Accuracy and precision of the statistical analysis methods used for brain activation maps are essential. Adjusting models to consider spatiotemporal correlation embedded in fMRI data may increase their accuracy, but it also introduces a high computational cost. The present study aimed apply assess Gaussian process (STGP) model improve accuracy reduce We applied both simulated experimental memor...

A popular approach to neural characterization describes neural responses in terms of a cascade of linear and nonlinear stages: a linear filter to describe stimulus integration, followed by a nonlinear function to convert the filter output to spike rate. However, real neurons respond to stimuli in a manner that depends on the nonlinear integration of excitatory and inhibitory synaptic inputs. He...

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in ris...

In this paper, we focus on predicting the visibility of packet losses in MPEG-2 compressed video streams. We develop a generalized linear model (GLM) to predict the probability that a packet loss will be visible to an average viewer. The GLM input consists of parameters that can be easily extracted from the video near the location of the loss, and outputs an estimate of the probability that tha...

OBJECTIVE To estimate the excess deaths attributed to influenza in Spain, using age-specific generalized linear models (GLM) and the Serfling model for the period 1999-2005. METHOD We reviewed mortality from influenza and pneumonia and all-cause deaths. We used an additive GLM procedure, including the numbers of weekly deaths as a response variable and the number of influenza virus and respir...

A popular approach to neural characterization describes neural responses in terms of a cascade of linear and nonlinear stages: a linear filter to describe stimulus integration, followed by a nonlinear function to convert the filter output to spike rate. However, real neurons respond to stimuli in a manner that depends on the nonlinear integration of excitatory and inhibitory synaptic inputs. He...

As a consequence of misspecification of the hemodynamic response and noise variance models, tests on general linear model coe cients are not valid. Robust estimation of the variance of the general linear model (GLM) coecients in fMRI time series is therefore essential. In this paper an alternative method to estimate the variance of the GLM coe cients accurately is suggested and compared to othe...

We study hybrid fuzzy differential equations (HFDEs) under the Hukuhara derivative numerically using Picard’s and general linear method (GLM). use trapezoidal triangular numbers as initial conditions. To demonstrate efficiency of proposed methods, exact well numerical solutions are presented graphically. In addition, a comparison is made between results from applying GLM those obtained when fif...

Language fMRI has been used to study brain regions involved in language processing and has been applied to pre-surgical language mapping. However, in order to provide clinicians with optimal information, the sensitivity and specificity of language fMRI needs to be improved. Type II error of failing to reach statistical significance when the language activations are genuinely present may be part...

Spatiotemporal data coherence widely exists in fMRI data and can be potentially used to increase brain activation detection. To assess this possibility, a two-stage fMRI data analysis method was presented in this work. Standard voxelwise general linear model (GLM) was used to first exclude voxels with low correlation to the functional design, and an intervoxel coherence map (ICM) was then calcu...