Altered Signal Propagation Patterns in the Brains of Individuals with Schizophrenia
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
488
Submission Type:
Abstract Submission
Authors:
Youngjo Song1, TAEWON KIM2
Institutions:
1MORESCIENCE, Seoul, AK, 2The Pennsylvania State University, University Park, PA
First Author:
Co-Author:
Introduction:
Schizophrenia (SZ) is a complex psychiatric disorder characterized by delusions, hallucinations, disorganized speech, and atypical behaviors. It is associated with altered brain dynamics (Georgiadis, et al., 2024; Menon, 2011; Roll, 2021). However, the exact nature of these disruptions remains unknown. We recently identified five fundamental large-scale signal propagation modes that effectively predict future BOLD activity and encompass various dynamic and operational aspects within the brain (Song et al., 2024). Here, we hypothesized that altered signal propagation patterns exist in the SZ brain. We applied this framework to investigate SZ-related brain dynamics, aiming to identify potential novel biomarkers for psychiatric disorders.
Methods:
We utilized publicly available COBRE fMRI data preprocessed with the NeuroImaging Analysis Kit (https://figshare.com/articles/dataset/COBRE_preprocessed_with_NIAK_0_12_4/1160600). The sample included 72 SZ individuals (F=14; mean age=38.17±13.89) and 74 demographically matched healthy controls (HC) (F=23; mean age=35.82±11.58). Following the resampling of the data to a 2 mm isotropic resolution, we applied a brain-wide parcellation approach by combining the Glasser cortical atlas with the Cole-Anticevic partition for subcortical and cerebellar structures, resulting in 716 ROIs. Subsequently, we regressed out 24 head-motion parameters, linear and quadratic trends, mean signals from ventricles and white matter, followed by band-pass filtering (0.01–0.1Hz). We then modeled each individual's fMRI dynamics using the signal propagation modes estimated from a large healthy cohort (n=1086, HCP S1200). This methodological framework characterizes individual differences in temporal evolution of BOLD signals, focusing on varying engagement, strength, and speed of each mode (Figure 1a; see Song et al., (2024) for details). Group differences in these dynamic characteristics were assessed using general linear models while controlling for confounding such as age, sex, handedness, average frame displacement before and after censoring frames with excessive motion, and the number of acceptable frames.
Results:
We found distinct patterns of large-scale signal propagation between SZ and HC. Specifically, individuals with SZ exhibited significantly lower engagement of unimodal-transmodal signal propagation (p=6.76×10-05, Bonferroni correction; Figure 1b), indicating a diminished integration of basic sensory information into higher-order cognitive processes, as well as reduced top-down modulation of sensory processing. These findings may underlie the altered multisensory perception (Gröhn et al., 2022). Conversely, we observed an increased relative engagement of the salience network shifting towards the default mode network (p=9.43×10-04, Bonferroni correction; Figure 1c), which may reflect aberrant salience attribution associated with positive or negative symptoms of SZ (Hare et al., 2019). Importantly, no significant difference in the strength or speed of propagation was found between SZ and HC (p>0.05). Furthermore, the engagement of unimodal-transmodal propagation exhibited a decline with age in individuals with SZ (p=0.0068), while remaining stable in HCs (p>0.9). This suggests an age-related deterioration of brain dynamics, which may contribute to the chronic and progressive nature of symptoms associated with SZ.
Conclusions:
By decomposing brain activity into discrete propagation modes, our approach provides a parsimonious and interpretable framework for characterizing altered brain dynamics in schizophrenia. These alterations have implications for integrating and extending previous findings regarding disrupted global signals (Yang, et al., 2014), functional gradients (Wang, et a., 2023), and changes within the default mode and salience networks in the SZ brains (Menon, 2011). In conclusion, this work establishes a foundational framework for identifying potential biomarkers pertinent to a wide array of psychiatric disorders.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Methods Development
Multivariate Approaches
Task-Independent and Resting-State Analysis
Keywords:
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Multivariate
Open Data
Psychiatric Disorders
Schizophrenia
1|2Indicates the priority used for review
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Provide references using APA citation style.
Gröhn, C., Norgren, E., & Eriksson, L. (2022). A systematic review of the neural correlates of multisensory integration in schizophrenia. Schizophrenia Research: Cognition, 27, 100219.
Hare, S. M., Ford, J. M., Mathalon, D. H., Damaraju, E., Bustillo, J., Belger, A., … & Turner, J. A. (2019). Salience–default mode functional network connectivity linked to positive and negative symptoms of schizophrenia. Schizophrenia Bulletin, 45(4), 892–901.
Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15(10), 483–506.
Rolls, E. T. (2021). Attractor cortical neurodynamics, schizophrenia, and depression. Translational Psychiatry, 11(1), 215.
Song, Y., Kim, P. S., Philip, B. A., & Kim, T. (2024). Large-scale signal propagation modes in the human brain. bioRxiv, 2024-11.
Wang, M., Yan, H., Tian, X., Yue, W., Liu, Y., Fan, L., … & Liu, B. (2023). Neuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia. Nature Mental Health, 1(9), 633–654.
Yang, G. J., Murray, J. D., Repovs, G., Cole, M. W., Savic, A., Glasser, M. F., … & Anticevic, A. (2014). Altered global brain signal in schizophrenia. Proceedings of the National Academy of Sciences, 111(20), 7438–7443.