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No effect of additional education on long-term brain structure, a preregistered natural experiment in thousands of individuals.

Nicholas Judd | Rogier Kievit
eLife | 2025

Education is related to a wide variety of beneficial health, behavioral, and societal outcomes. However, whether education causes long-term structural changes in the brain remains unclear. A pressing challenge is that individuals self-select into continued education, thereby introducing a wide variety of environmental and genetic confounders. Fortunately, natural experiments allow us to isolate the causal impact of increased education from individual (and societal) characteristics. Here, we exploit a policy change in the UK (the 1972 ROSLA act) that increased the amount of mandatory schooling from 15 to 16 years of age to study the impact of education on long-term structural brain outcomes in the UK Biobank. Using regression discontinuity (RD) - a causal inference method - we find no evidence of an effect from an additional year of education on any structural neuroimaging outcomes. This null result is robust across modalities, regions, and analysis strategies. An additional year of education is a substantial cognitive intervention, yet we find no evidence for sustained experience-dependent plasticity. Our results provide a challenge for prominent accounts of cognitive or 'brain reserve' theories which identify education as a major protective factor to lessen adverse aging effects. Our preregistered findings are one of the first implementations of RD on neural data - opening the door for causal inference in population-based neuroimaging.

Pubmed ID: 40709539

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Associated grants

  • Agency: Jacobs Foundation,
    Id: Jacobs Fellowship
  • Agency: Radboud Universitair Medisch Centrum,
    Id: Hypatia fellowship
  • Agency: Riksbankens Jubileumsfond,
    Id: Pro Futura Fellowship

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UK Biobank (tool)

RRID:SCR_012815

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Stan (tool)

RRID:SCR_018459

Probabilistic programming language for specifying statistical models. Defines log probability function over parameters conditioned on specified data and constants. Platform for statistical modeling and high performance statistical computation. Provides full Bayesian inference for posterior expectations including parameter estimation and posterior predictive inference by defining appropriate derived quantities of interest.

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BayesFactor (tool)

RRID:SCR_027116

Software R suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.

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