Brain Connectivity

Amyloid-beta (Aβ) pathology is the precipitating histopathological characteristic of Alzheimer's disease (AD). Although the formation of amyloid plaques in human brains is suggested to be a key factor in initiating AD pathogenesis, it is still not fully understood the upstream events that lead to Aβ plaque formation and its metabolism inside the brains.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) has been successfully introduced to study AD pathology in brain tissue both in AD mouse models and human samples. By using MALDI-MSI, a highly selective deposition of Aβ peptides in AD brains with a variety of cerebral amyloid angiopathy (CAA) involvement was observed.

MALDI-MSI visualized depositions of shorter peptides in AD brains; Aβ1–36 to Aβ1–39 were quite similarly distributed with Aβ1–40 as a vascular pattern, and deposition of Aβ1–42 and Aβ1–43 was visualized with a distinct senile plaque pattern distributed in parenchyma. Moreover, how MALDI-MSI covered in situ lipidomics of plaque pathology has been reviewed, which is of interest as aberrations in neuronal lipid biochemistry have been implicated in AD pathogenesis.

In this study, we introduce the methodological concepts and challenges of MALDI-MSI for the studies of AD pathogenesis. Diverse Aβ isoforms including various C- and N-terminal truncations in AD and CAA brain tissues will be visualized. Despite the close relationship between vascular and plaque Aβ deposition, the current strategy will define cross talk between neurodegenerative and cerebrovascular processes at the level of Aβ metabolism.

Matrix-assisted laser desorption ionization mass spectrometry-based chemical imaging has been successfully applied to comprehensively delineate spatial Aβ peptide- and neuronal lipid patterns in brains with Alzheimer's disease. This rather new approach overcomes major limitations inherent to commonly used biochemical methods and opens up for both static and dynamic biochemical interrogations of amyloid aggregation in situ.

Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations.

Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results.

Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease.

Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.

By assuming that functional connectivity is static over time, many previous studies have ignored the brain dynamics in Alzheimer's disease (AD) progression. Here, longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in AD progression. The result of this study would increase our understanding of the underlying mechanisms of AD and help in finding future treatment of this neurological disorder.

Recently, a new resting-state functional magnetic resonance imaging (rs-fMRI) measure to evaluate the concordance between different rs-fMRI metrics has been proposed and has not been investigated in Alzheimer's disease (AD).

3T rs-fMRI data were obtained from healthy young controls (YC, n = 26), healthy senior controls (SC, n = 29), and AD patients (n = 35). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were analyzed, followed by the calculation of their concordance using Kendall's W for each brain voxel across time. Group differences in the concordance were compared globally, within seven intrinsic brain networks, and on a voxel-by-voxel basis with covariates of age, sex, head motion, and gray matter volume.

The global concordance was lowest in AD among the three groups, with similar differences for the single metrics. When comparing AD to SC, reductions of concordance were detected in each of the investigated networks apart from the limbic network. For SC in comparison to YC, lower global concordance without any network-level difference was observed. Voxel-wise analyses revealed lower concordance in the right middle temporal gyrus in AD compared to SC and lower concordance in the left middle frontal gyrus in SC compared to YC. Lower fALFF were observed in the right angular gyrus in AD in comparison to SC, but ReHo and DC showed no group differences.

The concordance of resting-state measures differentiates AD from healthy aging and may represent a novel imaging marker in AD.

The usefulness of a new resting-state functional magnetic resonance imaging (rs-fMRI) measure to assess the concordance between different rs-fMRI metrics has been demonstrated in mental disorders such as depression and schizophrenia. Our study, to the best of our knowledge, is the first to confirm a decreased concordance in Alzheimer's disease (AD) patients compared to healthy young and senior individuals on global, network, and voxel-wise levels, which moreover seems to be sensitive in differentiating age-related from AD-related functional brain changes. Our findings suggest that the concordance of rs-fMRI metrics may be useful as a candidate biomarker for neurodegenerative disorders such as AD.

Regional hypermetabolism in Alzheimer's disease (AD), especially in the cerebellum, has been consistently observed but often neglected as an artefact produced by the commonly used proportional scaling procedure in the statistical parametric mapping. We hypothesize that the hypermetabolic regions are also important in disease pathology in AD.

Using fluorodeoxyglucose (FDG)-positron emission tomography (PET) images from 88 AD subjects and 88 age-sex matched normal controls (NL) from the publicly available Alzheimer's Disease Neuroimaging Initiative database, we developed a general linear model-based classifier that differentiated AD patients from normal individuals (sensitivity = 87.50%, specificity = 82.95%). We constructed region–region group-wise correlation matrices and evaluated differences in network organization by using the graph theory analysis between AD and control subjects.

We confirmed that hypermetabolism found in AD is not an artefact by replicating it using white matter as the reference region. The role of the hypermetabolic regions has been further investigated by using the graph theory. The differences in betweenness centrality (BC) between AD and NL network were correlated with region weights of FDG PET-based AD classifier. In particular, the hypermetabolism in cerebellum was accompanied with higher BC. The brain regions with higher BC in AD network showed a progressive increase in FDG uptake over 2 years in prodromal AD patients (n = 39).

This study suggests that hypermetabolism found in AD may play an important role in forming the AD-related metabolic network. In particular, hypermetabolic cerebellar regions represent a good candidate for further investigation in altered network organization in AD.

Cerebellar hypermetabolism is a commonly observed characteristic of Alzhiemer's disease (AD) neurodegeneration in neuroimaging studies. However, whether cerebellar hypermetabolism is relevant to disease progression or whether it is an artefact of proportional scaling is controversial. In this study, we developed a general linear model-based classifier for AD by using the white matter mean for image scaling. We demonstrate that cerebellar hypermetabolism is a robust neuroimaging feature of AD. Further, hypermetabolism in the cerebellum is associated with an increase in the betweenness centrality of this region, indicating an important role of the cerebellum in changes in brain connectivity during AD.