PsiConnect is a large-scale neuroimaging study designed to investigate context-dependent neural and subjective effects of psilocybin using multimodal neuroimaging. It combines functional, structural, and diffusion-weighted MRI with EEG to examine brain activity in 62 participants before and after a 19 mg dose of psilocybin. The design includes resting-state scans and three naturalistic conditions: guided meditation, music listening, and movie watching. Half of the cohort underwent an 8-week meditation training program, enabling exploration of interactions among meditation, psilocybin, and brain function. fMRI data was obtained through multi-echo fMRI, enhancing signal-to-noise ratio and reducing susceptibility artifacts to improve reliability. A comprehensive battery of behavioural and self-report measures captured acute and longitudinal cognitive and subjective effects, with follow-ups to one year post-administration. The large sample, multimodal imaging, contextual diversity, and behavioural follow-ups enable study of psilocybin-induced brain and behaviour changes with unprecedented comprehensiveness and reliability. Data is curated according to open science principles to ensure accessibility and compatibility with established neuroimaging pipelines, making PsiConnect a valuable, reusable resource for cognitive and computational neuroscience.
Novelli and colleagues introduce PsiConnect as a large, multimodal neuroimaging resource designed to characterise how psilocybin affects brain activity, subjective experience, and behaviour in different contexts. The paper frames prior psychedelic neuroimaging as limited by small samples, narrower task designs, and less comprehensive longitudinal follow-up, making it difficult to study context-dependent effects or to compare brain and behavioural changes across modalities. The study aims to provide an open, reusable dataset combining MRI, EEG, and behavioural measures collected before and after a standardised 19 mg psilocybin dose. A further aim is to examine how psilocybin interacts with contextual conditions such as rest, guided meditation, music listening, and movie watching, and to enable analysis of longer-term outcomes over follow-up periods extending to 12 months.
Papers cited by this study that are also in Blossom
Nicol, G. E. · Nature (2024)
Kaelen, M., Giribaldi, B., Raine, J. et al. · Psychopharmacology (2018)
Studerus, E., Gamma, A., Vollenweider, F. X. · PLOS ONE (2010)
Nour, M. R., Evans, J., Nutt, D. J. et al. · Frontiers in Human Neuroscience (2016)
PsiConnect was an open-label study with two main imaging sessions: a baseline session without psilocybin and a second session after administration of 19 mg psilocybin. The study was approved by Monash University’s ethics committee and registered with the Australian New Zealand Clinical Trials Registry. Written informed consent was obtained, and participants were screened for psychiatric history and other exclusions before enrolment. Sixty-five healthy adults aged 18-55 were recruited. The extracted text reports that all had no psychedelic experience and limited meditation exposure; no formal meditation practice was allowed. Two participants were later withdrawn before the psilocybin session because exclusion criteria were identified during the study period, and their baseline data were not analysed. Of the remaining participants, 63 received psilocybin. Each imaging session included MRI and EEG during four repeated conditions: resting state, guided meditation, music listening, and movie watching. In fMRI, the psilocybin session began about 80 minutes after dosing and followed a fixed order from rest to movie, apparently to preserve safety and experiential coherence. The EEG session used a different order, beginning with the movie and then the eyes-closed conditions. Half of the cohort completed an 8-week meditation training programme, allowing the researchers to examine interactions between meditation training and psilocybin. The imaging protocol was multimodal, including structural MRI, diffusion-weighted imaging, multi-echo fMRI, and EEG. The paper specifies that multi-echo fMRI was used to improve signal-to-noise ratio, reduce susceptibility-related signal loss, and support more reliable functional connectivity estimates. A broad behavioural battery was collected before, during, and after scanning, including measures of mood, mindfulness, personality, music-related traits, altered states of consciousness, mystical experience, ego dissolution, and follow-up outcomes at one day, one week, one, three, six, and twelve months. Data were organised according to BIDS standards and released openly via OpenNeuro. MRI data were preprocessed with fMRIPrep, cleaned with multi-echo ICA and an SPM12 general linear model, and projected to surface space for some analyses. EEG data were processed with the RELAX pipeline, including filtering, artefact reduction, independent component-based cleaning, and interpolation of rejected electrodes. Behavioural questionnaires were scored using the authors’ guidelines, with limited median imputation when fewer than half of the items on a scale were missing.
All 65 enrolled participants completed the baseline MRI and EEG sessions. For MRI, two participants were withdrawn before psilocybin administration, leaving 63 who received psilocybin. One did not complete any post-dose imaging, and another only partially completed the resting-state fMRI condition. The available post-psilocybin fMRI sample was therefore 62 for rest, 61 for meditation, 61 for music, and 61 for movie watching. For EEG, 63 participants had usable baseline data and 58 had usable post-dose EEG data after exclusions for missing event markers and incomplete sessions. The quality-control analyses found that head motion increased during the psilocybin MRI session compared with the baseline session. The authors provide recommended exclusions for some scans collected under psilocybin, but they retained and released the full dataset. Among anatomical images, eight psilocybin-session T1-weighted scans were excluded in the authors’ exploratory analysis, while all baseline anatomical scans were retained. The technical validation showed that multi-echo acquisition improved temporal signal-to-noise ratio (tSNR) relative to a single-echo proxy, and that ME-ICA cleaning improved it further. The largest gains were reported in regions prone to susceptibility-related signal dropout, including the temporal pole, ventromedial prefrontal cortex, and subcortical areas, with further improvement in superficial grey matter voxels after ME-ICA. The authors state that these patterns replicate earlier multi-echo findings and are especially relevant for psychedelic imaging because the affected regions are important to the studied phenomena. On the behavioural side, acute psilocybin effects were substantial on the 11-Dimension Altered States of Consciousness scale and the Mystical Experience Questionnaire. The extracted text does not report detailed group means in the prose, but it states that the acute data showed strong subjective effects after the standardised 19 mg dose. Clustered correlation matrices of behavioural measures suggested separable domains of positive self-boundary-dissolving experiences, sensory-hallucinogenic effects, and dysphoric effects. Negative experiences, particularly anxiety, were reportedly infrequent or low in intensity. In the follow-up and meditation-related measures, the correlations clustered into positive outcomes such as purpose, meaning, coherence, and well-being, versus negative outcomes such as stress, dysphoria, anxiety, and discomfort.
The authors interpret PsiConnect as a high-quality, unusually comprehensive dataset for studying psilocybin across brain, behaviour, and context. They argue that the combination of a relatively large sample, repeated multimodal imaging, naturalistic stimuli, meditation training in half the cohort, and longitudinal follow-up makes the resource well suited to questions about functional connectivity, brain network dynamics, subjective experience, and computational modelling. They position the technical findings as evidence that the chosen acquisition and preprocessing pipeline improved data quality. In particular, they note that multi-echo fMRI and ME-ICA were associated with better tSNR, especially in regions vulnerable to signal dropout, and that this is important for psychedelic neuroimaging. They also emphasise that the dataset is complementary to smaller earlier psilocybin datasets with more intensive repeated scanning of fewer participants. The authors acknowledge several trade-offs and limitations of the approach. They note that multi-echo imaging adds acquisition and preprocessing complexity, reduces spatial resolution, and increases scanner noise, which may be uncomfortable in psychedelic studies and potentially complicate auditory tasks. They also report greater head motion under psilocybin, which affected some scans and required recommended exclusions. The open-label design and the reliance on a fixed sequence of conditions are described in the methods and imply limited experimental control, although the extracted discussion text focuses mainly on technical and dataset-oriented limitations rather than making broader causal claims. Overall, the paper presents PsiConnect as an open-access resource intended to support future research across cognitive neuroscience, clinical research, and computational analysis. The authors state that the dataset has strong reuse potential because the raw and minimally processed data, along with detailed metadata and quality-control information, are made available in a format compatible with established pipelines.
The study protocol was approved by the Monash University Human Research Ethics Committee. The trial was registered with the Australian New Zealand Clinical Trials Registry under the registration number ACTRN12621001375842. Written consent was obtained at the start of the first expression of interest online questionnaire, then verbal consent was obtained prior to completing both the phone screening and the Structured Clinical Interview for DSM-5, Research version (SCID-5-RV), which was completed via video conferencing. Written informed consent was obtained from all participants prior to baseline imaging.
The study was open-label and included two imaging sessions: a baseline (no-psilocybin) session and a session following the administration of a 19 mg dose of psilocybin (see Fig.). The dose in mg/kg varied between 0.152 -0.388 (0.262 ± 0.057). Both sessions involved MRI and EEG scans, with four conditions repeated in each: resting state, guided meditation, music listening, and movie watching. In fMRI, conditions began ∼80 minutes post-dose and followed a fixed order (rest → meditation → music → movie). The sequence progressed from low-stimulus to high-stimulus contexts to prioritise safety and experiential coherence for psychedelic-naïve participants. To mitigate scanner noise, participants were fitted with S15 insert earphones using expanding foam tips to ensure effective passive attenuation. Audio levels were individually calibrated prior to scanning by first adjusting a normalised audio stimulus to a comfortable level, followed by presentation of a test sequence of scanner noise to confirm that the stimulus remained clearly audible over gradient sounds without discomfort. Volume was adjusted based on participant feedback. The EEG session started after a room transfer and 20-40 minutes of setup (cap placement and impedance checks), with the movie presented first, followed by the three eyes-closed conditions (movie → rest → meditation → music). In the resting-state condition (8 min MRI, 5 min EEG), participants were instructed to relax and keep their eyes closed while remaining still. During the guided meditation (6:30 min MRI, 5 min EEG), participants received brief instructions via MRI-safe audio, followed by silent periods of meditation. For the music listening condition (11:24 min MRI, 7 min EEG), a curated playlist was designed to evoke emotional depth and resonance. In the naturalistic movie condition (6 min MRI, 5 min EEG), These conditions were studied in both the baseline (no-psilocybin) and psilocybin sessions and were repeated in both fMRI and EEG, allowing for comprehensive cross-modal and longitudinal analyses of psilocybin's influence on brain activity and connectivity. Half of the participants were assigned to an Several behavioural measures were collected before and during the baseline (no-psilocybin) and psilocybin scans (see Behavioural Data Acquisition). The follow-up conducted the day after psilocybin administration included semi-structured, open-ended questions and experience ratings. Further follow-up measures were administered one week, and one, three, six and twelve months after psilocybin administration.
Sixty-five healthy adults aged 18-55 (37.3 ± 10.7) with no psychedelic experience were recruited for the study; participants reported their gender as 32 women, 1 non-binary person, and 32 men. Participants were required to have no formal meditation practice, and limited previous exposure to meditation. They were first screened via short online survey and then detailed screening was performed by a suitably trained staff member for excluding any psychopathology using the long form SCID-V. Exclusion criteria included a history of psychiatric disorders or suicidality, a On the day of psilocybin administration, they were assisted by a study doctor, researchers, lab staff with relevant training, and volunteers from the community drug harm reduction support organisation Dancewize. Psilocybin was generally well tolerated, although some adverse effects were reported, including reports of transient headaches during the night (n=3). Three participants received follow-up support from a clinical psychologist familiar with psychedelic integration. These follow-up calls were conducted over the phone, and no further support was required.
All the 65 enrolled participants completed the baseline MRI session (no psilocybin). Two participants were withdrawn prior to the psilocybin session due to meeting exclusion criteria identified during the study period, and their baseline data was not analysed. The remaining 63 participants received psilocybin. One did not undergo any post-dose imaging, and another only partially completed the resting-state condition of the fMRI scan. This partial resting-state fMRI was included in the technical validation presented here. In summary, the numbers of available post-psilocybin scans are: rest = 62, meditation = 61, music = 61, movie = 61.
All the 65 enrolled participants completed the baseline EEG session (no psilocybin). However, two missed event markers due to technical issues, so the full baseline EEG data including all conditions and event markers is available for 63 participants. Of the 63 participants who received psilocybin, 59 completed the EEG session. However, one missed event markers due to technical issues, so the full post-dose EEG data including all conditions and event markers is available for 58 participants.
Structural MRI data was acquired using a Siemens 3 Tesla Magnetom Skyra scanner at Monash Biomedical Imaging, Monash University, Australia. T1-weighted (T1w) anatomical images were obtained for each participant during two sessions: at baseline (no-psilocybin) and on the psilocybin administration day. The images were acquired using a 3D magnetisation-prepared rapid gradient-echo (MP-RAGE) sequence with a 32-channel head coil. The acquisition parameters were as follows: repetition time (TR) of 2300 ms, echo time (TE) of 2.07 ms, 192 slices per slab, 1 mm slice thickness,
Diffusion-weighted imaging data was acquired using a single-shell acquisition scheme with an echoplanar imaging sequence. The acquisition parameters were as follows: TR of 5900 ms, TE of 171.0 ms, 56 slices per slab, 2.5 mm slice thickness, and 2.5 mm isotropic voxel size. The field of view was 212 mm in the read direction and 100% in the phase direction. The Parallel Acquisition Technique mode used was GRAPPA, with an acceleration factor (PE) of 2 and 40 reference lines in the phase-encoding direction. Diffusion weighting was applied along 71 directions using a monopolar diffusion scheme. Two b-values were used: 0 s/mm 2 and 3000 s/mm 2 . The EPI factor was set to 86, and the bandwidth was 1038 Hz/Px. Fat suppression was implemented using a fat saturation technique.
EEG data was recorded using a 64-channel BrainAmp MR Plus amplifier (Brain Products GmbH, Germany) with BrainVision Recorder software (version 1.22.0001, Brain Products GmbH, Germany) and Ag/AgCl electrodes embedded in an actiCAP slim cap according to the standardized 10-10 system. The FCz electrode served as the reference and the FPz electrode served as the ground for online recording. The impedances were maintained below 10kΩ using an abrasive paste (Nuprep, Weaver and Company, USA) followed by applying a conductive gel (Easycap GmbH, Germany). EEG signals were sampled at 500 Hz with a bandpass filter of 0.01 -1000Hz. Participants were comfortably seated in an acoustically absorbent room to minimise environmental noise and their chin strap was securely fastened to ensure head stability. They were instructed to remain as still as possible and avoid jaw clenching during the recording to minimise artifacts. The EEG recording session included the same four conditions as the fMRI described above. The naturalistic movie (5 minutes) was presented first, followed by the three eyes-closed conditions: resting state (5 minutes), audio-guided meditation (5 minutes), and music listening (7 minutes). The raw data was acquired as a single continuous recording over all conditions and then split into four separate files using the event markers for the start and end of each condition. Due to technical issues, event markers were not recorded for two participants in the baseline session and one participant in the psilocybin session, so these recordings were excluded from pre-processing and analysis. In summary, pre-processed EEG is available for 63 participants in the baseline session and 58 in the psilocybin session. A synopsis of the behavioural measures and delivery times is provided in Tableand Supplementary Table. Survey presentation was randomised between participants, whereby all non-well-being related measures were presented in a random order first, excluding the Depression Anxiety and Stress Scale (DASS-21)and Inventory of Depression and Anxiety Symptoms (IDAS-II). Both these scales always appeared at the end, and were randomised within each other. Participants in the meditation arm completed the following measures prior to meditation training: DASS-21, selected scales from an abridged version of the Expanded Version of the IDAS-II, The Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), and State Adult Attachment Measure (SAAM). Each week of the meditation course, participants were instructed to complete the Mindfulness Adherence Questionnaire (MAQ). They also completed additional questions about their meditation practice, as described in the metadata (JSON) files. Prior to their baseline scanning sessions, all participants completed the following measures at home: Political Perspective Questionnaire (PPQ-5), Nature Relatedness Scale (NR-6), The Attachment Style Questionnaire (ASQ), Levenson Multidimensional Locus of Control Scales (LOC), NEO Five-Factor Inventory-3 (NEO-FFI-3) [19], Aesthetic Experiences Scale in Music (AES-M), Absorption in Music Scale (AIMS), Barcelona Music Reward Questionnaire (BMRQ), Short Test of Musical Preferences (STOMP-R), and Life Attitude Profile-Revised (LAP-R). On the baseline scan day, participants completed several measures after the MRI. They first completed a measure evaluating perceptions of patterns, faces, and imagery observed during the video of clouds (referred to as "clouds" or "clouds measure"). This was followed by a measure assessing ratings of music experience variables (liking, resonance, and openness). This measure is referred to here as LRO. During EEG setup, participants listened to six music samples, rating each using the LRO before advancing to the next sample. The order of the samples was randomised. The first EEG recording was the video of clouds. Following the video, participants completed the clouds measure. After this, EEG recording continued with rest, meditation, and music conditions. After EEG recording was completed, they completed the LRO. Shortly after, participants completed a series of psychological measures, including the Five Facet Mindfulness Questionnaire (FFMQ-SF), DASS-21, IDAS-II, WEMWBS, and the SAAM. Additionally, a 42-item version of the 5-Dimension Altered States of Consciousness (5D-ASC) scale was administered, allowing for the derivation of the 11-Dimension ASC (11D-ASC) solution, as described in. Participants in the meditation condition also completed the Meditation Depth Questionnaire (MEDEQ). On the administration day, participants again first completed the LRO and clouds measure after the MRI, followed by the Ego Dissolution Inventory (EDI). The order of these measures was kept consistent. During EEG setup, participants listened to six music samples, rating each using the LRO before advancing to the next sample. The order of the samples was randomised. The first EEG recording was the video of clouds. Following the video, participants completed the clouds measure. After this, EEG recording continued with rest, meditation, and music conditions. After the completion of EEG recording, the LRO was again administered. At the end of the session, participants completed the 94-item 5D-ASC (with 94 items used to construct the 5D-ASC and 42 items used to construct the 11D-ASC), the Mystical Experience Questionnaire -Revised (MEQ30), the AES-M, and a second administration of the EDI. The order of the measures was randomised. The metadata (JSON) files for all sessions provide the questions asked and more details. The one-day follow-up included semi-structured, open-ended questions, experience ratings, and meditation evaluations for the meditation arm. Ratings of personal meaningfulness and spiritual significance, subjective intensity, and anxiety at times throughout the day were also included. Further follow-up measures were administered one week, and one, three, six and twelve months after psilocybin administration. At each follow-up time point, participants completed the Self-Compassion Scale (SCS), FFMQ-SF, and meditation participants additionally completed the MAQ. The oneweek follow-up also included the Life Changes Inventory -Revised (LCI-R), AES-M, Emotional Breakthrough Inventory (EBI), LOC, and SAAM. The one-month follow-up also included: Persisting Effects Questionnaire Revised (PEQ60), Experiences Questionnaire (EQ), LAP-R, PPQ-5, NR-6, ASQ, LOC, DASS-21, IDAS-II, NEO-FFI-3, and meditation participants completed the Meditation Depth Questionnaire (MEDEQ) instead of the MAQ. The three-month follow-up also included LOC. The six-month follow-up also included LOC and LAP-R. Finally, the 12-month follow-up also included: LAP-R, PPQ-5, and NR-6.
The Digital Imaging and Communications in Medicine (DICOM) images acquired from the Siemens (Skyra) scanner were converted into the NIfTI format and organized according to the Brain Imaging Data Structure (BIDS) 1.7.0 using BIDScoin. Following the BIDS guidelines, each data file is accompanied by a corresponding metadata file in JSON format. These contain extensive lists of neuroimaging acquisition parameters, and the necessary information to interpret the behavioural measures. To de-identify the anatomical images, the facial features were removed using PyDeface (). The EEG data was also organised according to BIDS 1.7.0.
Anatomical MRI preprocessing was performed using fMRIPrep 22.0.2(RRID:SCR_016216), which is based on Nipype 1.8.5(RRID:SCR_002502). The baseline and administration T1weighted (T1w) images were corrected for intensity non-uniformity (INU) with N4 BiasFieldCorrection, distributed with ANTs 2.3.3(RRID:SCR_004757). The T1w-reference was then skullstripped with a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as the target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white matter (WM) and gray matter (GM) was performed on the brain-extracted T1w using fast
Functional MRI preprocessing was also performed using fMRIPrep 22.0.2. For each of the 8 BOLD runs per participant (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated from the shortest echo of the BOLD run using a custom methodology of fMRIPrep. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) were estimated before any spatiotemporal filtering using mcflirt(FSL 6.0.5.1:57b01774). BOLD runs were slice-time corrected to 0.401 s (0.5 of slice acquisition range 0s-0.802 s) using 3dTshift from AFNI(RRID:SCR_005927). The BOLD time-series (including slice-timing correction) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. A T2 ⋆ map was estimated from the preprocessed EPI echoes, by voxel-wise fitting the maximal number of echoes with reliable signal in that voxel to a monoexponential signal decay model with nonlinear regression. The T2 ⋆ /S0 estimates from a log-linear regression fit were used for initial values. The calculated T2 ⋆ map was then used to optimally combine preprocessed BOLD across echoes following the method described in. The optimally combined time series was carried forward as the preprocessed BOLD. The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration. Co-registration was configured with six degrees of freedom. Several confounding time series were calculated based on the preprocessed BOLD: framewise displacement (FD), DVARS and three region-wise global signals. FD was computed using two formulations following Power (absolute sum of relative motions,) and Jenkinson recommendations (relative root mean square displacement between affines,). FD and DVARS are calculated for each functional run, both using their implementations in Nipype, following the definitions by. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. The BOLD time series were resampled in standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space. All resamplings were performed with a single interpolation step by composing all the pertinent transformations (i.e. head-motion transform matrices and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using antsApplyTransforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels. Non-gridded (surface) resamplings were performed using mri_vol2surf (FreeSurfer). Many internal operations of fMRIPrep use Nilearn 0.9.1(RRID:SCR_001362), mostly within the functional processing workflow. For more details of the pipeline, see the section corresponding to workflows in fMRIPrep's documentation. This mask is obtained by dilating a GM mask extracted from the FreeSurfer's aseg segmentation, and it ensures components are not extracted from voxels containing a minimal fraction of GM. Finally, these masks are resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the k components with the largest singular values are retained, such that the retained components' time series are sufficient to explain 50% of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardized DVARS were annotated as motion outliers. Additional nuisance time series are calculated by means of principal components analysis of the signal found within a thin band (crown) of voxels around the edge of the brain, as proposed by.
The preprocessed and optimally combined data produced by fMRIPrep was cleaned via a single regression in SPM12using the general linear model. The regressors were: the white matter and cerebrospinal fluid signals computed by fMRIPrep, the frame-wise displacement, and the non-BOLD components identified via multi-echo ICA performed using tedana (version 0.0.12). In short, multi-echo ICA identifies non-BOLD components that are independent of the echo time. Finally, to facilitate surface-based analyses, the cleaned volumetric data was projected to the FreeSurfer left-right-symmetric cortical surface template with 32000 vertices for each hemisphere (fsLR32k).
The raw EEG signals were preprocessed using the default parameters of the automated RELAX pipelineimplemented in MATLAB using functions from EEGLABand FieldTriptoolboxes. First, the data was bandpass filtered between 0.25 and 80 Hz using a fourth-order Butterworth filter with zero phase, with a notch filter applied between "findNoisyChannels" function of the PREP pipeline. This was followed by the initial reduction of the artefacts related to eye movements, muscle activity and drift using a multi-channel Wiener filter, after which the data were re-referenced to the robust average reference. Residual artefacts were then removed by Wavelet-enhanced Independent Component Analysis (WICA) with the artefactual components identified by the automated ICLabel classifier. Electrodes rejected during the cleaning process were then restored to the data through spherical interpolation. Finally, all pre-processed data were visually inspected for data quality.
Questionnaires were administered online using Qualtrics. The raw data from all questionnaires were collected in their original Likert scale format, as defined by the authors of each measure. Raw data spreadsheets are provided for each time point. Subscale scores were computed according to the authors' guidelines, using the referenced papers listed in the metadata (JSON) file for each time point. If fewer than 50% of the required items for a scale were missing, the missing values were imputed using the median of the available items. Both the imputed and not imputed values are released as part of the dataset.
The data can be accessed from the OpenNeuro public repository (access number: ds006110), organised according to the Brain Imaging Data Structure (BIDS) specification, version 1.7.0. Specifically, the defaced raw data and the MRI and EEG derivatives are organised in the subfolders indicated in Table. The MRI derivatives are listed in the same order as they were computed via the preprocessing and cleaning pipeline: cortical surface reconstruction via FreeSurfer, minimal preprocessing via fMRIPrep, multi-echo ICA via tedana, and cleaning via a GLM performed using SPM12. The following sections will provide additional information about naming conventions and available formats for the files contained in the folders listed in Table.
• Task: Video, Filename: clouds_short.m4v, Author: Stoliker, D., Duration: 300.00 s. • Task: Meditation, Filename: meditation_short.wav, Author: Stoliker, D., Duration: 300.00 s. • Task: Music (part 1), Track: Sunset from the Ethereal Shore (Lauge Rework), Author: Spacecraft, Lauge, Abbreviated track duration., Notes: Subject to copyright (and thus omitted from repository). • Task: Music (part 2), Track: Sollys (Applefish Rework), Author: Lauge, Applefish, Abbreviated track duration, Notes: Subject to copyright (and thus omitted from repository).
Following BIDS conventions, behavioural data is stored in the phenotype folder, with subfolders for different surveys: those specific to the meditation arm (pre-training, weekly measures during the 8-week course, and post-training measures collected after both the 8-week course and psilocybin administration), as well as baseline, psilocybin administration, and follow-up surveys (Table). Each session folder contains one or more TSV files. Each TSV file refers to one scale and contains the scores for all the subscales involved. The scales are listed in Tableand described in Supplementary Table. Each TSV file is accompanied by a metadata (JSON) file that contains the description of the scale, a reference to the relevant literature, and information about how the items were aggregated into a score for each subscale (for example, by summation, average, or rescaling to the 0-100 range). In addition, each folder contains a JSON file listing the specific question asked for each item in the questionnaire. The raw data is provided in the phenotype/rawdata folder, which contains one spreadsheet per questionnaire in TSV format, each accompanied by a sidecar JSON file listing the item prompts.
Quality control was performed using the MRIQC BIDS app, which uses structural and functional MRI images to compute several quality metrics (see Supplementary Figs.andfor a comprehensive overview). These include the temporal signal-to-noise ratio and the frame-wise displacement that quantifies head motion. Results and group reports can be found in the derivatives/mriqc-22.0.6 folder. Principal component analysis (PCA) of these QC metrics revealed clusters corresponding to the four echos, and helped identify some outliers in the structural and functional images (see Fig.), which were further confirmed by visual inspection. In the case of fMRI, we found that head motion was the dominant factor causing outliers on the PCA plot, as confirmed by the square markers in Fig.that indicate scans with large head motion (mean frame-wise displacement greater than 0.5, which is often used as an exclusion threshold). Indeed, head motion, quantified via the mean framewise displacement, was the feature with the largest contribution to the second principal component (see Supplementary Fig.). A detailed analysis of the mean frame-wise displacement showed that participants moved their head more during the psilocybin MRI session than in the previous session without psilocybin (Fig.). While we retained and released all the data, we used the above process to generate a table of recommended exclusions for users who do not wish to re-run a dedicated quality control analysis (see Supplementary Table). There are no suggested exclusions for the fMRI images collected during baseline. Among the functional MRI images collected during psilocybin, we recommended excluding specific conditions for 7 participants: 2 rest, 2 meditation, 5 music, and 4 movie scans, as indicated in Fig.and Supplementary Table. If these suggestions conditions were excluded from analysis, in addition to the exclusions and attrition detailed in the Participants section above, the fMRI samples during psilocybin would be: rest = 60, meditation = 59, music = 56, and movie = 57. Among the anatomical MRI files, we excluded 8 structural T1w images from the psilocybin session but retained all the T1w images from the baseline session, as indicated in Fig.. These exclusions were applied in our exploratory analysis of the PsiConnect MRI data.
Functional MRI recordings often show a drift over time, due to magnetic field fluctuations caused by the scanner hardware, or by physiological factors such as slow variations in respiration or cardiac cycles that can introduce low-frequency fluctuations in the BOLD signal. This drift is evident in the raw data but is largely removed after cleaning via ME-ICA, and even more so after including white matter and CSF signals as regressors in the GLM (see Supplementary Fig.). Since cleaning didn't include global signal regression nor high-pass filtering, it is reasonable that some of these slow physiological drifts were captured by the confounding signals that were regressed.
The second echo was used as a proxy for single-echo data since the second echo time matches the typical value used in single-echo acquisitions (~30 ms). The temporal SNR (tSNR) of this pseudo single-echo data was compared to the optimally combined multi-echo data and to the cleaned ME-ICA data, both at the subject-and at the voxel-level. For each scan, the median tSNR across all voxels is higher in the optimally combined multi-echo data than in the single-echo data, and it further increases after ME-ICA cleaning (Fig.). This first result reveals the overall trend but ignores the spatial distribution of the tSNR across voxels. In order to locate these tSNR changes, we computed the median difference between tSNR values of each voxel (by subtracting the single-echo tSNR value from the optimally combined tSNR). The spatial maps shown in Fig.demonstrate that the largest improvement is achieved in brain regions prone to signal dropout, including the temporal pole, ventromedial prefrontal cortex, and subcortical areas. In addition, Fig.shows that the largest SNR improvement of ME-ICA over the optimally combined data is obtained in superficial grey matter voxels. Our results successfully replicate the median subject-level tSNR reported inand confirm that the benefits of the multi-echo data and ME-ICA procedures are region-specific. Importantly, the optimally combined multi-echo data shows higher tSNR in regions prone to signal dropout and in subcortical areas that are particularly relevant in the context of psychedelics. However, we must be careful not to understate the drawbacks of multi-echo fMRI sequences. The higher complexity in the acquisition and processing stages were mentioned above, as well as the reduced spatial resolution. In addition, the louder scanner noise could be distressing in psychedelic studies and might complicate task acquisitions involving complex auditory stimuli. Largest tSNR increase in the optimally combined multi-echo data compared to single-echo data. c) Further tSNR increase when using cleaned data via multi-echo ICA compared to the optimally combined data (thresholded at the top quartile to locate the voxels with the largest tSNR increase).
The acute effects assessed using the 11-Dimension Altered States of Consciousness (11-D ASC) scaleand the Mystical Experience Questionnaire (MEQ30)illustrate substantial group-level subjective effects intensity from the standardised 19mg dose of psilocybin (Fig.). To provide an overview of the other behavioural scores available in the dataset, we present the matrices of pairwise Pearson correlation between the scores acquired during the psilocybin and no-psilocybin sessions, as well as during the meditation training and the longitudinal follow-ups (Fig.). The matrices have been clustered to highlight sets of highly intercorrelated behavioural scores, which reflect key theoretical and conceptual distinctions in psychedelic effects. For example, the clusters identified during the psilocybin session (Fig.) distinguish between: positively felt self-and boundary-dissolving effects, which characterise the high-level associative, abstract and existential experiential qualities (e.g., transcendence, bliss, unity); sensory-hallucinogenic effects (e.g., visual imagery, synaesthesia); and dysphoric effects (e.g., impaired control and cognition, and anxiety). We also note that negative effects, particularly anxiety, were infrequent or rated as low by the participants. Two clusters also emerge in the follow-up questionnaires and in the meditation-related scores (Fig.,f), separating positive (purpose, meaning, coherence, well-being) and negative scores (stress, dysphoria, anxiety, discomfort).
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