ArticlePloS one2020
Interpretable brain age prediction using linear latent variable models of functional connectivity.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging.Psychophysiology · 2023Pooled it
- The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi-Omics Integration.Aging cell · 2026Review
- A Lightweight ScaleDense-Transformer Framework with Auxiliary Quantum-Inspired Bottleneck Module for Whole-Lifespan Brain Age Prediction.Brain sciences · 2026Article
- Dynamic Brain Age Modeling Identifies Network-Specific Cognitive Deficits in Schizophrenia.Research square · 2025Article
- Functional Brain Age Acceleration from Dynamic and Static Connectivity Predicts Working Memory and Attention Deficits in Schizophrenia.medRxiv : the preprint server for health sciences · 2025Article
- Second-order instantaneous causal analysis of spontaneous MEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Journal of cheminformatics · 2024Article
- Investigating the impact of motion in the scanner on brain age predictions.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Network-level enrichment provides a framework for biological interpretation of machine learning results.Network neuroscience (Cambridge, Mass.) · 2024Article
- Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study.Aging brain · 2024Article
- Revisiting the standard for modeling functional brain network activity: Application to consciousness.PloS one · 2024Article
- Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction.IEEE transactions on medical imaging · 2022Article
- A guided multiverse study of neuroimaging analyses.Nature communications · 2022Article
- Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI.Frontiers in computational neuroscience · 2022Article
- Correlation Constraints for Regression Models: Controlling Bias in Brain Age Prediction.Frontiers in psychiatry · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional connectivity while prioritizing model interpretability and understanding. This way, the models serve to both provide accurate estimates of brain age as well as allow us to investigate changes in functional connectivity which occur during the ageing process. The methods proposed in this work consist of a two-step procedure: first, linear latent variable models, such as PCA and its extensions, are employed to learn reproducible functional connectivity networks present across a cohort of subjects. The activity within each network is subsequently employed as a feature in a linear regression model to predict brain age. The proposed framework is employed on the data from the CamCAN repository and the inferred brain age models are further demonstrated to generalize using data from two open-access repositories: the Human Connectome Project and the ATR Wide-Age-Range.
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.