NeuroImage
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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.
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Previous studies have reported that the spontaneous, resting-state time course of the default-mode network is negatively correlated with that of the "task-positive network", a collection of regions commonly recruited in demanding cognitive tasks. However, all studies of negative correlations between the default-mode and task-positive networks have employed some form of normalization or regression of the whole-brain average signal ("global signal"); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the task-positive regions could be artifactually induced by global signal pre-processing. ⋯ Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within task-positive regions at the group-level (p<0.05, uncorrected; no regions at the group level were significant at FDR=0.05). Furthermore, physiological noise correction caused region-specific decreases in positive correlations within the default-mode network, reducing apparent false positives. It was observed that the low-frequency respiratory volume and cardiac rate regressors used within the physiological noise correction algorithm displayed significant (but not total) shared variance with the global signal, and constitute a model-based alternative to correcting for non-neural global noise.
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Simultaneously acquiring functional Near Infrared Spectroscopy (fNIRS) during Transcranial Magnetic Stimulation (rTMS) offers the possibility of directly investigating superficial cortical brain activation and connectivity. In addition, the effects of rTMS in distinct brain regions without quantifiable behavioral changes can be objectively measured. ⋯ Simultaneous rTMS/fNIRS provides a reliable measure of regional cortical brain activation and connectivity that could be very useful in studying brain disorders as well as cortical changes induced by rTMS.
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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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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.