Dataset Information

Investigation of serotonergic Parkinson's disease-related covariance pattern using [¹¹C]-DASB/PET.

ABSTRACT: We used positron emission tomography imaging with [¹¹C]-3-amino-4-(2-dimethylaminomethylphenylsulfanyl)- benzonitrile (DASB) and principal component analysis to investigate whether a specific Parkinson's disease (PD)-related spatial covariance pattern could be identified for the serotonergic system. We also explored if non-manifesting leucine-rich repeat kinase 2 (LRRK2) mutation carriers, with normal striatal dopaminergic innervation as measured with [¹¹C]-dihydrotetrabenazine (DTBZ), exhibit a distinct spatial covariance pattern compared to healthy controls and subjects with manifest PD. 15 subjects with sporadic PD, eight subjects with LRRK2 mutation-associated PD, nine LRRK2 non-manifesting mutation carriers, and nine healthy controls participated in the study. The analysis was applied to the DASB non-displaceable binding potential values evaluated in 42 pre-defined regions of interest. PD was found to be associated with a specific spatial covariance pattern, comprising relatively decreased DASB binding in the caudate, putamen and substantia nigra and relatively preserved binding in the hypothalamus and hippocampus; the expression of this pattern in PD subjects was significantly higher than in healthy controls (P?P?P?P?

SUBMITTER: Fu JF

PROVIDER: S-EPMC6014591 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Investigation of serotonergic Parkinson's disease-related covariance pattern using [11C]-DASB/PET.

Fu Jessie Fanglu JF Klyuzhin Ivan I Liu Shuying S Shahinfard Elham E Vafai Nasim N McKenzie Jessamyn J Neilson Nicole N Mabrouk Rostom R Sacheli Matthew A MA Wile Daryl D McKeown Martin J MJ Stoessl A Jon AJ Sossi Vesna V

NeuroImage. Clinical 20180521

We used positron emission tomography imaging with [11C]-3-amino-4-(2-dimethylaminomethylphenylsulfanyl)- benzonitrile (DASB) and principal component analysis to investigate whether a specific Parkinson's disease (PD)-related spatial covariance pattern could be identified for the serotonergic system. We also explored if non-manifesting leucine-rich repeat kinase 2 (LRRK2) mutation carriers, with normal striatal dopaminergic innervation as measured with [11C]-dihydrotetrabena ...[more]

PMID: 29946508

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Project description:Positron Emission Tomography (PET) is an important neuroimaging tool to quantify the distribution of specific molecules in the brain. The quantification is based on a series of individually designed data preprocessing steps (pipeline) and an optimal preprocessing strategy is per definition associated with less noise and improved statistical power, potentially allowing for more valid neurobiological interpretations. In spite of this, it is currently unclear how to design the best preprocessing pipeline and to what extent the choice of each preprocessing step in the pipeline minimizes subject-specific errors. To evaluate the impact of various preprocessing strategies, we systematically examined 384 different pipeline strategies in data from 30 healthy participants scanned twice with the serotonin transporter (5-HTT) radioligand [11C]DASB. Five commonly used preprocessing steps with two to four options were investigated: (1) motion correction (MC) (2) co-registration (3) delineation of volumes of interest (VOI's) (4) partial volume correction (PVC), and (5) kinetic modeling. To quantitatively compare and evaluate the impact of various preprocessing strategies, we used the performance metrics: test-retest bias, within- and between-subject variability, the intraclass-correlation coefficient, and global signal-to-noise ratio. We also performed a power analysis to estimate the required sample size to detect either a 5% or 10% difference in 5-HTT binding as a function of preprocessing pipeline. The results showed a complex downstream dependency between the various preprocessing steps on the performance metrics. The choice of MC had the most profound effect on 5-HTT binding, prior to the effects caused by PVC and kinetic modeling, and the effects differed across VOI's. Notably, we observed a negative bias in 5-HTT binding across test and retest in 98% of pipelines, ranging from 0 to 6% depending on the pipeline. Optimization of the performance metrics revealed a trade-off in within- and between-subject variability at the group-level with opposite effects (i.e. minimization of within-subject variability increased between-subject variability and vice versa). The sample size required to detect a given effect size was also compromised by the preprocessing strategy, resulting in up to 80% increases in sample size needed to detect a 5% difference in 5-HTT binding. This is the first study to systematically investigate and demonstrate the effect of choosing different preprocessing strategies on the outcome of dynamic PET studies. We provide a framework to show how optimal and maximally powered neuroimaging results can be obtained by choosing appropriate preprocessing strategies and we provide recommendations depending on the study design. In addition, the results contribute to a better understanding of methodological uncertainty and variability in preprocessing decisions for future group- and/or longitudinal PET studies.

Project description:BackgroundGait impairments are characteristic motor manifestations and significant predictors of poor quality of life in Parkinson's disease (PD). Neuroimaging biomarkers for gait impairments in PD could facilitate effective interventions to improve these symptoms and are highly warranted.ObjectiveThe aim of this study was to identify neural networks of discrete gait impairments in PD.MethodsFifty-five participants with early-stage PD and 20 age-matched healthy volunteers underwent quantitative gait assessment deriving 12 discrete spatiotemporal gait characteristics and [18 F]-2-fluoro-2-deoxyglucose-positron emission tomography measuring resting cerebral glucose metabolism. A multivariate spatial covariance approach was used to identify metabolic brain networks that were related to discrete gait characteristics in PD.ResultsIn PD, we identified two metabolic gait-related covariance networks. The first correlated with mean step velocity and mean step length (pace gait network), which involved relatively increased and decreased metabolism in frontal cortices, including the dorsolateral prefrontal and orbital frontal, insula, supplementary motor area, ventrolateral thalamus, cerebellum, and cuneus. The second correlated with swing time variability and step time variability (temporal variability gait network), which included relatively increased and decreased metabolism in sensorimotor, superior parietal cortex, basal ganglia, insula, hippocampus, red nucleus, and mediodorsal thalamus. Expression of both networks was significantly elevated in participants with PD relative to healthy volunteers and were not related to levodopa dosage or motor severity.ConclusionsWe have identified two novel gait-related brain networks of altered glucose metabolism at rest. These gait networks could serve as a potential neuroimaging biomarker of gait impairments in PD and facilitate development of therapeutic strategies for these disabling symptoms. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Dataset Information

Investigation of serotonergic Parkinson's disease-related covariance pattern using [¹¹C]-DASB/PET.

Publications

Investigation of serotonergic Parkinson's disease-related covariance pattern using [<sup>11</sup>C]-DASB/PET.

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