Evidence map›Paper›PMID 40567891›Full record

ArticleNeuroimage. Reports2025

Regional gray matter thickness correlations of the hearing and deaf feline brains.

Stephen G Gordon, Alessandra Sacco, Stephen G Lomber

Abstract read
In one paragraph

Article in Neuroimage. Reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Stephen G GordonIntegrated Program in Neuroscience, McGill University, Montreal, Canada.
Alessandra SaccoIntegrated Program in Neuroscience, McGill University, Montreal, Canada.
Stephen G LomberIntegrated Program in Neuroscience, McGill University, Montreal, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The overall function and associated structure of the brain changes dramatically following early-onset hearing loss in a process known as compensatory crossmodal plasticity. As the microscale changes to cerebral morphology driving these adaptations can be reflected macrostructurally in MRI analyses, high interregional correlations in features such as gray matter thickness are potentially indicative of functional relationships. To probe the changes in these associations following deafness using structure alone, perinatally-deafened and hearing control cats were scanned at 7T to obtain high-resolution T1-weighted images. After calculating regional thicknesses for 146 cortical areas, the 10,585 associated pairwise correlations were used to establish group-specific structural connectomes. Similar distributions of correlation strength were revealed between the two populations, however there was an overall increase in the density of the structurally-defined connectome following deafness. The connections demonstrating the most dramatic increases of correlational strength in the deprived group were those relating to the auditory and visual cortices, with a more balanced distribution of increases and decreases to connections involving solely non-sensory regions. In corroboration with previous feline structural- and diffusion-based neuroimaging literature, these results imply a reorganization of cortical gray matter to increase the overall processing of the remaining senses within a potentially less complex and more redundant connectome. The present study adds to the developing field of deafness literature through the implementation of novel analyses that add an additional perspective on neuroplasticity within the feline brain.

Indexed as

Compensatory crossmodal plasticityGray matterSensory cortexStructural MRI

Identifiers

PMID40567891
PMCPMC12172773

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Registered trials

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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.