Metabolic network as a progression biomarker of premanifest Huntington’s disease

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Abstract

Background. The evaluation of effective disease-modifying therapies for neurodegenerative disorders relies on objective and accurate measures of progression in at-risk individuals. Here we used a computational approach to identify a functional brain network associated with the progression of preclinical Huntington’s disease (HD).

Methods. Twelve premanifest HD mutation carriers were scanned with [¹⁸F]-fluorodeoxyglucose PET to measure cerebral metabolic activity at baseline and again at 1.5, 4, and 7 years. At each time point, the subjects were also scanned with [¹¹C]-raclopride PET and structural MRI to measure concurrent declines in caudate/putamen D₂ neuroreceptor binding and tissue volume. The rate of metabolic network progression in this cohort was compared with the corresponding estimate obtained in a separate group of 21 premanifest HD carriers who were scanned twice over a 2-year period.

Results. In the original premanifest cohort, network analysis disclosed a significant spatial covariance pattern characterized by progressive changes in striato-thalamic and cortical metabolic activity. In these subjects, network activity increased linearly over 7 years and was not influenced by intercurrent phenoconversion. The rate of network progression was nearly identical when measured in the validation sample. Network activity progressed at approximately twice the rate of single region measurements from the same subjects.

Conclusion. Metabolic network measurements provide a sensitive means of quantitatively evaluating disease progression in premanifest individuals. This approach may be incorporated into clinical trials to assess disease-modifying agents.

Trial registration. Registration is not required for observational studies.

Funding. NIH (National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering) and CHDI Foundation Inc.

Authors

Chris C. Tang, Andrew Feigin, Yilong Ma, Christian Habeck, Jane S. Paulsen, Klaus L. Leenders, Laura K. Teune, Joost C.H. van Oostrom, Mark Guttman, Vijay Dhawan, David Eidelberg