An ICA based approach for steady-state and transient analysis of task fMRI data: Application to study of thermal pain response

Background: Data driven analysis methods such as independent component analysis (ICA) offer the advantage of estimating subject contributions when used in a second-level analysis. With the traditionally used regression-based methods this is achieved with a design matrix that has to be specified a priori.

New method: We show that the ability of ICA to estimate subject contributions can be effectively used to perform steady-state as well as transient analysis of task functional magnetic resonance imaging (fMRI) data, which can help reveal important group differences.

Results: We apply the method to steady-state and transient analysis of block designed thermal pain stimulated fMRI data, and identify distinct sex differences, in parts of the pain matrix: brain stem, thalamus, amygdala, frontal pole (FP), temporal pole (TP), operculum (second somatosensory cortex, SII), anterior insular (AI), dorsal anterior cingulate cortex (dACC), and default mode network (DMN). We also show that the identified regions have significant correlation with weekly exercise and anxiety. Using transient analysis, we identify regions (SII, AI, dACC, DMN) specific to female group showing difference mainly in the initial stages of the experiments.

Comparison with existing method: With exact same spatial components input in the second level, permutation analysis of linear models cannot identify any significant group difference. In addition, the proposed transient analysis cannot be realized if user is required to input a design matrix as is the case with regression-based analyses.

Conclusions: The proposed two-level ICA is an effective multi-variate analysis method for both steady-state and transient analysis of task data.