Imaging transcriptomics of brain disorders

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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Overcoming bias in gene-set enrichment analyses of brain-wide transcriptomic data

10.1101/2020.04.24.058958 ◽

2020 ◽

Cited By ~ 4

Author(s):

Ben D. Fulcher ◽

Aurina Arnatkevičiūtė ◽

Alex Fornito

Keyword(s):

Gene Expression ◽

Brain Structure ◽

False Positive ◽

Structure And Function ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Transcriptomic Data ◽

Brain Structure And Function ◽

And Function

The recent availability of whole-brain atlases of gene expression, which quantify the transcriptional activity of thousands of genes across many different brain regions, has opened new opportunities to understand how gene-expression patterns relate to spatially varying properties of brain structure and function. To aid interpretation of a given neural phenotype, gene-set enrichment analysis (GSEA) has become a standard statistical methodology to identify functionally related groups of genes, annotated using systems such as the Gene Ontology (GO), that are associated with a given phenotype. While GSEA has identified groups of genes related to diverse aspects of brain structure and function in mouse and human, here we show that these results are affected by substantial statistical biases. Quantifying the falsepositive rates of individual GO categories across an ensemble of random phenotypic maps, we found an average 875-fold inflation of significant findings relative to expectation in mouse, and a 582-fold inflation in human, with some categories being judged as significant for over 20% of random phenotypes. Concerningly, the probability of a GO category being reported as significant in the extant literature increases with its estimated false-positive rate, suggesting that published reports are strongly affected by the reporting of false-positive bias. We show that the bias is primarily driven by within-category gene–gene coexpression and spatial autocorrelation, which are not accounted for in conventional GSEA nulls, and we introduce flexible ensemble-based null models that can account for these effects. Testing a range of structural connectivity and cell density phenotypes in mouse and human, we demonstrate that many GO categories that would conventionally be judged as highly significant are in fact consistent with ensembles of random phenotypes. Our results highlight major pitfalls with applying standard GSEA to brain-wide transcriptomic data and outline solutions to this pervasive problem, which is made available as an open toolbox.

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From molecules to mind

Eastern Mediterranean Health Journal ◽

10.26719/2001.7.3.363 ◽

2001 ◽

Vol 7 (3) ◽

pp. 363-366

Author(s):

L. Eisenberg

Keyword(s):

Gene Expression ◽

Environmental Factors ◽

Brain Structure ◽

Molecular Level ◽

Structure And Function ◽

External Factors ◽

Brain Structure And Function ◽

Organ Level ◽

And Function ◽

The Brain

Thispaper describes the relation between genes at the molecular level and the brain at the organ level, and biological, social and environmental factors. The malleability of the brain and the effect of external factors and experience on influencing gene expression and brain structure and function are discussed.

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