ArticlePLoS biology2022
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.
Article in PLoS biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses 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
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning prediction models for intravenous immunoglobulin resistance in Kawasaki disease: a meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Artificial Intelligence for Predicting Treatment Response in Patients With Anxiety Disorders After Cognitive Behavioral Therapy: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Neuroimaging Insights into the Public Health Burden of Neuropsychiatric Disorders: A Systematic Review of Electroencephalography-Based Cognitive Biomarkers.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Trial
- Functional brain growth trajectories across the first decade of life from a single longitudinal cohort.Research square · 2026Article
- Article
- A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort.Nature biomedical engineering · 2026Article
- Carriers of LRRK2 pathogenic variants show a milder, anatomically distinct brain signature of Parkinson's disease.Communications medicine · 2026Article
- A low-cost bedside nomogram integrating TSF and pain for predicting severe malnutrition in nasopharyngeal carcinoma: a prospective cohort study.Frontiers in nutrition · 2026Article
- Integrating MALDI-MSI-Based Spatial Proteomics and Machine Learning to Predict Chemoradiotherapy Outcomes in Head and Neck Cancer.International journal of molecular sciences · 2025Article
- Biological markers and psychosocial factors predict chronic pain conditions.Nature human behaviour · 2025Article
- Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia.Translational psychiatry · 2025Article
- Longer scans boost prediction and cut costs in brain-wide association studies.bioRxiv : the preprint server for biology · 2025Article
- Considering the interconnected nature of social identities in neuroimaging research.Nature neuroscience · 2025Review
- The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research.Biological psychiatry global open science · 2025Review
- Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease.Frontiers in artificial intelligence · 2025Article
- Heterogeneity Analysis of Associations Involving the Large-Scale Online MindCrowd Survey Memory Test.Gerontology · 2025Article
- Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort.PloS one · 2025Article
- Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.Scientific data · 2024Article
- Personalizing progressive changes to brain structure in Alzheimer's disease using normative modeling.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
13 authors.
Funding
Abstract
Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
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.