Technical note A simple and fast technique for on-line fMRI data analysis Stefano Salvador a , Andrea Brovelli b , Renata Longo a, * a Dipartimento di Fisica, Universita’ di Trieste, Trieste, Italy b SISSA-ISAS, Cognitive Neuroscience Sector, Trieste, Italy Received 15 December 2000; received in revised form 18 January 2002; accepted 18 January 2002 Abstract In the present work a simple technique for fMRI data analysis is presented. Artifacts due to random and stimulus-correlated motions are corrected without image registration procedures. The first step of our procedure is the calculation of the raw activation map by correlation analysis. The task related motion artifacts arise at the tissue interfaces, including vessels: when image intensity gradient is calculated the high values correspond to interface regions. To eliminate stimulus-correlated motion artifacts the intensity gradient image, obtained from the fMRI data set, is compared to the raw activation map. Since small random motions decrease the value of the correlation coefficient (R) of the external pixels of the activation areas, in the last step of our analysis procedures the clusters are extended to connected pixels having R values smaller than the defined threshold. Each cluster is expanded until the R value of the cluster average intensity is kept constant. The procedure has been tested with both GRE and EPI studies. The presented approach is a fast and robust technique useful for preliminary or on-line analysis of fMRI data. © 2002 Elsevier Science Inc. All rights reserved. Keywords: fMRI; BOLD; Data analysis 1. Introduction Extensive data manipulation is necessary to extract ac- tive brain areas from the acquired fMRI images. There are many available options for analysis and each choice may affect all subsequent results and conclusions. Therefore, fMRI data processing is an off-line procedure that takes a lot of time for both analysis procedure design and calcula- tions [1]. The high spatial resolution (in the order of millime- tres) of the fMRI images makes this technique extremely sensitive to head movements and a critical step of fMRI data analysis is the reduction of artifacts resulting from frame-to-frame motion of the subject [2]. The motion artifacts affecting fMRI examinations can be either due to stimulus-correlated movements and/or random move- ments. Stimulus-related movements often lead to false activated intensity time-courses in pixels at the tissue interface between high and low intensity structures (e.g., gray matter and CSF) [3]. Random movements, on the other hand, decrease the correlation between the fMRI signal and task profile in pixels on the boundary of the activation areas, thus reducing the number of pixels in the activation map. One of the most common approach for motion artifacts reduction is image realignment under rigid-body motion assumption that each image is acquired without motion artifacts. The rigid body registration hypothesis may be reasonable in EPI acquisition, but it does not fit GRE images that may suffer from motion blurring. Out-of-plane regis- tration presents unique difficulties because data are under- sampled in multislice fMRI studies, due to the slice thick- ness of 4 mm or larger. Moreover, since fMRI slices are not collected simultaneously, there exists no complete reference image set at any instant in time [2]. The high temporal resolution achieved with EPI tech- nique leads to the generation of very large data sets. Iterative registration algorithms require long computing time and a non-iterative one-pass approach may be a better choice, especially in preliminary or on-line data analysis [4]. In the present work, an fMRI data analysis procedure * Corresponding author. Tel.: +39-040-676-3383; fax: +39-040-676- 3350. E-mail address: renata.longo@ts.infn.it (R. Longo). Magnetic Resonance Imaging 20 (2002) 207–213 0730-725X/02/$ – see front matter © 2002 Elsevier Science Inc. All rights reserved. PII: S0730-725X(02)00465-4