Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease

Abstract

Alzheimer's disease (AD) is increasingly recognized as a disconnection syndrome, which leads to cognitive impairment due to the disruption of functional activity across large networks or systems of interconnected brain regions. We explored abnormal functional magnetic resonance imaging (fMRI) resting-state dynamics, functional connectivity, and weighted functional networks, in a sample of patients with severe AD (N = 18) and age-matched healthy volunteers (N = 21). We found that patients had reduced amplitude and regional homogeneity of low-frequency fMRI oscillations, and reduced the strength of functional connectivity, in several regions previously described as components of the default mode network, for example, medial posterior parietal cortex and dorsal medial prefrontal cortex. In patients with severe AD, functional connectivity was particularly attenuated between regions that were separated by a greater physical distance; and loss of long distance connectivity was associated with less efficient global and nodal network topology. This profile of functional abnormality in severe AD was consistent with the results of a comparable analysis of data on 2 additional groups of patients with mild AD (N = 17) and amnestic mild cognitive impairment (MCI; N = 18). A greater degree of cognitive impairment, measured by the mini-mental state examination across all patient groups, was correlated with greater attenuation of functional connectivity, particularly over long connection distances, for example, between anterior and posterior components of the default mode network, and greater reduction of global and nodal network efficiency. These results indicate that neurodegenerative disruption of fMRI oscillations and connectivity in AD affects long-distance connections to hub nodes, with the consequent loss of network efficiency. This profile was evident also to a lesser degree in the patients with less severe cognitive impairment, indicating that the potential of resting-state fMRI measures as biomarkers or predictors of disease progression in AD.

Keywords: Alzheimer's disease; disconnection; distance; functional connectivity; weighted brain networks.

Figure 1.

Schematic of data analysis pipeline. Regional mean fMRI time series were estimated by applying an anatomical template image to each subject's image. Univariate measures were estimated for each time series (ReHo and ALFF); bivariate measures of functional connectivity were estimated between each pair of regions; and weighted functional networks were constructed from the functional connectivity matrix for each participant. Gray matter volume was also estimated for each of the same set of 442 regions used to parcellate the fMRI data. The main statistical analyses compared the most impaired patient group, with severe AD, to the group of healthy comparison subjects on all neuroimaging markers; and correlated functional connectivity and network metrics with variation in MMSE scores over all patients (including mild AD and MCI as well as severe AD).

Figure 2.

Altered univariate fMRI measures and GM density in patients with AD and MCI. (A) Regions showing a significant difference in ALFF between NC and patients with severe AD; red/yellow voxels indicate reduced ALFF in patients and blue voxels indicate increased ALFF in patients. (B) Scatterplot showing a significant association between MMSE scores for all patients and ALFF in the left temporo-parietal cortex (medial temporal gyrus and angular gyrus; R = 0.309, P = 0.027). MCI patients are indicated by green triangles, mild AD patients by blue circles, and severe AD patients by red squares. (C) Regions showing a significant difference in ReHo between NC and patients with severe AD; voxels are color-coded as in A. (D) Scatterplot showing the significant association between MMSE scores for all patients and ReHo in the left occipito-parietal cortex (precuneus (PCUN) and cuneus; R = 0.361, P = 0.009); diagnostic groups are distinguished by point markers as in B. (E) Regions showing significant GM density differences between patients with severe AD and NC; voxels are color-coded as in A. (F) Scatterplot showing the significant association between MMSE scores for all patients and GM in the left occipito-parietal cortex (PCC and PCUN; R = 0.620, P < 0.0001); diagnostic groups are distinguished by point markers as in B.

Figure 3.

Altered functional connectivity in AD and in association with MMSE scores. (A) Nodal distribution of altered functional connectivities among the 442 brain regions. The color bar denotes the number of edges that had abnormally reduced the strength of connectivity to each node. (B) Graph showing the top 10% decreased functional and increased functional connections in patients with severe AD compared with NC (P < 0.05, false discovery rate (FDR) corrected). Blue line means decreased functional connectivity. Red line means increased functional connectivity. See also see Supplementary Table S1 and S2 and Figure S5 and S6 for details. (C) Scatterplot showing the relationship between the mean connectivity strength of the top 10% decreased functional connections and MMSE (R = 0.523, P = 0.0001). MCI patients are indicated by green triangles, mild AD patients by blue circles, and severe AD patients by red squares. (D) Graph showing the functional connections that are significantly correlated with MMSE in the MCI and AD groups (P < 0.05 FDR corrected).

Figure 4.

Decreased long-distance functional connectivity in AD. (A) Plot of the global mean connection strength at different connection distances (mm) in NC (black) and patients with severe AD (red) (error bar indicates standard deviation). Asterisk denotes a significant difference in global connection strength at P < 0.05. (B) Graph shows the functional connectivity pattern in NC, MCI, mild AD, and severe AD groups at connection density 1%. Here, we showed that the long-distance connections (> 83 mm = the mean physical distance of the brain) are significantly reduced in the patient groups, especially in severe AD. (C) Bar graph of connection strength at different distances for the connections that were significantly different in severe AD compared with NC: NC (black), MCI (green), mild AD (blue), and severe AD (red); error bars indicate standard deviation. Asterisk denotes a significant difference between NC and severe AD at P < 0.05, see Supplementary Table S1 and S2 and Figure S5 and S6 for details.

Figure 5.

Global network topological properties were abnormal in AD and correlated with MMSE. (A–C) Group differences of the normalized network properties—distance (A), clustering (B), and efficiency (C) —over a range of connection densities from 1% to 40%. Blue stars indicate that the brain network topological properties were significantly altered in severe AD (red line) compared with NC (black line; P < 0.05). Blue circles indicate the brain network topological properties were significant correlated with MMSE in the MCI and AD groups (P < 0.05). (D–F) Scatterplots of the relationships between network properties—distance (D), clustering (E), and efficiency (F)—and MMSE in the patient groups. MCI patients are indicated by green triangles, mild AD patients by blue circles, and severe AD patients by red squares. Magenta lines indicate that brain network measures were significantly correlated with MMSE in the MCI and AD groups. Blue dashed line indicates that brain network measures were significantly correlated with MMSE in the MCI group. Black line indicates that brain network measures were significantly correlated with MMSE in the mild AD group. See Supplementary Table S3 for details.

Figure 6.

Nodal distribution of the altered topological properties in AD. (A) Nodal distribution of the altered distance in severe AD; red/yellow voxels indicate reduced distance in severe AD. (B) Scatterplot of the relationship between nodal mean distance and MMSE in the MCI and AD groups (R = 0.389, P = 0.004). MCI patients are indicated by green triangles, mild AD patients by blue circles, and severe AD patients by red squares. (C) Nodal distribution of the altered global efficiency in severe AD; red/yellow voxels indicate reduced global efficiency in severe AD. (D) Scatterplot of the relationship between nodal efficiency and MMSE in the MCI and AD groups (R = 0.334, P = 0.015). MCI patients are indicated by green triangles, mild AD patients by blue circles, and severe AD patients by red squares.