Genetic and Network-Based Constraints on Gray Matter Volume Changes in Psychosis
Poster No:
461
Submission Type:
Abstract Submission
Authors:
Gabriella Chan1, Trang Cao1, Sidhant Chopra2, Aurina Arnatkevic̆iūtė1, James Pang1, Alex Fornito1
Institutions:
1Monash University, Clayton, Victoria, 2Orygen, Preston, Victoria
First Author:
Co-Author(s):
Introduction:
Differences in regional grey matter volumes (GMV) of the brain are a frequent finding in MRI studies of people with psychotic illness. These changes, which predominately consist of GMV reductions, consistently occur within disease-related functional networks (Segal et al., 2023). However, there is limited understanding of the mechanisms that shape the spatial patterning of these changes. Recent work from our group and others suggests that the progression of disease-associated GMV changes is constrained by brain network architecture (i.e., connectome), whereby GMV reductions spread through white matter tracts (Shafiei et al., 2020; Chopra et al., 2023). Here, we expand on existing models of GMV changes by integrating interactions between brain networks and genetic risk factors. Specifically, we apply an epidemiological agent-based spreading model to simulate the movement of pathological gene products along the connectome and predict the resulting spatial pattern of GMV reductions.
Methods:
Group-level GMV changes were assessed using standard voxel-based morphometry analyses on T1-weighted MRI data from 12 independent datasets and 24 different scan sites (1391 healthy controls and 940 patients with psychosis, 58.49% male). These maps were parcellated into 100 cortical and 32 subcortical regions (Schaefer et al., 2018; Tian et al., 2020). Structural connectivity matrices were derived from the diffusion MRI data of the Human Connectome Project, resulting in a 132×132 group-averaged connectome with 35% binary density. Gene expression profiles from microarray data across the whole brain were provided by the Allen Human Brain Atlas. Samples were assigned to each parcellated region and processed using established methods to produce a matrix of z-scored gene expression per region (Arnatkevic̆iūtė et al., 2019; Markello et al., 2021). Our disease model employed a multi-layered agent-based Susceptible-Infected-Removed (SIR) epidemic spreading model as previously described (Zheng et al., 2019), simulating contagion spread within regions based on gene expression and between regions according to the connectome. Model performance was compared with spatial null models generated using the BrainSMASH toolbox and rewired connectome nulls using the Maslov-Sneppen algorithm (Burt et al., 2020; Maslov & Sneppen, 2002).
Results:
We simulated pathological processes and subsequent atrophy across all pairwise combinations of potential risk and clearance genes. The resulting simulated atrophy maps were compared with empirical atrophy maps to determine a peak correlation per gene combination. Across all gene pairs, peak model fit reached r=0.58 (for exemplar risk/clearance gene pair [RNF144A/ RFXAP] see Fig. 1). Examining model fit demonstrates that as pathological processes propagate, model correlation increases up to a maximum value, after which it levels off (Fig 1a). The linear regional correlation between simulated and empirical atrophy at peak model fit is shown in Fig. 1b and spatial correspondence is shown in Fig 1c. To assess the validity of our results, we compared simulated atrophy maps with null models. Simulated atrophy from gene pairs with high model fit outperformed both the BrainSMASH spatial null (p<0.001, Fig 1d) and the Maslov-Sneppen rewired null (p < 0.001, Fig 1e).
Conclusions:
Our agent-based model integrates genomic and connectomic data to simulate grey matter volume changes in psychotic illnesses. Our results indicate that disease processes, constrained genomic expression, accumulate locally and propagate along the connectome to shape the distribution of GMV changes in psychosis.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetic Modeling and Analysis Methods
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Keywords:
DISORDERS
Modeling
MRI
Psychiatric Disorders
Schizophrenia
STRUCTURAL MRI
Other - Gene expression
1|2Indicates the priority used for review
Abstract Information
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Was this research conducted in the United States?
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.
Burt, J. B. (2020). Generative modeling of brain maps with spatial autocorrelation. NeuroImage, 220, 117038.
Chopra, S. (2023). Network-based spreading of gray matter changes across different stages of psychosis. JAMA psychiatry, 80(12), 1246-1257.
Markello, R. D. (2021). Standardizing workflows in imaging transcriptomics with the abagen toolbox. elife, 10, e72129.
Maslov, S. (2002). Specificity and stability in topology of protein networks. Science, 296(5569), 910-913.
Schaefer, A. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.
Segal, A. (2023). Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nature Neuroscience, 26(9), 1613-1629.
Shafiei, G. (2020). Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture. Biological psychiatry, 87(8), 727-735.
Tian, Y. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nature neuroscience, 23(11), 1421-1432.
Zheng, Y. Q. (2019). Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation. PLoS biology, 17(11), e3000495.