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Reliability of supraspinal correlates to lower urinary tract stimulation in healthy participants – A fMRI study

2019· article· en· W2914457644 on OpenAlexaff
Matthias Walter, Lorenz Leitner, Lars Michels, Martina D. Liechti, Patrick Freund, Thomas M. Kessler, Spyros Kollias, Ulrich Mehnert

Bibliographic record

VenueNeuroImage · 2019
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersContinence Foundation of AustraliaSwiss Continence FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsIntraclass correlationFunctional magnetic resonance imagingMagnetic resonance imagingNeuroimagingPsychologyMedicineBrain mappingPhysical medicine and rehabilitationNuclear medicineNeuroscienceRadiologyDevelopmental psychologyPsychometrics

Abstract

fetched live from OpenAlex

Previous functional neuroimaging studies provided evidence for a specific supraspinal network involved in lower urinary tract (LUT) control. However, data on the reliability of blood oxygenation level-dependent (BOLD) signal changes during LUT task-related functional magnetic resonance imaging (fMRI) across separate measurements are lacking. Proof of the latter is crucial to evaluate whether fMRI can be used to assess supraspinal responses to LUT treatments. Therefore, we prospectively assessed task-specific supraspinal responses from 20 healthy participants undergoing two fMRI measurements (test-retest) within 5-8 weeks. The fMRI measurements, conducted in a 3T magnetic resonance (MR) scanner, comprised a block design of repetitive bladder filling and drainage using an automated MR-compatible and MR-synchronized infusion-drainage device. Following transurethral catheterization and bladder pre-filling with body warm saline until participants perceived a persistent desire to void (START condition), fMRI was recorded during repetitive blocks (each 15 s) of INFUSION and WITHDRAWAL of 100 mL body warm saline into respectively from the bladder. BOLD signal changes were calculated for INFUSION minus START. In addition to whole brain analysis, we assessed BOLD signal changes within multiple 'a priori' region of interest (ROI), i.e. brain areas known to be involved in the LUT control from previous literature. To evaluate reliability of the fMRI results between visits, we applied different types of analyses: coefficient of variation (CV), intraclass correlation coefficient (ICC), Sørensen-Dice index, Bland-Altman method, and block-wise BOLD signal comparison. All participants completed the study without adverse events. The desire to void was rated significantly higher for INFUSION compared to START or WITHDRAWAL at both measurements without any effect of visit. At whole brain level, significant (p < 0.05, cluster corrected, k ≥ 41 voxels) BOLD signal changes were found for the contrast INFUSION compared to START in several brain areas. Overlap of activation maps from both measurements were observed in the orbitofrontal cortex, insula, ventrolateral prefrontal cortex (VLPFC), and inferior parietal lobe. The two highest ICCs, based on a ROI's mean beta weight, were 0.55 (right insular cortex) and 0.47 (VLPFC). Spatial congruency (Sørensen-Dice index) of all voxels within each ROI between measurements was highest in the insular cortex (left 0.55, right 0.44). In addition, the mean beta weight of the right insula and right VLPFC demonstrated the lowest CV and narrowest Bland and Altman 95% limits of agreement. In conclusion, the right insula and right VLPFC were revealed as the two most reliable task-specific ROIs using our automated, MR-synchronized protocol. Achieving high reliability using a viscero-sensory/interoceptive task such as repetitive bladder filling remains challenging and further endeavour is highly warranted to better understand which factors influence fMRI outcomes and finally to assess LUT treatment effects on the supraspinal level.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.382
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2019
Admission routes1
Has abstractyes

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