Evidence map›Paper›PMID 38339896›Full record

ArticleHuman brain mapping2024

Spinal fMRI demonstrates segmental organisation of functionally connected networks in the cervical spinal cord: A test-retest reliability study.

Olivia S Kowalczyk, Sonia Medina, Dimitra Tsivaka, Stephen B McMahon, Steven C R Williams, Jonathan C W Brooks, David J Lythgoe, Matthew A Howard

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
5.7field-weighted citation impact, top 4% 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

13 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
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  5. Spine-Prints: Transposing Brain Fingerprints to the Spinal Cord.bioRxiv : the preprint server for biology · 2025
    Article
  6. Rootlets-based registration to the PAM50 spinal cord template.Imaging neuroscience (Cambridge, Mass.) · 2025
    Article
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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

8 authors at 4 institutions in 2 countries.

Olivia S KowalczykDepartment of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK.ORCID 0000-0002-6543-290X
Sonia MedinaDepartment of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK.
Dimitra TsivakaDepartment of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK.
Stephen B McMahonWolfson Centre for Age Related Diseases, King's College London, London, UK.
Steven C R WilliamsDepartment of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK.
Jonathan C W BrooksSchool of Psychology, University of East Anglia, Norwich, UK.
David J LythgoeDepartment of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK.
Matthew A HowardDepartment of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK.
King's College London · GBUniversity of East Anglia · GBUniversity of Thessaly · GRWellcome Centre for Human Neuroimaging · GB

Funding

Medical Research Council MR/N026969/1
6 · The paper itself

Abstract

Resting functional magnetic resonance imaging (fMRI) studies have identified intrinsic spinal cord activity, which forms organised motor (ventral) and sensory (dorsal) resting-state networks. However, to facilitate the use of spinal fMRI in, for example, clinical studies, it is crucial to first assess the reliability of the method, particularly given the unique anatomical, physiological, and methodological challenges associated with acquiring the data. Here, we characterise functional connectivity relationships in the cervical cord and assess their between-session test-retest reliability in 23 young healthy volunteers. Resting-state networks were estimated in two ways (1) by estimating seed-to-voxel connectivity maps and (2) by calculating seed-to-seed correlations. Seed regions corresponded to the four grey matter horns (ventral/dorsal and left/right) of C5-C8 segmental levels. Test-retest reliability was assessed using the intraclass correlation coefficient. Spatial overlap of clusters derived from seed-to-voxel analysis between sessions was examined using Dice coefficients. Following seed-to-voxel analysis, we observed distinct unilateral dorsal and ventral organisation of cervical spinal resting-state networks that was largely confined in the rostro-caudal extent to each spinal segmental level, with more sparse connections observed between segments. Additionally, strongest correlations were observed between within-segment ipsilateral dorsal-ventral connections, followed by within-segment dorso-dorsal and ventro-ventral connections. Test-retest reliability of these networks was mixed. Reliability was poor when assessed on a voxelwise level, with more promising indications of reliability when examining the average signal within clusters. Reliability of correlation strength between seeds was highly variable, with the highest reliability achieved in ipsilateral dorsal-ventral and dorso-dorsal/ventro-ventral connectivity. However, the spatial overlap of networks between sessions was excellent. We demonstrate that while test-retest reliability of cervical spinal resting-state networks is mixed, their spatial extent is similar across sessions, suggesting that these networks are characterised by a consistent spatial representation over time.

Indexed as

Cervical CordAnimalsBrainGray MatterHumansMagnetic Resonance ImagingReproducibility of ResultsSpinal Cordreliabilityresting-state fMRIspinal fMRItest-retest

Identifiers

PMID38339896
PMCPMC10831202
OpenAlexW4391446082

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.