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Abstract #0317

MRI meets economics: Balancing sample size and scan duration

Leon Qi Rong Ooi1, Csaba Orban1, Thomas E Nichols2, Shaoshi Zhang1, Trevor Wei Kiat Tan1, Ru Kong1, Scott Marek3, Nico Dosenbach3, Timothy Laumann3, Evan Gordon3, Juan Helen Zhou1, Danilo Bzdok4, Simon Eickhoff5, Avram Holmes6, and B.T. Thomas Yeo1
1National University of Singapore, Singapore, Singapore, 2Big Data Institute, Oxford, United Kingdom, 3Washington University, St. Louis, MO, United States, 4McGill University, Montreal, QC, Canada, 5Research Center Jülich, Jülich, Germany, 6Rutgers University, Piscataway, NJ, United States

Synopsis

Keywords: fMRI Analysis, fMRI (resting state)

Motivation: Resting-state functional connectivity (RSFC) is widely used to predict behavioral traits in individuals.

Goal(s): A pervasive dilemma when collecting functional MRI data is whether to prioritize sample size or scan duration given fixed resources.

Approach: We systematically investigate the trade-off between sample size and scan time in the context of prediction accuracy and reliability of brain-behavior relationships using RSFC.

Results: Increasing sample size (with fixed scan time) or scan time (with fixed sample size) leads to similar accuracy. Reliability of brain-behavior association can only be improved with bigger sample sizes but not scan time.

Impact: Our findings establish an empirically informed reference for calibrating scan times and sample sizes to maximize prediction of behavioral performance and reliability of brain-behavior associations when using resting-state functional connectivity.

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Keywords