A method for generating reproducible evidence in fMRI studies.
Insights into cognitive neuroscience from neuroimaging techniques are now required to go beyond the localisation of well-known cognitive functions. Fundamental to this is the notion of reproducibility of experimental outcomes. This paper addresses the central issue that functional magnetic resonance imaging (fMRI) experiments will produce more desirable information if researchers begin to search for reproducible evidence rather than only p value significance. The study proposes a methodology for investigating reproducible evidence without conducting separate fMRI experiments. The reproducible evidence is gathered from the separate runs within the study. The associated empirical Bayes and ROC extensions of the linear model provide parameter estimates to determine reproducibility. Empirical applications of the methodology suggest that reproducible evidence is robust to small sample sizes and sensitive to both the magnitude and persistency of brain activation. It is demonstrated that research findings in fMRI studies would be more compelling with supporting reproducible evidence in addition to standard hypothesis testing evidence.
Liou
Michelle
M
Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan.
Su
Hong-Ren
HR
Lee
Juin-Der
JD
Aston
John A D
JA
Tsai
Arthur C
AC
Cheng
Philip E
PE
D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology; D003627:Data Interpretation, Statistical; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data*; D012372:ROC Curve; D015203:Reproducibility of Results; D018401:Sample Size