Neuroimaging and modern computational techniques like spatial covariance analysis have contributed greatly to the understanding of neural system abnormalities in neurodegenerative disorders such as Parkinson's disease (PD). The application of network analysis to metabolic PET data obtained from patients with PD has led to the identification and validation of two distinct spatial covariance patterns associated with the motor and cognitive manifestations of the disease. Quantifying the activity of these patterns in individual subjects has provided an objective tool for the assessment of treatment efficacy and differential diagnosis. We have found that activity of the PD motor-related network is modulated by antiparkinsonian treatments such as dopaminergic therapy, deep brain stimulation (DBS), and subthalamic nucleus (STN) gene therapy. By contrast, the cognitive-related network is not altered by these interventions for PD motor symptoms. This pattern may however change in response to therapies targeting the cognitive symptoms of this disorder. Recent work has focused on the identification of specific network biomarkers for atypical parkinsonian conditions such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). These disease-related patterns can potentially be used in an automated imaging-based algorithm to classify patients with these disorders.
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