Introduction
In this work we characterize functional magnetic resonance imaging (fMRI) data from event related and block design studies with typical values of several parameters. We use signal to noise ratio (SNR), percent signal change (PSC) and statistical parametric maps, estimated by SPM8 toolbox (beta, con and t-statistic maps). The aim of this work is to describe typical values appearing in datasets and to enhance knowledge about typical distributions of dataset鈥檚 parameters which could be used for estimation of data quality.Methods
We used data from four studies. Datasets were acquired using 1.5T MR scanner Siemens Symphony. First dataset is event-related visual oddball experiment (VOB) with 4 sessions, other datasets are block design studies: visual memory task (VMT), verbal fluency task (VFT) and semantic decision task (SDT). Parameters were calculated for three types of previously selected brain regions - regions related to the task (activations and deactivations), regions related to physiological artifacts and one cortical area not related to the task.
Results
In the event related study, typical PSC in task activated voxels was about 0.1%, in the block design studies was PSC higher, with mean values between 0.4% and 1%. Greater variance of SNR was observed in regions designated as sources of physiological noise in event related than in block design studies. We also observed significant correlation between t-values and PSC in VOB study (Pearson p = 0.8517) and stronger correlations in block design studies (Pearson p = {0.9902; 0.9299; 0.9697}).
Conclusion
Lower correlation of PSC and t-values in VOB study could be caused due to separation of experiment in four sessions. More robust explanation could be made after including more event related studies into this comparison. These dataset characterizations will provide useful basis for fMRI data simulations, for calculation of statistical power, and possibly for evaluation of fMRI data quality.
Thanks to Grant P103/12/0552 from GACR for funding.