Deep brain stimulation (DBS) is widely used for the treatment of movement disorders including Parkinson's disease, essential tremor, and dystonia and, to a lesser extent, certain treatment-resistant neuropsychiatric disorders including obsessive-compulsive disorder. Rather than a single unifying mechanism, DBS likely acts via several, nonexclusive mechanisms including local and network-wide electrical and neurochemical effects of stimulation, modulation of oscillatory activity, synaptic plasticity, and, potentially, neuroprotection and neurogenesis. These different mechanisms vary in importance depending on the condition being treated and the target being stimulated. Here we review each of these in turn and illustrate how an understanding of these mechanisms is inspiring next-generation approaches to DBS.

Mechanisms of deep brain stimulation.

Parkinson's disease (PD) is associated with abnormal activity in spatially distributed neural systems mediating the motor and cognitive manifestations of this disorder. Metabolic PET studies have demonstrated that this illness is characterized by a set of reproducible functional brain networks that correlate with these clinical features.The time at which these abnormalities appear is unknown, as is their relationship to concurrent clinical and dopaminergic indices of disease progression. In this longitudinal study, 15 early stage PD patients (age 58.0 � 10.2 years; Hoehn and Yahr Stage 1.2 � 0.3) were enrolled within 2 years of diagnosis. The subjects underwent multitracer PET imaging at baseline, 24 and 48 months. At each timepoint they were scanned with ( 18 F)-fluorodeoxyglucose (FDG) to assess longitudinal changes in regional glucose utilization and in the expression of the PD-related motor (PDRP) and cognitive metabolic covariance patterns (PDCP). At each timepoint the subjects also underwent PET imaging with ( 18 F)-fluoropropyl CIT (FP-CIT) to quantify longitudinal changes in caudate and putamen dopamine transpor- ter (DAT) binding. Regional metabolic changes across the three timepoints were localized using statistical para- metric mapping (SPM). Longitudinal changes in regional metabolism and network activity, caudate/putamen DAT binding, and Unified Parkinson's Disease Rating Scale (UPDRS) motor ratings were assessed using repeated measures analysis of variance (RMANOVA). Relationships between these measures of disease progression were assessed by computing within-subject correlation coefficients. We found that disease progression was associated with increasing metabolism in the subthalamic nucleus (STN) and internal globus pallidus (GPi) (P50.001), as well as in the dorsal pons and primary motor cortex (P50.0001). Advancing disease was also associated with declining metabolism in the prefrontal and inferior parietal regions (P50.001). PDRP expression was elevated at baseline relative to healthy control subjects (P50.04), and increased progressively over time (P50.0001). PDCP activity also increased with time (P50.0001). However, these changes in network activity were slower than for the PDRP (P50.04), reaching abnormal levels only at the final timepoint. Changes in PDRP activity, but not PDCP activity, correlated with concurrent declines in striatal DAT binding (P50.01) and increases in motor ratings (P50.005). Significant within-subject correlations (P50.01) were also evident between the latter two progression indices. The early stages of PD are associated with progressive increases and decreases in regional metabolism at key nodes of the motor and cognitive networks that characterize the illness. Potential disease-modifying therapies may alter the time course of one or both of these abnormal networks.

Changes in network activity with the progression of Parkinson's disease.

FDG PET in the differential diagnosis of parkinsonian disorders

https://www.cell.com/trends/neurosciences/pdf/S0166-2236(09)00131-3.pdf

Metabolic brain networks in neurodegenerative disorders: a functional imaging approach

It has been proposed that deep brain stimulation (DBS) of the subthalamic nucleus (STN DBS) and dopaminergic therapy ameliorate the symptoms of Parkinson's disease through similar functional mechanisms. We examined this notion using PET to compare the metabolic effects of these treatment approaches. Nine Parkinson's disease patients (age 61.7 ± 11.1 years) were scanned ON and OFF STN stimulation and nine others (age 60.0 ± 9.3 years) were scanned ON and OFF an individual titrated intravenous levodopa infusion. The two treatment groups were matched for baseline disease severity as well as clinical response to therapy. Similarities and differences in the effects of treatment on regional metabolism were assessed using statistical parametric mapping (SPM). In addition, we used network analysis to assess the effect of therapy on the expression of an abnormal Parkinson's disease-related spatial covariance pattern (PDRP). We found that both STN DBS and levodopa therapy were associated with significant (P 0.6). These findings support the results of previous imaging studies indicating that effective symptomatic therapies for Parkinson's disease involve a common mechanism. The modulation of pathological brain networks is a critical feature of the treatment response in parkinsonism.

Network modulation in the treatment of Parkinson's disease.

In a previous [F-18]fluorodeoxyglucose (FDG) PET study we analyzed regional metabolic data from a combined group of Parkinson's disease (PD) patients and healthy volunteers (N), using network analysis. By this method, we identified a unique pattern of regional metabolic covariation with an expression which accurately discriminated patients from healthy volunteers. To assess the reproducibility of this pattern as a potential marker for PD, we compared the pattern's topography with that of the disease-related covariance patterns identified in three other independent populations of patients with PD and healthy individuals studied in different PET laboratories. Methods: The following patient populations were studied: group A (original cohort: 22 PD, 20 N; resolution: 7.5 mm full width at half maximum [FWHM]); group B (18 PD, 12 N; resolution: 4.2 mm FWHM); group C (25 PD, 15 N; resolution: 8.0 mm FWHM); and group D (14 PD, 10 N; resolution: 10 mm FWHM). Region weights for the PD-related covariance pattern (PDRP) identified in the group A analysis were correlated with those for the disease-related patterns identified in the analyses of groups B, C and D, In addition, subject scores for the group A PDRP were computed prospectively for every individual in each of the study populations. PDRP scores for PD and N within each cohort were compared. Results: The PDRP topography identified in group A was highly correlated with each of the corresponding topographies identified in the other populations (r(2) similar to 0.60, P <0.0001). Prospectively computed subject scores for the group A PDRP significantly discriminated PD from N in each population (P <0.004). Conclusion: The PDRP topography identified previously in Group A is highly reproducible across patient populations and tomographs. Prospectively computed PDRP scores can accurately discriminate patients from controls in multiple populations studied with different tomographs. Brain network imaging with FDG PET can provide robust metabolic markers for the diagnosis of PD.

J Missimer

Papers

Reproducibility of regional metabolic covariance patterns: comparison of four populations.