scholarly journals A practical guide to linking brain-wide gene expression and neuroimaging data

2018 ◽  
Author(s):  
Aurina Arnatkevičiūtė ◽  
Ben D. Fulcher ◽  
Alex Fornito
Keyword(s):  
Gene Expression ◽  
Brain Atlas ◽  
Expression Data ◽  
Analysis Pipeline ◽  
Atlas Data ◽  
Processing Pipeline ◽  
Neuroimaging Data ◽  
Brain Structure And Function ◽  
Expression Atlas ◽  
And Function

AbstractThe recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on the molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

scholarly journals Topological gene-expression networks recapitulate brain anatomy and function

2018 ◽  
Author(s):  
Alice Patania ◽  
Pierluigi Selvaggi ◽  
Mattia Veronese ◽  
Ottavia Dipasquale ◽  
Paul Expert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Expression Data ◽  
Pharmacological Challenge ◽  
Topological Network ◽  
Dopaminergic Pathway ◽  
And Function

AbstractUnderstanding how gene expression translates to and affects human behaviour is one of the ultimate aims of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to produce and analyze genes co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas, and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene-set and find that co-expression networks produced by Mapper returned a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that topological network descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenge.

scholarly journals Topological gene expression networks recapitulate brain anatomy and function

2019 ◽  
Vol 3 (3) ◽  
pp. 744-762 ◽  
Author(s):  
Alice Patania ◽  
Pierluigi Selvaggi ◽  
Mattia Veronese ◽  
Ottavia Dipasquale ◽  
Paul Expert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Expression Data ◽  
Related Gene ◽  
Genetic Pathways ◽  
Dopaminergic Pathway ◽  
And Function

Understanding how gene expression translates to and affects human behavior is one of the ultimate goals of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to analyze gene co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene set and find that co-expression networks produced by Mapper return a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that network based descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenges.

scholarly journals Transcriptomic and Cellular Decoding of Regional Brain Vulnerability to Neurodevelopmental Disorders

2019 ◽  
Author(s):  
Jakob Seidlitz ◽  
Ajay Nadig ◽  
Siyuan Liu ◽  
Richard A.I. Bethlehem ◽  
Petra E. Vértes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Patterns ◽  
Neurodevelopmental Disorders ◽  
Copy Number Variations ◽  
Brain Anatomy ◽  
Specific Cell ◽  
Expression Data ◽  
Regional Brain ◽  
Genetic Risks ◽  
Neuroimaging Data

AbstractNeurodevelopmental disorders are highly heritable and associated with spatially-selective disruptions of brain anatomy. The logic that translates genetic risks into spatially patterned brain vulnerabilities remains unclear but is a fundamental question in disease pathogenesis. Here, we approach this question by integrating (i) in vivo neuroimaging data from patient subgroups with known causal genomic copy number variations (CNVs), and (ii) bulk and single-cell gene expression data from healthy cortex. First, for each of six different CNV disorders, we show that spatial patterns of cortical anatomy change in youth are correlated with spatial patterns of expression for CNV region genes in bulk cortical tissue from typically-developing adults. Next, by transforming normative bulk-tissue cortical expression data into cell-type expression maps, we further link each disorder’s anatomical change map to specific cell classes and specific CNV-region genes that these cells express. Finally, we establish convergent validity of this “transcriptional vulnerability model” by inter-relating patient neuroimaging data with measures of altered gene expression in both brain and blood-derived patient tissue. Our work clarifies general biological principles that govern the mapping of genetic risks onto regional brain disruption in neurodevelopmental disorders. We present new methods that can harness these principles to screen for potential cellular and molecular determinants of disease from readily available patient neuroimaging data.

scholarly journals Imaging transcriptomics of brain disorders

2021 ◽  
Author(s):  
Aurina Arnatkeviciute ◽  
Ben Fulcher ◽  
Mark Bellgrove ◽  
Alex Fornito
Keyword(s):  
Gene Expression ◽  
Brain Structure ◽  
Ex Vivo ◽  
Specific Gene ◽  
Brain Disorders ◽  
Spatiotemporal Resolution ◽  
Indirect Measures ◽  
Regional Vulnerability ◽  
Brain Structure And Function ◽  
And Function

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.

scholarly journals A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lianxin Zhong ◽  
Qingfang Meng ◽  
Yuehui Chen ◽  
Lei Du ◽  
Peng Wu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Small Sample ◽  
Expression Data ◽  
Cancer Subtypes ◽  
Cancer Pathogenesis ◽  
Depth Study ◽  
And Function

Abstract Background Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. Results In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. Conclusion The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.

scholarly journals Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ben D. Fulcher ◽  
Aurina Arnatkeviciute ◽  
Alex Fornito
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Brain Atlas ◽  
Positive Bias ◽  
Spatially Resolved ◽  
Brain Structure And Function ◽  
And Function ◽  
Gene Category

AbstractTranscriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associations, using gene-to-category annotation systems like the Gene Ontology (GO). Here, we show that applying standard GCEA methodology to spatial transcriptomic data is affected by substantial false-positive bias, with GO categories displaying an over 500-fold average inflation of false-positive associations with random neural phenotypes in mouse and human. The estimated false-positive rate of a GO category is associated with its rate of being reported as significantly enriched in the literature, suggesting that published reports are affected by this false-positive bias. We show that within-category gene–gene coexpression and spatial autocorrelation are key drivers of the false-positive bias and introduce flexible ensemble-based null models that can account for these effects, made available as a software toolbox.

scholarly journals Correspondence between gene expression and neurotransmitter receptor and transporter density in the human brain

2021 ◽  
Author(s):  
Justine Y Hansen ◽  
Ross D Markello ◽  
Lauri Tuominen ◽  
Martin Norgaard ◽  
Elena Kuzmin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Present Report ◽  
Neurotransmitter Receptors ◽  
Metabotropic Receptors ◽  
Microarray Gene Expression ◽  
Neurotransmitter Receptor ◽  
Neurotransmitter Systems ◽  
Brain Structure And Function ◽  
And Function ◽  
Direct Measures

Neurotransmitter receptors modulate the signaling between neurons. Thus, neurotransmitter receptors and transporters play a key role in shaping brain function. Due to the lack of comprehensive neurotransmitter receptor/transporter density datasets, microarray gene expression is often used as a proxy for receptor densities. In the present report, we comprehensively test the expression-density association for a total of 27 neurotransmitter receptors, receptor binding-sites, and transporters across 9 different neurotransmitter systems, using both PET and autoradiography imaging modalities. We find poor spatial correspondences between gene expression and density for all neurotransmitter receptors and transporters except four single-protein metabotropic receptors (5-HT1A, D2, CB1, and MOR). These expression-density associations are related to population variance and change across different classes of laminar differentiation. Altogether, we recommend using direct measures of receptor and transporter density when relating neurotransmitter systems to brain structure and function.

scholarly journals Desikan-Killiany-Tourville Atlas Compatible Version of M-CRIB Neonatal Parcellated Whole Brain Atlas: The M-CRIB 2.0

2018 ◽  
Author(s):  
Bonnie Alexander ◽  
Wai Yen Loh ◽  
Lillian G. Matthews ◽  
Andrea L. Murray ◽  
Chris Adamson ◽  
...  
Keyword(s):  
Brain Regions ◽  
Brain Atlas ◽  
Whole Brain ◽  
Brain Structure And Function ◽  
Postmenstrual Age ◽  
Mri Scans ◽  
Subcortical Regions ◽  
Cortical Regions ◽  
And Function ◽  
Ventral Diencephalon

AbstractOur recently published M-CRIB atlas comprises 100 neonatal brain regions including 68 compatible with the widely-used Desikan-Killiany adult cortical atlas. A successor to the Desikan-Killiany atlas is the Desikan-Killiany-Tourville atlas, in which some regions with unclear boundaries were removed, and many existing boundaries were revised to conform to clearer landmarks in sulcal fundi. Our first aim here was to modify cortical M-CRIB regions to comply with the Desikan-Killiany-Tourville protocol, in order to offer: a) compatibility with this adult cortical atlas, b) greater labelling accuracy due to clearer landmarks, and c) optimisation of cortical regions for integration with surface-based infant parcellation pipelines. Secondly, we aimed to update subcortical regions in order to offer greater compatibility with subcortical segmentations produced in FreeSurfer. Data utilized were the T2-weighted MRI scans in our M-CRIB atlas, for ten healthy neonates (postmenstrual age at MRI 40-43 weeks, 4 female), and corresponding parcellated images. Edits were performed on the parcellated images in volume space using ITK-SNAP. Cortical updates included deletion of frontal and temporal poles and ‘Banks STS’, and modification of boundaries of many other regions. Changes to subcortical regions included the addition of ‘ventral diencephalon’, and deletion of ‘subcortical matter’ labels. A detailed updated parcellation protocol was produced. The resulting whole-brain M-CRIB 2.0 atlas comprises 94 regions altogether. This atlas provides comparability with adult Desikan-Killiany-Tourville-labelled cortical data and FreeSurfer-labelled subcortical data, and is more readily adaptable for incorporation into surface-based neonatal parcellation pipelines. As such, it offers the ability to help facilitate a broad range of investigations into brain structure and function both at the neonatal time point and developmentally across the lifespan.

scholarly journals The mouse Gene Expression Database (GXD): 2021 update

2020 ◽  
Vol 49 (D1) ◽  
pp. D924-D931 ◽  
Author(s):  
Richard M Baldarelli ◽  
Constance M Smith ◽  
Jacqueline H Finger ◽  
Terry F Hayamizu ◽  
Ingeborg J McCright ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Mouse Genome Informatics ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Expression Database ◽  
Heat Map ◽  
Developmental Gene Expression ◽  
Related Information ◽  
Expression Atlas

Abstract The Gene Expression Database (GXD; www.informatics.jax.org/expression.shtml) is an extensive and well-curated community resource of mouse developmental gene expression information. For many years, GXD has collected and integrated data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot, and western blot experiments through curation of the scientific literature and by collaborations with large-scale expression projects. Since our last report in 2019, we have continued to acquire these classical types of expression data; developed a searchable index of RNA-Seq and microarray experiments that allows users to quickly and reliably find specific mouse expression studies in ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) and GEO (https://www.ncbi.nlm.nih.gov/geo/); and expanded GXD to include RNA-Seq data. Uniformly processed RNA-Seq data are imported from the EBI Expression Atlas and then integrated with the other types of expression data in GXD, and with the genetic, functional, phenotypic and disease-related information in Mouse Genome Informatics (MGI). This integration has made the RNA-Seq data accessible via GXD’s enhanced searching and filtering capabilities. Further, we have embedded the Morpheus heat map utility into the GXD user interface to provide additional tools for display and analysis of RNA-Seq data, including heat map visualization, sorting, filtering, hierarchical clustering, nearest neighbors analysis and visual enrichment.

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