Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain network...
We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome by combining multiple temporally-aligned myocardial velocity traces...
New neuroimaging techniques have led to significant advancements in our understanding of cerebral mechanisms of primary insomnia. However, the neuronal low-frequency oscillation remains largely uncharacterized in chronic primary insomnia (CPI). In th...
A better understanding of the behavior of individual grazing dairy cattle will assist in improving productivity and welfare. Global positioning systems (GPS) applied to cows could provide a means of monitoring grazing herds while overcoming the subst...
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over ti...
BACKGROUND: Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their app...
Anorexia nervosa (AN) is a disorder characterized by high levels of cognitive control and behavioral perseveration. The present study aims at exploring inhibitory control abilities and their functional connectivity correlates in patients with AN. Inh...
A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from resting state fMRI (rs-fMRI). As simultaneous electroencephalography (EEG) is often unavailable, decodin...
In this study, we present a method for measuring functional magnetic resonance imaging (fMRI) signal complexity using fuzzy approximate entropy (fApEn) and compare it with the established sample entropy (SampEn). Here we use resting state fMRI datase...
BACKGROUND: Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades.
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