ArticleScientific reports2021
Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- Breaking the norm: population-scale deviations of brain structure in depression and anxiety.Molecular psychiatry · 2026Article
- Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals.Nature communications · 2026Article
- Mild cognitive impairment cases affect the predictive power of Alzheimer's disease diagnostic models using routine clinical variables.npj aging · 2026Article
- Alzheimer's Disease Classification Using Population-Referenced Brain Volumetric Percentiles.Brain sciences · 2026Article
- Predicting developmental norms from baseline cortical thickness in longitudinal studies.Biology of sex differences · 2026Article
- Structural similarity networks reveal brain vulnerability in dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Generalizable MRI normative modelling to detect age-inappropriate neurodegeneration.Alzheimer's research & therapy · 2025Article
- The normative modelling framework for traumatic brain injury.Brain : a journal of neurology · 2025Review
- Understanding individual neurodegenerative progression in Parkinson's disease through normative modelling.Scientific reports · 2025Article
- Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.Alzheimer's research & therapy · 2025Article
- Analyzing heterogeneity in Alzheimer disease using multimodal normative modeling on imaging-based ATN biomarkers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Normative Modeling Reveals Age-Atypical Cortical Thickness Differences Between Hepatic Steatosis and Fibrosis in Non-Alcoholic Fatty Liver Disease.Brain and behavior · 2025Article
- Article
- A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.Artificial intelligence in medicine · 2025Article
- Neurofind: using deep learning to make individualised inferences in brain-based disorders.Translational psychiatry · 2025Article
- Parsing disease heterogeneity in structural and functional MRI-derived measures using normative modeling and Generative Adversarial Networks (GANs).Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.bioRxiv : the preprint server for biology · 2024Article
- Brain health in diverse settings: How age, demographics and cognition shape brain function.NeuroImage · 2024Article
- Quantifying impairment and disease severity using AI models trained on healthy subjects.NPJ digital medicine · 2024Article
- IMPROVING NORMATIVE MODELING FOR MULTI-MODAL NEUROIMAGING DATA USING MIXTURE-OF-PRODUCT-OF-EXPERTS VARIATIONAL AUTOENCODERS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer's disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community.
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.