Metabolic networks for assessment of therapy and diagnosis in Parkinson's disease

Abstract

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.

Covariant perfusion patterns provide clues to the origin of cognitive fluctuations and attentional dysfunction in Dementia with Lewy bodies

“…Use of these analyses applied to positron emission tomography (PET) and SPECT images is not without precedent in Lewy body disease (Eckert et al ., 2007b): in Parkinson's disease (PD), SCAs have helped characterize the relationship between disease-specific covariant metabolic networks and cognitive and motor dysfunction as well as providing a biometric probe for therapeutic response, for example, to a dopaminergic medication (Eckert et al ., 2007a; Hirano et al ., 2009). In addition, more recently we have shown that a multivariate approach can usefully discriminate between DLB and AD with high sensitivity and specificity (Colloby et al ., 2013).…”

Molecular imaging of neuropsychiatric symptoms in Alzheimer's and Parkinson's disease

“…Eidelberg et al identified a motor network in which higher metabolic activity in the lentiform nucleus and thalamus was associated with lower activity in the lateral frontal, paracentral, inferior parietal, and parieto-occipital areas and increased metabolism in the lentiform nucleus and thalamus (Eidelberg et al, 1994). Decreased expression of this network was associated with motor symptom improvement across interventions including pharmacotherapy (levo-dopa) and deep brain stimulation (Hirano et al, 2009). Greater brain activation in a motor sequence learning task in the left DLPFC, pre-supplementary motor area, and bilaterally in the superior parietal cortex was observed in the presence of a normal level of performance, which may suggest that increased activation of cortico-cortical pathways can compensate for striatal-cortical dysfunction (Fukuda et al, 2001).…”

Milestones in neuroimaging

“…By applying spatial covariance analysis, a network mapping approach based on principal components analysis, Eidelberg and colleagues have demonstrated characteristic alterations in functional brain organization in movement disorders 55, 56. The motor manifestations of PD are associated with the expression of a resting‐state metabolic network comprising increased regional activity in the globus pallidus, ventral thalamus, pons, and cerebellum, with relative reductions in parietal and premotor cortical activity (Fig.…”