The default-mode network (DMN) is a set of specific brain regions whose activity, predominant in the resting-state, is attenuated during cognitively demanding, externally-cued tasks. The cognitive correlates of this network have proven difficult to interrogate, but one hypothesis is that regions in the network process episodic memories and semantic knowledge integral to internally-generated mental activity. Here, we compare default-mode functional connectivity in the same group of subjects during rest and conscious sedation with midazolam, a state characterized by anterograde amnesia and a reduced level of consciousness. Although the DMN showed functional connectivity during both rest and conscious sedation, a direct comparison found that there was significantly reduced functional connectivity in the posterior cingulate cortex during conscious sedation. These results confirm that low-frequency oscillations in the DMN persist and remain highly correlated even at reduced levels of consciousness. We hypothesize that focal reductions in DMN connectivity, as shown here in the posterior cingulate cortex, may represent a stable correlate of reduced consciousness.
Independent component analysis (ICA) of functional MRI data is sensitive to model order selection. There is a lack of knowledge about the effect of increasing model order on independent components' (ICs) characteristics of resting state networks (RSNs). Probabilistic group ICA (group PICA) of 55 healthy control subjects resting state data was repeated 100 times using ICASSO repeatability software and after clustering of components, centrotype components were used for further analysis. Visual signal sources (VSS), default mode network (DMN), primary somatosensory (S(1)), secondary somatosensory (S(2)), primary motor cortex (M(1)), striatum, and precuneus (preC) components were chosen as components of interest to be evaluated by varying group probabilistic independent component analysis (PICA) model order between 10 and 200. At model order 10, DMN and VSS components fuse several functionally separate sources that at higher model orders branch into multiple components. Both volume and mean z-score of components of interest showed significant (P < 0.05) changes as a function of model order. In conclusion, model order has a significant effect on ICs characteristics. Our findings suggest that using model orders < or =20 provides a general picture of large scale brain networks. However, detection of some components (i.e., S(1), S(2), and striatum) requires higher model order estimation. Model orders 30-40 showed spatial overlapping of some IC sources. Model orders 70 +/- 10 offer a more detailed evaluation of RSNs in a group PICA setting. Model orders > 100 showed a decrease in ICA repeatability, but added no significance to either volume or mean z-score results.
Baseline activity of resting state brain networks (RSN) in a resting subject has become one of the fastest growing research topics in neuroimaging. It has been shown that up to 12 RSNs can be differentiated using an independent component analysis (ICA) of the blood oxygen level dependent (BOLD) resting state data. In this study, we investigate how many RSN signal sources can be separated from the entire brain cortex using high dimension ICA analysis from a group dataset. Group data from 55 subjects was analyzed using temporal concatenation and a probabilistic independent component analysis algorithm. ICA repeatability testing verified that 60 of the 70 computed components were robustly detectable. Forty-two independent signal sources were identifiable as RSN, and 28 were related to artifacts or other noninterest sources (non-RSN). The depicted RSNs bore a closer match to functional neuroanatomy than the previously reported RSN components. The non-RSN sources have significantly lower temporal intersource connectivity than the RSN (P < 0.0003). We conclude that the high model order ICA of the group BOLD data enables functional segmentation of the brain cortex. The method enables new approaches to causality and connectivity analysis with more specific anatomical details.
Improvement during the follow-up period was found in both the methylprednisolone and saline groups. The combination of methylprednisolone and bupivacaine seems to have a short-term effect, but at 3 and 6 months, the steroid group seems to experience a "rebound" phenomenon.
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