Academic Journal
Practical considerations for dense longitudinal neuroimaging: Ten simple rules for frequent scanning over short time periods.
| Τίτλος: | Practical considerations for dense longitudinal neuroimaging: Ten simple rules for frequent scanning over short time periods. |
|---|---|
| Συγγραφείς: | Cross E; Psychology and Human Development, Vanderbilt University, Nashville, TN, US., Compton AB; Psychology and Human Development, Vanderbilt University, Nashville, TN, US., Vess A; Psychology and Human Development, Vanderbilt University, Nashville, TN, US., Booth JR; Psychology and Human Development, Vanderbilt University, Nashville, TN, US., Kay K; Department of Radiology, University of Minnesota, Minneapolis, MN, US., Vinci-Booher S; Psychology and Human Development, Vanderbilt University, Nashville, TN, US; Department of Psychology, University of Notre Dame, Notre Dame, IN, US. Electronic address: svincibo@nd.edu. |
| Πηγή: | Developmental cognitive neuroscience [Dev Cogn Neurosci] 2026 Oct; Vol. 81, pp. 101802. Date of Electronic Publication: 2026 Aug 28. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101541838 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-9307 (Electronic) Linking ISSN: 18789293 NLM ISO Abbreviation: Dev Cogn Neurosci Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam : Elsevier |
| Ιατρικοί όροι (MeSH): | Brain*/diagnostic imaging , Brain*/growth & development , Neuroimaging*/methods , Research Design*, Child ; Humans ; Longitudinal Studies |
| Περίληψη: | 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. (Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.) |
| Competing Interests: | Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Grant Information: | R01 HD114489 United States HD NICHD NIH HHS |
| Contributed Indexing: | Keywords: Best practices; Dense; Longitudinal; Neuroimaging; Precision; Protocol |
| Entry Date(s): | Date Created: 20260908 Date Completed: 20261014 Latest Revision: 20261015 |
| Update Code: | 20261016 |
| PubMed Central ID: | PMC13573700 |
| DOI: | 10.1016/j.dcn.2026.101802 |
| PMID: | 42710322 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1878-9307 |
|---|---|
| DOI: | 10.1016/j.dcn.2026.101802 |