disease

In this paper, by utilizing surface diffeomorphic deformations, we constructed and analyzed subcortical shape morphometric networks in 210 healthy control (HC) subjects and 175 subjects with Alzheimer's disease (AD), aiming to identify AD-induced abnormalities in the subcortical shape network. We quantitatively analyzed pertinent network attributes of the entire network and each node. Further to this, hierarchical analyses were performed; group comparisons were conducted at the structure level first and then the sub-region level. The bilateral amygdalae, hippocampi, as well as the thalamus were all divided into multiple functionally distinct sub-regions. From the structure level analysis, we found significant HC-vs-AD group differences in the average local efficiency and average global efficiency. In addition, the local nodal efficiencies between the right thalamus and all three of the right hippocampus, right amygdala, and left thalamus, as well as that between the left amygdala and left hippocampus, decreased significantly in AD. According to the sub-regional network analyses, we observed significant AD-induced local efficiency decreases between different sub-regions within the right hippocampus itself and between the subiculum of the right hippocampus and the sub-region of the right thalamus connecting to the temporal lobe, indicating a degradation of circuit between the hippocampus, thalamus, and temporal lobe. Statistical comparisons were performed using 40,000 non-parametric permutation tests, with false discovery rate correction employed for multiple comparison correction.

Entorhinal and transentorhinal atrophy in mild cognitive impairment using longitudinal diffeomorphometry

INTRODUCTION

Autopsy findings have shown the entorhinal cortex and transentorhinal cortex are among the earliest sites of accumulation of pathology in patients developing Alzheimer's disease.

METHODS

Here, we study this region in subjects with mild cognitive impairment (n = 36) and in control subjects (n = 16). The cortical areas are manually segmented, and local volume and shape changes are quantified using diffeomorphometry, including a novel mapping procedure that reduces variability in anatomic definitions over time.

RESULTS

We find significant thickness and volume changes localized to the transentorhinal cortex through high field strength atlasing.

DISCUSSION

This demonstrates that in vivo neuroimaging biomarkers can detect these early changes among subjects with mild cognitive impairment.

BIOMARKER CHANGE-POINT ESTIMATION WITH RIGHT CENSORING IN LONGITUDINAL STUDIES

We consider in this paper a statistical two-phase regression model in which the change point of a disease biomarker is measured relative to another point in time, such as the manifestation of the disease, which is subject to right-censoring (i.e., possibly unobserved over the entire course of the study). We develop point estimation methods for this model, based on maximum likelihood, and bootstrap validation methods. The effectiveness of our approach is illustrated by numerical simulations, and by the estimation of a change point for amygdalar atrophy in the context of Alzheimer's disease, wherein it is related to the cognitive manifestation of the disease.

Hippocampal and Amygdala Gray Matter Loss in Elderly Controls with Subtle Cognitive Decline

In contrast to the idea that hippocampal and amygdala volume loss occur in late phases of neurodegeneration, recent contributions point to the relevance of preexisting structural deficits that are associated with aging and are independent of amyloid deposition in preclinical Alzheimer disease cases. The present work explores GM hippocampal and amygdala volumes in elderly controls displaying the first signs of cognitive decline. 455 subjects (263 females), including 374 controls (228 females) and 81 middle cognitive impairment subjects (35 females), underwent two neuropsychological evaluations (baseline and 18 months follow-up) and a MRI-T1 examination (only baseline). Clinical assessment included Mini-Mental State Examination (MMSE), Clinical Dementia Rating scale, Hospitalized Anxiety and Depression scale, the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery and RI-48 Cued Recall Test (RI-48) for episodic memory. Based on their cognitive performance, we defined the controls as stable controls (sCON) and deteriorating controls (dCONs). Analyses included volumetric assessment, shape analyses and linear regressions between GM volume loss and differences in clinical scores between baseline and follow-up. Significant GM volume decrease in hippocampus bilaterally and right amygdala was found in dCON compared to sCON (p < 0.05). Lower right amygdala volumes were measured in mild cognitive impairment (MCI) compared to sCON (p < 0.05). Shape analyses revealed that atrophy was more pronounced at the superior- posterior lateral side of the hippocampus and amygdala. Significant correlations were found between GM volume of left hippocampus and the delta of MMSE and RI-48 scores in dCON and MCI groups separately. Decreased hippocampal and right amygdala volumes precede the first signs of cognitive decline in healthy elderly controls at the pre-MCI state. Left hippocampus volume may also predict short-term changes of overall cognition in these vulnerable cases.

Two distinct classes of degenerative change are independently linked to clinical progression in mild cognitive impairment

We previously demonstrated 2 statistically distinct factors of degeneration in Alzheimer's disease: one strongly related to white matter damage and age interpreted as "age- and vascular-related", and the other related to cortical atrophy thought to represent "neurodegenerative changes associated with Alzheimer's disease". Those factors are now replicated in a distinct cross-sectional data set of 364 participants from the Alzheimer's Disease Neuroimaging Initiative and their interpretation is improved using correlations with CSF biomarkers. Furthermore, we now show that changes in both factors over 2 years are independently associated with decline in Mini-Mental State Examination score in a longitudinal subset of 116 individuals with mild cognitive impairment. Progression in the "age- and vascular-related" factor was greater for individuals with 2 APOE ε4 alleles and linked to a greater attributable change in Mini-Mental State Examination than the "neurodegenerative" factor. These results suggest benefits of targeting white matter and vascular health to complement interventions focused on the neurodegenerative aspect of the disease, even in individuals with little discernable vascular comorbidity.

Utility of real-time prospective motion correction (PROMO) for segmentation of cerebral cortex on 3D T1-weighted imaging: Voxel-based morphometry analysis for uncooperative patients

OBJECTIVE

To assess the utility of the motion correction method with prospective motion correction (PROMO) in a voxel-based morphometry (VBM) analysis for 'uncooperative' patient populations.

METHODS

High-resolution 3D T1-weighted imaging both with and without PROMO were performed in 33 uncooperative patients with Parkinson's disease (n = 11) or dementia (n = 22). We compared the grey matter (GM) volumes and cortical thickness between the scans with and without PROMO.

RESULTS

For the mean total GM volume with the VBM analysis, the scan without PROMO showed a significantly smaller volume than that with PROMO (p < 0.05), which was caused by segmentation problems due to motion during acquisition. The whole-brain VBM analysis showed significant GM volume reductions in some regions in the scans without PROMO (familywise error corrected p < 0.05). In the cortical thickness analysis, the scans without PROMO also showed decreased cortical thickness compared to the scan with PROMO (p < 0.05).

CONCLUSION

Our results with the uncooperative patients indicate that the use of PROMO can reduce misclassification during segmentation of the VBM analyses, although it may not prevent GM volume reduction.

KEY POINTS

• Motion artifacts pose significant problems for VBM analyses. • PROMO correction can reduce the motion artifacts in high-resolution 3D T1WI. • The use of PROMO may improve the precision of VBM analyses.

Life-Course Socioeconomic Position and Hippocampal Atrophy in a Prospective Cohort of Older Adults

OBJECTIVE

Low socioeconomic position (SEP) has been linked to an increased risk of dementia and cognitive decline. However, little is known about the association between SEP and morphologic brain changes in older age. This study examines the relationships between indicators of life-course SEP with both hippocampal volume (HcV) and HcV loss in a population-based cohort of 1328 older adults aged 65 to 80 years.

METHODS

Multivariable linear regression models were used to estimate the associations of SEP with baseline HcV and the annual rate of HcV atrophy according to three life-course conceptual models: the sensitive/critical periods model (which explored SEP in specific periods: in childhood [using parental education], early adulthood [based on participants' education], and midlife [based on participants' socioprofessional group]); the accumulation-of-risk model (life-course cumulative SEP), and the social mobility model (life-course SEP trajectories).

RESULTS

Participants with lower midlife SEP had smaller HcV (-0.08 cm; 95% confidence interval, -0.15 to -0.01) and 0.17% (95% confidence interval, 0.04%-0.30%) faster hippocampal atrophy than participants with higher midlife SEP. Childhood and early adulthood SEPs were not related to hippocampal measures. The accumulation-of-risk and the social mobility models revealed that the accumulation of socioeconomic disadvantage and declining socioeconomic trajectories were related to faster hippocampal atrophy.

CONCLUSIONS

In this cohort of older adults, lower socioprofessional attainment in midlife and disadvantageous life-course socioeconomic position were associated with faster hippocampal atrophy, a cerebral change linked to cognitive disorders. Results support the hypothesized links between socioenvironmental exposures related to stress and/or cognitive enrichment and brain/cognitive reserve capacities.

Utility of real-time prospective motion correction (PROMO) on 3D T1-weighted imaging in automated brain structure measurements

PROspective MOtion correction (PROMO) can prevent motion artefacts. The aim of this study was to determine whether brain structure measurements of motion-corrected images with PROMO were reliable and equivalent to conventional images without motion artefacts. The following T1-weighted images were obtained in healthy subjects: (A) resting scans with and without PROMO and (B) two types of motion scans (“side-to-side” and “nodding” motions) with and without PROMO. The total gray matter volumes and cortical thicknesses were significantly decreased in motion scans without PROMO as compared to the resting scans without PROMO (p < 0.05). Conversely, Bland–Altman analysis indicated no bias between motion scans with PROMO, which have good image quality, and resting scans without PROMO. In addition, there was no bias between resting scans with and without PROMO. The use of PROMO facilitated more reliable brain structure measurements in subjects moving during data acquisition.

Does medial temporal lobe thickness mediate the association between risk factor burden and memory performance in middle-aged or older adults with metabolic syndrome?

Metabolic syndrome (MetS) is a cluster of cardiovascular and metabolic abnormalities that together may increase the risk of developing cognitive decline and dementia; however, the neural substrate is incompletely understood. We investigated cortical thickness in the medial temporal lobe (MTL), hippocampal volume, as well as relationships among metabolic risk factor burden, structure and memory performance. Path-analytic models were tested to explore the relations between MetS risk factor, structure and memory performance. Participants were 65 non-demented, middle-aged and older adults, 34 with and 31 without metabolic syndrome. We analyzed archival T1-weighted magnetic resonance imaging (MRI) acquired at 3T and Total Recall and Delayed Recall scores from the Brief Visuospatial Memory Test Revised (BVMT-R). Middle-aged adults with MetS showed less MTL thickness, particularly in entorhinal cortex; while older adults showed a trend for left hippocampal volume loss. Lower MTL thickness, particularly in entorhinal cortex, was associated with greater metabolic risk factor burden in middle-aged adults. In older adults, hippocampal volume was associated with Total Recall and Delayed Recall, while in middle-age entorhinal cortical thickness mediated the association between metabolic disease burden and episodic memory function. The differential findings in middle-aged and older adults with MetS contribute to an understanding of the relationships between metabolic syndrome, structural changes in the brain and increased risk for cognitive decline.

Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to Alzheimer's disease

OBJECTIVE

To investigate whether subcortical grey matter atrophy predicts progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD), and to compare subcortical volumes between AD, MCI and controls. To assess the correlation between subcortical grey matter volumes and severity of cognitive impairment.

METHODS

We included 773 participants with three-dimensional T1-weighted MRI at 3 T, made up of 181 controls, who had subjective memory symptoms with normal cognition, 201 MCIs and 391 AD. During follow-up (2.0 ± 0.9 years), 35 MCIs converted to AD (progressive MCI) and 160 MCIs remained stable (stable MCI). We segmented volumes of six subcortical structures of the amygdala, thalamus, caudate nucleus, putamen, globus pallidus and nucleus accumbens, and of the hippocampus, using FMRIBs integrated registration and segmentation tool.

RESULTS

Analysis of variances, adjusted for sex and age, showed that all structures, except the globus pallidus, were smaller in AD than in controls. In addition, the amygdala, thalamus, putamen, nucleus accumbens and hippocampus were smaller in MCIs than in controls. Across groups, all subcortical greymatter volumes, except the globus pallidus, showed a positive correlation with cognitive function, as measured by Mini Mental State Examination (MMSE) (0.16<r<0.28, all p<0.05). Cox proportional hazards analyses adjusted for age, sex, education, Cambridge Cognitive Examination-Revised (CAMCOG-R) and MMSE showed that smaller volumes of the hippocampus and nucleus accumbens were associated with increased risk of progression from MCI to AD (HR (95% CI) 1.60 (1.15 to 2.21); 1.60 (1.09 to 2.35), p<0.05).

CONCLUSIONS

In addition to the hippocampus, the nucleus accumbens volume loss was also associated with increased risk of progression from MCI to AD. Furthermore, volume loss of subcortical grey matter structures was associated with severity of cognitive impairment.

Cerebral Microbleeds and Lacunar Infarcts Are Associated with Walking Speed Independent of Cognitive Performance in Middle-Aged to Older Adults

BACKGROUND

The positive relationship between cognitive and physical performance has been widely established. The influence of brain structure on both domains has been shown as well.

OBJECTIVE

We studied whether the relationship between brain structure and physical performance is independent of cognitive performance.

METHODS

This was a cross-sectional analysis of 297 middle-aged to older adults (mean age ± SD 65.4 ± 6.8 years). Memory function, executive function and physical performance measured by the Tandem Stance Test, Chair Stand Test, 4-meter walk and 25-meter walk were assessed. Magnetic resonance imaging was available in 237 participants and used to determine the (sub)cortical gray matter, white matter, hippocampal and basal ganglia volumes and the presence of cerebral small-vessel disease, i.e. white matter hyperintensities, cerebral microbleeds (CMBs) and lacunar infarcts (LIs). Regression analysis was used adjusting for age, gender, education and whole-brain volume. A Bonferroni correction was applied considering p values <0.017 as statistically significant.

RESULTS

Poor memory function was associated with a slower 4-meter walking speed (p < 0.01). No association was found between brain structure and cognitive performance. The presence of CMBs and LIs was associated with a slower 25-meter walking speed (p < 0.001). This result did not change after additional adjustment for cognitive performance.

CONCLUSIONS

In middle-aged to older adults, CMBs and LIs are associated with walking speed independent of cognitive performance. This emphasizes the clinical relevance of identifying each of the possible underlying mechanisms of physical performance, which is required for the development of timely and targeted therapies.

Brain structural, functional, and cognitive correlates of recent versus remote autobiographical memories in amnestic Mild Cognitive Impairment

Deficits in autobiographical memory appear earlier for recent than for remote life periods over the course of Alzheimer's disease (AD). The present study aims to further our understanding of this graded effect by investigating the cognitive and neural substrates of recent versus remote autobiographical memories in patients with amnestic Mild Cognitive Impairment (aMCI) thanks to an autobiographical fluency task. 20 aMCI patients and 25 Healthy elderly Controls (HC) underwent neuropsychological tests assessing remote (20-to-30 years old) and recent (the ten last years) autobiographical memory as well as episodic and semantic memory, executive function and global cognition. All patients also had a structural MRI and an FDG-PET scan. Correlations were assessed between each autobiographical memory score and the other tests as well as grey matter volume and metabolism. Within the aMCI, performances for the remote period correlated with personal semantic memory and episodic memory retrieval whereas performances for the recent period only correlated with episodic memory retrieval. Neuroimaging analyses revealed significant correlations between performances for the remote period and temporal pole and temporo-parietal cortex volumes and anterior cingulate gyrus metabolism, while performances for the recent period correlated with hippocampal volume and posterior cingulate, medial prefrontal and hippocampus metabolism. The brain regions related with the retrieval of events from the recent period showed greater atrophy/hypometabolism in aMCI patients compared to HC than those involved in remote memories. Recall of recent memories essentially relies on episodic memory processes and brain network while remote memories also involve other processes such as semantic memory. This is consistent with the semanticization of memories with time and may explain the better resistance of remote memory in AD.

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We studied cognitive correlates of recent and remote autobiographical memories in aMCI.

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We evaluated brain structural and functional correlates of each period of life.

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Recent memories rely on episodic memory processes and brain network.

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Remote memories also involve other processes such as semantic memory.

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These findings contribute to explain the better resistance of remote memory in aMCI.

Network Neurodegeneration in Alzheimer's Disease via MRI Based Shape Diffeomorphometry and High-Field Atlasing

This paper examines MRI analysis of neurodegeneration in Alzheimer’s Disease (AD) in a network of structures within the medial temporal lobe using diffeomorphometry methods coupled with high-field atlasing in which the entorhinal cortex is partitioned into eight subareas. The morphometry markers for three groups of subjects (controls, preclinical AD, and symptomatic AD) are indexed to template coordinates measured with respect to these eight subareas. The location and timing of changes are examined within the subareas as it pertains to the classic Braak and Braak staging by comparing the three groups. We demonstrate that the earliest preclinical changes in the population occur in the lateral most sulcal extent in the entorhinal cortex (alluded to as transentorhinal cortex by Braak and Braak), and then proceeds medially which is consistent with the Braak and Braak staging. We use high-field 11T atlasing to demonstrate that the network changes are occurring at the junctures of the substructures in this medial temporal lobe network. Temporal progression of the disease through the network is also examined via changepoint analysis, demonstrating earliest changes in entorhinal cortex. The differential expression of rate of atrophy with progression signaling the changepoint time across the network is demonstrated to be signaling in the intermediate caudal subarea of the entorhinal cortex, which has been noted to be proximal to the hippocampus. This coupled to the findings of the nearby basolateral involvement in amygdala demonstrates the selectivity of neurodegeneration in early AD.

Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer's disease

This study evaluated the utility of baseline and longitudinal magnetic resonance imaging (MRI) measures of medial temporal lobe brain regions collected when participants were cognitively normal and largely in middle age (mean age 57 years) to predict the time to onset of clinical symptoms associated with mild cognitive impairment (MCI). Furthermore, we examined whether the relationship between MRI measures and clinical symptom onset was modified by apolipoprotein E (ApoE) genotype and level of cognitive reserve (CR). MRI scans and measures of CR were obtained at baseline from 245 participants who had been followed for up to 18 years (mean follow-up 11 years). A composite score based on reading, vocabulary, and years of education was used as an index of CR. Cox regression models showed that lower baseline volume of the right hippocampus and smaller baseline thickness of the right entorhinal cortex predicted the time to symptom onset independently of CR and ApoE-ɛ4 genotype, which also predicted the onset of symptoms. The atrophy rates of bilateral entorhinal cortex and amygdala volumes were also associated with time to symptom onset, independent of CR, ApoE genotype, and baseline volume. Only one measure, the left entorhinal cortex baseline volume, interacted with CR, such that smaller volumes predicted symptom onset only in individuals with lower CR. These results suggest that MRI measures of medial temporal atrophy, ApoE-ɛ4 genotype, and the protective effects of higher CR all predict the time to onset of symptoms associated with MCI in a largely independent, additive manner during the preclinical phase of Alzheimer's disease.

Amygdalar atrophy in symptomatic Alzheimer's disease based on diffeomorphometry: the BIOCARD cohort

This article examines the diffeomorphometry of magnetic resonance imaging-derived structural markers for the amygdala, in subjects with symptomatic Alzheimer's disease (AD). Using linear mixed-effects models we show differences between those with symptomatic AD and controls. Based on template centered population analysis, the distribution of statistically significant change is seen in both the volume and shape of the amygdala in subjects with symptomatic AD compared with controls. We find that high-dimensional vertex based markers are statistically more significantly discriminating (p < 0.00001) than lower-dimensional markers and volumes, consistent with comparable findings in presymptomatic AD. Using a high-field 7T atlas, significant atrophy was found to be centered in the basomedial and basolateral subregions, with no evidence of centromedial involvement.

Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer's disease

This paper uses diffeomorphometry methods to quantify the order in which statistically significant morphometric change occurs in three medial temporal lobe regions, the amygdala, entorhinal cortex (ERC), and hippocampus among subjects with symptomatic and preclinical Alzheimer's disease (AD). Magnetic resonance imaging scans were examined in subjects who were cognitively normal at baseline, some of whom subsequently developed clinical symptoms of AD. The images were mapped to a common template, using shape-based diffeomorphometry. The multidimensional shape markers indexed through the temporal lobe structures were modeled using a changepoint model with explicit parameters, specifying the number of years preceding clinical symptom onset. Our model assumes that the atrophy rate of a considered brain structure increases years before detectable symptoms.

The results demonstrate that the atrophy changepoint in the ERC occurs first, indicating significant change 8–10 years prior to onset, followed by the hippocampus, 2–4 years prior to onset, followed by the amygdala, 3 years prior to onset. The ERC is significant bilaterally, in both our local and global measures, with estimates of ERC surface area loss of 2.4% (left side) and 1.6% (right side) annually. The same changepoint model for ERC volume gives 3.0% and 2.7% on the left and right sides, respectively. Understanding the order in which changes in the brain occur during preclinical AD may assist in the design of intervention trials aimed at slowing the evolution of the disease.

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We use diffeomorphometry to quantify the order in which statistically significant morphometric change occurs in three medial temporal lobe regions, the amygdala, entorhinal cortex (ERC), and hippocampus among subjects with symptomatic and preclinical Alzheimer's disease (AD).

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We introduce a model on anatomical shape change in which changepoint is inferred, taking place some period of time before cognitive onset of AD.

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The analysis uses a dataset arising from the BIOCARD study, in which all subjects were cognitively normal at baseline, some of whom subsequently developed clinical symptoms of AD.

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The results demonstrate that the atrophy changepoint in the ERC occurs first, indicating significant change 8-10 years prior to onset, followed by hippocampus, 2-4 years prior to onset, followed by amygdala, 3 years prior to onset.

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The ERC is significant bilaterally, in both our local and global measures, with estimates of ERC surface area loss of 2.4% (left side) and 1.6% (right side) annually.

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Understanding the order in which changes in the brain occur during preclinical AD may assist in the design of intervention trials aimed at slowing the evolution of the disease.

Automated algorithm to measure changes in medial temporal lobe volume in Alzheimer disease

BACKGROUND

The change in volume of anatomic structures is as a sensitive indicator of Alzheimer disease (AD) progression. Although several methods are available to measure brain volumes, improvements in speed and automation are required. Our objective was to develop a fully automated, fast, and reliable approach to measure change in medial temporal lobe (MTL) volume, including primarily hippocampus.

METHODS

The MTL volume defined in an atlas image was propagated onto each baseline image and a level set algorithm was applied to refine the shape and smooth the boundary. The MTL of the baseline image was then mapped onto the corresponding follow-up image to measure volume change (ΔMTL). Baseline and 24 months 3D T1-weighted images from the Alzheimer Disease Neuroimaging Initiative (ADNI) were randomly selected for 50 normal elderly controls (NECs), 50 subjects with mild cognitive impairment (MCI) and 50 subjects with AD to test the algorithm. The method was compared to the FreeSurfer segmentation tools.

RESULTS

The average ΔMTL (mean±SEM) was 68±35mm(3) in NEC, 187±38mm(3) in MCI and 300±34mm(3) in the AD group and was significantly different (p<0.0001) between all three groups. The ΔMTL was correlated with cognitive decline.

COMPARISON WITH EXISTING METHOD(S)

Results for the FreeSurfer software were similar but did not detect significant differences between the MCI and AD groups.

CONCLUSION

This novel segmentation approach is fully automated and provides a robust marker of brain atrophy that shows different rates of atrophy over 2 years between NEC, MCI, and AD groups.

Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: detecting, quantifying, and predicting

This article assesses the feasibility of using shape information to detect and quantify the subcortical and ventricular structural changes in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. We first demonstrate structural shape abnormalities in MCI and AD as compared with healthy controls (HC). Exploring the development to AD, we then divide the MCI participants into two subgroups based on longitudinal clinical information: (1) MCI patients who remained stable; (2) MCI patients who converted to AD over time. We focus on seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricles) in 754 MR scans (210 HC, 369 MCI of which 151 converted to AD over time, and 175 AD). The hippocampus and amygdala were further subsegmented based on high field 0.8 mm isotropic 7.0T scans for finer exploration. For MCI and AD, prominent ventricular expansions were detected and we found that these patients had strongest hippocampal atrophy occurring at CA1 and strongest amygdala atrophy at the basolateral complex. Mild atrophy in basal ganglia structures was also detected in MCI and AD. Stronger atrophy in the amygdala and hippocampus, and greater expansion in ventricles was observed in MCI converters, relative to those MCI who remained stable. Furthermore, we performed principal component analysis on a linear shape space of each structure. A subsequent linear discriminant analysis on the principal component values of hippocampus, amygdala, and ventricle leads to correct classification of 88% HC subjects and 86% AD subjects.

Alzheimer's disease clinical and research update for health care practitioners

Of the approximately 6.8 million Americans who have been diagnosed with dementia, over 5 million have been diagnosed with Alzheimer's Disease (AD). Due to the rise in the aging population, these figures are expected to double by 2050. The following paper provides an up-to-date review of clinical issues and relevant research. Research related to the methods of the earliest possible detection of AD is ongoing. Health care professionals should play a critical role in differentially diagnosing AD patients, as well as supporting their families. Novel interventions, including medications, natural supplements, and behavioral techniques, are constantly appearing in the literature. It is necessary for the health practitioner to remain current, regarding AD, as such information will facilitate better care for patients and their families.

Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging

The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.

The diagnosis and evaluation of dementia and mild cognitive impairment with emphasis on SPECT perfusion neuroimaging

As the world population ages, the incidence of dementing illnesses will dramatically increase. The number of people afflicted with dementia is expected to quadruple in the next 50 years. Since the neuropathology of the dementias precedes clinical symptoms often by several years, earlier detection and intervention could be key steps to mitigating the progression and burden of these diseases. This review will explore methods of evaluating, differentiating, and diagnosing the multiple forms of dementia. Particular emphasis will be placed on the diagnosis of mild cognitive impairment-the precursor to dementia. Anatomical imaging; cerebrospinal fluid markers; functional neuroimaging, such as positron emission tomography and single photon emission tomography; and molecular imaging, such as amyloid marker imaging, will be assessed in terms of sensitivity and specificity. Cost will also be a consideration, as the growing population afflicted with dementia represents an increasingly large financial encumbrance to the healthcare systems of every nation. In the face of expensive new markers and limited availability of cyclotrons, single photon emission computer tomography (SPECT) provides relatively high sensitivity and specificity at a comparatively low overall cost.

Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer's disease (AD). Using a sample from the Alzheimer's Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.

A review of the brain structure correlates of successful cognitive aging

Unimpaired cognition is an important feature of successful aging. Differences in cognitive performance among healthy older adults may be related to differences in brain structure. The authors reviewed the literature to examine the relationship between brain-structure size and cognitive performance in older adults. Eighty-three percent of studies found at least one positive relationship between these factors; however, findings were variable. Positive relationships emerged most consistently between the hippocampal formation and global cognition and memory and between frontal measures and executive function. Additional longitudinal study is needed to further evaluate structure-cognition relationships in older adulthood and across the adult lifespan.

Everyday episodic memory in amnestic mild cognitive impairment: a preliminary investigation

BACKGROUND

Decline in episodic memory is one of the hallmark features of Alzheimer's disease (AD) and is also a defining feature of amnestic Mild Cognitive Impairment (MCI), which is posited as a potential prodrome of AD. While deficits in episodic memory are well documented in MCI, the nature of this impairment remains relatively under-researched, particularly for those domains with direct relevance and meaning for the patient's daily life. In order to fully explore the impact of disruption to the episodic memory system on everyday memory in MCI, we examined participants' episodic memory capacity using a battery of experimental tasks with real-world relevance. We investigated episodic acquisition and delayed recall (story-memory), associative memory (face-name pairings), spatial memory (route learning and recall), and memory for everyday mundane events in 16 amnestic MCI and 18 control participants. Furthermore, we followed MCI participants longitudinally to gain preliminary evidence regarding the possible predictive efficacy of these real-world episodic memory tasks for subsequent conversion to AD.

RESULTS

The most discriminating tests at baseline were measures of acquisition, delayed recall, and associative memory, followed by everyday memory, and spatial memory tasks, with MCI patients scoring significantly lower than controls. At follow-up (mean time elapsed: 22.4 months), 6 MCI cases had progressed to clinically probable AD. Exploratory logistic regression analyses revealed that delayed associative memory performance at baseline was a potential predictor of subsequent conversion to AD.

CONCLUSIONS

As a preliminary study, our findings suggest that simple associative memory paradigms with real-world relevance represent an important line of enquiry in future longitudinal studies charting MCI progression over time.

The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease

The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of developing criteria for the symptomatic predementia phase of Alzheimer's disease (AD), referred to in this article as mild cognitive impairment due to AD. The workgroup developed the following two sets of criteria: (1) core clinical criteria that could be used by healthcare providers without access to advanced imaging techniques or cerebrospinal fluid analysis, and (2) research criteria that could be used in clinical research settings, including clinical trials. The second set of criteria incorporate the use of biomarkers based on imaging and cerebrospinal fluid measures. The final set of criteria for mild cognitive impairment due to AD has four levels of certainty, depending on the presence and nature of the biomarker findings. Considerable work is needed to validate the criteria that use biomarkers and to standardize biomarker analysis for use in community settings.

New tools for the study of Alzheimer's disease: what are biomarkers and morphometric markers teaching us?

Early detection is vital in the quest to develop a cure for Alzheimer's disease (AD), and CSF biomarkers (Aβ42, t-tau, p-tau) and MRI morphometry distinguish AD from healthy controls. Aβ42 and neurodegenerative biomarkers may precede clinical symptoms, but it is not clear whether AD invariably follows and whether neuropsychological tests are as sensitive. Aβ42 is related to plaque burden, which was assumed to be the main cause of AD. Evidence is now pointing to other forms of Aβ, for example, soluble Aβ oligomers, and it is possible that plaques are secondary rather than causative to neuronal damage. This makes it less obvious that CSF Aβ42 necessarily is the most potent marker. Atrophy has been regarded as a downstream event, but novel MRI analysis techniques detect atrophy at a stage where the cognitive reductions are small and possibly reversible, and MRI is superior to CSF biomarkers in the prediction of cognitive decline. The impact of biomarkers may be dynamic; changed Aβ42 is seen in cognitively normal, while atrophy causes decrements later. In conclusion, CSF and MRI biomarkers are extremely important, but it is not known whether they can distinguish events that will lead to AD from events that will not before cognitive reductions are measurable.