Evidence map›Paper›PMID 39810939›Full record

ArticleCurrent research in neurobiology2025

Distinct functional cerebral hypersensitivity networks during incisional and inflammatory pain in rats.

Silke Kreitz, Bruno Pradier, Daniel Segelcke, Saeedeh Amirmohseni, Andreas Hess, Cornelius Faber, Esther M Pogatzki-Zahn

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Article in Current research in neurobiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Silke KreitzInstitute of Experimental and Clinical Pharmacology and Toxicology, Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany.
Bruno PradierDepartment of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Germany.
Daniel SegelckeDepartment of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Germany.
Saeedeh AmirmohseniClinic of Radiology, University of Muenster, Germany.
Andreas HessInstitute of Experimental and Clinical Pharmacology and Toxicology, Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany.
Cornelius FaberClinic of Radiology, University of Muenster, Germany.
Esther M Pogatzki-ZahnDepartment of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the pathophysiology of pain has been investigated tremendously, there are still many open questions with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal neuroimaging pain studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. We developed a comprehensive framework for brain network analysis combining functional magnetic resonance imaging (MRI) with graph-theory (GT) and data classification by linear discriminant analysis. This enabled us to expand our knowledge of stimulus modalities processing under incisional (INC) and pathogen-induced inflammatory (CFA) pain entities compared to acute pain conditions. GT-analysis has uncovered specific features in pain modality processing that align well with those previously identified in humans. These include areas such as S1, M1, CPu, HC, piriform, and cingulate cortex. Additionally, we have identified unique Network Signatures of Pain Hypersensitivity (NSPH) for INC and CFA. This leads to a diminished ability to differentiate between stimulus modalities in both pain models compared to control conditions, while also enhancing aversion processing and descending pain modulation. Our findings further show that different pain entities modulate sensory input through distinct NSPHs. These neuroimaging signatures are an important step toward identifying novel cerebral pain biomarkers for certain diseases and relevant outcomes to evaluate target engagement of novel therapeutic and diagnostic options, which ultimately can be translated to the clinic.

Indexed as

Animal neuroimaging studiesCerebral pain biomarkersDisease-specific pain modelsFunctional MRI (fMRI)Graph theory in pain analysisIncisional pain (INC)Inflammatory pain (CFA)Linear discriminant analysisNetwork signatures of pain hypersensitivity (NSPH)Pain pathophysiology

Identifiers

PMID39810939
PMCPMC11731594

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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.