TY - JOUR
T1 - Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis
AU - Ma, Sai
AU - Calhoun, Vince D.
AU - Phlypo, Ronald
AU - Adali, Tülay
PY - 2014/4/15
Y1 - 2014/4/15
N2 - Recent work on both task-induced and resting-state functional magnetic resonance imaging (fMRI) data suggests that functional connectivity may fluctuate, rather than being stationary during an entire scan. Most dynamic studies are based on second-order statistics between fMRI time series or time courses derived from blind source separation, e.g., independent component analysis (ICA), to investigate changes of temporal interactions among brain regions. However, fluctuations related to spatial components over time are of interest as well. In this paper, we examine higher-order statistical dependence between pairs of spatial components, which we define as spatial functional network connectivity (sFNC), and changes of sFNC across a resting-state scan. We extract time-varying components from healthy controls and patients with schizophrenia to represent brain networks using independent vector analysis (IVA), which is an extension of ICA to multiple data sets and enables one to capture spatial variations. Based on mutual information among IVA components, we perform statistical analysis and Markov modeling to quantify the changes in spatial connectivity. Our experimental results suggest significantly more fluctuations in patient group and show that patients with schizophrenia have more variable patterns of spatial concordance primarily between the frontoparietal, cerebellar and temporal lobe regions. This study extends upon earlier studies showing temporal connectivity differences in similar areas on average by providing evidence that the dynamic spatial interplay between these regions is also impacted by schizophrenia.
AB - Recent work on both task-induced and resting-state functional magnetic resonance imaging (fMRI) data suggests that functional connectivity may fluctuate, rather than being stationary during an entire scan. Most dynamic studies are based on second-order statistics between fMRI time series or time courses derived from blind source separation, e.g., independent component analysis (ICA), to investigate changes of temporal interactions among brain regions. However, fluctuations related to spatial components over time are of interest as well. In this paper, we examine higher-order statistical dependence between pairs of spatial components, which we define as spatial functional network connectivity (sFNC), and changes of sFNC across a resting-state scan. We extract time-varying components from healthy controls and patients with schizophrenia to represent brain networks using independent vector analysis (IVA), which is an extension of ICA to multiple data sets and enables one to capture spatial variations. Based on mutual information among IVA components, we perform statistical analysis and Markov modeling to quantify the changes in spatial connectivity. Our experimental results suggest significantly more fluctuations in patient group and show that patients with schizophrenia have more variable patterns of spatial concordance primarily between the frontoparietal, cerebellar and temporal lobe regions. This study extends upon earlier studies showing temporal connectivity differences in similar areas on average by providing evidence that the dynamic spatial interplay between these regions is also impacted by schizophrenia.
KW - Dynamic spatial change
KW - FMRI
KW - Independent vector analysis
KW - Schizophrenia
KW - Spatial functional network connectivity
UR - http://www.scopus.com/inward/record.url?scp=84893550211&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893550211&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2013.12.063
DO - 10.1016/j.neuroimage.2013.12.063
M3 - Article
C2 - 24418507
AN - SCOPUS:84893550211
SN - 1053-8119
VL - 90
SP - 196
EP - 206
JO - NeuroImage
JF - NeuroImage
ER -