ArticleNeuroImage2024
High-resolution diffusion magnetic resonance imaging and spatial-transcriptomic in developing mouse brain.
Article in NeuroImage, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bibliometric analysis of neurite orientation dispersion and density imaging: research patterns, evolution, and frontier.Frontiers in neuroscience · 2026Pooled it
- Diffusion MRI reveals amyloid plaque-associated cellular signatures through spatial transcriptomic integration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.Science advances · 2026Article
- An anatomical and connectivity atlas of the tree shrew brain to bridge rodent and primate neuroanatomy.PLoS biology · 2026Article
- Detecting Beta-Amyloid Plaque via Low Rank Based Orthogonal Projection and Spatial-Spectrum Detector Using High-Resolution Quantitative Susceptibility Mapping for Preclinical Studies.IEEE transactions on bio-medical engineering · 2026Article
- The impact of small for gestational age on visual-motor integration in preterm infants: insights from early brain magnetic resonance imaging.Translational pediatrics · 2026Article
- Decoding gray matter, large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals.Communications biology · 2026Article
- Imaging the neurohiv brain in animal models: past, present, and future-toward longitudinal, whole-brain aging phenotypes.Frontiers in aging neuroscience · 2026Review
- A relaxation-diffusion MRI dataset of aging mouse brains at 9.4 Tesla.Scientific data · 2025Article
- Compressed Sensing Reconstruction with Zero-Shot Self-Supervised Learning for High-Resolution MRI of Human Embryos.Tomography (Ann Arbor, Mich.) · 2025Article
- MR-AIV revealsbioRxiv : the preprint server for biology · 2025Article
- Intrinsic Gene Expression Correlates of the Biophysically Modeled Diffusion Magnetic Resonance Imaging Signal.Biological psychiatry global open science · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level.
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Registered trials
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.