Optimizing fMRI preprocessing pipelines for block-design tasks as a function of age.

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Aging Research

Background:

• Functional Magnetic Resonance Imaging (fMRI) is crucial for studying brain function, but noise confounds significantly affect activation detection.
• Preprocessing pipelines heavily influence fMRI results, yet most studies focus on young adults, neglecting age-related differences.

Purpose of the Study:

• To investigate how preprocessing pipeline optimization in fMRI varies between young and older adults.
• To assess the impact of age and task contrast on preprocessing pipeline effectiveness using the NPAIRS framework.

Main Methods:

• Employed the NPAIRS cross-validation framework to optimize fMRI preprocessing pipelines based on prediction accuracy (P) and spatial reproducibility (R).
• Compared pipeline optimization effects between young (21-33 years) and older (61-82 years) cohorts across three block-design contrasts.
• Introduced novel statistical methods to address head motion biases in older adults.

Main Results:

• Optimal preprocessing pipelines were largely similar across age cohorts, with significant benefits of optimization observed in both groups.
• Head motion was a greater challenge in older adults, necessitating specialized statistical approaches.
• Task contrast choice had a greater impact on results than age cohort.
• Adaptive pipeline optimization per task run enhanced sensitivity to age-related brain activity, particularly for complex cognitive tasks.

Conclusions:

• Preprocessing pipeline optimization yields comparable benefits for both young and older adults in fMRI studies.
• Adaptive optimization strategies are crucial for detecting subtle, age-related changes in brain activity.
• This study provides the first comprehensive analysis of fMRI preprocessing across age groups, highlighting the importance of age-adjusted methods.