{
  "id": 18131001,
  "title": "Contributions of Site Heterogeneity in Multi-site Brain-Phenotype Predictions",
  "url": "https://urgent.news/2026/11/04/contributions-of-site-heterogeneity-in-multi-site-brain-phenotype",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-04T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763540v1?rss=1"
  },
  "original_language": "en",
  "account": "Machine learning studies of brain-phenotype associations commonly report accuracy with a single Pearson correlation coefficient. However, this metric can be influenced by several factors in harmonized multi-site data, including training-set size, feature variability, outcome variability, and true effect heterogeneity. The Reproducible Brain Charts consortium analyzed structural MRI data from 3486 participants across 9 sites. Three training set compositions were examined: within-site (Local), size-downsampled multi-site (Multi-Down), and full multi-site (Multi-Full) training. A fourth site-agnostic strategy (Overall) pooled all sites into a single dataset, ensuring the target site was included in both training and testing samples. Site-level heterogeneity was substantial, with an approximate I^2 of 95% for age prediction. The overall correlation of age prediction (r = 0.73) surpassed the within-site meta-analytic estimate (Multi-Full r = 0.63). This improvement in the pooled dataset was primarily due to an increase in outcome variance rather than a true advantage of multi-site training. Surprisingly, increasing the multi-site training sample size did not provide a significant advantage over within-site training. The perceived benefit of cross-dataset training appears to be more a result of training-set size than inherent multi-site composition. Similar patterns were observed for weaker brain-p-factor signals. These findings suggest that between-site heterogeneity in machine learning brain-phenotype prediction remains even after harmonization. The authors recommend reporting site-level prediction accuracy, feature dispersion, and the standard deviation of the outcome variable alongside any pooled estimates to enhance inter-site and inter-study comparisons of machine learning prediction accuracy.",
  "summary": "Machine learning studies of brain-phenotype associations typically summarize accuracy with a single Pearson correlation, yet in harmonized multi-site data this metric is shaped by training-set size, feature and outcome variability, and true effect heterogeneity. We analyzed structural MRI data from the Reproducible Brain Charts consortium. After preprocessing and quality control, 3486…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}