Experiments were performed at the Donders Institute for Brain, Cognition and Behaviour using a Siemens MAGNETOM Tim TRIO 3.0 Tesla scanner with a 32-channel head coil. First, high-resolution anatomical images were acquired using

an MPRAGE sequence (TE/TR = 3.03/2300 ms; 192 sagittal slices, isotropic voxel size of 1 × 1 × 1 mm). Then a real-time Crizotinib fMRI run was initiated and functional images were acquired using a single-shot gradient echo planar imaging sequence (TR/TE = 2000/30 ms; flip angle = 75°; voxel size = 3 × 3 × 3.3 mm; distance factor = 10%) with prospective acquisition correction (PACE) to minimize effects of head motion during data acquisition (Thesen et al., 2000). Twenty-eight ascending axial slices were acquired, oriented at about 30° relative to the anterior–posterior commissure. During the real-time fMRI run, all functional scans were acquired using a modified scanner sequence and in-house software that sent each acquired scan over Ethernet to another computer, which stored them in a FieldTrip (Oostenveld et al., 2011) raw data buffer. Each newly buffered raw scan was then

fed into a MATLAB-based (The Mathworks, Natick, MA, USA) preprocessing pipeline. The first preprocessing step involved selecting one of the two image series generated by the scanner sequence: the PACE series of images that is only prospectively corrected and the MoCo (motion-corrected) series that is both prospectively 5FU and retrospectively corrected (Thesen et al., 2000). We used the MoCo series of images as it contained the

least residual motion. Then scans were slice-time corrected, followed Glycogen branching enzyme by retrospective motion correction using an online rigid-body transformation algorithm with six degrees of freedom. This was done to remove any residual motion in the MoCo series. Then a recursive least-squares GLM was applied to each scan to remove nuisance signals (Bagarinao et al., 2003). Five regressors corresponding to DC offset, linear drift and three translational motion parameters were used in the model. Next, we removed white matter and cerebral spinal fluid voxels from all scans using a gray matter mask, which was obtained from high-resolution anatomical images using SPM8s (Wellcome Department of Cognitive Neurology, Queens Square, London, UK) unified segmentation-normalization procedure (Ashburner & Friston, 2005). Volumes were resliced to the resolution of the functional scans using the first acquired functional scan as reference. After gray matter masking, top and bottom slices in each scan were masked to avoid using the bad voxels in these slices formed during online retrospective motion correction. Each scan, now fully preprocessed, was saved in a FieldTrip preprocessed data buffer. The entire real-time fMRI pipeline is shown in Fig. 2. Once preprocessed, scans were then used for training and decoding.