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The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source(More)
The authors propose a new procedure for reducing faking on personality tests within selection contexts. This computer-based procedure attempts to identify and warn potential fakers early on during the testing process and then give them a chance for recourse. Two field studies were conducted to test the efficacy of the proposed procedure. Study 1(More)
Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age.(More)
Pyroglutamate helix B surface peptide (pHBSP) is an 11 amino acid peptide, designed to interact with a novel cell surface receptor, composed of the classical erythropoietin (EPO) receptor disulfide linked to the beta common receptor. pHBSP has the cytoprotective effects of EPO without stimulating erythropoiesis. Effects on early cerebral hemodynamics and(More)
Mild traumatic brain injury (mTBI) results in an estimated 75-90% of the 1.7 million TBI-related emergency room visits each year. Post-concussion symptoms, which can include impaired memory problems, may persist for prolonged periods of time in a fraction of these cases. The purpose of this study was to determine if an erythropoietin-mimetic peptide,(More)
Functional data analysis (FDA) considers the continuity of the curves or functions, and is a topic of increasing interest in the statistics community. FDA is commonly applied to time-series and spatial-series studies. The development of functional brain imaging techniques in recent years made it possible to study the relationship between brain and mind over(More)
In a recent article in The Journal of General Psychology, J. B. Hittner, K. May, and N. C. Silver (2003) described their investigation of several methods for comparing dependent correlations and found that all can be unsatisfactory, in terms of Type I errors, even with a sample size of 300. More precisely, when researchers test at the .05 level, the actual(More)
Classification problems involving a categorical class label Y and a functional predictor X(t) are becoming increasingly common. Since X(t) is infinite dimensional, some form of dimension reduction is essential in these problems. Conventional dimension reduction techniques for functional data usually suffer from one or both of the following problems. First,(More)
This paper is concerned with classifying high dimensional data into one of two categories. In various settings, such as when dealing with fMRI and microarray data, the number of variables is very large, which makes well-known classification techniques impractical. The number of variables might be reduced via principal component analysis or some robust(More)