Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensional...

PURPOSE The aim of this study was to estimate the incidence of sinus membrane perforation in maxillary sinus augmentation surgery using a lateral approach and the impact of sinus integrity on incidence of sinusitis and bone graft survival in the maxillary sinus. PATIENTS AND METHODS A total of 359 sinus augmentation procedures (208 patients) were evaluated retrospectively for sinus integrity ...

In search for suitable tools to study brain activation in natural environments, where the stimuli are multimodal, poorly predictable and irregularly varying, we collected functional magnetic resonance imaging data from 6 subjects during a continuous 8-min stimulus sequence that comprised auditory (speech or tone pips), visual (video clips dominated by faces, hands, or buildings), and tactile fi...

In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional mode...

In functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: "How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection", NeuroImage, vol. 141, pp. 469-489; http://dx.doi.org/10.1016/j.neuroimage.2016.07.047), we have introduced cross-valida...

[1] This paper shows how Gumbel-distributed data can be related to explanatory variables by using generalized linear models (GLMs) fitted by using a modified form of the iteratively weighted least squares algorithm (IWLS). Typical applications include (1) testing for trend in annual flood data, as a possible consequence of changing land cover or other factors; (2) testing for trend in annual ma...

This report proposes a double simulated annealing (DSA) for functional magnetic resonance image (fMRI) analysis. The first simulated annealing (SA) is used to disconnect the brain from the skull. The second SA is applied to locate the activation area of the fMRIs. The performance evaluation of this approach includes receiver-operating characteristic (ROC) analysis, similarity analysis, and comp...

In task-based fMRI, the generalized linear model (GLM) is widely used to detect activated brain regions. A fundamental assumption in the GLM model for fMRI activation detection is that the brain's response, represented by the blood-oxygenation level dependent (BOLD) signals of volumetric voxels, follows the shape of stimulus paradigm. Based on this same assumption, we use the dynamic functional...

In this paper we review neglected issues of simultaneous statistical inference and statistical power in survey research applications of the general linear model, and we find that classical hypothesis testing as it is currently applied, is inadequate for the purposes of social research. The intelligent use of statistical inference demands control over the overall level of Type I error and knowle...