.. 2. Data-driven methods ICA is a data-driven multivariate analysis method that can be used to separate any multivariate signal into subcomponents that are mutually statistically independent. Since the fMRI signal observed www.selleckchem.com/products/SB-203580.html is a summation of signals from multiple independent networks (ICNs) in the brain, ICA is ideally suited to separate each of the ICNs. ICA does not necessitate the a priori definition of regions from which low-frequency fluctuations are to be extracted and can extract ICNs by determining the maximal spatial and temporal independence of signals in the TF-fMRI data. This can be done at both the subject and group levels [17]. Examples of several ICNs that were identified as independent components at the group level in the same data used in the seed analyses are displayed in Figures ?Figures1b1b and ?and2b.

2b. These group-level ICNs can then be used to back-reconstruct individual subject ICNs [18]. Pitfalls Several special considerations need to be accounted for while analyzing TF-fMRI studies. Some of the more prominent issues are listed here: 1. Signal contamination All of these analyses necessitate several preprocessing steps to avoid signal contamination from non-neuronal sources of fluctuations in the signal time course, most prominently from movement and low-frequency oscillations induced from the cardiac and respiratory cycle [19]. Regressing out nuisance covariates (that is, bulk head motion parameters, white matter signal, cerebrospinal fluid signal, and global signal) from the signal time courses attempts to deal with these confounds [20].

However, bulk head motion may remain as a significant confound, specifically in patients with dementia; therefore, scanning sessions contaminated by significant motion are typically excluded from subsequent analysis. Removal of global mean signal improves the specificity of connectivity analysis [20] and is an attractive alternative to using physiologic cardiac and respiratory inputs as regressors [19] for reducing spurious direct correlations when MRI-compatible physiologic measuring systems are not available. This is necessary because gray matter has a capillary density significantly greater than that of white matter [21] and this variability is not accounted for by cerebral spinal fluid and white matter regression alone [20].

However, this increases the regions that have negative correlations GSK-3 or so-called ‘anti-correlations’ as the mean value for all voxels at every time point will be zero [22]. Figure ?Figure1a1a shows the positive correlations, and Figure ?Figure2a2a shows the negative correlations (that is, anti-correlations). Surprisingly, the regions that are anti-correlated are consistent within and between subjects for any given seed; however, the physiologic meaning of this relationship remains uncertain. This relationship is most prominent between regions defined as the task-positive network Ku 0059436 and the task-negative network [23].