Poster No:
1560
Submission Type:
Abstract Submission
Authors:
Anna Pidnebesna1, David Hartman1,2, Jaroslav Hlinka3
Institutions:
1Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic, 2Computer Science Institute of Charles University, Prague, Czech Republic, Prague, Czech Republic, 3National Institute of Mental Health, Klecany, Czech Republic
First Author:
Anna Pidnebesna
Institute of Computer Science of the Czech Academy of Sciences
Prague, Czech Republic
Co-Author(s):
David Hartman
Institute of Computer Science of the Czech Academy of Sciences|Computer Science Institute of Charles University, Prague, Czech Republic
Prague, Czech Republic|Prague, Czech Republic
Jaroslav Hlinka
National Institute of Mental Health
Klecany, Czech Republic
Introduction:
Symmetries play an important role in the human brain function. Understanding the symmetry patterns, particularly in clinical populations, can provide valuable insights into brain organisation. However, the complexity of the brain structure combined with the inaccuracy of network edge estimation often leads to complications in exploring brain symmetry. In this study, we utilise a recently introduced notion of approximate symmetry to analyse resting-state fMRI data from schizophrenia patients and healthy controls to examine the symmetry of brain network connectivity and its potential role in the disorder.
Methods:
We analysed resting-state fMRI data from 90 schizophrenia patients and 90 healthy controls acquired on a 3T Siemens Magnetom Trio scanner at IKEM, Prague. Data preprocessing followed standard pipelines, see the details in [1,3]. Time-series data were extracted from 90 regions of interest (ROIs) based on the AAL atlas. Functional connectivity was computed for every subject using Pearson correlation, and connectivity matrices were thresholded to retain a 10% dense graph.
Recently introduced global approximate symmetries of networks are based on approximate automorphisms, i.e. permutations that preserve the maximum of the network structure computed using a Quadratic Symmetry Approximator [2]. We use a set of symmetry estimates computed for every subject using a set of initializations to calculate symmetry-based characterizations of network nodes. For every node, we estimated an average rate of cases when the node was a fixed point in permutation for a given subject. The higher the rate, the more unique the node's role in the connectivity structure. Finally, the described rates were compared per node between groups of patients and healthy controls. The Mann-Whitney U test was used to compare the distributions; the significance level was set to 0.05 (Bonferroni corrected).
Results:
We found that the average proportion of fixed points is generally higher for the group of patients. The significant difference between groups was found in the following regions: Middle Frontal Gyrus (left and right), Superior Parietal Gyrus (right), Inferior Parietal Gyrus (left and right), Middle Temporal Gyrus (right), Insula (left and right), Rolandic Operculum (left), Inferior Frontal Operculum (left and right), Inferior Frontal Triangularis (left and right), and the Middle Frontal Orbital Gyrus (left and right). Thus, schizophrenia patients exhibit decreases in approximate symmetry patterns of functional connectivity compared to healthy controls, with significant changes in brain regions involved in the Default Mode Network (DMN), Salience Network (SN), and Fronto-Parietal Network (FPN). The regions with significant differences, including the Middle Frontal Gyrus, Parietal Gyrus, Insula, and Temporal Gyrus, are crucial for the functions of these networks, which are implicated across many functions including cognitive control, higher-level sensory processing, and self-referential thought.
Conclusions:
The results of our analysis suggest that schizophrenia patients, compared to healthy individuals, might have a higher rate of fixed points in the regions involved in DMN, SN and FPN. The high fixed point rate can be interpreted as a local uniqueness of the connectivity pattern, which can also mean its low redundancy. This conclusion is in line with the findings from various studies that suggest that healthy controls have more redundancy (and therefore more robustness) in their DMN, SN, and FPN connections compared to schizophrenia patients, who exhibit significant dysconnectivity and impaired network dynamics. This supports the notion that a well-functioning brain network architecture is crucial for cognitive and emotional regulation, which is disrupted in schizophrenia.
The study was supported by ERDF-Project Brain dynamics No. CZ.02.01.01/00/22_008/0004643 and Czech Health Research Council Project No. NU21-08-00432
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Methods Development 1
Task-Independent and Resting-State Analysis
Keywords:
Data analysis
FUNCTIONAL MRI
Psychiatric Disorders
Schizophrenia
Statistical Methods
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
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.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
FSL
Provide references using APA citation style.
[1] Caputi, L., Pidnebesna, A., & Hlinka, J. (2021). Promises and pitfalls of topological data analysis for brain connectivity analysis. NeuroImage, 238, 118245. https://doi.org/10.1016/j.neuroimage.2021.118245
[2] Pidnebesna, A., Hartman, D., Pokorná, A., Straka, M., & Hlinka, J. (2023). Computing approximate symmetries of complex networks. ArXiv. https://arxiv.org/abs/2312.08042
[3] Tomecek, D., Kolenic, M., Rehak Buckova, B., Tintera, J., Spaniel, F., Horacek, J. & Hlinka, J. (2024). Resting-state hyper- and hypo-connectivity in early schizophrenia: which tip of the iceberg should we focus on? bioRxiv 2024.09.20.613853; doi: https://doi.org/10.1101/2024.09.20.613853
No