A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we exte...

Running title GLM with natural sounds 2 2 ABSTRACT 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 stimul...

When the sensory cortex is stimulated and directly receiving afferent input, modulations can also be observed in the activity of other brain regions comprising spatially distributed, yet intrinsically connected networks, suggesting that these networks support brain function during task performance. Such networks can exhibit subtle or unpredictable task responses which can pass undetected by con...

The general linear model (GLM) has been used to analyze simultaneous EEG-fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of indepe...

This study was designed to quantify nitric oxide synthase (NOS) activity in microdissected glomeruli (Glm), pars convoluta, pars recta, cortical collecting duct, cortical thick ascending limb, outer medullary collecting duct, medullary thick ascending limb and thin limb, inner medullary collecting duct (IMCD) and thin limb, and vasa recta (VR). Total protein from microdissected segments was inc...

We provide a brief survey of the progress made in modelling covariance matrices from the perspective of generalized linear models (GLM) and the use of link functions (factorizations) that may lead to statistically meaningful and unconstrained reparameterization. We highlight the advantage of the Cholesky decomposition in dealing with the normal likelihood maximization and compare the findings w...

BACKGROUND Brain voltage activity displays distinct neuronal rhythms spanning a wide frequency range. How rhythms of different frequency interact - and the function of these interactions - remains an active area of research. Many methods have been proposed to assess the interactions between different frequency rhythms, in particular measures that characterize the relationship between the phase ...

In this paper, we propose variable selection and estimation in generalized linear models using the seamless L0 (SELO) penalized likelihood approach. The SELO penalty is a smooth function that very closely resembles the discontinuous L0 penalty. We develop an e cient algorithm to fit the model, and show that the SELO-GLM procedure has the oracle property in the presence of a diverging number of ...

In the current estimation of a GLM model, the correlation structure of regressors is not used as the basis on which to lean strong predictive dimensions. Looking for linear combinations of regressors that merely maximize the likelihood of the GLM has two major consequences: 1) collinearity of regressors is a factor of estimation instability, and 2) as predictive dimensions may lean on noise, bo...

Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the numbe...