Unveiling Fetal Cortical Folding: Neuroimaging and Genetic Insights
Presented During:
Monday, June 24, 2024: 5:45 PM - 7:00 PM
COEX
Room:
Grand Ballroom 101-102
Poster No:
1295
Submission Type:
Abstract Submission
Authors:
Xinyi Xu1, Ruike Chen1, Tianshu Zheng1, Zhiyong Zhao1, Mingyang Li1, Dan Wu1
Institutions:
1College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang
First Author:
Xinyi Xu
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
Co-Author(s):
Ruike Chen
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
Tianshu Zheng
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
Zhiyong Zhao
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
Mingyang Li
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
Dan Wu
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
College of Biomedical Engineering & Instrument Science, Zhejiang University
Hangzhou, Zhejiang
Introduction:
The human fetal cerebral cortex undergoes genetically orchestrated gyrification (cortical folding) during the prenatal period. Recent fetal brain MRI advancements allow spatiotemporal quantification of macro- and microstructures during cortical folding (Figure 1a). Researchers aim to understand the relationship between morphological changes and underlying microstructural alterations associated with cortical folding (Garcia et al., 2021).
Transcriptomic technologies revealed regional gene expression patterns during prenatal development (Miller et al., 2014, Li et al., 2018). Pioneering studies by (Huang et al., 2013) and (Vasung et al., 2021) correlated MRI measurements with relevant genes using ex-vivo and in-vivo fetal brains but fell short in describing cortical folding. Therefore, we aim to comprehensively characterize spatiotemporal macro- and microstructural development of the fetal cerebral cortex by integrating in-utero fetal structural MRI, diffusion MRI, and gene expression.
Transcriptomic technologies revealed regional gene expression patterns during prenatal development (Miller et al., 2014, Li et al., 2018). Pioneering studies by (Huang et al., 2013) and (Vasung et al., 2021) correlated MRI measurements with relevant genes using ex-vivo and in-vivo fetal brains but fell short in describing cortical folding. Therefore, we aim to comprehensively characterize spatiotemporal macro- and microstructural development of the fetal cerebral cortex by integrating in-utero fetal structural MRI, diffusion MRI, and gene expression.
Methods:
Fetal brain MRI data
We used fetal brain T2 atlas (Xinyi et al., 2022) and diffusion MRI atlas data (Chen et al., 2022) from 23-38w gestational age (GA), served as standard average representations of a healthy fetal population. Macro- and microstructural characterization of cortical folding was conducted weekly (Figure 1a). Curvature, indicating the degree of cortical folding, was computed as the morphological representation. The Diffusion Basis Spectrum Imaging (DBSI) model (Wang et al., 2011) estimated the fiber, restricted, hindered, and water components of brain tissue using the DBSI toolbox (Spees et al., 2018).
Bulk tissue gene expression data
Cortical gene expression data, quantified as Reads Per Kilobase Million (RPKM), were obtained from PsychEncode project (Li et al., 2018). Postmortem human brains underwent regional dissection encompassing 11 neocortical regions (Figure 1b). For this study, we selectively incorporated RPKM data from 4 prenatal specimens (two at 21 pcw, one at 35 pcw, and another at 37 pcw) to match GAs of fetal brain MRI atlases (Figure 1c).
Structural equation modeling (SEM)
To examine directional influence between early macrostructural cortical folding changes and late microstructural alterations, we conducted SEM in R (lavaan package). This analysis focused on curvature and four DBSI metrics at early (23w) and late (35w-38w) stages (Figure 1d). FDR correction was applied within each DBSI metric.
Correlation of Gene experession and Macro-/Micro-structural Measures
We compared regional variation in cortical MRI measures (curvature and DBSI metrics) with prenatal gene expression in correspondent cortical regions (Figure 1b). Pearson correlation between MRI measures and log of RPKM for selected 5438 marker genes (similar selection process to (Ball et al., 2020)) was performed. Enrichment analysis of significant associated genes was conducted using WebGestalt (https://www.webgestalt.org/#).
We used fetal brain T2 atlas (Xinyi et al., 2022) and diffusion MRI atlas data (Chen et al., 2022) from 23-38w gestational age (GA), served as standard average representations of a healthy fetal population. Macro- and microstructural characterization of cortical folding was conducted weekly (Figure 1a). Curvature, indicating the degree of cortical folding, was computed as the morphological representation. The Diffusion Basis Spectrum Imaging (DBSI) model (Wang et al., 2011) estimated the fiber, restricted, hindered, and water components of brain tissue using the DBSI toolbox (Spees et al., 2018).
Bulk tissue gene expression data
Cortical gene expression data, quantified as Reads Per Kilobase Million (RPKM), were obtained from PsychEncode project (Li et al., 2018). Postmortem human brains underwent regional dissection encompassing 11 neocortical regions (Figure 1b). For this study, we selectively incorporated RPKM data from 4 prenatal specimens (two at 21 pcw, one at 35 pcw, and another at 37 pcw) to match GAs of fetal brain MRI atlases (Figure 1c).
Structural equation modeling (SEM)
To examine directional influence between early macrostructural cortical folding changes and late microstructural alterations, we conducted SEM in R (lavaan package). This analysis focused on curvature and four DBSI metrics at early (23w) and late (35w-38w) stages (Figure 1d). FDR correction was applied within each DBSI metric.
Correlation of Gene experession and Macro-/Micro-structural Measures
We compared regional variation in cortical MRI measures (curvature and DBSI metrics) with prenatal gene expression in correspondent cortical regions (Figure 1b). Pearson correlation between MRI measures and log of RPKM for selected 5438 marker genes (similar selection process to (Ball et al., 2020)) was performed. Enrichment analysis of significant associated genes was conducted using WebGestalt (https://www.webgestalt.org/#).
Results:
Early changes (23w) in curvature were significantly associated with changes in DBSI fiber and hindered fractions at later stages (35w-38w) (Figure 2a-c).
Based on these SEM results, we further analyzed the correlations of curvature at 23w and two DBSI metrics at 37w-38w with gene expression data. Of 5438 genes, 87 correlated significantly with curvature, 79 with DBSI fiber fraction, and 30 with DBSI hindered fraction after FDR correction (p<0.05) (Figure 2d). Significant enrichment of the genes that were significantly associated with curvature identified neurodevelopmental terms, including neurogenesis and neuron differentiation (Figure 2e). Genes significantly correlated with DBSI fiber and hindered fractions were both enriched for cell adhesion, biological adhesion, and extracellular matrix organization.
Based on these SEM results, we further analyzed the correlations of curvature at 23w and two DBSI metrics at 37w-38w with gene expression data. Of 5438 genes, 87 correlated significantly with curvature, 79 with DBSI fiber fraction, and 30 with DBSI hindered fraction after FDR correction (p<0.05) (Figure 2d). Significant enrichment of the genes that were significantly associated with curvature identified neurodevelopmental terms, including neurogenesis and neuron differentiation (Figure 2e). Genes significantly correlated with DBSI fiber and hindered fractions were both enriched for cell adhesion, biological adhesion, and extracellular matrix organization.
Conclusions:
Our study combines advanced imaging modalities and genetic analysis to probe fetal cortex development dynamics, enhancing our understanding of macro- and microstructural interplay in neurodevelopment.
Genetics:
Transcriptomics
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Normal Development 2
Keywords:
Other - Fetal brain; Cortical folding; Gene expression; in-utero MRI; atlas; development
1|2Indicates the priority used for review
Provide references using author date format
2. Chen R, et al. (2022), 'Deciphering the developmental order and microstructural patterns of early white matter pathways in a diffusion MRI based fetal brain atlas', Neuroimage, vol. 264, no. pp. 119700.
3. Garcia K E, et al. (2021), 'A model of tension-induced fiber growth predicts white matter organization during brain folding', Nature Communications, vol. 12, no. 1, pp. 6681.
4. Huang H, et al. (2013), 'Coupling Diffusion Imaging with Histological and Gene Expression Analysis to Examine the Dynamics of Cortical Areas across the Fetal Period of Human Brain Development', Cerebral Cortex, vol. 23, no. 11, pp. 2620-2631.
5. Li M, et al. (2018), 'Integrative functional genomic analysis of human brain development and neuropsychiatric risks', Science (New York, N.Y.), vol. 362, no. 6420, pp. eaat7615.
6. Miller J A, et al. (2014), 'Transcriptional landscape of the prenatal human brain', Nature, vol. 508, no. 7495, pp. 199-206.
7. Spees W M, et al. (2018), 'MRI-based assessment of function and dysfunction in myelinated axons', Proceedings of the National Academy of Sciences, vol. 115, no. 43, pp. E10225-E10234.
8. Vasung L, et al. (2021), 'Association between Quantitative MR Markers of Cortical Evolving Organization and Gene Expression during Human Prenatal Brain Development', Cerebral Cortex, vol. 31, no. 8, pp. 3610-3621.
9. Wang Y, et al. (2011), 'Quantification of increased cellularity during inflammatory demyelination', Brain, vol. 134, no. Pt 12, pp. 3590-3601.
10. Xinyi X, et al. (2022), 'Spatiotemporal Atlas of the Fetal Brain Depicts Cortical Developmental Gradient', The Journal of Neuroscience, vol. 42, no. 50, pp. 9435.