Factors Contributing to Short-Term Structural Variability in a Longitudinal MRI Dataset.

Area of Science:

• Neuroimaging
• Brain Structure Analysis
• Longitudinal Studies

Background:

• Longitudinal magnetic resonance imaging (MRI) studies require careful consideration of confounding factors affecting brain structural changes over time.
• Understanding intraindividual variability in brain structure is crucial for accurate longitudinal analysis.

Purpose of the Study:

• To identify factors contributing to intraindividual variability of brain structure within a short timeframe.
• To investigate the impact of various factors on regional gray matter volume changes in longitudinal MRI data.

Main Methods:

• Multilevel sparse partial least squares regression was employed.
• Analysis was performed on the longitudinal Day2day MRI dataset to examine regional gray matter volume.
• Factors influencing image quality variability were also investigated.

Main Results:

• Regional gray matter (GM) volume estimations were primarily influenced by image quality.
• Outdoor temperature and time since baseline were key predictors of volumetric changes in insular and diencephalic regions.
• Factors associated with image quality variability were identified.

Conclusions:

• Image quality is a primary driver of GM volume changes in short-term longitudinal MRI.
• Environmental factors (outdoor temperature) and study duration (time since baseline) affect specific brain regions.
• Maintaining adequate participant arousal during MRI scanning is essential for reliable data.