Exploring influence of subliminal interoception on whole-brain functional network connectivity dynamics

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.

[1]  K. Yau,et al.  Interoception: the sense of the physiological condition of the body , 2003, Current Opinion in Neurobiology.

[2]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[3]  A. Belger,et al.  Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia , 2014, NeuroImage: Clinical.

[4]  V. Calhoun,et al.  The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.

[5]  Vince D. Calhoun,et al.  A method for functional network connectivity among spatially independent resting-state components in schizophrenia , 2008, NeuroImage.

[6]  Martin Sarter,et al.  Ascending visceral regulation of cortical affective information processing , 2003, The European journal of neuroscience.

[7]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[8]  G. Jackson,et al.  Effect of prior cognitive state on resting state networks measured with functional connectivity , 2005, Human brain mapping.

[9]  Neuroimaging of Interoception: Deciphering the Neural Correlates of the Primordial Emotions , 2015 .

[10]  O. Cameron Visceral Sensory Neuroscience: Interoception , 2001 .

[11]  Rex E. Jung,et al.  A Baseline for the Multivariate Comparison of Resting-State Networks , 2011, Front. Syst. Neurosci..

[12]  Dante Mantini,et al.  Effect of interoception on intra- and inter-network connectivity of human brain — An independent component analysis of fMRI data , 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

[13]  Vince D. Calhoun,et al.  Mutually temporally independent connectivity patterns: A new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender , 2015, NeuroImage.

[14]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.