Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease

Interactions between nanoparticles and pathological changes of vascular in Alzheimer's disease

Emerging evidence suggests that vascular pathological changes play a pivotal role in the pathogenesis of Alzheimer's disease (AD). The dysfunction of the cerebral vasculature occurs in the early course of AD, characterized by alterations in vascular morphology, diminished cerebral blood flow (CBF), impairment of the neurovascular unit (NVU), vasculature inflammation, and cerebral amyloid angiopathy. Vascular dysfunction not only facilitates the influx of neurotoxic substances into the brain, triggering inflammation and immune responses but also hampers the efflux of toxic proteins such as Aβ from the brain, thereby contributing to neurodegenerative changes in AD. Furthermore, these vascular changes significantly impact drug delivery and distribution within the brain. Therefore, developing targeted delivery systems or therapeutic strategies based on vascular alterations may potentially represent a novel breakthrough in AD treatment. This review comprehensively examines various aspects of vascular alterations in AD and outlines the current interactions between nanoparticles and pathological changes of vascular.

Distance-weighted Sinkhorn loss for Alzheimer's disease classification

Traditional loss functions such as cross-entropy loss often quantify the penalty for each mis-classified training sample without adequately considering its distance from the ground truth class distribution in the feature space. Intuitively, the larger this distance is, the higher the penalty should be. With this observation, we propose a penalty called distance-weighted Sinkhorn (DWS) loss. For each mis-classified training sample (with predicted label A and true label B), its contribution to the DWS loss positively correlates to the distance the training sample needs to travel to reach the ground truth distribution of all the A samples. We apply the DWS framework with a neural network to classify different stages of Alzheimer's disease. Our empirical results demonstrate that the DWS framework outperforms the traditional neural network loss functions and is comparable or better to traditional machine learning methods, highlighting its potential in biomedical informatics and data science.

MRI or 18F-FDG PET for Brain Age Gap Estimation: Links to Cognition, Pathology, and Alzheimer Disease Progression

Deviations of brain age from chronologic age, known as the brain age gap (BAG), have been linked to neurodegenerative diseases such as Alzheimer disease (AD). Here, we compare the associations of MRI-derived (atrophy) or 18F-FDG PET-derived (brain metabolism) BAG with cognitive performance, neuropathologic burden, and disease progression in cognitively normal individuals (CNs) and individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). Methods: Machine learning pipelines were trained to estimate brain age from 185 matched T1-weighted MRI or 18F-FDG PET scans of CN from the Alzheimer's Disease Neuroimaging Initiative and validated in external test sets from the Open Access of Imaging and German Center for Neurodegenerative Diseases-Longitudinal Cognitive Impairment and Dementia studies. BAG was correlated with measures of cognitive performance and AD neuropathology in CNs, SCD subjects, and MCI subjects. Finally, BAG was compared between cognitively stable and declining individuals and subsequently used to predict disease progression. Results: MRI (mean absolute error, 2.49 y) and 18F-FDG PET (mean absolute error, 2.60 y) both estimated chronologic age well. At the SCD stage, MRI-based BAG correlated significantly with beta-amyloid1-42 (Aβ1-42) in cerebrospinal fluid, whereas 18F-FDG PET BAG correlated with memory performance. At the MCI stage, both BAGs were associated with memory and executive function performance and cerebrospinal fluid Aβ1-42, but only MRI-derived BAG correlated with phosphorylated-tau181/Aβ1-42 Lastly, MRI-estimated BAG predicted MCI-to-AD progression better than 18F-FDG PET-estimated BAG (areas under the curve, 0.73 and 0.60, respectively). Conclusion: Age was reliably estimated from MRI or 18F-FDG PET. MRI BAG reflected cognitive and pathologic markers of AD in SCD and MCI, whereas 18F-FDG PET BAG was sensitive mainly to early cognitive impairment, possibly constituting an independent biomarker of brain age-related changes.

Voxel-wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer's Disease Classification using Deep Multimodal Learning

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder requiring accurate and early diagnosis for effective treatment. Resting-state functional magnetic resonance imaging (rs-fMRI) and gray matter volume analysis from structural MRI have emerged as valuable tools for investigating AD-related brain alterations. However, the potential benefits of integrating these modalities using deep learning techniques remain unexplored. In this study, we propose a novel framework that fuses composite images of multiple rs-fMRI networks (called voxelwise intensity projection) and gray matter segmentation images through a deep learning approach for improved AD classification. We demonstrate the superiority of fMRI networks over commonly used metrics such as amplitude of low-frequency fluctuations (ALFF) and fractional ALFF in capturing spatial maps critical for AD classification. We use a multi-channel convolutional neural network incorporating the AlexNet dropout architecture to effectively model spatial and temporal dependencies in the integrated data. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset of AD patients and cognitively normal (CN) validate the efficacy of our approach, showcasing improved classification performance of 94.12% test accuracy and an area under the curve (AUC) score of 97.79 compared to existing methods. Our results show that the fusion results generally outperformed the unimodal results. The saliency visualizations also show significant differences in the hippocampus, amygdala, putamen, caudate nucleus, and regions of basal ganglia which are in line with the previous neurobiological literature. Our research offers a novel method to enhance our grasp of AD pathology. By integrating data from various functional networks with structural MRI insights, we significantly improve diagnostic accuracy. This accuracy is further boosted by the effective visualization of this combined information. This lays the groundwork for further studies focused on providing a more accurate and personalized approach to AD diagnosis. The proposed framework and insights gained from fMRI networks provide a promising avenue for future research in deep multimodal fusion and neuroimaging analysis.

A monocarboxylate transporter rescues frontotemporal dementia and Alzheimer's disease models

Brains are highly metabolically active organs, consuming 20% of a person's energy at resting state. A decline in glucose metabolism is a common feature across a number of neurodegenerative diseases. Another common feature is the progressive accumulation of insoluble protein deposits, it's unclear if the two are linked. Glucose metabolism in the brain is highly coupled between neurons and glia, with glucose taken up by glia and metabolised to lactate, which is then shuttled via transporters to neurons, where it is converted back to pyruvate and fed into the TCA cycle for ATP production. Monocarboxylates are also involved in signalling, and play broad ranging roles in brain homeostasis and metabolic reprogramming. However, the role of monocarboxylates in dementia has not been tested. Here, we find that increasing pyruvate import in Drosophila neurons by over-expression of the transporter bumpel, leads to a rescue of lifespan and behavioural phenotypes in fly models of both frontotemporal dementia and Alzheimer's disease. The rescue is linked to a clearance of late stage autolysosomes, leading to degradation of toxic peptides associated with disease. We propose upregulation of pyruvate import into neurons as potentially a broad-scope therapeutic approach to increase neuronal autophagy, which could be beneficial for multiple dementias.

Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis

With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.

Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2  ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.

Longitudinal Spatial Relationships Between Atrophy and Hypometabolism Across the Alzheimer's Disease Continuum

BACKGROUND

The A/T/N framework allows for the assessment of pathology-specific markers of MRI-derived structural atrophy and hypometabolism on 18FDG-PET. However, how these measures relate to each other locally and distantly across pathology-defined A/T/N groups is currently unclear.

OBJECTIVE

To determine the regions of association between atrophy and hypometabolism in A/T/N groups both within and across time points.

METHODS

We examined multivariate multimodal neuroimaging relationships between MRI and 18FDG-PET among suspected non-Alzheimer's disease pathology (SNAP) (A-T/N+; n = 14), Amyloid Only (A+T-N-; n = 24) and Probable AD (A+T+N+; n = 77) groups. Sparse canonical correlation analyses were employed to model spatially disjointed regions of association between MRI and 18FDG-PET data. These relationships were assessed at three combinations of time points -cross-sectionally, between baseline visits and between month 12 (M-12) follow-up visits, as well as longitudinally between baseline and M-12 follow-up.

RESULTS

In the SNAP group, spatially overlapping relationships between atrophy and hypometabolism were apparent in the bilateral temporal lobes when both modalities were assessed at the M-12 timepoint. Amyloid-Only subjects showed spatially discordant distributed atrophy-hypometabolism relationships at all time points assessed. In Probable AD subjects, local correlations were evident in the bilateral temporal lobes when both modalities were assessed at baseline and at M-12. Across groups, hypometabolism at baseline correlated with non-local, or distant, atrophy at M-12.

CONCLUSION

These results support the view that local concordance of atrophy and hypometabolism is the result of a tau-mediated process driving neurodegeneration.

Lithium Enhances Hippocampal Glucose Metabolism in an In Vitro Mice Model of Alzheimer's Disease

Impaired cerebral glucose metabolism is an early event that contributes to the pathogenesis of Alzheimer's disease (AD). Importantly, restoring glucose availability by pharmacological agents or genetic manipulation has been shown to protect against Aβ toxicity, ameliorate AD pathology, and increase lifespan. Lithium, a therapeutic agent widely used as a treatment for mood disorders, has been shown to attenuate AD pathology and promote glucose metabolism in skeletal muscle. However, despite its widespread use in neuropsychiatric disorders, lithium's effects on the brain have been poorly characterized. Here we evaluated the effect of lithium on glucose metabolism in hippocampal neurons from wild-type (WT) and APPSwe/PS1ΔE9 (APP/PS1) mice. Our results showed that lithium significantly stimulates glucose uptake and replenishes ATP levels by preferential oxidation of glucose through glycolysis in neurons from WT mice. This increase was also accompanied by a strong increase in glucose transporter 3 (Glut3), the major carrier responsible for glucose uptake in neurons. Similarly, using hippocampal slices from APP-PS1 mice, we demonstrate that lithium increases glucose uptake, glycolytic rate, and the ATP:ADP ratio in a process that also involves the activation of AMPK. Together, our findings indicate that lithium stimulates glucose metabolism and can act as a potential therapeutic agent in AD.

Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification

Structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) classification and its prodromal stage-mild cognitive impairment (MCI) classification have attracted many attentions and been widely investigated in recent years. Owing to the high dimensionality, representation of the sMRI image becomes a difficult issue in AD classification. Furthermore, regions of interest (ROI) reflected in the sMRI image are not characterized properly by spatial analysis techniques, which has been a main cause of weakening the discriminating ability of the extracted spatial feature. In this study, we propose a ROI-based contourlet subband energy (ROICSE) feature to represent the sMRI image in the frequency domain for AD classification. Specifically, a preprocessed sMRI image is first segmented into 90 ROIs by a constructed brain mask. Instead of extracting features from the 90 ROIs in the spatial domain, the contourlet transform is performed on each of these ROIs to obtain their energy subbands. And then for an ROI, a subband energy (SE) feature vector is constructed to capture its energy distribution and contour information. Afterwards, SE feature vectors of the 90 ROIs are concatenated to form a ROICSE feature of the sMRI image. Finally, support vector machine (SVM) classifier is used to classify 880 subjects from ADNI and OASIS databases. Experimental results show that the ROICSE approach outperforms six other state-of-the-art methods, demonstrating that energy and contour information of the ROI are important to capture differences between the sMRI images of AD and HC subjects. Meanwhile, brain regions related to AD can also be found using the ROICSE feature, indicating that the ROICSE feature can be a promising assistant imaging marker for the AD diagnosis via the sMRI image. Code and Sample IDs of this paper can be downloaded at https://github.com/NWPU-903PR/ROICSE.git.

Multi-Modal Feature Selection with Feature Correlation and Feature Structure Fusion for MCI and AD Classification

Feature selection for multiple types of data has been widely applied in mild cognitive impairment (MCI) and Alzheimer's disease (AD) classification research. Combining multi-modal data for classification can better realize the complementarity of valuable information. In order to improve the classification performance of feature selection on multi-modal data, we propose a multi-modal feature selection algorithm using feature correlation and feature structure fusion (FC2FS). First, we construct feature correlation regularization by fusing a similarity matrix between multi-modal feature nodes. Then, based on manifold learning, we employ feature matrix fusion to construct feature structure regularization, and learn the local geometric structure of the feature nodes. Finally, the two regularizations are embedded in a multi-task learning model that introduces low-rank constraint, the multi-modal features are selected, and the final features are linearly fused and input into a support vector machine (SVM) for classification. Different controlled experiments were set to verify the validity of the proposed method, which was applied to MCI and AD classification. The accuracy of normal controls versus Alzheimer's disease, normal controls versus late mild cognitive impairment, normal controls versus early mild cognitive impairment, and early mild cognitive impairment versus late mild cognitive impairment achieve 91.85 ± 1.42%, 85.33 ± 2.22%, 78.29 ± 2.20%, and 77.67 ± 1.65%, respectively. This method makes up for the shortcomings of the traditional multi-modal feature selection based on subjects and fully considers the relationship between feature nodes and the local geometric structure of feature space. Our study not only enhances the interpretation of feature selection but also improves the classification performance, which has certain reference values for the identification of MCI and AD.

Sex differences in Alzheimer's disease: metabolic reprogramming and therapeutic intervention

Studies on the sporadic form of Alzheimer's disease (AD) have revealed three classes of risk factor: age, genetics, and sex. These risk factors point to a metabolic dysregulation as the origin of AD. Adaptive alterations in cerebral metabolism are the rationale for the Metabolic Reprogramming (MR) Theory of the origin of AD. The theory contends that the progression toward AD involves three adaptive events: a hypermetabolic phase, a prolonged prodromal phase, and a metabolic collapse. This article exploits the MR Theory to elucidate the effect of hormonal changes on the origin and progression of AD in women. The theory invokes bioenergetic signatures of the menopausal transition to propose sex-specific diagnostic program and therapeutic strategies.

Evaluation of Age and Sex-Related Metabolic Changes in Healthy Subjects: An Italian Brain 18F-FDG PET Study

Background: 18F-fluorodeoxyglucose (18F-FDG) positron-emission-tomography (PET) allows detection of cerebral metabolic alterations in neurological diseases vs. normal aging. We assess age- and sex-related brain metabolic changes in healthy subjects, exploring impact of activity normalization methods. Methods: brain scans of Italian Association of Nuclear Medicine normative database (151 subjects, 67 Males, 84 Females, aged 20–84) were selected. Global mean, white matter, and pons activity were explored as normalization reference. We performed voxel-based and ROI analyses using SPM12 and IBM-SPSS software. Results: SPM proved a negative correlation between age and brain glucose metabolism involving frontal lobes, anterior-cingulate and insular cortices bilaterally. Narrower clusters were detected in lateral parietal lobes, precuneus, temporal pole and medial areas bilaterally. Normalizing on pons activity, we found a more significant negative correlation and no positive one. ROIs analysis confirmed SPM results. Moreover, a significant age × sex interaction effect was revealed, with worse metabolic reduction in posterior-cingulate cortices in females than males, especially in post-menopausal age. Conclusions: this study demonstrated an age-related metabolic reduction in frontal lobes and in some parieto-temporal areas more evident in females. Results suggested pons as the most appropriate normalization reference. Knowledge of age- and sex-related cerebral metabolic changes is critical to correctly interpreting brain 18F-FDG PET imaging.

Impairments in Brain Bioenergetics in Aging and Tau Pathology: A Chicken and Egg Situation?

The brain is the most energy-consuming organ of the body and impairments in brain energy metabolism will affect neuronal functionality and viability. Brain aging is marked by defects in energetic metabolism. Abnormal tau protein is a hallmark of tauopathies, including Alzheimer's disease (AD). Pathological tau was shown to induce bioenergetic impairments by affecting mitochondrial function. Although it is now clear that mutations in the tau-coding gene lead to tau pathology, the causes of abnormal tau phosphorylation and aggregation in non-familial tauopathies, such as sporadic AD, remain elusive. Strikingly, both tau pathology and brain hypometabolism correlate with cognitive impairments in AD. The aim of this review is to discuss the link between age-related decrease in brain metabolism and tau pathology. In particular, the following points will be discussed: (i) the common bioenergetic features observed during brain aging and tauopathies; (ii) how age-related bioenergetic defects affect tau pathology; (iii) the influence of lifestyle factors known to modulate brain bioenergetics on tau pathology. The findings compiled here suggest that age-related bioenergetic defects may trigger abnormal tau phosphorylation/aggregation and cognitive impairments after passing a pathological threshold. Understanding the effects of aging on brain metabolism may therefore help to identify disease-modifying strategies against tau-induced neurodegeneration.

Integrated transcriptomic and neuroimaging brain model decodes biological mechanisms in aging and Alzheimer's disease

Both healthy aging and Alzheimer's disease (AD) are characterized by concurrent alterations in several biological factors. However, generative brain models of aging and AD are limited in incorporating the measures of these biological factors at different spatial resolutions. Here, we propose a personalized bottom-up spatiotemporal brain model that accounts for the direct interplay between hundreds of RNA transcripts and multiple macroscopic neuroimaging modalities (PET, MRI). In normal elderly and AD participants, the model identifies top genes modulating tau and amyloid-β burdens, vascular flow, glucose metabolism, functional activity, and atrophy to drive cognitive decline. The results also revealed that AD and healthy aging share specific biological mechanisms, even though AD is a separate entity with considerably more altered pathways. Overall, this personalized model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying effective genetic targets for extending healthy aging and treating AD progression.

The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer's disease than MMSE alone

Background

Alzheimer’s disease (AD) is assessed by carefully examining a patient’s cognitive impairment. However, previous studies reported inadequate diagnostic accuracy for dementia in primary care settings. Many hospitals use the automated quantitative evaluation method known as the Voxel-based Specific Regional Analysis System for Alzheimer’s Disease (VSRAD), wherein brain MRI data are used to evaluate brain morphological abnormalities associated with AD. Similarly, an automated quantitative evaluation application called the easy Z-score imaging system (eZIS), which uses brain SPECT data to detect regional cerebral blood flow decreases associated with AD, is widely used. These applications have several indicators, each of which is known to correlate with the degree of AD. However, it is not completely known whether these indicators work better when used in combination in real-world clinical practice.

Methods

We included 112 participants with mild cognitive impairment (MCI) and 128 participants with early AD in this study. All participants underwent MRI, SPECT, and the Mini-Mental State Examination (MMSE). Demographic and clinical characteristics were assessed by univariate analysis, and logistic regression analysis with a combination of MMSE, VSRAD and eZIS indicators was performed to verify whether the diagnostic accuracy in discriminating between MCI and early AD was improved.

Results

The area under the receiver operating characteristic curve (AUC) for the MMSE score alone was 0.835. The AUC was significantly improved to 0.870 by combining the MMSE score with two quantitative indicators from the VSRAD and eZIS that assessed the extent of brain abnormalities.

Conclusion

Compared with the MMSE score alone, the combination of the MMSE score with the VSRAD and eZIS indicators significantly improves the accuracy of discrimination between patients with MCI and early AD. Implementing VSRAD and eZIS does not require professional clinical experience in the treatment of dementia. Therefore, the accuracy of dementia diagnosis by physicians may easily be improved in real-world primary care settings.

Disruption of Endoplasmic Reticulum Proteostasis in Age-Related Nervous System Disorders

Endoplasmic reticulum (ER) stress is a prominent cellular alteration of diseases impacting the nervous system that are associated to the accumulation of misfolded and aggregated protein species during aging. The unfolded protein response (UPR) is the main pathway mediating adaptation to ER stress, but it can also trigger deleterious cascades of inflammation and cell death leading to cell dysfunction and neurodegeneration. Genetic and pharmacological studies in experimental models shed light into molecular pathways possibly contributing to ER stress and the UPR activation in human neuropathies. Most of experimental models are, however, based on the overexpression of mutant proteins causing familial forms of these diseases or the administration of neurotoxins that induce pathology in young animals. Whether the mechanisms uncovered in these models are relevant for the etiology of the vast majority of age-related sporadic forms of neurodegenerative diseases is an open question. Here, we provide a systematic analysis of the current evidence linking ER stress to human pathology and the main mechanisms elucidated in experimental models. Furthermore, we highlight the recent association of metabolic syndrome to increased risk to undergo neurodegeneration, where ER stress arises as a common denominator in the pathogenic crosstalk between peripheral organs and the nervous system.

Andrographolide restores glucose uptake in rat hippocampal neurons

Cerebral glucose hypometabolism is a common pathophysiological characteristic of many neurodegenerative diseases. This metabolic dysfunction includes alterations in glucose transport from the blood into the neurons by the facilitative glucose transporters (GLUTs). Several studies suggest that metabolic disturbances precede clinical symptoms and correlate with disease progression. Some groups have started to explore the use of therapeutic strategies that target decreased cerebral glucose metabolism to promote its availability. We selected Andrographolide (Andro), a natural product obtained from Andrographis paniculate that has both anti-hyperglycemic and anti-diabetic effects. Although it was shown to promote glucose uptake in vivo, the underlying mechanisms remain unclear. Here, we evaluated the acute effects of Andro on glucose transport and metabolism using primary rat hippocampal neuronal cultures. Our results showed that Andro enhances neuronal glucose uptake and stimulates glucose metabolism by inducing GLUT3 and 4 expression in neurons, as well as by promoting glycolysis. We also observed that Andro-mediated effects depend on the activity of AMP-activated protein kinase (AMPK), one of the central regulators of glucose metabolism. Our studies open the possibility to use Andro as a drug to restore glucose levels in neurodegenerative diseases.

The accuracy of hippocampal volumetry and glucose metabolism for the diagnosis of patients with suspected Alzheimer's disease, using automatic quantitative clinical tools

The hippocampus is one of the earliest sites involved in the pathology of Alzheimer's disease (AD). Therefore, we specifically investigated the sensitivity and specificity of hippocampal volume and glucose metabolism in patients being evaluated for AD, using automated quantitative tools (NeuroQuant - magnetic resonance imaging [MRI] and Scenium - positron emission tomography [PET]) and clinical evaluation.This retrospective study included adult patients over the age of 45 years with suspected AD, who had undergone fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET-CT) and MRI. FDG-PET-CT images were analyzed both qualitatively and quantitatively. In quantitative volumetric MRI analysis, the percentage of the total intracranial volume of each brain region, as well as the total hippocampal volume, were considered in comparison to an age-adjusted percentile. The remaining brain regions were compared between groups according to the final diagnosis.Thirty-eight patients were included in this study. After a mean follow-up period of 23 ± 11 months, the final diagnosis for 16 patients was AD or high-risk mild cognitive impairment (MCI). Out of the 16 patients, 8 patients were women, and the average age of all patients was 69.38 ± 10.98 years. Among the remaining 22 patients enrolled in the study, 14 were women, and the average age was 67.50 ± 11.60 years; a diagnosis of AD was initially excluded, but the patients may have low-risk MCI. Qualitative FDG-PET-CT analysis showed greater accuracy (0.87), sensitivity (0.76), and negative predictive value (0.77), when compared to quantitative PET analysis, hippocampal MRI volumetry, and specificity. The positive predictive value of FDG-PET-CT was similar to the MRI value.The performance of FDG-PET-CT qualitative analysis was significantly more effective compared to MRI volumetry. At least in part, this observation could corroborate the sequential hypothesis of AD pathophysiology, which posits that functional changes (synaptic dysfunction) precede structural changes (atrophy).

Ketogenic Medium Chain Triglycerides Increase Brain Energy Metabolism in Alzheimer's Disease

BACKGROUND

In Alzheimer's disease (AD), it is unknown whether the brain can utilize additional ketones as fuel when they are derived from a medium chain triglyceride (MCT) supplement.

OBJECTIVE

To assess whether brain ketone uptake in AD increases in response to MCT as it would in young healthy adults.

METHODS

Mild-moderate AD patients sequentially consumed 30 g/d of two different MCT supplements, both for one month: a mixture of caprylic (55%) and capric acids (35%) (n = 11), followed by a wash-out and then tricaprylin (95%; n = 6). Brain ketone (11C-acetoacetate) and glucose (FDG) uptake were quantified by PET before and after each MCT intervention.

RESULTS

Brain ketone consumption doubled on both types of MCT supplement. The slope of the relationship between plasma ketones and brain ketone uptake was the same as in healthy young adults. Both types of MCT increased total brain energy metabolism by increasing ketone supply without affecting brain glucose utilization.

CONCLUSION

Ketones from MCT compensate for the brain glucose deficit in AD in direct proportion to the level of plasma ketones achieved.

The db mutation improves memory in younger mice in a model of Alzheimer's disease

Alzheimer's disease (AD) is the most common age-related neurodegenerative disease, while obesity is a major global public health problem associated with the metabolic disorder type 2 diabetes mellitus (T2DM). Chronic obesity and T2DM have been identified as invariant risk factors for dementia and late-onset AD, while their impacts on the occurrence and development of AD remain unclear. As shown in our previous study, the diabetic mutation (db, Lepr^db/db) induces mixed or vascular dementia in mature to middle-aged APP^ΔNL/ΔNL x PS1^P264L/P264L knock-in mice (db/AD). In the present study, the impacts of the db mutation on young AD mice at 10 weeks of age were evaluated. The db mutation not only conferred young AD mice with severe obesity, impaired glucose regulation and activated mammalian target of rapamycin (mTOR) signaling pathway in the mouse cortex, but lead to a surprising improvement in memory. At this young age, mice also had decreased cerebral Aβ content, which we have not observed at older ages. This was unlikely to be related to altered Aβ synthesis, as both β- and γ-secretase were unchanged. The db mutation also reduced the cortical IL-1β mRNA level and IBA1 protein level in young AD mice, with no significant effect on the activation of microglia and astrocytes. We conclude that the db mutation could transitorily improve the memory of young AD mice, a finding that may be partially explained by the relatively improved glucose homeostasis in the brains of db/AD mice compared to their counterpart AD mice, suggesting that glucose regulation could be a strategy for prevention and treatment of neurodegenerative diseases like AD.

Voxel-wise deviations from healthy aging for the detection of region-specific atrophy

The identification of pathological atrophy in MRI scans requires specialized training, which is scarce outside dedicated centers. We sought to investigate the clinical usefulness of computer-generated representations of local grey matter (GM) loss or increased volume of cerebral fluids (CSF) as normalized deviations (z-scores) from healthy aging to either aid human visual readings or directly detect pathological atrophy.

Two experienced neuroradiologists rated atrophy in 30 patients with Alzheimer's disease (AD), 30 patients with frontotemporal dementia (FTD), 30 with dementia due to Lewy-body disease (LBD) and 30 healthy controls (HC) on a three-point scale in 10 anatomical regions as reference gold standard. Seven raters, varying in their experience with MRI diagnostics rated all cases on the same scale once with and once without computer-generated volume deviation maps that were overlaid on anatomical slices. In addition, we investigated the predictive value of the computer generated deviation maps on their own for the detection of atrophy as identified by the gold standard raters.

Inter and intra-rater agreements of the two gold standard raters were substantial (Cohen's kappa κ > 0.62). The intra-rater agreement of the other raters ranged from fair (κ = 0.37) to substantial (κ = 0.72) and improved on average by 0.13 (0.57 < κ < 0.87) when volume deviation maps were displayed. The seven other raters showed good agreement with the gold standard in regions including the hippocampus but agreement was substantially lower in e.g. the parietal cortex and did not improve with the display of atrophy scores. Rating speed increased over the course of the study and irrespective of the presentation of voxel-wise deviations.

Automatically detected large deviations of local volume were consistently associated with gold standard atrophy reading as shown by an area under the receiver operator characteristic of up to 0.95 for the hippocampus region. When applying these test characteristics to prevalences typically found in a memory clinic, we observed a positive or negative predictive value close to or above 0.9 in the hippocampus for almost all of the expected cases. The volume deviation maps derived from CSF volume increase were generally better in detecting atrophy.

Our study demonstrates an agreement of visual ratings among non-experts not further increased by displaying, region-specific deviations of volume. The high predictive value of computer generated local deviations independent from human interaction and the consistent advantages of CSF-over GM-based estimations should be considered in the development of diagnostic tools and indicate clinical utility well beyond aiding visual assessments.

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The visual identification of atrophy is most accurate in the temporal lobe.

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Voxel-wise deviations of tissue volume from normal aging is easy to implement.

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Displaying voxel-wise deviations subjectively but not objectively aids readers.

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Voxel-wise deviations themselves show high agreement with expert readings.

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Information on tissue deviations should be provided with cerebral MRI scans.

The vascular facet of late-onset Alzheimer's disease: an essential factor in a complex multifactorial disorder

PURPOSE OF REVIEW

This article provides a brief overview of relevant cerebrovascular mechanisms implicated in late-onset Alzheimer's disease (LOAD) development, and highlights the main reasons for incorporating novel cerebrovascular biomarkers to the models defining a multifactorial LOAD pathogenesis. We also discuss how novel brain mapping techniques and multifactorial data-driven models are having a critical role on understanding LOAD and may be particularly useful for identifying effective therapeutic agents for this disorder.

RECENT FINDINGS

A growing body of evidence supports that LOAD is a complex disorder, causally associated to a high multiplicity of pathologic mechanisms. New experimental and neuroimaging data, in combination with the recent use of integrative multifactorial data-driven models, support the early role of vascular factors in LOAD genesis and development. Among other relevant roles, the cerebrovascular system has a key modulatory effect on prion-like propagation, deposition and toxicity (e.g. Aβ, tau proteins). The early signs of vascular dysregulation during LOAD progression are notable both at the microscopic and the macroscopic scales.

SUMMARY

We emphasize that LOAD should be studied as a complex multifactorial disorder, not dominated by a dominant biological factor (e.g. Aβ), and without disregarding any relevant pathologic factor, such as vascular dysregulation. Cerebrovascular biomarkers are invaluable for defining multifactorial disease progression models as well as for evaluating the effectiveness of different therapeutic strategies.

Separating Symptomatic Alzheimer's Disease from Depression based on Structural MRI

Older patients with depression or Alzheimer’s disease (AD) at the stage of early dementia or mild cognitive impairment may present with objective cognitive impairment, although the pathology and thus therapy and prognosis differ substantially. In this study, we assessed the potential of an automated algorithm to categorize a test set of 65 T1-weighted structural magnetic resonance images (MRI). A convenience sample of elderly individuals fulfilling clinical criteria of either AD (n = 28) or moderate and severe depression (n = 37) was recruited from different settings to assess the potential of the pattern recognition method to assist in the differential diagnosis of AD versus depression. We found that our algorithm learned discriminative patterns in the subject’s grey matter distribution reflected by an area under the receiver operator characteristics curve of up to 0.83 (confidence interval ranged from 0.67 to 0.92) and a balanced accuracy of 0.79 for the separation of depression from AD, evaluated by leave-one-out cross validation. The algorithm also identified consistent structural differences in a clinically more relevant scenario where the data used during training were independent from the data used for evaluation and, critically, which included five possible diagnoses (specifically AD, frontotemporal dementia, Lewy body dementia, depression, and healthy aging). While the output was insufficiently accurate to use it directly as a means for classification when multiple classes are possible, the continuous output computed by the machine learning algorithm differed between the two groups that were investigated. The automated analysis thus could complement, but not replace clinical assessments.

Study of the Influence of Age in 18F-FDG PET Images Using a Data-Driven Approach and Its Evaluation in Alzheimer's Disease

Objectives

¹⁸F-FDG PET scan is one of the most frequently used neural imaging scans. However, the influence of age has proven to be the greatest interfering factor for many clinical dementia diagnoses when analyzing ¹⁸F-FDG PET images, since radiologists encounter difficulties when deciding whether the abnormalities in specific regions correlate with normal aging, disease, or both. In the present paper, the authors aimed to define specific brain regions and determine an age-correction mathematical model.

Methods

A data-driven approach was used based on 255 healthy subjects.

Results

The inferior frontal gyrus, the left medial part and the left medial orbital part of superior frontal gyrus, the right insula, the left anterior cingulate, the left median cingulate, and paracingulate gyri, and bilateral superior temporal gyri were found to have a strong negative correlation with age. For evaluation, an age-correction model was applied to 262 healthy subjects and 50 AD subjects selected from the ADNI database, and partial correlations between SUVR mean and three clinical results were carried out before and after age correction.

Conclusion

All correlation coefficients were significantly improved after the age correction. The proposed model was effective in the age correction of both healthy and AD subjects.

Predicting progression of amnesic MCI: The integration of episodic memory impairment with perfusion SPECT

The present study aimed at assessing if the ability to predict progression from amnesic Mild Cognitive Impairment (aMCI) to dementia is improved by considering the presence at the baseline of Single Photon Emission Computed Tomography (SPECT) perfusion abnormalities in addition to a defect of long term memory. The Episodic Memory Score (EMS), a global index which integrates results obtained in subtests of the Rey's Verbal Learning Test and the Rey-Osterrieth Figure recall, were taken into account to evaluate defects of long term memory. The study sample consisted of 42 subjects affected by aMCI, who were followed-up during a two-year period. At the final follow-up 15 subjects progressed to AD. The EMS predicted progression from aMCI to dementia with a high level of sensitivity and a lower level of specificity, but the association of neuropsychological (EMS) and SPECT data (hypoperfusion in the Posterior Cingulate Cortex) increased the accuracy in predicting conversion from aMCI to AD. The association of results obtained by aMCI patients on memory tests and perfusion SPECT may improve the accuracy in detecting subjects who will progress to dementia. The use of currently available and low-cost investigations could be advantageous in terms of public health policies.

Increased Brain Glucose Uptake After 12 Weeks of Aerobic High-Intensity Interval Training in Young and Older Adults

CONTEXT

Aerobic exercise training can increase brain volume and blood flow, but the impact on brain metabolism is less known.

OBJECTIVE

We determined whether high-intensity interval training (HIIT) increases brain metabolism by measuring brain glucose uptake in younger and older adults.

DESIGN

Brain glucose uptake was measured before and after HIIT or a sedentary (SED) control period within a larger exercise study.

SETTING

Study procedures were performed at the Mayo Clinic in Rochester, MN.

PARTICIPANTS

Participants were younger (18 to 30 years) or older (65 to 80 years) SED adults who were free of major medical conditions. Group sizes were 15 for HIIT (nine younger and six older) and 12 for SED (six younger and six older).

INTERVENTION

Participants completed 12 weeks of HIIT or SED. HIIT was 3 days per week of 4 × 4 minute intervals at over 90% of peak aerobic capacity (VO2peak) with 2 days per week of treadmill walking at 70% VO2peak.

MAIN OUTCOME MEASURES

Resting brain glucose uptake was measured using 18F-fluorodeoxyglucose positron emission tomography scans at baseline and at week 12. Scans were performed at 96 hours after exercise. VO2peak was measured by indirect calorimetry.

RESULTS

Glucose uptake increased significantly in the parietal-temporal and caudate regions after HIIT compared with SED. The gains with HIIT were not observed in all brain regions. VO2peak was increased for all participants after HIIT and did not change with SED.

CONCLUSION

We demonstrate that brain glucose metabolism increased after 12 weeks of HIIT in adults in regions where it is reduced in Alzheimer's disease.

Clinical validity of brain fluorodeoxyglucose positron emission tomography as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework

The use of Alzheimer's disease (AD) biomarkers is supported in diagnostic criteria, but their maturity for clinical routine is still debated. Here, we evaluate brain fluorodeoxyglucose positron emission tomography (FDG PET), a measure of cerebral glucose metabolism, as a biomarker to identify clinical and prodromal AD according to the framework suggested for biomarkers in oncology, using homogenous criteria with other biomarkers addressed in parallel reviews. FDG PET has fully achieved phase 1 (rational for use) and most of phase 2 (ability to discriminate AD subjects from healthy controls or other forms of dementia) aims. Phase 3 aims (early detection ability) are partly achieved. Phase 4 studies (routine use in prodromal patients) are ongoing, and only preliminary results can be extrapolated from retrospective observations. Phase 5 studies (quantify impact and costs) have not been performed. The results of this study show that specific efforts are needed to complete phase 3 evidence, in particular comparing and combining FDG PET with other biomarkers, and to properly design phase 4 prospective studies as a basis for phase 5 evaluations.

Biomarkers for Alzheimer Disease: Classical and Novel Candidates' Review

The biomarkers may be useful for predictive diagnosis of Alzheimer's disease (AD). The current challenge is to diagnose it in its preclinical phase. The combination of cerebrospinal fluid (CSF) biomarkers and imaging has been investigated extensively for a number of years. It can provide an increased diagnostic accuracy. This review discusses the contribution of classical biomarkers to predict AD and highlights novel candidates identified as potential markers for AD. We referred to the electronic databases PubMed/Medline and Web of Science to search for articles that were published until February 2016. Sixty-two records were included in qualitative synthesis. In the first section, the results show the contribution of biomarkers to predict and track AD considered as classical biomarkers. In the second section, the results highlight the involvement of novel candidates that should be considered for future evaluation in the characterization of the AD progression. Reported findings open prospect to define noninvasive biomarkers to predict AD before symptoms onset.

Taming Alzheimer's disease: New perspectives, newer horizons

Alzheimer's disease (AD) is the leading cause of dementia. However, current therapies do not prevent progression of the disease. New research into the pathogenesis of the disease has brought about a greater understanding of the "amyloid cascade" and associated receptor abnormalities, the role of genetic factors, and revealed that the disease process commences 10 to 20 years prior to the appearance of clinical signs. This greater understanding of the disease has prompted development of novel disease-modifying therapies (DMTs) which may prevent onset or delay progression of the disease. Using genetic biomarkers like apolipoprotein E (ApoE) ε4, biochemical biomarkers like cerebrospinal fluid (CSF) amyloid and tau proteins, and imaging biomarkers like magnetic resonance imaging (MRI) and positron emission tomography (PET), it is now possible to detect preclinical AD and also monitor its progression in asymptomatic people. These biomarkers can be used in the selection of high-risk populations for clinical trials and also to monitor the efficacy and side-effects of DMT. To validate and standardize these biomarkers and select the most reliable, repeatable, easily available, cost-effective and complementary options is the challenge ahead.

Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease

Previous studies have demonstrated altered brain activity in Alzheimer's disease using task based functional MRI (fMRI), network based resting-state fMRI, and glucose metabolism from ¹⁸F fluorodeoxyglucose-PET (FDG-PET). Our goal was to define a novel indicator of neuronal activity based on a first-order textural feature of the resting state functional MRI (RS-fMRI) signal. Furthermore, we examined the association between this neuronal activity metric and glucose metabolism from ¹⁸F FDG-PET. We studied 15 normal elderly controls (NEC) and 15 probable Alzheimer disease (AD) subjects from the AD Neuroimaging Initiative. An independent component analysis was applied to the RS-fMRI, followed by template matching to identify neuronal components (NC). A regional brain activity measurement was constructed based on the variation of the RS-fMRI signal of these NC. The standardized glucose uptake values of several brain regions relative to the cerebellum (SUVR) were measured from partial volume corrected FDG-PET images. Comparing the AD and NEC groups, the mean brain activity metric was significantly lower in the accumbens, while the glucose SUVR was significantly lower in the amygdala and hippocampus. The RS-fMRI brain activity metric was positively correlated with cognitive measures and amyloid β_1–42 cerebral spinal fluid levels; however, these did not remain significant following Bonferroni correction. There was a significant linear correlation between the brain activity metric and the glucose SUVR measurements. This proof of concept study demonstrates that this novel and easy to implement RS-fMRI brain activity metric can differentiate a group of healthy elderly controls from a group of people with AD.

Positive affect predicts cerebral glucose metabolism in late middle-aged adults

Positive affect is associated with a number of health benefits; however, few studies have examined the relationship between positive affect and cerebral glucose metabolism, a key energy source for neuronal function and a possible index of brain health. We sought to determine if positive affect was associated with cerebral glucose metabolism in late middle-aged adults (n = 133). Participants completed the positive affect subscale of the Center for Epidemiological Studies Depression Scale at two time points over a two-year period and underwent 18F-fluorodeoxyglucose-positron emission tomography scanning. After controlling for age, sex, perceived health status, depressive symptoms, anti-depressant use, family history of Alzheimer’s disease, APOE ε4 status and interval between visits, positive affect was associated with greater cerebral glucose metabolism across para-/limbic, frontal, temporal and parietal regions. Our findings provide evidence that positive affect in late midlife is associated with greater brain health in regions involved in affective processing and also known to be susceptible to early neuropathological processes. The current findings may have implications for interventions aimed at increasing positive affect to attenuate early neuropathological changes in at-risk individuals.

Enhanced Feedback-Related Negativity in Alzheimer's Disease

Alzheimer’s disease (AD), the most common cause of dementia in the elderly, results in the impairment of executive function, including that of performance monitoring. Feedback-related negativity (FRN) is an electrophysiological measure reflecting the activity of this monitoring system via feedback signals, and is generated from the anterior cingulate cortex. However, there have been no reports on FRN in AD. Based on prior aging studies, we hypothesized that FRN would decrease in AD patients. To assess this, FRN was measured in healthy individuals and those with AD during a simple gambling task involving positive and negative feedback stimuli. Contrary to our hypothesis, FRN amplitude increased in AD patients, compared with the healthy elderly. We speculate that this may reflect the existence of a compensatory mechanism against the decline in executive function. Also, there was a significant association between FRN amplitude and depression scores in AD, and the FRN amplitude tended to increase insomuch as the Self-rating Depression Scale (SDS) was higher. This result suggests the existence of a negative bias in the affective state in AD. Thus, the impaired functioning monitoring system in AD is a more complex phenomenon than we thought.

Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity

This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation.

Brain imaging evidence of early involvement of subcortical regions in familial and sporadic Alzheimer's disease

Recent brain imaging studies have found changes in subcortical regions in presymptomatic autosomal dominant Alzheimer's disease (ADAD). These regions are also affected in sporadic Alzheimer's disease (sAD), but whether such changes are seen in early-stage disease is still uncertain. In this review, we discuss imaging studies published in the past 12 years that have found evidence of subcortical involvement in early-stage ADAD and/or sAD. Several papers have reported amyloid deposition in the striatum of presymptomatic ADAD mutation carriers, prior to amyloid deposition elsewhere. Altered caudate volume has also been implicated in early-stage ADAD, but findings have been variable. Less is known about subcortical involvement in sAD: the thalamus and striatum have been found to be atrophied in symptomatic patients, but their involvement in the preclinical phase remains unclear, in part due to the difficulties of studying this stage in sporadic disease. Longitudinal imaging studies comparing ADAD mutation carriers with individuals at high-risk for sAD may be needed to elucidate the significance of subcortical involvement in different AD clinical stages.

Increased Glucose Transport into Neurons Rescues Aβ Toxicity in Drosophila

Glucose hypometabolism is a prominent feature of the brains of patients with Alzheimer's disease (AD). Disease progression is associated with a reduction in glucose transporters in both neurons and endothelial cells of the blood-brain barrier. However, whether increasing glucose transport into either of these cell types offers therapeutic potential remains unknown. Using an adult-onset Drosophila model of Aβ (amyloid beta) toxicity, we show that genetic overexpression of a glucose transporter, specifically in neurons, rescues lifespan, behavioral phenotypes, and neuronal morphology. This amelioration of Aβ toxicity is associated with a reduction in the protein levels of the unfolded protein response (UPR) negative master regulator Grp78 and an increase in the UPR. We further demonstrate that genetic downregulation of Grp78 activity also protects against Aβ toxicity, confirming a causal effect of its alteration on AD-related pathology. Metformin, a drug that stimulates glucose uptake in cells, mimicked these effects, with a concomitant reduction in Grp78 levels and rescue of the shortened lifespan and climbing defects of Aβ-expressing flies. Our findings demonstrate a protective effect of increased neuronal uptake of glucose against Aβ toxicity and highlight Grp78 as a novel therapeutic target for the treatment of AD.

Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study

Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC >  0.9) but substantially less accurate (AUC = 0.76 for AD, AUC = 0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD-abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

Medium-chain plasma acylcarnitines, ketone levels, cognition, and gray matter volumes in healthy elderly, mildly cognitively impaired, or Alzheimer's disease subjects

Aging, amyloid deposition, and tau-related pathology are key contributors to the onset and progression of Alzheimer's disease (AD). However, AD is also associated with brain hypometabolism and deficits of mitochondrial bioenergetics. Plasma acylcarnitines (ACCs) are indirect indices of altered fatty acid beta-oxidation, and ketogenesis has been found to be decreased on aging. Furthermore, in elderly subjects, alterations in plasma levels of specific ACCs have been suggested to predict conversion to mild cognitive impairment (MCI) or AD. In this study, we assayed plasma profiles of ACCs in a cohort of healthy elderly control, MCI subjects, and AD patients. Compared with healthy controls or MCI subjects, AD patients showed significant lower plasma levels of several medium-chain ACCs. Furthermore, in AD patients, these lower concentrations were associated with lower prefrontal gray matter volumes and the presence of cognitive impairment. Interestingly, lower levels of medium-chain ACCs were also found to be associated with lower plasma levels of 2-hydroxybutyric acid. Overall, these findings suggest that altered metabolism of medium-chain ACCs and impaired ketogenesis can be metabolic features of AD.

Use of dynamic (18)F-fluorodeoxyglucose positron emission tomography to investigate choroid plexus function in Alzheimer's disease

Choroid plexuses (CPs) are structures involved in CSF production and cerebral regulation and present atypical glucose metabolism. In addition, CPs impairment may be related to Alzheimer disease (AD). In the present study, we present the first results pointing out glucose metabolism in the CP with dynamic fluorodeoxyglucose positron emission tomography (dynamic (18)F-FDG-PET). We studied 47 elderly adults who were classified into three classes: healthy subjects (HS), amnestic mild cognitive impairment (aMCI) and AD. All participants have undergone dynamic (18)F-FDG-PET for 45 min. Acquisitions were divided into 34 frames to extract tissue time-activity curves (TTACs) in various structures including CSF and CPs. Results showed a decreased CPs (18)F-FDG metabolism in AD compared with aMCI and HS. Conversely, dynamic uptake was higher in CSF for AD compared with the other groups. ROC analysis showed that CPs TTACs are a promising tool as it yielded sensitivity of 85.7% and a specificity of 83.3%. Our study showed a disturbance of glucose exchange at the blood-CSF barrier level which is in favour of a key-role of the CPs in AD.

Memory suppression in Alzheimer's disease

An important challenge for memory is the competition between appropriate and inappropriate information during retrieval. This competition is normally reduced thanks to controlled inhibitory processes that suppress irrelevant memories. In Alzheimer's disease (AD), compromise of suppression ability may result in strong competition between relevant and irrelevant memories during retrieval. The present review highlights this issue by examining studies using the directed forgetting method in AD. This method in which participants are typically instructed to forget no longer relevant information is argued to reflect suppression in memory. Studies using the directed forgetting method suggest that AD participants experience difficulties when they are asked to suppress no longer relevant information in working, autobiographical, source and destination memory. Difficulties in suppressing irrelevant information, as may be observed in AD, may hamper memory retrieval by activating irrelevant memories at the expense of relevant ones.

PET Imaging of Epigenetic Influences on Alzheimer's Disease

The precise role of environment-gene interactions (epigenetics) in the development and progression of Alzheimer's disease (AD) is unclear. This review focuses on the premise that radiotracer-specific PET imaging allows clinicians to visualize epigenetically influenced events and that such imaging may provide new, valuable insights for preventing, diagnosing, and treating AD. Current understanding of the role of epigenetics in AD and the principles underlying the use of PET radiotracers for in vivo diagnosis are reviewed. The relative efficacies of various PET radiotracers for visualizing the epigenetic influences on AD and their use for diagnosis are discussed. For example, [¹⁸F]FAHA demonstrates sites of differential HDAC activity, [¹⁸F]FDG indirectly illuminates sites of neuronal hypomethylation, and the carbon-11 isotope-containing Pittsburgh compound B ([¹¹C]PiB) images amyloid-beta plaque deposits. A definitive AD diagnosis is currently achievable only by postmortem histological observation of amyloid-beta plaques and tau neurofibrillary tangles. Therefore, reliable in vivo neuroimaging techniques could provide opportunities for early diagnosis and treatment of AD.

Nanotechnology-based drug delivery systems for the treatment of Alzheimer's disease

Alzheimer's disease is a neurological disorder that results in cognitive and behavioral impairment. Conventional treatment strategies, such as acetylcholinesterase inhibitor drugs, often fail due to their poor solubility, lower bioavailability, and ineffective ability to cross the blood-brain barrier. Nanotechnological treatment methods, which involve the design, characterization, production, and application of nanoscale drug delivery systems, have been employed to optimize therapeutics. These nanotechnologies include polymeric nanoparticles, solid lipid nanoparticles, nanostructured lipid carriers, microemulsion, nanoemulsion, and liquid crystals. Each of these are promising tools for the delivery of therapeutic devices to the brain via various routes of administration, particularly the intranasal route. The objective of this study is to present a systematic review of nanotechnology-based drug delivery systems for the treatment of Alzheimer's disease.