ArticleJournal of magnetic resonance imaging : JMRI2021
Cross-Scanner Harmonization of Neuromelanin-Sensitive MRI for Multisite Studies.
Article in Journal of magnetic resonance imaging : JMRI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed.
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Locus Coeruleus MR Measured Signal Intensity in Fibromyalgia Relative to Healthy Controls.European journal of pain (London, England) · 2025Article
- Improved Injury Detection Through Harmonizing Multi-Site Neuroimaging Data after Experimental TBI: A Translational Outcomes Project in NeuroTrauma (TOP-NT) Consortium Study.bioRxiv : the preprint server for biology · 2025Article
- Neuromelanin-MRI identifies locus coeruleus and substantia nigra degeneration as key differentiators in isolated rapid eye movement sleep behavior disorder.Brain imaging and behavior · 2025Article
- Improved injury detection through harmonizing multi-site neuroimaging data after experimental TBI: a Translational Outcomes Project in Neurotrauma consortium study.Frontiers in neurology · 2025Article
- Neuromelanin-sensitive MRI for mechanistic research and biomarker development in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Neuromelanin-sensitive MRI as a promising biomarker of catecholamine function.Brain : a journal of neurology · 2024Review
- Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals.JAMA psychiatry · 2024Article
- Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.Scientific data · 2024Article
- Diagnostic utility of 7T neuromelanin imaging of the substantia nigra in Parkinson's disease.NPJ Parkinson's disease · 2024Article
- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
- Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.An international journal on information fusion · 2022Article
- Standardized Data Acquisition for Neuromelanin-sensitive Magnetic Resonance Imaging of the Substantia Nigra.Journal of visualized experiments : JoVE · 2021Article
Corrections and comments
- Commented on by
Authors and funding
7 authors.
Funding
Abstract
backgroundNeuromelanin-sensitive magnetic resonance imaging (NM-MRI) is a validated measure of neuromelanin concentration in the substantia nigra-ventral tegmental area (SN-VTA) complex and is a proxy measure of dopaminergic function with potential as a noninvasive biomarker. The development of generalizable biomarkers requires large-scale samples necessitating harmonization approaches to combine data collected across sites. PURPOSE: To develop a method to harmonize NM-MRI across scanners and sites. STUDY TYPE: Prospective. POPULATION: A total of 128 healthy subjects (18-73 years old; 45% female) from three sites and five MRI scanners. FIELD STRENGTH/SEQUENCE: 3.0 T; NM-MRI two-dimensional gradient-recalled echo with magnetization-transfer pulse and three-dimensional T1-weighted images. ASSESSMENT: NM-MRI contrast (contrast-to-noise ratio [CNR]) maps were calculated and CNR values within the SN-VTA (defined previously by manual tracing on a standardized NM-MRI template) were determined before harmonization (raw CNR) and after ComBat harmonization (harmonized CNR). Scanner differences were assessed by calculating the classification accuracy of a support vector machine (SVM). To assess the effect of harmonization on biological variability, support vector regression (SVR) was used to predict age and the difference in goodness-of-fit (Δr) was calculated as the correlation (between actual and predicted ages) for the harmonized CNR minus the correlation for the raw CNR. STATISTICAL TESTS: Permutation tests were used to determine if SVM classification accuracy was above chance level and if SVR Δr was significant. A P-value <0.05 was considered significant.
resultsIn the raw CNR, SVM MRI scanner classification was above chance level (accuracy = 86.5%). In the harmonized CNR, the accuracy of the SVM was at chance level (accuracy = 29.5%; P = 0.8542). There was no significant difference in age prediction using the raw or harmonized CNR (Δr = -0.06; P = 0.7304). DATA
conclusionComBat harmonization removes differences in SN-VTA CNR across scanners while preserving biologically meaningful variability associated with age. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: 1.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.