Assessment of the progression of Parkinson's disease: a metabolic network approach

Abstract

Clinical research into Parkinson's disease has focused increasingly on the development of interventions that slow the neurodegeneration underlying this disorder. These investigations have stimulated interest in finding objective biomarkers that show changes in the rate of disease progression with treatment. Through radiotracer-based imaging of nigrostriatal dopaminergic function, a specific class of biomarkers to monitor the progression of Parkinson's disease has been identified, and these biomarkers were used in the clinical trials of drugs with the potential to modify the course of the disease. However, in some of these studies there was discordance between the imaging outcome measures and blinded clinical ratings of disease severity. Research is underway to identify and validate alternative ways to image brain metabolism, through which the efficacy of new therapies for Parkinson's disease and related disorders can be assessed. During recent years, spatial covariance analysis has been used with (18)F-fluorodeoxyglucose PET to detect abnormal patterns of brain metabolism in patients with neurodegenerative disorders. Rapid, automated, voxel-based algorithms have been used with metabolic imaging to quantify the activity of disease-specific networks. This approach has helped to characterise the unique metabolic patterns associated with the motor and cognitive features of Parkinson's disease. The results of several studies have shown correction of abnormal motor, but not cognitive, network activity by treatment with dopaminergic therapy and deep brain stimulation. The authors of a longitudinal imaging study of early-stage Parkinson's disease reported substantial differences in the development of these metabolic networks over a follow-up of 4 years. WHERE NEXT?: Developments in network imaging have provided the basis for several new applications of metabolic imaging in the study of Parkinson's disease. A washout study is currently underway to determine the long-duration effects of dopaminergic therapy on the network activity related to Parkinson's disease, which will be useful to plan future trials of disease-modifying drugs. Network approaches are also being applied to the study of atypical parkinsonian syndromes. The characterisation of specific patterns associated with atypical parkinsonian syndromes and classic Parkinson's disease will be the basis for a fully automated imaging-based procedure for early differential diagnosis. Efforts are underway to quantify the networks related to Parkinson's disease with less invasive imaging methods. Assessments of network activity with perfusion-weighted MRI show excellent concordance with measurements done with established radiotracer techniques. This approach will ultimately enable the assessment of abnormal network activity in people who are genetically at risk of Parkinson's disease.