Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer's Disease Neuroimaging Initiative cohort.

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10-8 and 4.3 × 10-7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granu la ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.

Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis.

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples' transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with l2,1 -norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN.

This study aims to develop an optimally performing convolutional neural network to classify Alzheimer's disease into mild cognitive impairment, normal controls, or Alzheimer's disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer's disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer's disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer's disease versus normal controls, Alzheimer's disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.

Mapping of Alzheimer's disease related data elements and the NIH Common Data Elements.

BACKGROUND: Alzheimer's Disease (AD) is a devastating disease that destroys memory and other cognitive functions. There has been an increasing research effort to prevent and treat AD. In the US, two major data sharing resources for AD research are the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI); Additionally, the National Institutes of Health (NIH) Common Data Elements (CDE) Repository has been developed to facilitate data sharing and improve the interoperability among data sets in various disease research areas. METHOD: To better understand how AD-related data elements in these resources are interoperable with each other, we leverage different representation models to map data elements from different resources: NACC to ADNI, NACC to NIH CDE, and ADNI to NIH CDE. We explore bag-of-words based and word embeddings based models (Word2Vec and BioWordVec) to perform the data element mappings in these resources. RESULTS: The data dictionaries downloaded on November 23, 2021 contain 1,195 data elements in NACC, 13,918 in ADNI, and 27,213 in NIH CDE Repository. Data element preprocessing reduced the numbers of NACC and ADNI data elements for mapping to 1,099 and 7,584 respectively. Manual evaluation of the mapping results showed that the bag-of-words based approach achieved the best precision, while the BioWordVec based approach attained the best recall. In total, the three approaches mapped 175 out of 1,099 (15.92%) NACC data elements to ADNI; 107 out of 1,099 (9.74%) NACC data elements to NIH CDE; and 171 out of 7,584 (2.25%) ADNI data elements to NIH CDE. CONCLUSIONS: The bag-of-words based and word embeddings based approaches showed promise in mapping AD-related data elements between different resources. Although the mapping approaches need further improvement, our result indicates that there is a critical need to standardize CDEs across these valuable AD research resources in order to maximize the discoveries regarding AD pathophysiology, diagnosis, and treatment that can be gleaned from them.

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques.

Background: Amyloid-ß (Aß) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aß plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aß plaques. Objective: To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aß42/40 ratio. Methods: We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aß42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results: Results from ADNI (mean age 72.6, Aß+ rate 49.5%) and BAI (mean age 66.2, Aß+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aß42/40 (0.73 and 0.81) predictors. CONCLUSIONS: Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aß plaques and structural MRI.

Exploring Imaging Genetic Markers of Alzheimer's Disease Based on a Novel Nonlinear Correlation Analysis Algorithm.

Alzheimer's disease (AD) is an irreversible neurological disorder characterized by insidious onset. Identifying potential markers in its emergence and progression is crucial for early diagnosis and treatment. Imaging genetics typically merges genetic variables with multiple imaging parameters, employing various association analysis algorithms to investigate the links between pathological phenotypes and genetic variations, and to unearth molecular-level insights from brain images. However, most existing imaging genetics algorithms based on sparse learning assume a linear relationship between genetic factors and brain functions, limiting their ability to discern complex nonlinear correlation patterns and resulting in reduced accuracy. To address these issues, we propose a novel nonlinear imaging genetic association analysis method, Deep Self-Reconstruction-based Adaptive Sparse Multi-view Deep Generalized Canonical Correlation Analysis (DSR-AdaSMDGCCA). This approach facilitates joint learning of the nonlinear relationships between pathological phenotypes and genetic variations by integrating three different types of data: structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism (SNP), and gene expression data. By incorporating nonlinear transformations in DGCCA, our model effectively uncovers nonlinear associations across multiple data types. Additionally, the DSR algorithm clusters samples with identical labels, incorporating label information into the nonlinear feature extraction process and thus enhancing the performance of association analysis. The application of the DSR-AdaSMDGCCA algorithm on real data sets identified several AD risk regions (such as the hippocampus, parahippocampus, and fusiform gyrus) and risk genes (including VSIG4, NEDD4L, and PINK1), achieving maximum classification accuracy with the fewest selected features compared to baseline algorithms. Molecular biology enrichment analysis revealed that the pathways enriched by these top genes are intimately linked to AD progression, affirming that our algorithm not only improves correlation analysis performance but also identifies biologically significant markers.

Amyloid-ß prediction machine learning model using source-based morphometry across neurocognitive disorders.

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aß) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aß-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aß-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

Characterization of monoamine oxidase-B (MAO-B) as a biomarker of reactive astrogliosis in Alzheimer's disease and related dementias.

Reactive astrogliosis accompanies the two neuropathological hallmarks of Alzheimer's disease (AD)-Aß plaques and neurofibrillary tangles-and parallels neurodegeneration in AD and AD-related dementias (ADRD). Thus, there is growing interest in developing imaging and fluid biomarkers of reactive astrogliosis for AD/ADRD diagnosis and prognostication. Monoamine oxidase-B (MAO-B) is emerging as a target for PET imaging radiotracers of reactive astrogliosis. However, a thorough characterization of MAO-B expression in postmortem control and AD/ADRD brains is lacking. We sought to: (1) identify the primary cell type(s) expressing MAO-B in control and AD brains; (2) quantify MAO-B immunoreactivity in multiple brain regions of control and AD donors as a proxy for PET radiotracer uptake; (3) correlate MAO-B level with local AD neuropathological changes, reactive glia, and cortical atrophy; (4) determine whether the MAOB rs1799836 SNP genotype impacts MAO-B expression level; (5) compare MAO-B immunoreactivity across AD/ADRD, including Lewy body diseases (LBD) and frontotemporal lobar degenerations with tau (FTLD-Tau) and TDP-43 (FTLD-TDP). We found that MAO-B is mainly expressed by subpial and perivascular cortical astrocytes as well as by fibrous white matter astrocytes in control brains, whereas in AD brains, MAO-B is significantly upregulated by both cortical reactive astrocytes and white matter astrocytes across temporal, frontal, and occipital lobes. By contrast, MAO-B expression level was unchanged and lowest in cerebellum. Cortical MAO-B expression was independently associated with cortical atrophy and local measures of reactive astrocytes and microglia, and significantly increased in reactive astrocytes surrounding Thioflavin-S+ dense-core Aß plaques. MAO-B expression was not affected by the MAOB rs1799836 SNP genotype. MAO-B expression was also significantly increased in the frontal cortex and white matter of donors with corticobasal degeneration, Pick's disease, and FTLD-TDP, but not in LBD or progressive supranuclear palsy. These findings support ongoing efforts to develop MAO-B-based PET radiotracers to image reactive astrogliosis in AD/ADRD.

The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment.

Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer's disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.

Evaluating the effect of rapamycin treatment in Alzheimer's disease and aging using in vivo imaging: the ERAP phase IIa clinical study protocol.

BACKGROUND: Rapamycin is an inhibitor of the mechanistic target of rapamycin (mTOR) protein kinase, and preclinical data demonstrate that it is a promising candidate for a general gero- and neuroprotective treatment in humans. Results from mouse models of Alzheimer's disease have shown beneficial effects of rapamycin, including preventing or reversing cognitive deficits, reducing amyloid oligomers and tauopathies and normalizing synaptic plasticity and cerebral glucose uptake. The "Evaluating Rapamycin Treatment in Alzheimer's Disease using Positron Emission Tomography" (ERAP) trial aims to test if these results translate to humans through evaluating the change in cerebral glucose uptake following six months of rapamycin treatment in participants with early-stage Alzheimer's disease. METHODS: ERAP is a six-month-long, single-arm, open-label, phase IIa biomarker-driven study evaluating if the drug rapamycin can be repurposed to treat Alzheimer's disease. Fifteen patients will be included and treated with a weekly dose of 7 mg rapamycin for six months. The primary endpoint will be change in cerebral glucose uptake, measured using [18F]FDG positron emission tomography. Secondary endpoints include changes in cognitive measures, markers in cerebrospinal fluid as well as cerebral blood flow measured using magnetic resonance imaging. As exploratory outcomes, the study will assess change in multiple age-related pathological processes, such as periodontal inflammation, retinal degeneration, bone mineral density loss, atherosclerosis and decreased cardiac function. DISCUSSION: The ERAP study is a clinical trial using in vivo imaging biomarkers to assess the repurposing of rapamycin for the treatment of Alzheimer's disease. If successful, the study would provide a strong rationale for large-scale evaluation of mTOR-inhibitors as a potential disease-modifying treatment in Alzheimer's disease. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT06022068, date of registration 2023-08-30.

Subclinical epileptiform discharges in Alzheimer's disease are associated with increased hippocampal blood flow.

BACKGROUND: In epilepsy, the ictal phase leads to cerebral hyperperfusion while hypoperfusion is present in the interictal phases. Patients with Alzheimer's disease (AD) have an increased prevalence of epileptiform discharges and a study using intracranial electrodes have shown that these are very frequent in the hippocampus. However, it is not known whether there is an association between hippocampal hyperexcitability and regional cerebral blood flow (rCBF). The objective of the study was to investigate the association between rCBF in hippocampus and epileptiform discharges as measured with ear-EEG in patients with Alzheimer's disease. Our hypothesis was that increased spike frequency may be associated with increased rCBF in hippocampus. METHODS: A total of 24 patients with AD, and 15 HC were included in the analysis. Using linear regression, we investigated the association between rCBF as measured with arterial spin-labelling MRI (ASL-MRI) in the hippocampus and the number of spikes/sharp waves per 24 h as assessed by ear-EEG. RESULTS: No significant difference in hippocampal rCBF was found between AD and HC (p-value = 0.367). A significant linear association between spike frequency and normalized rCBF in the hippocampus was found for patients with AD (estimate: 0.109, t-value = 4.03, p-value < 0.001). Changes in areas that typically show group differences (temporal-parietal cortex) were found in patients with AD, compared to HC. CONCLUSIONS: Increased spike frequency was accompanied by a hemodynamic response of increased blood flow in the hippocampus in patients with AD. This phenomenon has also been shown in patients with epilepsy and supports the hypothesis of hyperexcitability in patients with AD. The lack of a significant difference in hippocampal rCBF may be due to an increased frequency of epileptiform discharges in patients with AD. TRIAL REGISTRATION: The study is registered at clinicaltrials.gov (NCT04436341).

Behaviorally meaningful functional networks mediate the effect of Alzheimer's pathology on cognition.

Tau pathology is associated with cognitive impairment in both aging and Alzheimer's disease, but the functional and structural bases of this relationship remain unclear. We hypothesized that the integrity of behaviorally meaningful functional networks would help explain the relationship between tau and cognitive performance. Using resting state fMRI, we identified unique networks related to episodic memory and executive function cognitive domains. The episodic memory network was particularly related to tau pathology measured with positron emission tomography in the entorhinal and temporal cortices. Further, episodic memory network strength mediated the relationship between tau pathology and cognitive performance above and beyond neurodegeneration. We replicated the association between these networks and tau pathology in a separate cohort of older adults, including both cognitively unimpaired and mildly impaired individuals. Together, these results suggest that behaviorally meaningful functional brain networks represent a functional mechanism linking tau pathology and cognition.

Molecular Imaging of Alzheimer's Disease-Related Sigma-1 Receptor in the Brain via a Novel Ru-Mediated Aromatic 18F-deoxyfluorination Probe.

Sigma-1 receptor (σ1R) is an intracellular protein implicated in a spectrum of neurodegenerative conditions, notably Alzheimer's disease (AD). Positron emission tomography (PET) imaging of brain σ1R could provide a powerful tool for better understanding the underlying pathomechanism of σ1R in AD. In this study, we successfully developed a 18F-labeled σ1R radiotracer [18F]CNY-05 via an innovative ruthenium (Ru)-mediated 18F-deoxyfluorination method. [18F]CNY-05 exhibited preferable brain uptake, high specific binding, and slightly reversible pharmacokinetics within the PET scanning time window. PET imaging of [18F]CNY-05 in nonhuman primates (NHP) indicated brain permeability, metabolic stability, and safety. Moreover, autoradiography and PET studies of [18F]CNY-05 in the AD mouse model found a significantly decreased brain uptake compared to that in wild-type mice. Collectively, we have provided a novel 18F-radiolabeled σ1R PET probe, which enables visualizing brain σ1R in health and neurological diseases.

Pathophysiology characterization of Alzheimer's disease in South China's aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS).

INTRODUCTION: The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86 million population and faces a significant challenge of Alzheimer's disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort. METHODS: Following the standard protocols of the Alzheimer's Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), ß-amyloid (Aß) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aß42/Aß40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aß PET, and tau PET, respectively. Plasma biomarkers, Aß PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aß PET positivity were also determined in different diagnostic groups. RESULTS: The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aß PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported. DISCUSSION: The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aß PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aß PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China's older population.

Predicting the efficacy of donepezil intervention in Alzheimer's disease patients using regional homogeneity in the inferior orbitofrontal cortex.

BACKGROUND: Although donepezil is a commonly used drug for treating Alzheimer's disease (AD), the mechanisms by which it affects patients' functional brain activity, and thus modulates clinical symptoms, remain unclear. METHODS: In the present study, we used resting-state functional magnetic resonance imaging (MRI) and regional homogeneity (ReHo) to investigate the effects of donepezil on local brain activity in AD patients. Resting-state functional MRI data were collected from 32 subjects: 16 healthy controls and 16 AD patients. All 16 AD patients underwent 6 months of donepezil treatment and received two MRI scans (pre- and post-intervention). Analysis of covariance and post hoc analyses were used to compare ReHo differences among the healthy controls, pre-intervention AD patients, and post-intervention AD patients. Pearson correlation analysis was used to examine relationships between ReHo values in differential brain regions and clinical symptoms. RESULTS: Compared with healthy controls, post-intervention AD patients had reduced ReHo in the orbital part of the inferior frontal gyrus, and pre-intervention AD patients had reduced ReHo in the orbital part of the right inferior frontal gyrus. Pattern recognition models revealed that pre-intervention ReHo values in abnormal brain regions of AD patients were 76% accurate for predicting the efficacy of donepezil on cognitive function and 65% accurate for predicting its efficacy on depressive symptoms. CONCLUSIONS: These findings deepen our understanding of the brain mechanisms underlying the clinical efficacy of donepezil in AD patients, and provide a novel way to predict its clinical efficacy in such patients.

Alteration of medial temporal lobe metabolism related to Alzheimer's disease and dementia with lewy bodies.

BACKGROUND: Association of medial temporal lobe (MTL) metabolism with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) has not been evaluated considering their mixed disease (MD). METHODS: 131 patients with AD, 133 with DLB, 122 with MD, and 28 normal controls (NCs) underwent neuropsychological tests, assessments for parkinsonism, cognitive fluctuation (CF), and visual hallucinations (VH), and 18F-fluorodeoxyglucose PET to quantify MTL metabolism in the amygdala, hippocampus, and entorhinal cortex. The effects of AD and DLB on MTL metabolism were evaluated using general linear models (GLMs). Associations between MTL metabolism, cognition, and clinical features were evaluated using GLMs or logistic regression models separately performed for the AD spectrum (NC + AD + MD), DLB spectrum (NC + DLB + MD), and disease groups (AD + DLB + MD). Covariates included age, sex, and education. RESULTS: AD was associated with hippocampal/entorhinal hypometabolism, whereas DLB was associated with relative amygdalar/hippocampal hypermetabolism. Relative MTL hypermetabolism was associated with lower attention/visuospatial/executive scores and severe parkinsonism in both the AD and DLB spectra and disease groups. Left hippocampal/entorhinal hypometabolism was associated with lower verbal memory scores, whereas right hippocampal hypometabolism was associated with lower visual memory scores in both the AD spectrum and disease groups. Relative MTL hypermetabolism was associated with an increased risk of CF and VH in the disease group, and relative amygdalar hypermetabolism was associated with an increased risk of VH in the DLB spectrum. CONCLUSIONS: Entorhinal-hippocampal hypometabolism and relative amygdala-hippocampal hypermetabolism could be characteristics of AD- and DLB-related neurodegeneration, respectively.

Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis.

BACKGROUND: Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease. METHODS: Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression. RESULTS: Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease. CONCLUSIONS: Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.

Probable Novel APP Met671Leu Mutation in a Chinese Han Family with Early-Onset Alzheimer's Disease.

Familial Alzheimer's disease (AD) is a rare disease caused by autosomal-dominant mutations. APP (encoding amyloid precursor protein), PSEN1 (encoding presenilin 1), and PSEN2 (encoding presenilin 2) are the most common genes cause dominant inherited AD. This study aimed to demonstrate a Chinese early-onset AD pedigree presenting as progressive memory impairment, apraxia, visual-spatial disorders, psychobehavioral disorders, and personality changes with a novel APP gene mutation. The family contains four patients, three carries and three normal family members. The proband underwent brain magnetic resonance imaging (MRI), 18F-fludeoxyglucose positron emission tomography (18F-FDG-PET), cerebrospinal fluid amyloid detection, 18F-florbetapir (AV-45) Positron Emission Computed Tomography (PET) imaging, whole-exome sequencing and Sanger sequencing. Brain MRI images showed brain atrophy, especially in the entorhinal cortex, temporal hippocampus, and lateral ventricle dilation. The FDG-PET showed hypometabolism in the frontotemporal, parietal, and hippocampal regions. 18F-florbetapir (AV-45) PET imaging showed cerebral cortex Aß protein deposition. The cerebrospinal fluid amyloid protein test showed Aß42/Aß40 ratio decreases, pathological phosphor-tau level increases. Whole-exome sequencing detected a new missense mutation of codon 671 (M671L), which was a heterozygous A to T point mutation at position 2011 (c.2011A > T) in exon 16 of the amyloid precursor protein, resulting in the replacement of methionine to Leucine. The co-separation analysis was validated in this family. The mutation was found in 3 patients, 3 clinical normal members in the family, but not in the other 3 unaffected family members, 100 unrelated normal subjects, or 100 sporadic patients with AD. This mutation was probably pathogenic and novel in a Chinese Han family with early-onset AD.

An Insertion Within SIRPß1 Shows a Dual Effect Over Alzheimer's Disease Cognitive Decline Altering the Microglial Response.

Background: Microglial dysfunction plays a causative role in Alzheimer's disease (AD) pathogenesis. Here we focus on a germline insertion/deletion variant mapping SIRPß1, a surface receptor that triggers amyloid-ß(Aß) phagocytosis via TYROBP. Objective: To analyze the impact of this copy-number variant in SIRPß1 expression and how it affects AD molecular etiology. Methods: Copy-number variant proxy rs2209313 was evaluated in GERALD and GR@ACE longitudinal series. Hippocampal specimens of genotyped AD patients were also examined. SIRPß1 isoform-specific phagocytosis assays were performed in HEK393T cells. Results: The insertion alters the SIRPß1 protein isoform landscape compromising its ability to bind oligomeric Aß and its affinity for TYROBP. SIRPß1 Dup/Dup patients with mild cognitive impairment show an increased cerebrospinal fluid t-Tau/Aß ratio (p = 0.018) and a higher risk to develop AD (OR = 1.678, p = 0.018). MRIs showed that Dup/Dup patients exhibited a worse initial response to AD. At the moment of diagnosis, all patients showed equivalent Mini-Mental State Examination scores. However, AD patients with the duplication had less hippocampal degeneration (p < 0.001) and fewer white matter hyperintensities. In contrast, longitudinal studies indicate that patients bearing the duplication allele show a slower cognitive decline (p = 0.013). Transcriptional analysis also shows that the SIRPß1 duplication allele correlates with higher TREM2 expression and an increased microglial activation. Conclusions: The SIRPß1 internal duplication has opposite effects over MCI-to-Dementia conversion risk and AD progression, affecting microglial response to Aß. Given the pharmacological approaches focused on the TREM2-TYROBP axis, we believe that SIRPß1 structural variant might be considered as a potential modulator of this causative pathway.