{
  "id": 14728136,
  "title": "A parsimonious, age-invariant, and psychometrically validated neural signature of task-control network functioning",
  "url": "https://urgent.news/2026/10/15/a-parsimonious-age-invariant-and-psychometrically-validated-neural",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-15T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.08.757721v1?rss=1"
  },
  "original_language": "en",
  "account": "Neuroimaging holds great promise for uncovering brain markers that underpin individual and developmental variations in cognition. However, issues of measure complexity, unreliability, and unclear developmental aspects currently hinder this progress. To tackle these obstacles, the researchers employed latent variable modeling on demand-related responses from the task-control frontoparietal and dorsal attention networks (FPN, DAN) in two large-scale studies: the Adolescent Brain Cognitive Development Study and the Human Connectome Project. The task-control activations were effectively modeled using a bifactor structure consisting of a general factor encompassing FPN/DAN and network-specific factors, with this model showing remarkable consistency across middle childhood to early adolescence and displaying a strong generalization to adulthood. Utilizing trait-state-occasion modeling, the study identified a general task-control trait that demonstrates dependable correlations with cognitive abilities, clinical symptoms, and academic performance. Moreover, incorporating repeated neuroimaging data further strengthened these associations. By developing a parsimonious neural signature that transcends sample-specificities, remains stable throughout development, and possesses wide-ranging implications, this research paves the way for a deeper understanding of the developmental and individual differences in goal-directed behavior.",
  "summary": "Neuroimaging has vast potential for identifying brain features that support individual and developmental differences in cognition, but challenges related to the dimensionality, unreliability, and unknown developmental properties of neuroimaging measures prevent this from being realized. We overcome these challenges by applying latent variable modeling to demand-related responses in the…",
  "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."
}