Cortical Metabolic and Cognitive Correlates of Disorientation in Alzheimer’s Disease

The increased effective connectivity from left middle occipital gyrus to right medial septum/diagonal bands in AD patients after donepezil intervention

Introduction

Donepezil enhances the function of cholinergic nerves by increasing the concentration of acetylcholine, thereby improving clinical symptoms in patients with Alzheimer's disease (AD). However, the neural mechanisms of how donepezil modulates the effective connectivity (EC) network of cholinergic system in AD patients remain unknown. We speculated that the effective network of the cholinergic system changes in AD patients after donepezil intervention.

Methods

We employed resting-state functional magnetic resonance imaging and Granger causality analysis approach to explore changes in the effective connectivity network of the basal forebrain in AD patients before and after donepezil intervention. This study included 32 participants, including 16 healthy controls (HCs) and 16 AD patients. In a 3T MRI scanner, the 16 AD patients were scanned before and after the donepezil intervention. To compare EC differences between the three groups of participants, ANOVA and post-hoc t-tests analysis were employed.

Results

Compared to baseline status, AD patients after donepezil intervention had an increased EC from left middle occipital gyrus to right medial septum/diagonal bands. Compared to HCs, AD patients after donepezil intervention had an increased EC from right inferior frontal gyrus/orbit part to right medial septum/diagonal bands, AD patients before donepezil intervention had a reduced EC from right precuneus to right medial septum/diagonal bands. A significant positive correlation was found between EC values in right precuneus and Mini-Mental State Examination in pre-intervention AD patients (r = 0.7338, p = 0.0012).

Discussion

Our study showed that effective connectivity of brain regions associated with the default mode network in the cholinergic pathway was enhanced after donepezil intervention. The results of this study will help us to better understand the neural mechanisms of donepezil intervention in AD and to find clinical targets for intervention.

Identification of image genetic biomarkers of Alzheimer's disease by orthogonal structured sparse canonical correlation analysis based on a diagnostic information fusion

Alzheimer's disease (AD) is an irreversible neurodegenerative disease, and its incidence increases yearly. Because AD patients will have cognitive impairment and personality changes, it has caused a heavy burden on the family and society. Image genetics takes the structure and function of the brain as a phenotype and studies the influence of genetic variation on the structure and function of the brain. Based on the structural magnetic resonance imaging data and transcriptome data of AD and healthy control samples in the Alzheimer's Disease Neuroimaging Disease database, this paper proposed the use of an orthogonal structured sparse canonical correlation analysis for diagnostic information fusion algorithm. The algorithm added structural constraints to the region of interest (ROI) of the brain. Integrating the diagnostic information of samples can improve the correlation performance between samples. The results showed that the algorithm could extract the correlation between the two modal data and discovered the brain regions most affected by multiple risk genes and their biological significance. In addition, we also verified the diagnostic significance of risk ROIs and risk genes for AD. The code of the proposed algorithm is available at https://github.com/Wanguangyu111/OSSCCA-DIF.

Associating brain imaging phenotypes and genetic risk factors via a hypergraph based netNMF method

Abstract

Alzheimer's disease (AD) is a severe neurodegenerative disease for which there is currently no effective treatment. Mild cognitive impairment (MCI) is an early disease that may progress to AD. The effective diagnosis of AD and MCI in the early stage has important clinical significance.

Methods

To this end, this paper proposed a hypergraph-based netNMF (HG-netNMF) algorithm for integrating structural magnetic resonance imaging (sMRI) of AD and MCI with corresponding gene expression profiles.

Results

Hypergraph regularization assumes that regions of interest (ROIs) and genes were located on a non-linear low-dimensional manifold and can capture the inherent prevalence of two modalities of data and mined high-order correlation features of the two data. Further, this paper used the HG-netNMF algorithm to construct a brain structure connection network and a protein interaction network (PPI) with potential role relationships, mine the risk (ROI) and key genes of both, and conduct a series of bioinformatics analyses.

Conclusion

Finally, this paper used the risk ROI and key genes of the AD and MCI groups to construct diagnostic models. The AUC of the AD group and MCI group were 0.8 and 0.797, respectively.