Analysis of brain connectivity in 151 schizophrenia patients and 160 healthy controls reveals distinct connectivity patterns.
Schizophrenia patients showed stronger connectivity between subcortical, auditory, and visual networks compared to healthy controls.
Patients had lower connectivity in the sensorimotor network relative to controls.
Distinct clustering patterns in connectivity gradients were observed between patients and healthy controls.
Patients spent more time in a subcortical/cerebellar state, while healthy controls preferred a sensorimotor/default mode state.
Gradient synchrony analysis indicated more shifts between networks in patients, suggesting altered brain dynamics.
The findings may enhance understanding of functional dysconnectivity in schizophrenia.
Simplified
(dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilize fixed spatial maps and evaluate transient changes in coupling among time courses estimated from (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each time point to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HCs). Functional dysconnectivity between different brain regions has been reported in SZ, yet the neural mechanisms behind it remain elusive. Using resting-state fMRI and ICA on a dataset consisting of 151 SZ patients and 160 age and gender-matched HCs, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD), and visual (VIS) networks in patients, as well as hypoconnectivity in the sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/default mode network (DMN), as well as SC/AUD/SM/cerebellar (CB) and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/SC networks and transmodal CC/DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM, and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in SZ patients. By employing dFNG, we highlight a new perspective to capture large-scale fluctuations across the brain while maintaining the convenience of brain networks and low-dimensional summary measures.
Key numbers
151 of 311
Cohort Size
Number of schizophrenia patients analyzed in the study.
Higher duration in state 4 () for SZ patients
Increased Time in State 4
Duration spent in network state based on analysis.
Stronger connectivity between , , and networks in SZ patients
Stronger Connectivity
Connectivity differences assessed through static analysis.
Full Text
We can’t show the full text here under this license.
