Another frequently used discrimination approach is multivoxel pat

Another frequently used discrimination approach is multivoxel pattern analysis (MVPA), which uses pattern-classification techniques to extract the signal across multiple voxels. Many studies have used MVPA to discriminate cognitive changes successfully. For example, MVPA has been used to predict the time course of recall behavior in a free-recall task (Polyn et al. 2005), and it has also been used to predict second-by-second changes in perceived stimulus dominance during a binocular rivalry task (Haynes and Rees Inhibitors,research,lifescience,medical 2005). The most important obstacle to the

extensive use of the voxel-based discrimination approach is the large number of voxel sets to be scanned. However, if improvements are made in the computational algorithms, the voxel-based approach will be highly promising as a tool for characterizing and understanding of how information is represented and processed in the brain. In many functional connectivity analysis, the term ROI-wise or voxel-wise is occasionally Inhibitors,research,lifescience,medical used in different documents or software (e.g., in Resting-State fMRI Data Analysis Toolkit (REST) provided by Beijing Normal University (http://www.restfmri.net/), one can calculate ROI-wise or voxel-wise functional connectivity Inhibitors,research,lifescience,medical directly), indicating that both ROI-wise analysis and voxel-wise analysis in functional

connectivity are seed-based approaches. The ROI-wise analysis Inhibitors,research,lifescience,medical estimates the brain connectivity by computing correlation between temporal signals from two predefined ROIs, whereas the voxel-wise analysis correlates functional temporal signals of a seed region with those of other brain voxels (Craddock et al. 2011; Valsasina et al. 2011). The selection of ROIs typically requires a priori knowledge about the underlying problem; therefore, both of these approaches are conceptually different from the reversal coarse-grained method proposed Inhibitors,research,lifescience,medical here. In summary, the current study compared coarse-grained analysis with reversal coarse-grained

analysis by analyzing the functional abnormalities of the hate circuit studied previously by us in patients with MDD over a fine spatial scale (Tao et al. 2011). By computing the intensity of each voxel, we were able to precisely localize the changed site of the hate circuit. Furthermore, our results demonstrated that the voxel-wise time series extracted from the reversal coarse-grained Carfilzomib analysis had several advantages: (1) a larger amplitude of fluctuations was detected, which indicates that the BOLD signals are more synchronized; (2) more significant differences were observed in the functional connectivity related to the ROIs between patients and controls; and (3) a better performance was observed in the discrimination tasks. From a global perspective, coarse-grained analysis is an appropriate method to investigate the significantly different ROIs and functional connectivity.

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