Poster No:
690
Submission Type:
Abstract Submission
Authors:
Ming-Hsuan Lu1, Chang-Le Chen2, Chih-Min Liu1,3
Institutions:
1Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan, 2Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 3Department of Psychiatry, College of Medicine, National Taiwan University, Taipei, Taiwan
First Author:
Ming-Hsuan Lu
Department of Psychiatry, National Taiwan University Hospital
Taipei, Taiwan
Co-Author(s):
Chang-Le Chen
Department of Bioengineering, University of Pittsburgh
Pittsburgh, PA
Chih-Min Liu
Department of Psychiatry, National Taiwan University Hospital|Department of Psychiatry, College of Medicine, National Taiwan University
Taipei, Taiwan|Taipei, Taiwan
Introduction:
White matter microstructural change is a validated brain imaging phenotype of schizophrenia (SCZ). However, the regional pattern of white matter disruption is highly heterogeneous among individuals with schizophrenia. We hypothesize that the heterogeneity may be better accounted for by looking at systems-level changes in white matter tracts. Meanwhile, white matter microstructure is highly heritable and polygenic. We aim to uncover patient subgroups based on their pattern of white matter disruption and explore their polygenic basis.
Methods:
We analyzed diffusion MRI (dMRI) and genotype data from two cohorts. Cohort 1 includes 222 subjects from 97 families (97 patients, 90 unaffected siblings, 35 unaffected parents), whereas Cohort 2 includes 296 subjects (184 patients, 112 controls). We applied a normative model built from 482 healthy Taiwanese subjects. All subjects (n=1000) were scanned in a single MRI machine. Briefly, we analyzed z-scores of generalized fractional anisotropy (GFA-Z) of 45 tracts. We grouped the tracts into six systems optimized for clustering validation metrics and quantified systems-level changes of white matter microstructure with a normalized enrichment score (NES). K-means clustering of NES was applied independently to patients from the two cohorts to classify patients into white matter subtypes. To adjust for family background, we calculated family-adjusted NES (faNES) by subtracting GFA-Z of unaffected relatives from that of the patient as a preprocessing step before enrichment analysis. For genotype data, we calculated the whole-genome polygenic score with PRScs and pathway polygenic score (pPGS) with PRSet2 for SCZ based on summary statistics from PGC3. Pathways were selected from the Gene Ontology (GO) and Canonical Pathways (CP) from the Molecular Signature Database (MSigDB). GO terms were prioritized by appropriate gene count (100–500), relevance to schizophrenia (top 20% in PGC3 GO enrichment), and redundancy elimination, yielding 194 valid gene sets. We tested logistic regression with white matter subtype as dependent variables, PRScs or pPGS as independent variables, and 18 genetic PCs as covariates. In the pPGS model, we included PRScs as an additional covariate to adjust for the whole-genome polygenic burden. We meta-analyzed the two cohorts.
Results:
In both cohorts, we consistently identified three white matter subtypes of schizophrenia. Cluster 1 (28.9% in Cohort 1, 34.8% in Cohort 2) mainly features prefrontal deficits. Cluster 2 presents with limbic and commissural systems deviation (44.3% in Cohort 1, 40.8% in Cohort 2). Cluster 3 exhibits relatively preserved white matter systems except for the sensorimotor system (26.8% in Cohort 1, 24.5% in Cohort 2). The clusters assigned with NES and faNES were highly consistent (94/97=96.7%), suggesting that our white matter subtyping of SCZ is robust to family background. Cluster 1 was associated with higher PRScs (p=0.00838, Nagelkerke's R2=0.0352). In pPGS models, Cluster 1 was associated with gene sets relevant to intracellular calcium level (GO:0010522 p=0.00218, GO:0060402 p=0.0110), and telencephalon development (GO:0021537 p=0.0280). Cluster 2 was associated with pPGS for oxidative stress (GO:0034599 p=7.45E-5, GO:0006979 p=0.00498). Cluster 3 was associated with pPGS for "intraspecies interaction between organisms" (GO:0051703 p=7.53E-4). The association of Cluster 2 with pPGS for oxidative stress remained significant after Benjamini-Hochberg's adjustment for multiple comparisons (pBH=0.043). pPGS for canonical pathways suggested that Cluster 3 was associated with WNT signaling (R-HSA-201681 p=7.70E-5).
Conclusions:
White matter subtypes of SCZ can be reliably identified based on systems-level statistics. The white matter subtypes are associated with polygenic risk in specific gene sets with interpretable biological relevance. Current findings on white matter subtypes and pPGS may inform patient stratification.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Genetics:
Genetic Modeling and Analysis Methods 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
Diffusion MRI 2
Keywords:
White Matter
Other - Polygenic score
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):
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Was this research conducted in the United States?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Were any animal research approved by the relevant IACUC or other animal research panel?
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Please indicate which methods were used in your research:
Diffusion MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
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DSI-Studio
Provide references using APA citation style.
1) Lv, J. et al. (2021), 'Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort', Mol Psychiatry, vol. 26, no. 7, pp. 3512-3523. 2) Tseng, I.W.Y. et al. (2021), 'Microstructural differences in white matter tracts across middle to late adulthood: a diffusion MRI study on 7167 UK Biobank participants', Neurobiol Aging. vol. 98, pp. 160-172. 3) Koshiyama, D. et al. (2020), 'White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals', Mol Psychiatry, vol. 25, no. 4, pp. 883-895. 4) Warren, T.L. et al. (2024), 'Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis', Mol Psychiatry, vol. 29, no. 8, pp. 2389-2398.
No