Use dense repeated-measurement datasets to estimate baseline brain-measure variability before interpreting intervention-related plasticity¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The authors recommend using dense longitudinal MRI datasets to estimate the baseline variability of brain parameters so that plastic changes observed in intervention studies can be compared against naturally occurring fluctuation, and to test the stability of MRI-derived parameters across processing pipelines. They argue that short-term MRI variability is often interpreted as noise but may arise from physiological and psychological states or changing environmental factors, so "the present dataset might also be used to estimate the baseline variability of brain parameters to compare plastic changes in the context of intervention studies".

Design Implications¶

Context¶

Requirements¶

• Repeated MRI scanning of the same individuals over many sessions within a short period, with consistent scanner settings

Constraints¶

• Repeated MRI of the same individual within a short time often yields differences in neuroimaging parameters even with consistent scanner settings

Target Learners¶

• Researchers designing longitudinal neuroimaging or intervention studies

Target Learning Goals¶

• Distinguishing true plastic change from temporary fluctuation in brain measures
• Assessing reliability of MRI processing pipelines

Claims¶

• Daily physiological changes in non-experimental situations influence measures of brain structure and function [+W]

Related Strategies¶

Examples¶

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Key Sources¶

• Falkenstein Kim, Pauley Claire, Kühn Simone. (2026). Investigating effects of day-to-day variations in environmental exposure on the human brain: study protocol for the Day2Day Environment project. BMC Neuroscience. https://doi.org/10.1186/s12868-026-01052-z