Functional imaging, notably fMRI and PET, have demonstrated the existence of numerous distributed networks in the human brain that have a spontaneous, large activity at rest. This group is about all experimental or simulation results related to these resting-state networks. http://www.citeulike.org/groupfunc/3138 CiteULike.org en-gb Copyright © 2004-2007 Oversity Limited http://www.citeulike.org/group/3138/article/6098155 <i>no abstract</i> 2009-11-11T14:35:40+00:00 http://www.citeulike.org/group/3138/article/6098145 A computer simulation model was developed to investigate the use of interregional correlations of cerebral metabolic rates to analyze functional interactions in the brain. The model generates simulated metabolic data for individual brain regions in a specified number of subjects, where there are defined functional couplings amongst the regions. Random numbers provide the variability seen in measured metabolic data. Correlational analysis is performed on these simulated data sets. The parameters of the model can be chosen so that simulated and actual metabolic data are very similar. The model demonstrates that the change in the correlation coefficient between normalized metabolic data in two brain regions is related to the change in the strength of the functional association between the two regions. The model also is used to explore the relations between patterns of correlations and the underlying sets of functional couplings. The results indicate that correlational analysis provides more information about regional involvement in neural systems than does region-by-region comparisons of absolute metabolic rates. 2009-11-11T14:24:29+00:00 http://www.citeulike.org/group/3138/article/6083254 Functional magnetic resonance imaging (fMRI) has recently proved its utility in studying brain large-scale networks through fluctuations in resting-state data. To process such rest acquisitions, exploratory methods such as independent component analysis (ICA) are of particular interest. Yet, while successfully applied at the individual level, existing ICA methods still fail to provide robust functional network detection at the group level. In this paper, we propose a method for detecting group functional large-scale networks in fMRI using ICA, which allows to systematically control the consistency of the group results with the individual ones. This approach, called NEDICA (network detection using ICA), was applied on resting-state data from twenty healthy subjects and the robustness of the resulting networks was assessed by a bootstrap sampling procedure. We found seven functional networks that were very representative of the population and highly reproducible on the basis of bootstrap tests. These results were in good agreement with the existing literature and confirmed the ability of fMRI to noninvasively reveal large-scale interactions in the brain. 2009-11-07T19:05:42+00:00 http://www.citeulike.org/group/3138/article/5310617 This work examines the influence of changes in baseline activity on the intrinsic functional connectivity fMRI (fc-fMRI) in humans. Baseline brain activity was altered by inducing anesthesia (sevoflurane end-tidal concentration 1%) in human volunteers and fc-fMRI maps between the pre-anesthetized and anesthetized conditions were compared across different brain networks. We particularly focused on low-level sensory areas (primary somatosensory, visual, and auditory cortices), the thalamus, and pain (insula), memory (hippocampus) circuits, and the default mode network (DMN), the latter three to examine higher-order brain regions. The results indicate that, while fc-fMRI patterns did not significantly differ ( p  < 0.005; 20-voxel cluster threshold) in sensory cortex and in the DMN between the pre- and anesthetized conditions, fc-fMRI in high-order cognitive regions (i.e. memory and pain circuits) was significantly altered by anesthesia. These findings provide further evidence that fc-fMRI reflects intrinsic brain properties, while also demonstrating that 0.5 MAC sevoflurane anesthesia preferentially modulates higher-order connections. 2009-11-04T19:35:52+00:00 http://www.citeulike.org/group/3138/article/5312316 An increasing number of fMRI studies are using the correlation of low-frequency fluctuations between brain regions, believed to reflect synchronized variations in neuronal activity, to infer “functional connectivity”. In studies of autism spectrum disorder (ASD), decreases in this measure of connectivity have been found by focusing on the response to task modulation, by using only the rest periods, or by analyzing purely resting-state data. This difference in connectivity, however, could result from a number of different mechanisms — differences in noise, task-related fluctuations, task performance, or spontaneous neuronal activity. In this study, we investigate the difference in functional connectivity between adolescents with high-functioning ASD and typically developing control subjects by examining the residual fluctuations occurring on top of the fMRI response to an overt verbal fluency task. We find decreased correlations of these residuals (a decreased “connectivity”) in ASD subjects. Furthermore, we find that this decrease was not due to task-related effects, block-to-block variations in task performance, or increased noise, and the difference was greatest when primarily rest periods are considered. These findings suggest that the estimate of disrupted functional connectivity in ASD is likely driven by differences in task-unrelated neuronal fluctuations. 2009-11-04T19:34:21+00:00