Neural correlates of the LSD experience revealed by multimodal neuroimaging

Abstract

Rest1 and Rest3 are resting state Rest2 is music, three subjects had technical problems with the music and should not be used (S03, S12, S15) ratings: subjects rated the questions with a VAS after each scan. The 11D-ASC was rated at the end of the scanning day. LSD, BOLD Pre-processing Four different but complementary imaging software packages were used to analyse the fMRI data. Specifically, FMRIB Software Library (FSL), AFNI , Freesurfer and Advanced Normalization Tools (ANTS) were used. One subject did not complete the BOLD scans due to anxiety and an expressed desire to exit the scanner and four others were discarded from the group analyses due to excessive head movement. Principally, motion was measured using frame-wise displacement (FD) (Power et al., 2014). The criterion for exclusion was subjects with >15% scrubbed volumes when the scrubbing threshold is FD = 0.5. After discarding these subjects we reduced the threshold to FD = 0.4. The between-condition difference in mean FD for the 4 subjects that were discarded was 0.323±0.254 and for the 15 subjects that were used in the analysis the difference in mean FD was 0.046 ±0.032. The following pre-processing stages were performed: 1) removal of the first three volumes; 2) de-spiking (3dDespike, AFNI); 3) slice time correction (3dTshift, AFNI); 4) motion correction (3dvolreg, AFNI) by registering each volume to the volume most similar, in the least squares sense, to all others (in-house code); 5) brain extraction (BET, FSL); 6) rigid body registration to anatomical scans (twelve subjects with FSL’s BBR, one subject with Freesurfer’s bbregister and two subjects manually); 7) non-linear registration to 2mm MNI brain (Symmetric Normalization (SyN), ANTS); 8) scrubbing (Power et al., 2012) - using an FD threshold of 0.4 (the mean percentage of volumes scrubbed for placebo and LSD was 0.4 ±0.8% and 1.7 ±2.3%, respectively). The maximum number of scrubbed volumes per scan was 7.1%) and scrubbed volumes were replaced with the mean of the surrounding volumes. Additional pre-processing steps included: 9) spatial smoothing (FWHM) of 6mm (3dBlurInMask, AFNI); 10) band-pass filtering between 0.01 to 0.08 Hz (3dFourier, AFNI); 11) linear and quadratic de-trending (3dDetrend, AFNI); 12) regressing out 9 nuisance regressors (all nuisance regressors were bandpassed filtered with the same filter as in step 10): out of these, 6 were motion-related (3 translations, 3 rotations) and 3 were anatomically-related (not smoothed). Specifically, the anatomical nuisance regressors were: 1) ventricles (Freesurfer, eroded in 2mm space), 2) draining veins (DV) (FSL’s CSF minus Freesurfer’s Ventricles, eroded in 1mm space) and 3) local white matter (WM) (FSL’s WM minus Freesurfer’s subcortical grey matter (GM) structures, eroded in 2mm space). Regarding WM regression, AFNI’s 3dLocalstat was used to calculate the mean local WM time-series for each voxel, using a 25mm radius sphere centred on each voxel (Jo et al., 2010). fMRI motion correction After discarding four subjects due to head motion, fifteen were left for the BOLD analysis. There was still a significant between-condition difference in motion for these subjects however (mean FD of placebo = 0.074 ±0.032, mean FD of LSD = 0.12 ±0.05, p = 0.0002). RSFC analysis is extremely sensitive to head motion (Power et al., 2012) and therefore special consideration was given to the pre-processing pipeline to account for motion. This section goes into more detail about the pre-processing steps that were performed to reduce artefacts associated with motion as well as other non-neural sources of noise. De-spiking has been shown to improve motion-correction and create more accurate FD values (Jo et al., 2013) and low-pass filtering at 0.08 Hz has been shown to perform well in removing high frequency motion (Satterthwaite et al., 2013). Six motion regressors were used as covariates in linear regression. It was decided that using more than six (e.g., “Friston 24-p

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