Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics

Bruno, Jennifer; Hosseini, S. M. Hadi; Saggar, Manish; Quintin, Eve-Marie; Raman, Mira; Reiss, Allan L.

doi:10.1093/cercor/bhw055

Cited by 32 publications

(37 citation statements)

References 60 publications

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“…For example, we detected a downregulation of Efna5 and Epha7 in Fmr1 -KO neurons, both critical to cortex development and plasticity and involved in the establishment of corticothalamic projections [42][43][44] . This downregulation could therefore be involved in the deficit of cortical-subcortical circuits reported in individuals with FXS 45 . Conversely, we observed an upregulation of Ephb3 and Epha4 receptors in astrocytes, which are activated by ephrin-B produced by neurons ( Figure 3b).…”

Section: Multiple Pathways Display Opposite Effects In Astrocytes Andmentioning

confidence: 99%

Single Cell Transcriptomics Reveals Dysregulated Cellular and Molecular Networks in a Fragile X Syndrome model

Donnard

Shu

Garber

2020

Preprint

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Despite advances in understanding the pathophysiology of Fragile X syndrome (FXS), its molecular bases are still poorly understood. Whole brain tissue expression profiles have proved surprisingly uninformative. We applied single cell RNA sequencing to profile a FXS mouse model. We found that FXS results in a highly cell type specific effect and it is strongest among different neuronal types. We detected a downregulation of mRNAs bound by FMRP and this effect is prominent in neurons. Metabolic pathways including translation are significantly upregulated across all cell types with the notable exception of excitatory neurons. These effects point to a potential difference in the activity of mTOR pathways, and together with other dysregulated pathways suggest an excitatory-inhibitory imbalance in the FXS cortex which is exacerbated by astrocytes. Our data demonstrate the cell-type specific complexity of FXS and provide a resource for interrogating the biological basis of this disorder.

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Section: Multiple Pathways Display Opposite Effects In Astrocytes Andmentioning

confidence: 99%

Single Cell Transcriptomics Reveals Dysregulated Cellular and Molecular Networks in a Fragile X Syndrome model

Donnard

Shu

Garber

2020

Preprint

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“…The extent that transcriptomic similarity mediates covariance, particularly in relation to connectivity, remains to be seen, however. Nevertheless, given this link between neurodevelopment, genetics, and structural covariance, it is not surprising that alterations in structural covariance arise in relation with aberrant gene expression (Pezawas et al, 2008;Schmitt et al, 2016;Bruno et al, 2016) or early sensory deprivation (Voss & Zatorre, 2015).…”

Section: Introductionmentioning

confidence: 99%

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

et al. 2018

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An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings. Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed. We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

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“…Nevertheless, given this link between neurodevelopment, genetics, and structural covariance, it is not surprising that alterations in structural covariance arise in relation with aberrant gene expression (Pezawas et al, 2008;Schmitt et al, 2016;Bruno et al, 2016) or early sensory deprivation (Voss & Zatorre,50 2015).…”

mentioning

confidence: 99%

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

Yee

Fernandes

French

et al. 2017

Preprint

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An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings.Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed.We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance * Corresponding author Email address: yohan.yee@sickkids.ca (Yohan Yee) Preprint submitted to BioRxivAugust 31, 2017 . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/183004 doi: bioRxiv preprint first posted online (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

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Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics

Cited by 32 publications

References 60 publications

Single Cell Transcriptomics Reveals Dysregulated Cellular and Molecular Networks in a Fragile X Syndrome model

Single Cell Transcriptomics Reveals Dysregulated Cellular and Molecular Networks in a Fragile X Syndrome model

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

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