Peter Mondrup Rasmussen

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In analysis of multi-channel event related EEG signals indepedent component analysis (ICA) has become a widely used tool to attempt to separate the data into neural activity, physiological and non-physiological artifacts. High density elctrode systems offer an opportunity to estimate a corresponding large number of independent components (ICs). However, too(More)
CONTEXT Both hallucinogens and 3,4-methylenedioxymethamphetamine (MDMA or "ecstasy") have direct agonistic effects on postsynaptic serotonin(2A) receptors, the key site for hallucinogenic actions. In addition, MDMA is a potent releaser and reuptake inhibitor of presynaptic serotonin. OBJECTIVE To assess the differential effects of MDMA and hallucinogen(More)
UNLABELLED Stress sensitivity and serotonergic neurotransmission interact, e.g. individuals carrying the low-expressing variants (S and LG) of the 5-HTTLPR promoter polymorphism of the serotonin transporter (SERT) gene are at higher risk for developing mood disorders when exposed to severe stress and display higher cortisol responses when exposed to(More)
We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising procedure is performed within a data-driven split-half evaluation(More)
There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus on visualization of such nonlinear kernel models. Specifically, we(More)
Large amounts of neuroimaging studies are collected and have changed our view on human brain function. By integrating multiple studies in meta-analysis a more complete picture is emerging. Brain locations are usually reported as coordinates with reference to a specific brain atlas, thus some of the databases offer so-called coordinate-based searching to the(More)
The effects of physiological noise may significantly limit the reproducibility and accuracy of BOLD fMRI. However, physiological noise evidences a complex, undersampled temporal structure and is often non-orthogonal relative to the neuronally-linked BOLD response, which presents a significant challenge for identifying and removing such artifact. This paper(More)
We explore functional connectivity in nine subjects measured with 1.5T fMRI-BOLD in a longitudinal study of recovery from unilateral stroke affecting the motor area (Small et al., 2002). We found that several measures of complexity of covariance matrices show strong correlations with behavioral measures of recovery. In Schmah et al. (2010), we applied(More)
Classification models are becoming increasing popular tools in the analysis of neuroimaging data sets. Besides obtaining good prediction accuracy, a competing goal is to interpret how the classifier works. From a neuroscientific perspective, we are interested in the brain pattern reflecting the underlying neural encoding of an experiment defining multiple(More)