Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

Li, Jiao; Seidlitz, Jakob; Suckling, John; Fan, Fuyou; Ji, Gong‐Jun; Meng, Yao; Yang, Siqi; Wang, Kai; Qiu, Jiang; Chen, Huafu; Liao, Wei

doi:10.1038/s41467-021-21943-5

Cited by 123 publications

(114 citation statements)

References 81 publications

(123 reference statements)

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“…Thirty-four of those genes were also transcriptionally dysregulated in post-mortem tissues, mirroring previous findings (Anderson, Collins, Kong, et al, 2020;Romero-Garcia et al, 2020). These associations were specific to depression and not evident for genes dysregulated in other psychiatric disorders (Li et al, 2021). Functional MRI analysis has revealed that temporal fluctuations in regional signal homogeneity are linked to the expression of several neurobiological gene categories that include synaptic signaling, metabolism, central nervous system development, and neurodegenerative disease (Xue et al, 2020).…”

Section: Psychiatric Disorderssupporting

confidence: 76%

“…Studies of Major Depressive Disorder (MDD) have consistently identified links between IDPs and the spatial expression pattern of genes that are also transcriptionally dysregulated in post-mortem studies. Li et al (2021) used multivariate analysis to identify linear combinations of almost 3000 genes enriched in synapse-related terms and other neurobiologically relevant categories that were related to regional case-control differences in MS.…”

Section: Psychiatric Disordersmentioning

confidence: 99%

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Imaging transcriptomics of brain disorders

Arnatkevičiūtė¹,

Fulcher²,

Bellgrove³

et al. 2021

Preprint

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Non-invasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function invivo and has been used extensively to map the neural changes associated with different brain disorders. However,most neuroimaging techniques have limited spatiotemporal resolution and offer only indirect measures ofunderlying pathological mechanisms. The recent development of anatomically comprehensive gene-expressionatlases has opened new opportunities for studying the transcriptional correlates of non-invasively measured neuralphenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms.Here, we overview some fundamental methods in imaging transcriptomics and outline their application tounderstanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidenceindicates that spatial variations in gene expression are linked to normative changes in brain structure during agerelatedmaturation and neurodegeneration that are in part associated with cell-specific gene expression markersof gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked totranscriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstratethat spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spreadof disease across the brain. This growing body of work supports the utility of transcriptional atlases in testinghypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimagingphenotypes.

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Section: Psychiatric Disorderssupporting

confidence: 76%

Section: Psychiatric Disordersmentioning

confidence: 99%

Imaging transcriptomics of brain disorders

Arnatkevičiūtė¹,

Fulcher²,

Bellgrove³

et al. 2021

Preprint

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show abstract

“…All these methods have been used to investigate network alterations in brain-related diseases (Yao et al, 2010;Zheng et al, 2015;Bethlehem et al, 2017;Yu et al, 2018). Seidlitz and colleagues proposed a morphometric similarity network that captures cortical cytoarchitecture and is linked to individual cognitive performance (Seidlitz et al, 2018), and it has been applied to understand major depressive disorder (Li et al, 2021). Despite the progress in constructing different brain networks, a well-validated and widely accessible model for mapping the brain network architecture of anatomical brain regions in an individual human brain is needed.…”

Section: Introductionmentioning

confidence: 99%

Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis

Zhao

Zheng

Che

et al. 2021

Network Neuroscience

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A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject. We further assessed the small-world properties of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of enriched genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient >0.7). In addition, the small-word property (σ>2) and the high correlation between gene expression (R=0.29, P<0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable and biologically plausible method to understand human morphological covariance based on sMRI.

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“…Resting-state intrinsic brain synchronization is also supported by related gene expression ( Richiardi et al, 2015 ). Combing neuroimaging and gene transcripts provides insights into how disease-related aberrance at the microscale architecture drives macroscale brain abnormalities in mental disorders such as depression and schizophrenia ( Romero-Garcia et al, 2020 ; Li and Seidlitz, 2021 ). The details of the underlying transcriptional mechanisms of brain age remain unknown.…”

Section: Introductionmentioning

confidence: 99%

The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures

Fang

Han

et al. 2021

Front. Neurosci.

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Recent studies combining neuroimaging with machine learning methods successfully infer an individual’s brain age, and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated an individual’s brain age in the Southwest University Adult Lifespan Dataset (N = 492) from the gray matter volumes (GMV) derived from T1-weighted MRI scans by means of Gaussian process regression. Computational lesion analysis was performed to determine the importance of each brain network in brain age prediction. Then, we identified brain age-related genes by using prior brain-wide gene expression data, followed by gene enrichment analysis using Metascape. As a result, the prediction model successfully inferred an individual’s brain age and the computational lesion prediction results identified the central executive network as a vital network in brain age prediction (Steiger’s Z = 2.114, p = 0.035). In addition, the brain age-related genes were enriched in Gene Ontology (GO) processes/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways grouped into numbers of clusters, such as regulation of iron transmembrane transport, synaptic signaling, synapse organization, retrograde endocannabinoid signaling (e.g., dopaminergic synapse), behavior (e.g., memory and associative learning), neurotransmitter secretion, and dendrite development. In all, these results reveal that the GMV of the central executive network played a vital role in predicting brain age and bridged the gap between transcriptome and neuroimaging promoting an integrative understanding of the pathophysiology of brain age.

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Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

Cited by 123 publications

References 81 publications

Imaging transcriptomics of brain disorders

Imaging transcriptomics of brain disorders

Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis

The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures

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