20 research outputs found
Impulsivity is Associated with Increased Metabolism in the Fronto-Insular Network in Parkinsonās Disease
Front. Behav. Neurosci. 9:317. doi: 10.3389/fnbeh.2015.00317 Various neuroimaging studies demonstrated that the fronto-insular network is implicated in impulsive behavior. We compared glucose metabolism (as a proxy measure of neural activity) among 24 patients with Parkinsonās disease (PD) who presented with low or high levels of impulsivity based on the Barratt Impulsiveness Scale 11 (BIS) scores. Subjects underwent 18-fluorodeoxyglucose positron emission tomography (FDG-PET) and the voxel-wise group difference of FDG-metabolism was analyzed in Statistical Parametric Mapping (SPM8). Subsequently, we performed a partial correlation analysis between the FDG-metabolism and BIS scores, controlling for covariates (i.e., age, sex, severity of disease and levodopa equivalent daily doses). Voxel-wise group comparison revealed higher FDG-metabolism in the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and right insula in patients with higher impulsivity scores. Moreover, there was a positive correlation between the FDG-metabolism and BIS scores. Our findings provide evidence that high impulsivity is associated with increased FDG-metabolis
New Computer-Aided Diagnosis of Dementia Using Positron Emission Tomography: Brain Regional Sensitivity-Mapping Method
Purpose: We devised a new computer-aided diagnosis method to segregate dementia using one estimated index (Total Z
score) derived from the Brodmann area (BA) sensitivity map on the stereotaxic brain atlas. The purpose of this study is to
investigate its accuracy to differentiate patients with Alzheimerās disease (AD) or mild cognitive impairment (MCI) from
normal adults (NL).
Methods: We studied 101 adults (NL: 40, AD: 37, MCI: 24) who underwent 18FDG positron emission tomography (PET)
measurement. We divided NL and AD groups into two categories: a training group with (Category A) and a test group
without (Category B) clinical information. In Category A, we estimated sensitivity by comparing the standard uptake value
per BA (SUVR) between NL and AD groups. Then, we calculated a summated index (Total Z score) by utilizing the sensitivitydistribution
maps and each BA z-score to segregate AD patterns. To confirm the validity of this method, we examined the
accuracy in Category B. Finally, we applied this method to MCI patients.
Results: In Category A, we found that the sensitivity and specificity of differentiation between NL and AD were all 100%. In
Category B, those were 100% and 95%, respectively. Furthermore, we found this method attained 88% to differentiate ADconverters
from non-converters in MCI group.
Conclusions: The present automated computer-aided evaluation method based on a single estimated index provided good
accuracy for differential diagnosis of AD and MCI. This good differentiation power suggests its usefulness not only for
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