2.2. fMRI image acquisition, protocol and preprocessing

We acquired and preprocessed the resting-state neuroimaging data according to standard procedures in several of our manuscripts (Nicholson et al., 2015, Nicholson et al., 2017, Nicholson et al., 2018). We utilized a 3 Tesla MRI Scanner (Trio, Siemens Medical Solutions, Erlangen, Germany) with a 32-channel phased array head coil for brain imaging. During the resting-state scan, 120 volumes of whole brain BOLD (blood oxygen level dependent) images were acquired with the manufacturer's standard T2* gradient-echo planar imaging (EPI) pulse sequence (single-shot, blipped-EPI, interleaved slice acquisition order and tridimensional prospective acquisition correction) with the following parameters: TR = 3000 ms, TE = 20 ms, isotropic resolution 2 mm, FOV = 192 × 192 × 128 mm³ (94 × 94 matrix, 64 slices), flip angle = 90° High-resolution T1-weighted anatomical images were acquired with a Magnetization-Prepared Rapid Acquisition Gradient Echo sequence (192 slices, 1 mm isotropic resolution). For the resting-state procedure, participants were instructed to close their eyes and let their minds wander while trying not to focus on anything in particular for 6 minutes (Bluhm et al., 2009; Fransson, 2005; Harricharan et al., 2016), after which we assessed state-based clinical symptoms experienced during the scans (see below).

Preprocessing of the functional images was performed with SPM12 (Wellcome Department of Cognitive Neurology, London, UK) within Matlab 2017a. After discarding the 4 initial volumes, the standard preprocessing routine included spatial alignment to the mean image using a rigid body transformation, reslicing, and co-registration of the functional mean image to the anatomical image. The co-registered images were segmented using the “New Segment” method implemented in SPM12. The functional images were normalized to MNI space (Montréal Neurological Institute) and were subsequently smoothed with a FWHM (full-width at half-maximum) Gaussian kernel of 6 mm. Additional correction for motion was implemented using the ART software package (Gabrieli Lab, McGovern Institute for Brain Research, Cambridge, MA), which computes regressors that account for outlier volumes, in addition to the six movement regressors computed during standard realignment in general linear modeling. The smoothed functional images were subsequently bandpass filtered (high-pass 0.012 Hz, low-pass 0.1 Hz) (software by co-author Jean Théberge).