The status of olfactory function and the striatal dopaminergic system in drug-induced parkinsonism

Olfactory impairment has been reported in drug-induced parkinsonism (DIP), but the relationship between dopaminergic dysfunction and smell deficits in DIP patients has not been characterized. To this end, we studied 16 DIP patients and 13 patients affected by Parkinson's disease (PD) using the "Sniffin' Sticks" test and [(123)I] FP-CIT SPECT (single-photon emission computed tomography). DIP patients were divided based on normal (n = 9) and abnormal (n = 7) putamen dopamine transporter binding. Nineteen healthy age- and sex-matched subjects served as controls of smell function. Patients with DIP and pathological putamen uptake had abnormal olfactory function. In this group of patients, olfactory TDI scores (odor threshold, discrimination and identification) correlated significantly with putamen uptake values, as observed in PD patients. By contrast, DIP patients with normal putamen uptake showed odor functions-with the exception of the threshold subtest-similar to control subjects. In this group of patients, no significant correlation was observed between olfactory TDI scores and putamen uptake values. The results of our study suggest that the presence of smell deficits in DIP patients might be more associated with dopaminergic loss rather than with a drug-mediated dopamine receptor blockade. These preliminary results might have prognostic and therapeutic implications, as abnormalities in these individuals may be suggestive of an underlying PD-like neurodegenerative process.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.