Widespread Increases in Dynamic Inter-Network Connectivity Entropy in Schizophrenia
Poster No:
1265
Submission Type:
Abstract Submission
Authors:
Natalia Maksymchuk1, Robyn Miller1, Vince Calhoun1
Institutions:
1GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Understanding complex dynamic processes in the brain and detecting critical brain states that underlie mental health conditions is essential for advancing fundamental neuroscience research and clinical practice. These perspectives imply developing advanced analytic tools and whole-brain computational models that operate in high-dimensional spaces, which results in an overwhelming number of parameters and requires intensive computational resources. Addressing this challenge, we applied a new measure, dynamic inter-network connectivity entropy (DICE) as a more interpretable and conclusive tool for evaluating brain network interactions. Our results demonstrate that DICE captures key connectivity patterns and group differences between healthy controls (HC) and schizophrenia (SZ) patients, comparable to the standard dynamic functional network connectivity (dFNC) approach while reducing dimensionality. Furthermore, DICE extends dFNC by uncovering novel properties of brain network dynamics, offering a higher-level perspective and improved interpretability.
Methods:
We utilized resting-state fMRI data from 311 participants, including 160 HC and 151 SZ subjects, matched for age and gender. The data were obtained through the multi-site fBIRN project (Potkin, 2009). Preprocessing was conducted using a standard pipeline described in (Damaraju, 2014), followed by group-independent component analysis, which produced 100 group-level functional network spatial maps and their corresponding time courses. Of these, 53 were identified as intrinsic connectivity networks (ICNs). To compute the DICE metric, we first derived network connectivity distributions from dynamic functional network connectivity matrices and calculated the entropies of these distributions. Subsequently, k-means clustering, fast Fourier transform (FFT), and Kalman filter approaches were applied. Group differences between SZ and HC participants were analyzed using a linear regression model. P-values were corrected for multiple comparisons using the false discovery rate method at a significance threshold of α = 0.05. The regression model accounted for potential confounding variables, including age, gender, and head motion.
Results:
We revealed that baseline entropy in SZ patients is significantly elevated across 41 of 53 brain networks compared to HC. Using K-means clustering, we found that SZ subjects' brains spend more time in states where entropy fluctuates minimally from the mean and exhibit significantly lower transition probability to states where entropy is substantially higher or lower than the mean. In contrast, healthy brains display lower baseline entropy and can achieve a wider range of entropy states and transitions, reflecting greater neural adaptability.
Spectral analysis of DICE time series via FFT revealed significantly reduced low-frequency power in SZ subjects, reflecting altered network dynamics and diminished synchronization across brain networks. Additionally, a Kalman filter approach revealed that SZ subjects exhibit less dynamic whole-brain connectivity, suggesting greater rigidity and reduced adaptability in their neural network dynamics. These effects were most prominent in the subcortical, sensorimotor, visual, cognitive control, and cerebellar brain domains.
Spectral analysis of DICE time series via FFT revealed significantly reduced low-frequency power in SZ subjects, reflecting altered network dynamics and diminished synchronization across brain networks. Additionally, a Kalman filter approach revealed that SZ subjects exhibit less dynamic whole-brain connectivity, suggesting greater rigidity and reduced adaptability in their neural network dynamics. These effects were most prominent in the subcortical, sensorimotor, visual, cognitive control, and cerebellar brain domains.
·Figure 1. SZ patients exhibit an increase in mean DICE across majority of ICNs. ICNs where mean DICE is significantly higher are marked with asterisks.
Conclusions:
Our DICE approach reveals patterns of altered brain network dynamics in SZ patients, reflecting the rigidity and diminished complexity of network interactions compared to healthy controls. These findings complement and extend previous work on the meta-state framework for dynamic analysis of dFNC data, which demonstrated significantly reduced dynamism in functional connectivity patterns in SZ subjects (Miller, 2016). Thus, DICE provides a robust framework for assessing connectivity dynamics and highlights its potential as a biomarker for SZ, offering both a reduction in dimensionality and a deeper understanding of brain network alterations in this disorder.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Keywords:
Data analysis
FUNCTIONAL MRI
Psychiatric Disorders
Schizophrenia
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Miller R.L. (2016). Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients. PLoS One, 11(3), e0149849.
Potkin, S.G. (2009). Widespread cortical dysfunction in schizophrenia: the FBIRN imaging consortium. Schizophr Bull, 35(1), 15-18.