Early Mild Cognitive Impairment Subjects Research Articles

Differential Microglial activation and Astrocyte reactivity in Alzheimer’s trajectory

AbstractBackgroundMicroglial activation and astrocyte activation plays a significant role in the pathogenesis of AD. Here, we evaluate microglial activation, the interrelationship between microglial activation, amyloid and tau pathologies, and the impact of microglial activation in Alzheimer’s disease trajectory. We specifically focus on microglia as a major player in neuroinflammation and evaluate the spatial and temporal variation of microglial activation in the disease trajectory. Additionally, we evaluate astroglial activation using novel astrocyte marker and evaluate the relationship between amyloid, MRI volume changes and glucose metabolism in Alzheimer’s trajectory.MethodWe evaluated AD, MCI and cognitively normal subjects using amyloid PET, tau PET and neuroinflammation using TSPO PET scans. A separate group of AD, MCI and control subjects were evaluated using novel astrocyte marker [11C]BU99008 PET along with [18F]FDG PET and amyloid PET. [11C]PBR28 and [11C]BU99008 PET scans were analysed using two tissue compartmental model, while [18F]Flutemetamol was analysed using target to cerebellar ratio. [18F]FDG PET was analysed using pons as a reference region. We performed regional and voxel wise analysis for all the tracers.ResultMicroglial activation was elevated in different cortical regions in both AD and MCI subjects. Microglial activation was associated with lower grey matter density in later stages of the disease, while higher grey matter volume was associated with increased microglial activation in early mild cognitive impairment subjects. There were areas of increased astrocyte activation in both AD and MCI. Higher microglial activation was associated with increased tau formation and neuronal damage in later stages of the disease. Interestingly, there was increased astrocyte reactivity in the early stages of the disease.ConclusionWhile microglial activation in the late phase of the disease is associated with neuronal damage, however, in the early stages microglial activation was associated with higher grey matter volume. Microglial activation demonstrated a biphasic pattern of activation. In contrast to microglial activation, astrocyte activation was prominent in the early stage of the disease and may be associated with neuroprotection. This suggests that in targeting glial activation as a therapeutic strategy, we must target astrocyte and microglia separately and in different stages of the disease.

Multimodal ensemble model for Alzheimer's disease conversion prediction from Early Mild Cognitive Impairment subjects

Alzheimer's Disease (AD) is the most common type of dementia. Predicting the conversion to Alzheimer's from the mild cognitive impairment (MCI) stage is a complex problem that has been studied extensively. This study centers on individualized EMCI (the earliest MCI subset) to AD conversion prediction on multimodal data such as diffusion tensor imaging (DTI) scans and electronic health records (EHR) for their patients using the combination of both a balanced random forest model alongside a convolutional neural network (CNN) model. Our random forest model leverages EHR's patient biometric and neuropsychiatric test score features, while our CNN model uses the patient's diffusion tensor imaging (DTI) scans for conversion prediction. To accomplish this, 383 Early Mild Cognitive Impairment (EMCI) patients were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Within this set, 49 patients would eventually convert to AD (EMCI_C), whereas the remaining 335 did not convert (EMCI_NC). For the EHR-based classifier, 288 patients were used to train the random forest model, with 95 set aside for testing. For the CNN classifier, 405 DTI images were collected across 90 distinct patients. Nine clinical features were selected to be combined with the visual predictor. Due to the imbalanced classes, oversampling was performed for the clinical features and augmentation for the DTI images. A grid search algorithm is also used to determine the ideal weighting between our two models. Our results indicate that an ensemble model was effective (98.81% accuracy) at EMCI to AD conversion prediction. Additionally, our ensemble model provides explainability as feature importance can be assessed at both the model and individual prediction levels. Therefore, this ensemble model could serve as a diagnostic support tool or a means for identifying clinical trial candidates.

Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects.

Alzheimer's Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.

Sparse structure deep network embedding for transforming brain functional network in early mild cognitive impairment classification

AbstractCurrently, it remains one of the most challenging issues to distinguish brain functional networks of early mild cognitive impairment subjects (eMCIs) and normal control subjects (NCs). Unlike images, functional networks are non‐Euclidean data and not easily classified by dilated convolutional neural network (DCNN). To address this problem, we developed a sparse structure deep network embedding (SSDNE) method which transforms brain functional networks into a double‐channel image in eMCI classification. First, the eigenvector of each node in the functional network was obtained by SSDNE, and principal component analysis (PCA) was employed to sort the importance of eigenvectors. Next, two eigenvector groups with the highest contribution rate were extracted in turn and divided into several equal‐length intervals along its direction respectively, and the numbers of nodes that fall into the intervals were counted to obtain the corresponding two‐dimensional histograms. Then, the both histograms were stacked up and down into a double‐channel image. Finally, a double‐channel image was input into DCNN for feature learning to achieve final classification results. Experimental results show that, SSDNE performed better in maintaining the original structure of brain functional networks, and the transformed double‐channel image achieved comparably identifying results on eMCI classification compared with other network embedding algorithms. This novel method solved the problem that brain functional networks cannot be directly applied to convolutional neural networks for feature extraction and classification. Meanwhile, it can provide a reference for the early auxiliary diagnosis of Alzheimer's disease (AD).

Identifying brain functional connectivity alterations during different stages of Alzheimer’s disease

Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed. Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated individually in the early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), AD, and control subjects. Results: In the whole-brain analysis, during one year of disease progression, no significant changes were observed in none of the study groups. However, the intergroup comparison showed that in different stages (from healthy to AD) the efficiencies, clustering coefficient, transitivity, and modularity of the brain network have significantly changed. In the DMN network analysis, the EMCI subjects demonstrated significant alterations but no significant changes were observed in other study groups. In the nodal analysis of the DMN, the participation, clustering, and degree were among the measures significantly changed with the AD progression. Conclusions: Functional connectivity alterations are more in the first stage of AD. Since AD progresses slowly whole brain alterations are not significant in one year but DMN exhibits significant changes. Cingulum anterior and posterior areas were the first affected regions of interest (ROI) in the DMN network afterwards, the frontal superior medial ROI was declined in the functional connectivity.

A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging

BackgroundDiagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer’s disease (AD) with its delineation from the cognitively normal (CN) group remains a challenging but essential step for the planning of early treatment. Although several studies have focused on the MCI diagnosis, this study introduces the early stage of MCI to assess more thoroughly the earliest signs of disease manifestation and progression. New methodWe used random forest feature selection model with a Gaussian-based algorithm to perform method evaluation. This integrated method serves to define multivariate normal distributions in order to classify different stages of AD, with the focus placed on detecting EMCI subjects in the most challenging classification of CN vs. EMCI. ResultsUsing 896 participants classified into the four categories of CN, EMCI, late mild cognitive impairment (LMCI) and AD, the results show that the EMCI group can be delineated from the CN group with a relatively high accuracy of 78.8% and sensitivity of 81.3%. Comparison with existing method(s)The feature selection model and classifier are compared with some other prominent algorithms. Although higher accuracy has been achieved using the Gaussian process (GP) model (78.8%) over the SVM classifier (75.6%) for CN vs. EMCI classification, with 0.05 being the cutoff for significance, and based on student’s t-test, it was determined that the differences for accuracy, sensitivity, specificity between the GP method and support vector machine (SVM) are not statistically significant. ConclusionAddressing the challenging classification of CN vs. EMCI provides useful information to help clinicians and researchers determine essential measures that can help in the early detection of AD.

Default Mode Network Functional Connectivity in Early and Late Mild Cognitive Impairment: Results From the Alzheimer's Disease Neuroimaging Initiative.

Default mode network (DMN) functional connectivity is one of the neuroimaging candidate biomarkers of Alzheimer disease. However, no studies have investigated DMN connectivity at different stages of mild cognitive impairment (MCI). The aim of this study was to investigate patterns of DMN connectivity and its breakdown among cognitively normal (CN), early MCI (EMCI), and late MCI (LMCI) subjects. Magnetic resonance imaging data and neuropsychological test scores from 130 subjects (CN=43, EMCI=47, LMCI=40) were obtained from the Alzheimer's Disease Neuroimaging Initiative. DMN functional connectivity was extracted using independent components analysis and compared between groups. Functional connectivity in the precuneus, bilateral medial frontal, parahippocampal, middle temporal, right superior temporal, and left angular gyri was decreased in EMCI subjects compared with CN subjects. When the 2 MCI groups were directly compared, LMCI subjects exhibited decreased functional connectivity in the precuneus, bilateral medial frontal gyri, and left angular gyrus. There was no significant difference in gray matter volume among the 3 groups. Amyloid-positive EMCI subjects revealed more widespread breakdown of DMN connectivity than amyloid-negative EMCI subjects. A quantitative index of DMN connectivity correlated well with measures of cognitive performance. Our results suggest that the breakdown of DMN connectivity may occur in the early stage of MCI.

Comparing positron emission tomography imaging and cerebrospinal fluid measurements of β‐amyloid

We examined agreement and disagreement between 2 biomarkers of β-amyloid (Aβ) deposition (amyloid positron emission tomography [PET] and cerebrospinal fluid [CSF] Aβ1-42 ) in normal aging and dementia in a large multicenter study. Concurrently acquired florbetapir PET and CSF Aβ were measured in cognitively normal, mild cognitive impairment (MCI), and Alzheimer's disease participants (n = 374) from the Alzheimer's Disease Neuroimaging Initiative. We also compared Aβ measurements in a separate group with serial CSF measurements over 3.1 ± 0.8 years that preceded a single florbetapir session. Additional biomarker and cognitive data allowed us to further examine profiles of discordant cases. Florbetapir and CSF Aβ were inversely correlated across all diagnostic groups, and dichotomous measurements were in agreement in 86% of subjects. Among subjects showing the most disagreement, the 2 discordant groups had different profiles: the florbetapir(+) /CSF Aβ(-) group was larger (n = 13) and was made up of only normal and early MCI subjects, whereas the florbetapir(-) /CSF Aβ(+) group was smaller (n = 7) and had poorer cognitive function and higher CSF tau, but no ApoE4 carriers. In the longitudinal sample, we observed both stable longitudinal CSF Aβ trajectories and those actively transitioning from normal to abnormal, but the final CSF Aβ measurements were in good agreement with florbetapir cortical retention. CSF and amyloid PET measurements of Aβ were consistent in the majority of subjects in the cross-sectional and longitudinal populations. Based on our analysis of discordant subjects, the available evidence did not show that CSF Aβ regularly becomes abnormal prior to fibrillar Aβ accumulation early in the course of disease.