Differences in Topography of Individual Amyloid Brain Networks by Amyloid PET Images in Healthy Control, Mild Cognitive Impairment, and Alzheimer's Disease

Abstract

Amyloid plaques, implicated in Alzheimer's disease, exhibit a spatial propagation pattern through interconnected brain regions, suggesting network-driven dissemination. This study utilizes PET imaging to investigate these brain connections and introduces an innovative method for analyzing the amyloid network. A modified version of a previously established method is applied to explore distinctive patterns of connectivity alterations across cognitive performance domains. PET images illustrate differences in amyloid accumulation, complemented by quantitative network indices. The normal control group shows minimal amyloid accumulation and preserved network connectivity. The MCI group displays intermediate amyloid deposits and partial similarity to normal controls and AD patients, reflecting the evolving nature of cognitive decline. Alzheimer's disease patients exhibit high amyloid levels and pronounced disruptions in network connectivity, which are reflected in low levels of global efficiency (Eg) and local efficiency (Eloc). It is mostly in the temporal lobe where connectivity alterations are found, particularly in regions related to memory and cognition. Network connectivity alterations, combined with amyloid PET imaging, show potential as discriminative markers for different cognitive states. Dataset-specific variations must be considered when interpreting connectivity patterns. The variability in MCI and AD overlap emphasizes the heterogeneity in cognitive decline progression, suggesting personalized approaches for neurodegenerative disorders. This study contributes to understanding the evolving network characteristics associated with normal cognition, MCI, and AD, offering valuable insights for developing diagnostic and prognostic markers.

Keywords: Amyloid plaque; Brain connectivity; Graph theory; Positron emission tomography.

Fig. 1

Schematic workflow of calculating an individual amyloid matrix of correlation coefficients by first calculating the individual weight matrix (W) and normal matrix (M_NC) from the normal group, and then multiplying both W and M_NC to obtain the individual correlation coefficient matrix (M). Finally, the individual correlation coefficient matrix transfers to the optima-individual amyloid brain network by OMSTs method

Fig. 2

The data utilized in the first row of the figure, spanning from the first to the third columns, originates from (ADNI, Linkou, and Kaohsiung). Matrices depicting correlation coefficients between brain regions of the right and left hemispheres (vertical axis) are presented. The inter-hemispheric connectivity is visualized through correlation coefficient matrices between the right hemisphere (horizontal axis) and left hemisphere (vertical axis) brain regions for NC, MCI, and AD. These matrices are derived using the normal template (initial column) and average individual connectivity matrices (second to fourth columns) employing the proposed methodology

Fig. 3

The number of edges, serving as a measure of network connectivity, reaches its peak in the NC group across a broad spectrum of correlation thresholds (0 < correlation coefficient values < 1) for three datasets (ADNI, Linkou, and Kaohsiung) in the NC, MCI, and AD conditions

Fig. 4

Brain connectivity graphs illustrate present NC, MCI, and AD across three datasets. The inter-hemispheric connectivity graphs were visualized for the three groups in each dataset, derived from thresholding the correlation coefficient matrix. In the graphical representation, brain connections are depicted by black lines, while nodes are represented by color-coded dots. Specifically, red dots denote the central lobule, pink dots represent the frontal lobe, yellow dots correspond to the parietal lobe, green dots signify the temporal lobe, light blue dots represent the occipital lobe, deep blue dots indicate the Limbic lobe and deep red dots represent other regions. This visualization provides a comprehensive overview of the inter-hemispheric connectivity patterns in the brain for individuals in the NC, MCI, and AD groups across the three datasets

Fig. 5

The differences in network efficiency between NC, MCI, and AD groups. NC, healthy controls; MCI, mild cognitive impairment; AD, Alzheimer’s disease; The differences in small world metrics (Eg, Eloc, Cp, Gamma, Lambda Lp, and Sigma) of the brain functional networks between NC, MCI, and AD groups. Error bars represent standard errors

Fig. 6

This figure depicts three distinct datasets from top to bottom. Moving from left to right within each dataset, the box plots represent the distribution of the global efficiency (Eg), local efficiency (Eloc), and clustering coefficient (Cp) parameters

Fig. 7

Selected representative amyloid PET images of individuals with varying levels of cognitive performance are presented from left to right in three datasets. These images depict cases from three groups: normal controls, subjects with mild cognitive impairment (MCI), and those diagnosed with Alzheimer’s disease (AD). Each displayed image is accompanied by corresponding values of Eg and Eloc, representing each patient's specific measures