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Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease
Peter McColgan, Kiran K. Seunarine, Sarah Gregory, Adeel Razi, Marina Papoutsi, Jeffrey D. Long, James A. Mills, Eileanoir Johnson, Alexandra Durr, Raymund A.C. Roos, Blair R. Leavitt, Julie C. Stout, Rachael I. Scahill, Chris A. Clark, Geraint Rees, Sarah J. Tabrizi, the Track-On HD Investigators
Peter McColgan, Kiran K. Seunarine, Sarah Gregory, Adeel Razi, Marina Papoutsi, Jeffrey D. Long, James A. Mills, Eileanoir Johnson, Alexandra Durr, Raymund A.C. Roos, Blair R. Leavitt, Julie C. Stout, Rachael I. Scahill, Chris A. Clark, Geraint Rees, Sarah J. Tabrizi, the Track-On HD Investigators
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Research Article Neuroscience

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

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Abstract

We lack a mechanistic explanation for the stereotyped pattern of white matter loss seen in Huntington’s disease (HD). While the earliest white matter changes are seen around the striatum, within the corpus callosum, and in the posterior white matter tracts, the order in which these changes occur and why these white matter connections are specifically vulnerable is unclear. Here, we use diffusion tractography in a longitudinal cohort of individuals yet to develop clinical symptoms of HD to identify a hierarchy of vulnerability, where the topological length of white matter connections between a brain area and its neighbors predicts the rate of atrophy over 24 months. This demonstrates a new principle underlying neurodegeneration in HD, whereby brain connections with the greatest topological length are the first to suffer damage that can account for the stereotyped pattern of white matter loss observed in premanifest HD.

Authors

Peter McColgan, Kiran K. Seunarine, Sarah Gregory, Adeel Razi, Marina Papoutsi, Jeffrey D. Long, James A. Mills, Eileanoir Johnson, Alexandra Durr, Raymund A.C. Roos, Blair R. Leavitt, Julie C. Stout, Rachael I. Scahill, Chris A. Clark, Geraint Rees, Sarah J. Tabrizi, the Track-On HD Investigators

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Figure 3

Connection length varies according to connection type and correlates with rate of connection degeneration over 2 years in premanifest Huntington’s Disease (preHD).

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Connection length varies according to connection type and correlates wit...
(A) Illustration of shortest weighted path length between A and D in an example network. Numbers represent connection weights. When calculating shortest weighted path, length connections are weighted by the inverse of streamline weights, as stronger connections represent shorter paths in graph theory. (B) Comparison of shortest weighted path length for different classes of connection. Intra-M, intramodular (magenta); Intra-H, intrahemispheric (green); Inter-H, interhemispheric (red); CS, cortico-striatal (blue). Lower line, minimum; upper line, maximum; middle-box line, median; lower-box line, 1st quartile; upper-box line: 3rd quartile. Red crosses indicate outliers. (C) Cross-sectional analysis: Z-scores, denoting loss of connection strength, were transformed into positive atrophy measures using a logistic transform. Average transformed connection strength Z-score for preHD participants was plotted against connection-weighted path length for average control, and Spearman rank correlations were performed. Connections are color coded according to type. (D) Longitudinal analysis: Z-scores, denoting connection rate of atrophy over 3 time points, were transformed into a positive rate of atrophy measure using a logistic transform. Average transformed connection rate of change Z-scores for preHD participants were plotted against connection-weighted path length for average control, and Spearman rank correlations were performed. For both C and D, each data point represents a brain connection. The black line represents a least-squares linear regression line. df, degrees of freedom (2,695 data points displayed for each figure).

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