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

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 sampl...