Evidence map›Paper›PMID 35953450›Full record

ArticleBrain : a journal of neurology2023

Mapping astrogliosis in the individual human brain using multidimensional MRI.

Dan Benjamini, David S Priemer, Daniel P Perl, David L Brody, Peter J Basser

Open access · bronzeAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 49 citations in OpenAlex.

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  15. Quantitative MRI of the hippocampus reveals microstructural trajectories of aging and Alzheimer's disease pathology.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Dan BenjaminiSection on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20891, USA.ORCID 0000-0002-9558-7056
David S PriemerDepartment of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA.
Daniel P PerlDepartment of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA.
David L BrodyCenter for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA.
Peter J BasserSection on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20891, USA.
Uniformed Services University of the Health Sciences · USHenry M. Jackson Foundation · US

Funding

Development of Brain MRI Contrast AgentsZIANS003170 · NINDS · NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE · PI KORETSKY, ALAN · 2019 to 2024
$7.7M
Quantitative imaging of astrogliosis and neuroinflammationZIAAG000354 · NIA · NATIONAL INSTITUTE ON AGING · PI BENJAMINI, DAN · 2022 to 2025
$956k
6 · The paper itself

Abstract

There are currently no non-invasive imaging methods available for astrogliosis assessment or mapping in the central nervous system despite its essential role in the response to many disease states, such as infarcts, neurodegenerative conditions, traumatic brain injury and infection. Multidimensional MRI is an increasingly employed imaging modality that maximizes the amount of encoded chemical and microstructural information by probing relaxation (T1 and T2) and diffusion mechanisms simultaneously. Here, we harness the exquisite sensitivity of this imagining modality to derive a signature of astrogliosis and disentangle it from normative brain at the individual level using machine learning. We investigated ex vivo cerebral cortical tissue specimens derived from seven subjects who sustained blast-induced injuries, which resulted in scar-border forming astrogliosis without being accompanied by other types of neuropathological abnormality, and from seven control brain donors. By performing a combined post-mortem radiology and histopathology correlation study we found that astrogliosis induces microstructural and chemical changes that are robustly detected with multidimensional MRI, and which can be attributed to astrogliosis because no axonal damage, demyelination or tauopathy were histologically observed in any of the cases in the study. Importantly, we showed that no one-dimensional T1, T2 or diffusion MRI measurement can disentangle the microscopic alterations caused by this neuropathology. Based on these findings, we developed a within-subject anomaly detection procedure that generates MRI-based astrogliosis biomarker maps ex vivo, which were significantly and strongly correlated with co-registered histological images of increased glial fibrillary acidic protein deposition (r = 0.856, P < 0.0001; r = 0.789, P < 0.0001; r = 0.793, P < 0.0001, for diffusion-T2, diffusion-T1 and T1-T2 multidimensional data sets, respectively). Our findings elucidate the underpinning of MRI signal response from astrogliosis, and the demonstrated high spatial sensitivity and specificity in detecting reactive astrocytes at the individual level, and if reproduced in vivo, will significantly impact neuroimaging studies of injury, disease, repair and aging, in which astrogliosis has so far been an invisible process radiologically.

Indexed as

Brain Injuries, TraumaticGliosisAstrocytesBrainGlial Fibrillary Acidic ProteinHumansMagnetic Resonance ImagingGlial Fibrillary Acidic ProteinastrogliosisblastdiffusionGFAPmachine learningmultidimensional MRIradiological–pathological correlationsrelaxationtraumatic brain injury

Identifiers

PMID35953450
PMCPMC9976979
OpenAlexW4290999458

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.