ArticleNeuroImage. Clinical2015
Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models.
Article in NeuroImage. Clinical, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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Who cites it
17 citing papers in PubMed, 31 citations in OpenAlex.
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- Linking maternal disrupted interaction and infant limbic volumes: The role of infant cortisol output.Psychoneuroendocrinology · 2023Article
- Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions.Frontiers in aging neuroscience · 2023Review
- Inter-scanner reproducibility of brain volumetry: influence of automated brain segmentation software.BMC neuroscience · 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
- Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Automated segmentation of cerebral deep gray matter from MRI scans: effect of field strength on sensitivity and reliability.BMC neurology · 2017Article
- Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis.AJNR. American journal of neuroradiology · 2017Article
- Spaceflight-induced neuroplasticity in humans as measured by MRI: what do we know so far?NPJ microgravity · 2017Article
- Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2016Review
- The Effects of Hemodynamic Changes on Pulse Wave Velocity in Cardiothoracic Surgical Patients.BioMed research international · 2016Observational
- Influence of equipment changes on MRI measures of brain atrophy and brain microstructure in a placebo-controlled trial of ibudilast in progressive multiple sclerosis.Multiple sclerosis journal - experimental, translational and clinicalArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Magnetic resonance imaging (MRI) of the brain provides important outcome measures in the longitudinal evaluation of disease activity and progression in MS subjects. Two common measures derived from brain MRI scans are the brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV), and these measures are routinely assessed longitudinally in clinical trials and observational studies. When measuring each outcome longitudinally, observed changes may be potentially confounded by variability in MRI acquisition parameters between scans. In order to accurately model longitudinal change, the acquisition parameters should thus be considered in statistical models. In this paper, several models for including protocol as well as individual MRI acquisition parameters in linear mixed models were compared using a large dataset of 3453 longitudinal MRI scans from 1341 subjects enrolled in the CLIMB study, and model fit indices were compared across the models. The model that best explained the variance in BPF data was a random intercept and random slope with protocol specific residual variance along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. The model that best explained the variance in T2LV was a random intercept and random slope along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. In light of these findings, future studies pertaining to BPF and T2LV outcomes should carefully account for the protocol factors within longitudinal models to ensure that the disease trajectory of MS subjects can be assessed more accurately.
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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.