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. 2015 Aug;138(Pt 8):2332-46.
doi: 10.1093/brain/awv145. Epub 2015 Jun 9.

Network Topology and Functional Connectivity Disturbances Precede the Onset of Huntington's Disease

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Free PMC article

Network Topology and Functional Connectivity Disturbances Precede the Onset of Huntington's Disease

Deborah L Harrington et al. Brain. .
Free PMC article

Abstract

Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease burden increased. These disturbances were often related to long-range connections involving peripheral nodes and interhemispheric connections. A strong association was found between weaker connectivity and decreased rich-club organization, indicating that whole-brain simple connectivity partially expressed disturbances in the communication of highly-connected hubs. However, network topology and network-based statistic connectivity metrics did not correlate with key markers of executive dysfunction (Stroop Test, Trail Making Test) in prodromal Huntington's disease, which instead were related to whole-brain connectivity disturbances in nodes (right inferior parietal, right thalamus, left anterior cingulate) that exhibited multiple aberrant connections and that mediate executive control. Altogether, our results show for the first time a largely disease burden-dependent functional reorganization of whole-brain networks in prodromal Huntington's disease. Both analytic approaches provided a unique window into brain reorganization that was not related to brain atrophy or motor symptoms. Longitudinal studies currently in progress will chart the course of functional changes to determine the most sensitive markers of disease progression.

Keywords: Huntington disease; graph theory; network based statistic; network topology; resting-state connectivity.

Figures

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Early intervention trials in Huntington’s disease would benefit from markers of prodromal disease progression. By applying graph theory analyses and network-based statistics to whole-brain resting-state fMRI data, Harrington et al. reveal decreased connectivity of rich-club network hubs, plus weakening of frontostriatal and strengthening of frontal-posterior connections with increasing disease burden.
Figure 1
Figure 1
Network topology measures in the gene-negative and the gene-positive groups. Bar graphs display the mean (standard error) standardized age-adjusted residuals for global efficiency, rich-club area under the curve (AUC), density, and average clustering coefficient in each group. Plotted values can be negative as they are standardized residuals. Supplementary Table 2 reports the unadjusted means and standard deviations for these measures, for which all values are positive. The main effect of group (Negative, Low, Medium, and High) was significant for global efficiency and rich-club AUC. For these measures, asterisks designate significant differences in the means between the Negative group and a prodromal Huntington’s disease group (global efficiency: Medium, P = 0.035, High, P = 0.002; rich-club AUC: Medium, P = 0.007, High, P = 0.001).
Figure 2
Figure 2
Association between network topology measures and disease burden in gene-positive participants. Scatter plots show the relationship between the CAP score and the age-adjusted residuals for global efficiency (r = 0.33, P = 0.022) and rich-club area under the curve (r = −0.32, P = 0.024). The solid lines show the best-fitting linear regression line and the 95% confidence intervals.
Figure 3
Figure 3
Rich-club ratio plots for gene-negative and gene-positive groups as a function of node strength. The graph shows the association between the mean (standard error) ratios of rich-club to a random network as a function of node strength (k) for each of the groups. The figure shows that differences between the Negative and the prodromal Huntington’s disease groups emerge as node strength increases. A k-value of 130 was the highest node-strength value for which all subjects had data and the functions were well-behaved, exhibiting no sharp discontinuities (vertical dotted line).
Figure 4
Figure 4
Rich-club anatomy for the entire sample. Fifty nodes are shown whose strength (k) was greater than 130 in 90% of the subjects in one or more of the groups. Images are displayed in neurological view (left side = left hemisphere). The red–yellow colour bar denotes the average node strength across all subjects. High-strength nodes were prominent in midline central [right supplementary motor area (SMA), bilateral middle cingulate] and posterior (superior parietal, precuneus, cuneus, lingual and fusiform gyrus) cortices, sensorimotor cortex (precentral and postcentral gyrus), lateral temporal (bilateral superior temporal and Heschl’s gyrus; right middle temporal) and occipital (superior and middle occipital) cortices, and the cerebellum (lobule VI). The node strength of midline anterior and posterior areas was particularly notable. Group differences in node strength were not significant (FDR adjusted) for any of these nodes.
Figure 5
Figure 5
Aberrant functional connectivity in the gene-positive groups. To identify anatomical sources of functional connectivity disturbances in prodromal Huntington’s disease (prHD), the fitted z-scores of correlation coefficients (standardized age-adjusted residuals) for all edges connecting the 300 nodes were compared between the Negative group and each of the prodromal Huntington’s disease groups using the network-based statistic. The glass brains illustrate the aberrant functional connections that were identified for each prodromal Huntington’s disease group. The brains at the top (Negative > prHD) display functional connections that were weaker (red edges) in each prodromal Huntington’s disease group relative to the Negative group. The brains at the bottom (prHD > Negative) display functional connections that were stronger (blue edges) in each prodromal Huntington’s disease group relative to the Negative group. Empty glass brains signify no group differences in functional connectivity.
Figure 6
Figure 6
Whole-brain functional connectivity of nodes with the highest number of weakened and strengthened aberrant connections in gene-positive individuals. Displayed nodes are those showing a significant main effect of group (Negative, Low, Medium, High) (Supplementary Table 4). Their anatomical location is shown on lateral and medial sections of brains. The bar graphs display the mean (standard error) of the whole-brain summed z-scores (standardized age-adjusted residuals) for each group. An asterisk designates significant differences in the means between the Negative group and a prodromal Huntington’s disease group. The scatter plot (left) illustrates the significant association between CAP scores and whole-brain functional connectivity of the right inferior parietal lobe (r = 0.31, P = 0.032).
Figure 7
Figure 7
Association between whole-brain functional connectivity in regions of interest and cognitive performances in gene-positive participants. Graphs plot the age- and education-adjusted residuals for the cognitive measures against the age-adjusted residuals of the regional summed z-scores in the prodromal Huntington’s disease subjects. The solid lines on scatter plots show the best-fitting linear regression line and the 95% confidence intervals. Black, red, and green dots designate participants in the Low, Medium, and High CAP groups, respectively. Left: Association between Stroop Color Naming and whole-brain functional connectivity of the right inferior parietal lobule (r = −0.434, P = 0.002). Middle: Association between Stroop Interference and whole-brain connectivity of the right thalamus (r = −0.494, P = 0.0004). Right: Association between Trails B-A and whole-brain connectivity of the left anterior cingulate (r = −0.332, P = 0.02).

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