ArticleNeuroImage2015
Estimating anatomical trajectories with Bayesian mixed-effects modeling.
Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Cumulative Socioeconomic Status Risk is Associated With Greater Increase in Serum Neurofilament Light Chain Levels Among Middle-Aged Black Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
- Plasma inflammation for predicting phenotypic conversion and clinical progression of autosomal dominant frontotemporal lobar degeneration.Journal of neurology, neurosurgery, and psychiatry · 2023Article
- Cognitive Decline Related to Diet Pattern and Nutritional Adequacy in Alzheimer's Disease Using Surface-Based Morphometry.Nutrients · 2022Article
- Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease.Scientific reports · 2022Article
- Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling.NeuroImage. Clinical · 2022Article
- Computationally derived anatomic subtypes of behavioral variant frontotemporal dementia show temporal stability and divergent patterns of longitudinal atrophy.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- Rates of Brain Atrophy Across Disease Stages in Familial Frontotemporal Dementia Associated With MAPT, GRN, and C9orf72 Pathogenic Variants.JAMA network open · 2020Article
- Revised Self-Monitoring Scale: A potential endpoint for frontotemporal dementia clinical trials.Neurology · 2020Article
- Regional and hemispheric susceptibility of the temporal lobe to FTLD-TDP type C pathology.NeuroImage. Clinical · 2020Article
- Interregional causal influences of brain metabolic activity reveal the spread of aging effects during normal aging.Human brain mapping · 2019Article
- Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning.Human brain mapping · 2019Article
- Progressive neurodegeneration following spinal cord injury: Implications for clinical trials.Neurology · 2018Article
- Patterns of progressive atrophy vary with age in Alzheimer's disease patients.Neurobiology of aging · 2018Article
- Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Review
- Article
- Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference.BMJ open · 2017Article
- Making use of longitudinal information in pattern recognition.Human brain mapping · 2016Article
- Staging Alzheimer's Disease Risk by Sequencing Brain Function and Structure, Cerebrospinal Fluid, and Cognition Biomarkers.Journal of Alzheimer's disease : JAD · 2016Article
- A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging.NeuroImage · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We introduce a mass-univariate framework for the analysis of whole-brain structural trajectories using longitudinal Voxel-Based Morphometry data and Bayesian inference. Our approach to developmental and aging longitudinal studies characterizes heterogeneous structural growth/decline between and within groups. In particular, we propose a probabilistic generative model that parameterizes individual and ensemble average changes in brain structure using linear mixed-effects models of age and subject-specific covariates. Model inversion uses Expectation Maximization (EM), while voxelwise (empirical) priors on the size of individual differences are estimated from the data. Bayesian inference on individual and group trajectories is realized using Posterior Probability Maps (PPM). In addition to parameter inference, the framework affords comparisons of models with varying combinations of model order for fixed and random effects using model evidence. We validate the model in simulations and real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We further demonstrate how subject specific characteristics contribute to individual differences in longitudinal volume changes in healthy subjects, Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD).
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.