How Many Is Enough? Effect of Sample Size in Inter-Subject Correlation Analysis of fMRI

• Published in: Computational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 325 - 334-023
• Main Authors: Pajula, Juha, Tohka, Jussi
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2016; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Adult; Aged; Aged, 80 and over; Bioengineering; Brain - blood supply; Brain - physiology; Brain Mapping; Correlation analysis; Datasets; Female; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Middle Aged; Reproducibility; Reproducibility of Results; Sample Size; Samples; Sampling; Statistical analysis; Statistical methods; Statistical power; Statistics; Statistics as Topic; Technology application; Time series; Young Adult
• DOI: 10.1155/2016/2094601
• ISSN: 1687-5265; 1687-5273

Inter-subject correlation (ISC) is a widely used method for analyzing functional magnetic resonance imaging (fMRI) data acquired during naturalistic stimuli. A challenge in ISC analysis is to define the required sample size in the way that the results are reliable. We studied the effect of the sample size on the reliability of ISC analysis and additionally addressed the following question: How many subjects are needed for the ISC statistics to converge to the ISC statistics obtained using a large sample? The study was realized using a large block design data set of 130 subjects. We performed a split-half resampling based analysis repeatedly sampling two nonoverlapping subsets of 10–65 subjects and comparing the ISC maps between the independent subject sets. Our findings suggested that with 20 subjects, on average, the ISC statistics had converged close to a large sample ISC statistic with 130 subjects. However, the split-half reliability of unthresholded and thresholded ISC maps improved notably when the number of subjects was increased from 20 to 30 or more.