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Dense longitudinal neuroimaging reveals individual brain change trajectories

  • Sophia Vinci-Booher
  • , Xueying Ren
  • , Kendrick Kay
  • , Chen Yu
  • , Franco Pestilli
  • , James R. Booth

Research output: Contribution to journalReview articlepeer-review

Abstract

Longitudinal measurements of brain structure and function are critical for understanding how humans change over time. Traditional longitudinal approaches sample sparsely across large windows of time to estimate coarse, long-term brain changes. This review showcases insights from dense longitudinal neuroimaging (DLN), an emerging approach that samples densely across relatively short windows of time to precisely estimate individual trajectories of brain change. DLN measures multiple samples from individuals throughout critical periods of rapid change. It allows precise estimates of nonlinear trajectories to advance a mechanistic understanding of brain change. Novel findings from this approach are improving our understanding of human cognition, such as the role of the motor system in visual development and learning.

Original languageEnglish (US)
Pages (from-to)464-476
Number of pages13
JournalTrends in Cognitive Sciences
Volume30
Issue number5
DOIs
StatePublished - May 2026

Bibliographical note

Publisher Copyright:
© 2025 The Authors.

Keywords

  • dense
  • development
  • learning
  • longitudinal
  • mechanism
  • neuroimaging
  • precision
  • trajectory

PubMed: MeSH publication types

  • Journal Article
  • Review
  • Research Support, N.I.H., Extramural

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