Reliability of Structural Equation Modeling in Examining Resting State Motor Network in Healthy Subjects

Resting state connectivity studies are of growing significance and interest in the current neuroimaging literature due to their potential in explaining various underlying brain mechanisms and, therefore, their utility in clinical applications. While functional connectivity has been extensively examined in the human brain, effective connectivity is a burgeoning field in functional neuroimaging studies, and there is an increased interest in quantifying effective connectivity that takes into account the directional influences of various brain regions active in a particular functional network. Studies have shown the presence of multiple functional networks in the resting state, which have been shown to be consistent across subjects and between sessions. However, this is not the case with resting state effective connectivity.;In this thesis we evaluate effective connectivity of the resting state motor network in normal subjects using structural equation modeling (SEM), a linear statistical analysis method. It has been shown that signals related to cardiac pulsatality and respiration effects can confound functional MRI results. Thus, we have investigated the effect of various filtering strategies on the reliability of effective connectivity measurements. In this thesis, we examined the effect of four methods of physiological filtering of resting state data: (a) preprocessed data without any filtering, (b) removal of prospectively recorded cardiac and respiratory fluctuations using RETROICOR, (c) removal of global average signal from all the brain voxels time series, (d) regressing out average signal of the white matter (WM) and cerebrospinal fluid (CSF), and (e) temporal filtering to remove frequencies pertinent to cardiac and respiratory sources. The resulting effect of each of these methods on the estimation of resting state motor network effective connectivity was examined in this thesis.;We identified two plausible models for resting state effective connectivity, that were both functionally feasible and provided good fits to the datasets considered. We also examined the effects of the filtering methods on the path coefficient values of the directional connections. While in model 2 there is a significant effect of the filtering methods on the effective connectivity estimates, this is not so clear with model 1, wherein the RETROICOR filtered dataset does not significantly change the path coefficient values from the dataset with no filtering. We also show that while the physiologically filtered dataset and the global signal filtered dataset yielded the most reliable estimates of path coefficients, global filtered and WmCsf filtered dataset were the least reliable amongst the datasets considered. The results also show that the path coefficients obtained at the subject level and the session level displayed a high degree of variability. Given the variability of effective connectivity from SEM, we suggest that the results of effective connectivity be interpreted with caution.