Metabolic PET imaging provides clues to developing Huntington’s disease

A functional neural network is under investigation as a major player in the progression of Huntington’s disease. Metabolic imaging of this region could serve as a tool for future treatments, according to a study published online Aug. 29 in the Journal of Clinical Investigation.

Researchers including Chris C. Tang, MD, from the Center for Neurosciences at The Feinstein Institute for Medical Research in Manhasset, N.Y., and colleagues evaluated a computational method of metabolic brain FDG PET for 12 healthy carriers of a mutated gene linked to the neurodegenerative disorder. These patients were imaged over a seven year period and scientists were able to assess disease progression from its asymptomatic state into the clear presence of disease and found linear increases in cerebral metabolic activity were matched by a breakdown in caudate and putamen tissue volume and neuro-receptor binding as evidenced by separate C-11 raclopride PET imaging and MRI. These findings were matched independently by another study of mutated gene carrying patients in the Netherlands who were imaged over a two-year period.

“Metabolic network measurements provide a sensitive means of quantitatively evaluating disease progression in premanifest individuals,” wrote Tang et al. “This approach may be incorporated into clinical trials to assess disease-modifying agents.”

Subjects underwent FDG PET brain imaging at the start of the study and again at 1.5, 4 and 7 years to evaluate cerebral metabolic activity and progression of disease. Neural network analyses revealed a substantial spatial covariance pattern and alteration in striato-thalamic and cortical metabolic activity overtime, which was also validated in the cohort from the Netherlands. Metabolic changes between baseline and the four-year mark accounted for a significant 9.7 percent of overall variance.

The identification of a functional network involved in Huntington’s disease progression could provide a better means of evaluating future drug treatments targeted toward those at risk of developing the disorder.

“To date, disease-related brain networks have been identified mainly by analyzing functional imaging data from clinically affected individuals who vary in the duration and severity of symptoms,” wrote the authors. “Nonetheless, few approaches have been developed to isolate specifically those networks that relate to the progression of the underlying process. Such networks are particularly relevant to the study of ‘at-risk’ individuals, in whom consistent clinical manifestations of the disease have yet to appear. Indeed, accurate determination of the preclinical progression rate is crucial for the evaluation of interventions designed to delay, or altogether prevent, the onset of symptoms.”