Learning networks in health and Parkinson's disease: reproducibility and treatment effects

In a previous H(2) (15)O/PET study of motor sequence learning, we used principal components analysis (PCA) of region of interest (ROI) data to identify performance-related activation patterns in normal subjects and patients with Parkinson's disease (PD). In the present study, we determined whether these patterns predicted learning performance in subsequent normal and untreated PD cohorts. Using a voxel-based PCA approach, we correlated the changes in network activity that occurred during antiparkinsonian treatment and their relationship to learning performance. We found that the previously identified ROI-based patterns correlated with learning performance in the prospective normal (P < 0.01) and untreated PD (P < 0.05) cohorts. Voxel analysis revealed that target retrieval was related to a network characterized by bilateral activation of the dorsolateral prefrontal, premotor and anterior cingulate cortex, the precuneus, and the occipital association areas as well as the right ventral prefrontal and inferior parietal regions. Target acquisition was associated with a different network involving activation of the caudate, putamen, and right dentate nucleus, as well as the left ventral prefrontal and inferior parietal areas. Antiparkinsonian therapy gave rise to changes in retrieval performance that correlated with network modulation (P < 0.01). Increases in network activation and learning performance occurred with internal pallidal deep brain stimulation (GPi DBS); decrements in these measures were present with levodopa. Our findings suggest that network analysis of activation data can provide stable descriptors of learning performance. Network quantification can provide an objective means of assessing the effects of therapy on cognitive functioning in neurodegenerative disorders.