NeuroImage
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A least angle regression method for fMRI activation detection in phase-encoded experimental designs.
This paper presents a new regression method for functional magnetic resonance imaging (fMRI) activation detection. Unlike general linear models (GLM), this method is based on selecting models for activation detection adaptively which overcomes the limitation of requiring a predefined design matrix in GLM. This limitation is because GLM designs assume that the response of the neuron populations will be the same for the same stimuli, which is often not the case. ⋯ This paper found that GLM with fixed design matrix was inferior compared to the described LARS method for fMRI activation detection in a phased-encoded experimental design. In addition, the proposed method has the advantage of increasing the degrees of freedom in the regression analysis. We conclude that the method described provides a new and novel approach to the detection of fMRI activation which is better than GLM based analyses.
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We previously proposed an integrated electroencephalography (EEG), magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI) model based on an extended neural mass model (ENMM) within a single cortical area. In the ENMM, a cortical area contains several minicolumns where strengths of their connections diminish exponentially with their distances. The ENMM was derived based on the physiological principles of the cortical minicolumns and their connections within a single cortical area to generate EEG, MEG, and fMRI signals. ⋯ Using simulations, we evaluate effects of parameters of the model on its dynamics and, based on stability analysis, find valid ranges for parameters of the model. In addition, we evaluate reducing redundancy of the model parameters using simulation results and conclude that the parameters of the model can be limited to the LRCs and SRCs while the intra-minicolumn parameters stay at their physiological mean values. The proposed multi-area integrated E/MEG model provides an efficient neuroimaging technique for effective connectivity analysis in healthy subjects as well as neurological and psychiatric patients.
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The structural changes that arise as the brain ages influence its functionality. In many cases, the anatomical degradation simply leads to normal aging. In others, the neurodegeneration is large enough to cause neurological disorders (e.g. ⋯ In particular, we study in detail the effect of the thalamic input on specific cortical regions, the long-range connectivity between cortical regions, and the short-range coupling within the same cortical area. Once the influence of each type of connectivity is determined, we characterize the regions of parameter space compatible with the EEG recordings of the populations under study. Our results show that the different types of connectivity must be fine-tuned to maintain the brain in a healthy functioning state independently of its age and brain condition.
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This research describes a new Bayesian spatiotemporal model to analyse BOLD fMRI studies. In the temporal dimension, we describe the shape of the hemodynamic response function (HRF) with a transfer function model. The spatial continuity and local homogeneity of the evoked responses are modelled by a Gaussian Markov random field prior on the parameter indicating activations. ⋯ Bayesian spatiotemporal model of fMRI data, Neuroimage, 49: 442-456], offering more flexibility in the estimation of the HRF and computational advantages in the resulting MCMC algorithm. Simulations from the model are performed in order to ascertain the performance of the sampling scheme and the ability of the posterior to estimate model parameters, as well as to check the model sensitivity to signal to noise ratio. Results are shown on synthetic data and on a real data set from a block-design fMRI experiment.
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Task switching requires executive control processes that undergo age-related decline. Previous neuroimaging studies have identified age-related differences in brain activation associated with global switching effects (dual-task blocks versus single-task blocks), but age-related differences in activation during local switching effects (switch trials versus repeat trials, within blocks) have not been investigated. This experiment used functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI), to examine adult age differences in task switching across adjacent trials (i.e., local task switching). ⋯ Further, individual differences in cue-related functional connectivity shared a substantial portion of the age-related variability in the efficiency of target categorization response (drift rate). This age-related difference in functional connectivity, however, was independent of white matter integrity within task-relevant regions. These findings highlight the functional connectivity of frontoparietal activation as a potential source of age-related decline in executive control.