Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature

AbstractAlzheimer’s disease (AD) is a common neurodegenerative disease in the elderly, early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most of the brain regions related to AD were identified based on the imaging method, which can only identify some atrophic brain regions. In this work, we used mathematical models to find out the potential brain regions related to AD. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, we set a new local feature index 2hop-connectivity to measure the correlation among different areas. And for this, we proposed a novel algorithm named 2hopRWR to measure 2hop-connectivity. At last, we proposed a new index GFS (Global Feature Score) based on global feature by combing 5 local features: degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are likely related to Alzheimer’s Disease. As a result, all the top ten brain regions in GFS scoring difference between the AD group and the non-AD group were related to AD by literature verification. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the mini-mental state examination (MMSE) scale and montreal cognitive assessment (MoCA) scale. So, we believe the GFS can also be used as a new index to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method in other areas of network analysis.

Mixed pathology, with both Alzheimer’s disease and vascular abnormalities, is the most common cause of clinical dementia in the elderly. While usually thought to be concurrent diseases, the fact that changes in cerebral blood flow are a prominent early and persistent alteration in Alzheimer’s disease raises the possibility that vascular alterations and Alzheimer pathology are more directly linked. Here, we report that aged tau-overexpressing mice develop changes to blood vessels including abnormal, spiraling morphologies; reduced blood vessel diameters; and increased overall blood vessel density in cortex. Blood flow in these vessels was altered, with periods of obstructed flow rarely observed in normal capillaries. These changes were accompanied by cortical atrophy as well as increased expression of angiogenesis-related genes such as Vegfa, Serpine1, and Plau in CD31-positive endothelial cells. Interestingly, mice overexpressing nonmutant forms of tau in the absence of frank neurodegeneration also demonstrated similar changes. Furthermore, many of the genes we observe in mice are also altered in human RNA datasets from Alzheimer patients, particularly in brain regions classically associated with tau pathology such as the temporal lobe and limbic system regions. Together these data indicate that tau pathological changes in neurons can impact brain endothelial cell biology, altering the integrity of the brain’s microvasculature.

Abstract The most replicated blood biomarker for monitoring Alzheimer’s disease is neurofilament light (NFL). Recent evidence revealed that the plasma level of the NFL has a strong predictive value in cognitive decline and is elevated in AD patients. The Diffusion Tensor Imaging (DTI) is understood to reflect white matter disruption, neurodegeneration largely, and synaptic damage in AD. However, there is no investigation of the association between plasma NFL and white matter microstructure integrity. we have investigated the cross-sectional associations of plasma NFL, CSF tau, p tau, and Aβ with white matter microstructural changes as measured by DTI in 92 mild cognitive impairment (MCI) participants. We investigated potential correlations of the DTI values of each region of the MNI atlas, with plasma NFL, CSF total tau, CSF p tau, and as well as CSF Aβ, separately using a partial correlation model controlled for the effect of age, sex and APOE ε4 genotype. Our findings revealed a significant correlation between plasma and CSF biomarkers with altered white matter microstructural changes in widespread brain regions. Plasma NFL has a negative correlation with FA and positive correlation with RD, AD, and MD values in different regions. Plasma NFL promises to be an early biomarker of microstructural changes in MCI and for MCI progression to AD.

AbstractAlzheimer’s disease (AD) is the most frequent cause of dementia in the elderly, characterized by the presence of cerebral amyloid plaques and neurofibrillary tangles. The causes of the disease are not well understood, especially considering that more than 95% of AD patients are non-familial. Due to the similarity of brain regions affected in herpes simplex encephalitis to those mainly affected in AD, and owing to the very high prevalence of latent herpes simplex virus type 1 (HSV1) infection, reactivation of HSV1 was proposed as one of the possible causes of AD. The trigeminal ganglion, located only a few millimeters from the entorhinal cortex, is the primary site of HSV1 latency, although other sites including the sensory neurons, the nodose ganglion of the vagus nerve and other regions of the brain may be involved, possibly in relation to very early neurofibrillary AD changes in the dorsal raphe, locus coeruleus and other brainstem nuclei. Novel data obtained upon infection of cultured neuronal cells and mouse brain with HSV1 further show that HSV1 infection causes intracellular amyloid-beta protein accumulation, as well as abnormal phosphorylation of tau protein, the major component of tangles. Another interesting fact is the existence of a significant degree of homology between HSV1 components and AD susceptibility genes. In this review we summarize findings that reveal connections between the two conditions, as well as different suggestions for the mechanisms of HSV1-induced AD. As most of the available results support a connection of AD and HSV1 infection, antiviral therapy should be taken into consideration for AD treatment following early diagnosis.

Background: Hippocampal atrophy is a characteristic of Alzheimer’s disease (AD). However, alterations in structural connectivity (number of connecting fibers) between the hippocampus and whole brain regions due to hippocampal atrophy remain largely unknown in AD and its prodromal stage, amnestic mild cognitive impairment (aMCI).Methods: We collected high-resolution structural MRI (sMRI) and diffusion tensor imaging (DTI) data from 36 AD patients, 30 aMCI patients, and 41 normal control (NC) subjects. First, the volume and structural connectivity of the bilateral hippocampi were compared among the three groups. Second, correlations between volume and structural connectivity in the ipsilateral hippocampus were further analyzed. Finally, classification ability by hippocampal volume, its structural connectivity, and their combination were evaluated.Results: Although the volume and structural connectivity of the bilateral hippocampi were decreased in patients with AD and aMCI, only hippocampal volume correlated with neuropsychological test scores. However, positive correlations between hippocampal volume and ipsilateral structural connectivity were displayed in patients with AD and aMCI. Furthermore, classification accuracy (ACC) was higher in AD vs. aMCI and aMCI vs. NC by the combination of hippocampal volume and structural connectivity than by a single parameter. The highest values of the area under the receiver operating characteristic (ROC) curve (AUC) in every two groups were all obtained by combining hippocampal volume and structural connectivity.Conclusions: Our results showed that the combination of hippocampal volume and structural connectivity (number of connecting fibers) is a new perspective for the discrimination of AD and aMCI.

Abstract The most replicated blood biomarker for monitoring Alzheimer’s disease is neurofilament light (NFL). Recent evidence revealed that the plasma level of the NFL has a strong predictive value in cognitive decline and is elevated in AD patients. The Diffusion Tensor Imaging (DTI) is understood to reflect white matter disruption, neurodegeneration, and synaptic damage in AD. However, few investigations have been carried out on the association between plasma NFL and white matter microstructure integrity. We have investigated the cross-sectional associations of plasma NFL, CSF total tau, phosphorylated tau, and Amyloid β with white matter microstructural changes as measured by DTI in 92 mild cognitive impairment (MCI) participants. We investigated potential correlations of the DTI values of each region of the MNI atlas, with plasma NFL, separately using a partial correlation model controlled for the effect of age, sex, and APOE ε4 genotype. Our findings revealed a significant correlation between plasma and CSF biomarkers with altered white matter microstructural changes in widespread brain regions. Plasma NFL negatively correlates with FA and the positive correlation with RD, DA, and MD values in different regions. Our findings showed that plasma NFL is associated with white matter changes and AD-related features, including atrophy and hypometabolism. Plasma NFL promises to be an early biomarker of microstructural changes in MCI and MCI progression to AD.

Abstract Background Alzheimer's disease (AD) dementia is the most common form of dementia in the elderly. Chronic periodontitis (CP) is a progressive destructive disease in the periodontal tissues, which is also common in the elderly. CP is known to be associated with an increase in cognitive decline in Alzheimer’s disease (AD). Recently, a potential role for pathogenic microbes in the development or exacerbation of AD pathology has been proposed. To reveal the association between periodontitis-related microbes and AD, we investigated the oral microbiome in AD patients with CP. Methods Fifteen AD dementia (AD) with CP and 14 cognitively unimpaired (CU) participants with CP were recruited. Buccal, supragingival and subgingival plaque samples were collected with the full-mouth periodontal examination. Alpha diversity, beta diversity, LEfSe (linear discriminant analysis effect size), metabolic pathway prediction and network analysis were applied to compare the microbiome features. Results All participants had moderate to severe chronic periodontitis. The level of alpha diversity in subgingival microbiota of the AD group was higher than the CU group. Also, principle coordinate analysis showed significant difference in subgingival samples. When significant taxa were analyzed by LEfSe, various Prevotella spp. were more prevalent in subgingival samples from AD group. Furthermore, subgingival microbiome network analysis showed distinctive network complexity in AD compared to CU group. Conclusion We found that subgingival microbiome of AD patients had increased microbial diversity. The composition of subgingival microbiome was different between the AD and the CU groups. This pilot study provides a novel view at the changes of subgingival microbiome in AD patients with CP. Our findings need further well-designed studies with adequate sample size to confirm oral microbiome characteristics in AD with CP.

Alzheimer’s disease (AD) is the most prevalent type of dementia. Pathological changes in the AD brain include amyloid-β(Aβ) plaques and neurofibrillary tangles (NFTs), as well as neuronal death and synaptic loss. Matrix metalloproteinases (MMPs) play an important role as inflammatory components in the pathogenesis of AD. MMP-2 might be assumed to have a protective role in AD and is the major MMP which is directly linked to Aβin the brain. Synthesis of MMP-9 can be induced by Aβ, and the enzymes appear to exert multiple effects in AD in senile plaque homoeostasis. The proaggregatory influence on tau oligomer formation in strategic brain regions may be a potential neurotoxic side effect of MMP-9. MMP-3 levels are correlated to the duration of AD and correlate with the CSF T-tau and P-tau levels in the elderly controls. Elevated brain levels of MMP-3 might result in increased MMP-9 activity and indirectly facilitate tau aggregation. At present, the clinical utility of these proteins, particularly in plasma or serum, as potential early diagnostic biomarkers for AD remains to be established. More research is needed to understand the diverse roles of these proteases to design specific drugs and devise therapeutic strategies for AD.

The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.

To explore the evaluation of white matter structural network analysis in the differentiation of Alzheimer’s disease (AD) and subcortical ischemic vascular dementia (SIVD), 67 participants [31 AD patients, 19 SIVD patients, and 19 normal control (NC)] were enrolled in this study. Each participant underwent 3.0T MRI scanning. Diffusion tensor imaging (DTI) data were analyzed by graph theory (GRETNA toolbox). Statistical analyses of global parameters [gamma, sigma, lambda, global shortest path length (Lp), global efficiency (Eg), and local efficiency (Eloc)] and nodal parameters [betweenness centrality (BC)] were obtained. Network-based statistical analysis (NBS) was employed to analyze the group differences of structural connections. The diagnosis efficiency of nodal BC in identifying different types of dementia was assessed by receiver operating characteristic (ROC) analysis. There were no significant differences of gender and years of education among the groups. There were no significant differences of sigma and gamma in AD vs. NC and SIVD vs. NC, whereas the Eg values of AD and SIVD were statistically decreased, and the lambda values were increased. The BC of the frontal cortex, left superior parietal gyrus, and left precuneus in AD patients were obviously reduced, while the BC of the prefrontal and subcortical regions were decreased in SIVD patients, compared with NC. SIVD patients had decreased structural connections in the frontal, prefrontal, and subcortical regions, while AD patients had decreased structural connections in the temporal and occipital regions and increased structural connections in the frontal and prefrontal regions. The highest area under curve (AUC) of BC was 0.946 in the right putamen for AD vs. SIVD. White matter structural network analysis may be a potential and promising method, and the topological changes of the network, especially the BC change in the right putamen, were valuable in differentiating AD and SIVD patients.