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Noninvasive visualization of human dopamine dynamics from PET images.


ABSTRACT: We recently introduced strategies for extracting temporal patterns of brain dopamine fluctuations from dynamic positron emission tomography (PET) data using the tracer [11C]-raclopride. Each of our methods yields a collection of time-concentration curves for endogenous dopamine. Given a spatially dense collection of curves (i.e., one at every voxel in a region of interest), we produce image volumes of dopamine (DA) concentration, DA(X, t), at multiple voxel locations and each time-frame. The volume over time-frames constitutes a 4D dataset that can be thought of as a DA "movie". There are a number of ways to visualize such data. Viewing cine loops of a slice through the DA volume is one way. Creating images of dopamine peak-time, Tpeak(X), derived from a movie, is another. Each visualization may reveal spatio-temporal patterns of neurotransmitter activity heretofore unobservable. We conducted an initial validation experiment in which identical DA responses were induced by an identical task, initiated at different times by the same subject, in two separate PET scans. A comparison of the resulting Tpeak(X) images revealed a large contiguous cluster of striatal voxels, on each side, whose DA timing was consistent with the relative timing of the tasks. Hence, the DA movies and their respective peak-time images were shown to be new types of functional images that contain bonafide timing information about a neurotransmitter's response to a stimulus.

SUBMITTER: Morris ED 

PROVIDER: S-EPMC3725608 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Noninvasive visualization of human dopamine dynamics from PET images.

Morris E D ED   Constantinescu C C CC   Sullivan J M JM   Normandin M D MD   Christopher L A LA  

NeuroImage 20100104 1


We recently introduced strategies for extracting temporal patterns of brain dopamine fluctuations from dynamic positron emission tomography (PET) data using the tracer [11C]-raclopride. Each of our methods yields a collection of time-concentration curves for endogenous dopamine. Given a spatially dense collection of curves (i.e., one at every voxel in a region of interest), we produce image volumes of dopamine (DA) concentration, DA(X, t), at multiple voxel locations and each time-frame. The vol  ...[more]

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