ArticleNeuroImage2020
Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 30 citations in OpenAlex.
- Functional changes in sleep-related arousal after ketamine administration in individuals with treatment-resistant depression.Translational psychiatry · 2024Trial
- Age and sex estimation using post-mortem CT scout view.International journal of legal medicine · 2026Article
- Aging beyond diagnosis: the MRI brain age gap across disorders.GeroScience · 2026Review
- Smooth Normative Brain Mapping of Three-Dimensional Morphometry Imaging Data Using Skew-Normal Regression.Human brain mapping · 2025Article
- Article
- Traumatic stress load and stressor reactivity score associated with accelerated gray matter maturation in youths indexed by normative models.Molecular psychiatry · 2023Article
- Deep Relation Learning for Regression and Its Application to Brain Age Estimation.IEEE transactions on medical imaging · 2022Article
- Tissue volume estimation and age prediction using rapid structural brain scans.Scientific reports · 2022Article
- An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling.Science advances · 2022Article
- Environmental pollution and energy research and development: an Environmental Kuznets Curve model through quantile simulation approach.Environmental science and pollution research international · 2021Article
- The Age Structure, Stringency Policy, Income, and Spread of Coronavirus Disease 2019: Evidence From 209 Countries.Frontiers in psychology · 2020Article
- Lactoferrin-Loaded PEG/PLA Block Copolymer Targeted With Anti-Transferrin Receptor Antibodies for Alzheimer Disease.Dose-response : a publication of International Hormesis SocietyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised functional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject.
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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.