NeuroImage
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The objective of this study was to investigate total volume and spatial distribution of white matter hyperintensities (WMH) in a large sample of newly diagnosed Parkinson's disease (PD) patients with and without mild cognitive impairment (MCI) compared to normal controls (NC). Furthermore, we aimed to examine the impact of the WMH on attention-executive performance in PD. MCI is regarded as a pre-dementia stage. ⋯ Analysis showed that there were no significant differences between the 3 groups in total volume or spatial distribution of WMH. In addition there was no significant relationship between total volume or spatial distribution of WMH and attention-executive functions in PD. We conclude that in this PD cohort, cognitive impairment seems to be independent of WMH damage.
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Both affective neuroscience and decision science focus on the role of emotions in decisions. Regret and disappointment are emotions experienced with negative decision outcomes. The present research examines the neural substrates of regret and disappointment as well as the role of regret and disappointment in decision making. ⋯ Both regret and disappointment activated anterior insula and dorsomedial prefrontal cortex relative to fixation, with greater activation in regret than in disappointment. In contrast to disappointment, regret also showed enhanced activation in the lateral orbitofrontal cortex. These findings suggest that regret and disappointment, emotions experienced during decision-related loss, share a general neural network but differ in both the magnitude of subjective feelings and with regret activating some regions with greater intensity.
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Resting-state data sets contain coherent fluctuations unrelated to neural processes originating from residual motion artefacts, respiration and cardiac action. Such confounding effects may introduce correlations and cause an overestimation of functional connectivity strengths. In this study we applied several multidimensional linear regression approaches to remove artificial coherencies and examined the impact of preprocessing on sensitivity and specificity of functional connectivity results in simulated data and resting-state data sets from 40 subjects. ⋯ Results in simulated data sets compared with result of human data strongly suggest that anticorrelations are indeed introduced by global signal regression and should therefore be interpreted very carefully. In addition, global signal regression may also reduce the sensitivity for detecting true correlations, i.e. increase the number of false negatives. Concluding from our results we suggest that is highly recommended to apply correction against realignment parameters, white matter and ventricular time courses, as well as the global signal to maximize the specificity of positive resting-state correlations.
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Rates of brain atrophy derived from serial magnetic resonance (MR) studies may be used to assess therapies for Alzheimer's disease (AD). These measures may be confounded by changes in scanner voxel sizes. For this reason, the Alzheimer's Disease Neuroimaging Initiative (ADNI) included the imaging of a geometric phantom with every scan. ⋯ We used the registration algorithm to quantify any residual scaling errors, and found the algorithm to be unbiased, with no significant (p=0.97) difference between control (n=79) and AD subjects (n=50), but with a mean (SD) absolute volume change of 0.20 (0.20) % due to linear scalings. 9DOF registration was shown to be comparable to geometric phantom correction in terms of the effect on atrophy measurement and unbiased with respect to disease status. These results suggest that the additional expense and logistic effort of scanning a phantom with every patient scan can be avoided by registration-based scaling correction. Furthermore, based upon the atrophy rates in the AD subjects in this study, sample size requirements would be approximately 10-12% lower with (either) correction for voxel scaling than if no correction was used.
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We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). ⋯ For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.