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.
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: Sarah J Tabrizi; Ralf Reilmann; Raymund A C Roos; Alexandra Durr; Blair Leavitt; Gail Owen; Rebecca Jones; Hans Johnson; David Craufurd; Stephen L Hicks; Christopher Kennard; Bernhard Landwehrmeyer; Julie C Stout; Beth Borowsky; Rachael I Scahill; Chris Frost; Douglas R Langbehn Journal: Lancet Neurol Date: 2011-12-02 Impact factor: 44.182
Authors: H Diana Rosas; David H Salat; Stephanie Y Lee; Alexandra K Zaleta; Nathanael Hevelone; Steven M Hersch Journal: Ann N Y Acad Sci Date: 2008-12 Impact factor: 5.691
Authors: A Feigin; C Tang; Y Ma; P Mattis; D Zgaljardic; M Guttman; J S Paulsen; V Dhawan; D Eidelberg Journal: Brain Date: 2007-09-24 Impact factor: 13.501
Authors: J C H van Oostrom; M Dekker; A T M Willemsen; B M de Jong; R A C Roos; K L Leenders Journal: Eur J Neurol Date: 2008-12-09 Impact factor: 6.089
Authors: Sarah J Tabrizi; Douglas R Langbehn; Blair R Leavitt; Raymund Ac Roos; Alexandra Durr; David Craufurd; Christopher Kennard; Stephen L Hicks; Nick C Fox; Rachael I Scahill; Beth Borowsky; Allan J Tobin; H Diana Rosas; Hans Johnson; Ralf Reilmann; Bernhard Landwehrmeyer; Julie C Stout Journal: Lancet Neurol Date: 2009-07-29 Impact factor: 44.182
Authors: María C Rodriguez-Oroz; Belen Gago; Pedro Clavero; Manuel Delgado-Alvarado; David Garcia-Garcia; Haritz Jimenez-Urbieta Journal: Curr Neurol Neurosci Rep Date: 2015-07 Impact factor: 5.081
Authors: Casey A Maguire; Servio H Ramirez; Steven F Merkel; Miguel Sena-Esteves; Xandra O Breakefield Journal: Neurotherapeutics Date: 2014-10 Impact factor: 7.620
Authors: Meike Herben-Dekker; Joost C H van Oostrom; Raymund A C Roos; Caroline K Jurgens; Marie-Noëlle W Witjes-Ané; Hubertus P H Kremer; Klaus L Leenders; Jacoba M Spikman Journal: J Neurol Date: 2014-04-30 Impact factor: 4.849
Authors: Jan Van den Stock; François-Laurent De Winter; Rawaha Ahmad; Stefan Sunaert; Koen Van Laere; Wim Vandenberghe; Mathieu Vandenbulcke Journal: Hum Brain Mapp Date: 2015-04-08 Impact factor: 5.038
Authors: Christopher A Ross; Elizabeth H Aylward; Edward J Wild; Douglas R Langbehn; Jeffrey D Long; John H Warner; Rachael I Scahill; Blair R Leavitt; Julie C Stout; Jane S Paulsen; Ralf Reilmann; Paul G Unschuld; Alice Wexler; Russell L Margolis; Sarah J Tabrizi Journal: Nat Rev Neurol Date: 2014-03-11 Impact factor: 42.937
Authors: Ji Hyun Ko; Andrew Feigin; Paul J Mattis; Chris C Tang; Yilong Ma; Vijay Dhawan; Matthew J During; Michael G Kaplitt; David Eidelberg Journal: J Clin Invest Date: 2014-07-18 Impact factor: 14.808