Exploring influence of subliminal interoception on whole-brain functional network connectivity dynamics - Jarrahi, Behnaz; Mantini, Dante; Mehnert, Ulrich; Kollias, Spyros (2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015)
Recent fMRI studies have highlighted a dynamic relation across large-scale intrinsic connectivity networks (ICNs) of human brain. The origin of such temporal variations in functional connectivity especially during the task-free (resting-state) fMRI is still a matter of debate and ongoing investigation. In this exploratory study, we sought to determine whether subliminal differences in interoception (e.g., distention pressure on the viscera) can influence the dynamics of whole-brain functional network connectivity. A group of healthy right-handed female subjects, close in age (n = 15, mean age ± SD = 30.33 ± 8.7 years) underwent a series of eyes-open resting-state fMRI scans under different interoceptive conditions including catheterization and partial bladder filling. Using a high-dimensional independent component analysis, the functional imaging data were parcellated into 75 components, out of which 33 were identified as non-artifactual ICNs. Changes in dynamic functional network connectivity (dFNC) were evaluated using the sliding-time window approach and k-means clustering algorithm. We used subject medians for each cluster state and compared differences in dFNC correlations using a paired t-test. Following a false discovery rate multiple comparison correction threshold of p<;0.05, no significant differences in dFNC were found. However, different dwell times for each (pseudo-)resting-state were observed. More liberal statistical criteria (uncorrected p<;0.005) also indicated differences in dFNC between ICN pairs especially involving the salience, subcortical, sensorimotor, cerebellar and brainstem networks. Further investigations of the effect of internal (bodily) sensations on the time-varying aspects of functional connectivity can improve our understanding of the nature of temporal fluctuations in interrelations between intrinsic brain networks.