Dataset Information

Striatofrontal Deafferentiation in MSA-P: Evaluation with [18F]FDG Brain PET.

ABSTRACT:

Background

Although cognitive impairment is not a consistent feature of multiple system atrophy (MSA), increasing evidence suggests that cognitive impairment is common in MSA with predominant parkinsonism (MSA-P). It is assumed that the cognitive impairment in MSA-P is caused by the striatal dysfunction and disruption of striatofrontal connections. The aim of this study was to evaluate the relationship between regional glucose metabolism in the frontal cortex and striatum in patients with MSA-P using [18F]FDG brain PET.

Methods

Twenty-nine patients with MSA-P and 28 healthy controls underwent [18F]FDG brain PET scan. The [18F]FDG brain PET images were semiquantitatively analyzed on the basis of a template in standard space. The regional glucose metabolism of the cerebral cortex and striatum were compared between MSA-P and healthy control groups. The correlations between age, symptom duration, H&Y stage, UPDRS III score, MMSE score, and glucose metabolism in the cerebellum and striatum to glucose metabolism in the frontal cortex were evaluated by multivariate analysis.

Results

The glucose metabolism in the frontal cortex and striatum in MSA-P patients were significantly lower than those in healthy controls. Glucose metabolism in the striatum was the most powerful determinant of glucose metabolism in the frontal cortex in MSA-P. Only age and glucose metabolism in the cerebellum were independent variables affecting the glucose metabolism in the frontal cortex in healthy controls.

Conclusion

The decrease in frontal glucose metabolism in MSA-P is related to the decrease in striatal glucose metabolism. This result provided evidence of striatofrontal deafferentiation in patients with MSA-P.

SUBMITTER: Kim HW

PROVIDER: S-EPMC5234778 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Striatofrontal Deafferentiation in MSA-P: Evaluation with [18F]FDG Brain PET.

Kim Hae Won HW Oh Minyoung M Oh Jungsu S JS Oh Seung Jun SJ Lee Sang Ju SJ Chung Sun Ju SJ Kim Jae Seung JS

PloS one 20170113 1

<h4>Background</h4>Although cognitive impairment is not a consistent feature of multiple system atrophy (MSA), increasing evidence suggests that cognitive impairment is common in MSA with predominant parkinsonism (MSA-P). It is assumed that the cognitive impairment in MSA-P is caused by the striatal dysfunction and disruption of striatofrontal connections. The aim of this study was to evaluate the relationship between regional glucose metabolism in the frontal cortex and striatum in patients wit ...[more]

PMID: 28085923

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Project description:IntroductionMetabolic brain network analysis based on graph theory using FDG PET imaging is potentially useful for investigating brain activity alternation due to metabolism changes in different stages of Alzheimer's disease (AD). Most studies on metabolic network construction have been based on group data. Here a novel approach in building an individual metabolic network was proposed to investigate individual metabolic network abnormalities.MethodFirst, a weighting matrix was calculated based on the interregional effect size difference of mean uptake between a single subject and average normal controls (NCs). Then the weighting matrix for a single subject was multiplied by a group-based connectivity matrix from an NC cohort. To study the performance of the proposed individual metabolic network, inter- and intra-hemispheric connectivity patterns in the groups of NC, sMCI (stable mild cognitive impairment), pMCI (progressive mild cognitive impairment), and AD using the proposed individual metabolic network were constructed and compared with those from the group-based results. The network parameters of global efficiency and clustering coefficient and the network density score (NDS) in the default-mode network (DMN) of generated individual metabolic networks were estimated and compared among the disease groups in AD.ResultsOur results show that the intra- and inter-hemispheric connectivity patterns estimated from our individual metabolic network are similar to those from the group-based method. In particular, the key patterns of occipital-parietal and occipital-temporal inter-regional connectivity deficits detected in the groupwise network study for differentiating different disease groups in AD were also found in the individual network. A reduction trend was observed for network parameters of global efficiency and clustering coefficient, and also for the NDS from NC, sMCI, pMCI, and AD. There was no significant difference between NC and sMCI for all network parameters.ConclusionWe proposed a novel method in constructing the individual metabolic network using a single-subject FDG PET image and a group-based NC connectivity matrix. The result has shown the effectiveness and feasibility of the proposed individual metabolic network in differentiating disease groups in AD. Future studies should include investigation of inter-individual variability and the correlation of individual network features to disease severities and clinical performance.

Project description:The aim of the study was to analyze the use of block sequential regularized expectation maximization (BSREM) with different β-values for the detection of brain metastases in digital fluorine-18 labeled 2-deoxy-2-fluoro-D-glucose (18F-FDG) PET/CT in lung cancer patients. We retrospectively analyzed staging/restaging 18F-FDG PET/CT scans of 40 consecutive lung cancer patients with new brain metastases, confirmed by MRI. PET images were reconstructed using BSREM (β-values of 100, 200, 300, 400, 500, 600, 700) and OSEM. Two independent blinded readers (R1 and R2) evaluated each reconstruction using a 4-point scale for general image quality, noise, and lesion detectability. SUVmax of metastases, brain background, target-to-background ratio (TBR), and contrast recovery (CR) ratio were recorded for each reconstruction. Among all reconstruction techniques, differences in qualitative parameters were analyzed using non-parametric Friedman test, while differences in quantitative parameters were compared using analysis of variances for repeated measures. Cohen's kappa (k) was used to measure inter-reader agreement. The overall detectability of brain metastases was highest for BSREM200 (R1: 2.83 ± 1.17; R2: 2.68 ± 1.32) and BSREM300 (R1: 2.78 ± 1.23; R2: 2.68 ± 1.36), followed by BSREM100, which had lower accuracy owing to noise. The highest median TBR was found for BSREM100 (R1: 2.19 ± 1.05; R2: 2.42 ± 1.08), followed by BSREM200 and BSREM300. Image quality ratings were significantly different among reconstructions (p < 0.001). The median quality score was higher for BSREM100-300, and both noise and metastases' SUVmax decreased with increasing β-value. Inter-reader agreement was particularly high for the detectability of photopenic metastases and blurring (all k > 0.65). BSREM200 and BSREM300 yielded the best results for the detection of brain metastases, surpassing both BSREM400 and OSEM, typically used in clinical practice.

Dataset Information

Striatofrontal Deafferentiation in MSA-P: Evaluation with [18F]FDG Brain PET.

Background

Methods

Results

Conclusion

Publications

Striatofrontal Deafferentiation in MSA-P: Evaluation with [18F]FDG Brain PET.

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