Practical considerations for dense longitudinal neuroimaging: Ten simple rules for frequent scanning over short time periods.

E Cross, A B Compton, A Vess, J R Booth, K Kay, S Vinci-Booher

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Article in Developmental cognitive neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

E CrossPsychology and Human Development, Vanderbilt University, Nashville, TN, US.

A B ComptonPsychology and Human Development, Vanderbilt University, Nashville, TN, US.

A VessPsychology and Human Development, Vanderbilt University, Nashville, TN, US.

J R BoothPsychology and Human Development, Vanderbilt University, Nashville, TN, US.

K KayDepartment of Radiology, University of Minnesota, Minneapolis, MN, US.

S Vinci-BooherPsychology and Human Development, Vanderbilt University, Nashville, TN, US; Department of Psychology, University of Notre Dame, Notre Dame, IN, US. Electronic address: svincibo@nd.edu.

Funding

CRCNS: Dense longitudinal neuroimaging to evaluate learning in childhoodR01HD114489 · NICHD · VANDERBILT UNIVERSITY · PI VINCI-BOOHER, SOPHIA · 2023 to 2025

$1.4M

NICHD NIH HHS R01 HD114489

6 · The paper itself

Abstract

Dense longitudinal neuroimaging (DLN) is a precision neuroimaging approach that samples frequently throughout a targeted window of time to characterize individual trajectories of brain change. The approach enables the answering of fundamental questions about the underlying mechanisms of cognitive development and learning. However, acquiring DLN data presents practical challenges that go beyond those associated with acquiring conventional longitudinal data. In this paper, we discuss key practical considerations for designing and executing DLN studies, especially with respect to collecting data from children and other populations with limited scan tolerance. We provide ten simple rules for study design, measurement, recruitment and retention, and compliance to assist research teams seeking to conduct DLN studies.

Indexed as

Best practicesDenseLongitudinalNeuroimagingPrecisionProtocol

Identifiers

PMID42710322

DOI10.1016/j.dcn.2026.101802

PMCPMC13573700

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Textmetadata

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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 Socratic graph.