ArticleNeuroImage2016
Power estimation for non-standardized multisite studies.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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.
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.
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Who cites it
22 citing papers in PubMed, 43 citations in OpenAlex.
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- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
- Spinal Cord Atrophy Predicts Progressive Disease in Relapsing Multiple Sclerosis.Annals of neurology · 2022Observational
- Criminal arrests associated with reduced regional brain volumes in an adult population with documented childhood lead exposure.Environmental research · 2021Article
- Comparison of structural MRI brain measures between 1.5 and 3 T: Data from the Lothian Birth Cohort 1936.Human brain mapping · 2021Article
- Estimating the effect of a scanner upgrade on measures of grey matter structure for longitudinal designs.PloS one · 2021Article
- Intensity warping for multisite MRI harmonization.NeuroImage · 2020Article
- Article
- Constructing and evaluating a cortical surface atlas and analyzing cortical sex differences in young Chinese adults.Human brain mapping · 2020Article
- Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2020Article
- Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2020Article
- TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis.NeuroImage. Clinical · 2020Article
- Accurate, rapid and reliable, fully automated MRI brainstem segmentation for application in multiple sclerosis and neurodegenerative diseases.Human brain mapping · 2019Article
- The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocolsJournal of psychiatry & neuroscience : JPN · 2019Article
- Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data.Human brain mapping · 2018Article
- SUMMIT (Serially Unified Multicenter Multiple Sclerosis Investigation): creating a repository of deeply phenotyped contemporary multiple sclerosis cohorts.Multiple sclerosis (Houndmills, Basingstoke, England) · 2018Article
- Dual-Sensitivity Multiple Sclerosis Lesion and CSF Segmentation for Multichannel 3T Brain MRI.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2018Article
- Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis.AJNR. American journal of neuroradiology · 2017Article
Corrections and comments
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Authors and funding
57 authors at 14 institutions in 8 countries.
Funding
Abstract
A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions.
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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.