ArticleHuman brain mapping2022
Statistical testing in transcriptomic-neuroimaging studies: A how-to and evaluation of methods assessing spatial and gene specificity.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 46 citations in OpenAlex.
- Relationship Between Dp140 Genotype and Cortical Similarity Network Phenotype in Duchenne Muscular Dystrophy: Preliminary T1 Weighted Study.Journal of magnetic resonance imaging : JMRI · 2026Article
- BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment.Human brain mapping · 2026Article
- Connectome-based growth models reveal individual heterogeneity and neurophysiological subtypes of subthreshold depression.Molecular psychiatry · 2026Article
- Shared neurogenetic architecture links adolescent neurodevelopmental deviations to adult psychopathological procrastination.Molecular psychiatry · 2026Article
- Recent developments in imaging transcriptomics for psychiatric disorders.Psychoradiology · 2026Review
- A Critical Evaluation of Background Gene Omission in Imaging Transcriptomics.Biological psychiatry global open science · 2025Article
- The emergence of genetic variants linked to brain and cognitive traits in human evolution.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Individual Variability in the Structural Connectivity Architecture of the Human Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Brain change trajectories in healthy adults correlate with Alzheimer's related genetic variation and memory decline across life.Nature communications · 2024Article
- Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer's disease.Cell reports. Medicine · 2024Review
- A shifting role of thalamocortical connectivity in the emergence of cortical functional organization.Nature neuroscience · 2024Article
- Deciphering the functional specialization of whole-brain spatiomolecular gradients in the adult brain.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics.NeuroImage · 2024Article
- Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease.Cell reports · 2024Article
- The alteration of cortical microstructure similarity in drug-resistant epilepsy correlated with mTOR pathway genes.EBioMedicine · 2023Article
- Novel effects of Ras-MAPK pathogenic variants on the developing human brain and their link to gene expression and inhibition abilities.Translational psychiatry · 2023Article
- Novel effects of Ras-MAPK pathogenic variants on the developing human brain and their link to gene expression and inhibition abilities.Research square · 2023Article
- Long-range functional connections mirror and link microarchitectural and cognitive hierarchies in the human brain.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Cortical anatomical variations, gene expression profiles, and clinical phenotypes in patients with schizophrenia.NeuroImage. Clinical · 2023Article
- Alzheimer's genetic risk effects on cerebral blood flow across the lifespan are proximal to gene expression.Neurobiology of aging · 2022Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Multiscale integration of gene transcriptomic and neuroimaging data is becoming a widely used approach for exploring the molecular underpinnings of large-scale brain organization in health and disease. Proper statistical evaluation of determined associations between imaging-based phenotypic and transcriptomic data is key in these explorations, in particular to establish whether observed associations exceed "chance level" of random, nonspecific effects. Recent approaches have shown the importance of statistical models that can correct for spatial autocorrelation effects in the data to avoid inflation of reported statistics. Here, we discuss the need for examination of a second category of statistical models in transcriptomic-neuroimaging analyses, namely those that can provide "gene specificity." By means of a couple of simple examples of commonly performed transcriptomic-neuroimaging analyses, we illustrate some of the potentials and challenges of transcriptomic-imaging analyses, showing that providing gene specificity on observed transcriptomic-neuroimaging effects is of high importance to avoid reports of nonspecific effects. Through means of simulations we show that the rate of reported nonspecific effects (i.e., effects that cannot be specifically linked to a specific gene or gene-set) can run as high as 60%, with only less than 5% of transcriptomic-neuroimaging associations observed through ordinary linear regression analyses showing both spatial and gene specificity. We provide a discussion, a tutorial, and an easy-to-use toolbox for the different options of null models in transcriptomic-neuroimaging analyses.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.