{"dataset":{"id":"53731","dataset_id":"on003633","name":" ForrestGump-MEG","description":"ForrestGump-MEG is a multimodal neuroimaging dataset comprising magnetoencephalography (MEG) recordings from 11 subjects during passive viewing of a 2-hour audio-visual movie stimulus (Chinese-dubbed Forrest Gump), along with structural MRI data. The dataset includes raw 275-channel CTF MEG data, preprocessed MEG recordings with independent component analysis decomposition, anatomically preprocessed T1-weighted images, and MEG-MRI co-registration information. This resource enables investigation of neural dynamics during naturalistic audiovisual perception and brain-behavior relationships in ecologically valid contexts.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on003633","concept_doi":"10.82901/nemar.on003633","latest_version_doi":"10.82901/nemar.on003633.v1.0.0","created_at":"2026-06-22 08:01:29","updated_at":"2026-07-10 22:17:50","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \" ForrestGump-MEG\",\n  \"description\": \"ForrestGump-MEG is a multimodal neuroimaging dataset comprising magnetoencephalography (MEG) recordings from 11 subjects during passive viewing of a 2-hour audio-visual movie stimulus (Chinese-dubbed Forrest Gump), along with structural MRI data. The dataset includes raw 275-channel CTF MEG data, preprocessed MEG recordings with independent component analysis decomposition, anatomically preprocessed T1-weighted images, and MEG-MRI co-registration information. This resource enables investigation of neural dynamics during naturalistic audiovisual perception and brain-behavior relationships in ecologically valid contexts.\",\n  \"methods_description\": \"MEG data were acquired using a 275-channel CTF system during movie watching. Preprocessing involved: (1) bad channel detection and removal, (2) high-pass filtering at 1 Hz to remove slow drifts, and (3) artifact removal using independent component analysis. Structural T1-weighted images were minimally preprocessed using fMRIPrep's anatomical pipeline with default settings. MEG-MRI co-registration and surface reconstruction were performed using MNE-BIDS and FreeSurfer.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Xingyu Liu\": {},\n    \"Yuxuan Dai\": {},\n    \"Hailun Xie\": {},\n    \"Zonglei Zhen\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"Magnetoencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D015225\"\n    },\n    {\n      \"term\": \"movie watching\"\n    },\n    {\n      \"term\": \"naturalistic stimuli\"\n    },\n    {\n      \"term\": \"audiovisual perception\"\n    },\n    {\n      \"term\": \"structural MRI\"\n    },\n    {\n      \"term\": \"independent component analysis\"\n    },\n    {\n      \"term\": \"Forrest Gump\"\n    },\n    {\n      \"term\": \"CTF MEG\"\n    },\n    {\n      \"term\": \"fMRIPrep\"\n    },\n    {\n      \"term\": \"FreeSurfer\"\n    },\n    {\n      \"term\": \"passive viewing\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on003633\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on003633\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds003633\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds000113\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds003633.v1.0.3\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"National Key R&D Program of China\",\n      \"award_number\": \"2019YFA0709503\"\n    },\n    {\n      \"funder_name\": \"National Natural Science Foundation of China\",\n      \"award_number\": \"31771251\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"meg\"\n  ],\n  \"sizes\": [\n    \"165.7 GB (5350 files)\"\n  ],\n  \"formats\": [\n    \".2mm/README\",\n    \".3d\",\n    \".H\",\n    \".K\",\n    \".README\",\n    \".acq\",\n    \".annot\",\n    \".area\",\n    \".avg\",\n    \".avg_curv\",\n    \".avg_sulc\",\n    \".avg_thickness\",\n    \".bak\",\n    \".cfg\",\n    \".cls\",\n    \".cmd\",\n    \".crv\",\n    \".csv\",\n    \".ctab\",\n    \".curv\",\n    \".dat\",\n    \".de\",\n    \".defect_borders\",\n    \".defect_chull\",\n    \".defect_labels\",\n    \".done\",\n    \".ds/BadChannels\",\n    \".eeg\",\n    \".env\",\n    \".fif\",\n    \".flat\",\n    \".gii\",\n    \".gitattributes\",\n    \".gz\",\n    \".h5\",\n    \".hc\",\n    \".hist\",\n    \".inflated\",\n    \".inflated_avg\",\n    \".inflated_pre\",\n    \".infods\",\n    \".jacobian_white\",\n    \".json\",\n    \".label\",\n    \".left_right\",\n    \".local-copy\",\n    \".log\",\n    \".lta\",\n    \".m\",\n    \".m3z\",\n    \".md\",\n    \".meg4\",\n    \".mgh\",\n    \".mgz\",\n    \".mid\",\n    \".midthickness\",\n    \".mrk\",\n    \".newds\",\n    \".nofix\",\n    \".old\",\n    \".orig\",\n    \".orig_avg\",\n    \".pial\",\n    \".pial_avg\",\n    \".pial_semi_inflated\",\n    \".preaparc\",\n    \".py\",\n    \".reg\",\n    \".res4\",\n    \".seghead\",\n    \".segments\",\n    \".sh\",\n    \".smoothwm\",\n    \".sphere\",\n    \".stats\",\n    \".sulc\",\n    \".surf\",\n    \".thickness\",\n    \".touch\",\n    \".tsv\",\n    \".txt\",\n    \".volume\",\n    \".white\",\n    \".white_avg\",\n    \".xfm\",\n    \".yml\"\n  ],\n  \"source_hash\": \"5cafb6ab59b5fb99c66b2ff9d92936c53583d70b60eec6797923d03e7410bb77\"\n}","last_activity_at":"2026-06-22 08:01:29","source":"openneuro","source_id":"ds003633","subject_count":12,"modalities":"anat,meg","age_min":19,"age_max":25,"file_size":165969896874,"total_files":7528,"tasks":"movie,noise","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Xingyu Liu, Yuxuan Dai, Hailun Xie, Zonglei Zhen","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003633-blue)](https://doi.org/10.82901/nemar.on003633)\n\n\n**ForrestGump-MEG: A audio-visual movie watching MEG dataset**\n\nFor details please refer to our paper on https://www.biorxiv.org/content/10.1101/2021.06.04.446837v1.\n\nThis dataset contains MEG data recorded from 11 subjects while watching the 2h long Chinese-dubbed audio-visual movie 'Forrest Gump'. The data were acquired with a 275-channel CTF MEG. Auxiliary data (T1w) as well as derivation data such as preprocessed data and MEG-MRI co-registration are also included. \n\n\n**Pre-process procedure description**\n\nThe T1w images stored as NIFTI files were minimally-preprocessed using the anatomical preprocessing pipeline from fMRIPrep with default settings. \n\nMEG data were pre-processed using MNE following a three-step procedure: 1. bad channels were detected and removed. 2. a high-pass filter of 1 Hz was applied to remove possible slow drifts from the continuous MEG data. 3. artifacts removal was performed with ICA.\n\n\n**Stimulus material**\n\nThe audio-visual stimulus materials were from the Chinese-dubbed 'Forrest Gump' DVD released in 2013 (ISBN: 978-7-7991-3934-0), which cannot be publicly released due to copyright restrictions. The stimulus materials are available upon reasonable request and on condition of a research-only data use agreement (correspondence with Xingyu Liu, liuxingyu987@foxmail.com).\n\n\n**Dataset content overview**\n\nthe data were organized following the MEG-BIDS using MNE-BIDS toolbox.\n\n*the pre-processed MEG data*\n\nThe preprocessed MEG recordings including the preprocessed MEG data, the event files, the ICA decomposition and label files and the MEG-MRI coordinate transformation file are hosted here. \n\n\t|---./derivatives/preproc_meg-mne_mri-fmriprep/sub-xx/ses-movie/meg/\n\t\t|---sub-xx_ses-movie_coordsystem.json\n\t\t|---sub-xx_ses-movie_task-movie_run-xx_channels.tsv\n\t\t|---sub-xx_ses-movie_task-movie_run-xx_decomposition.tsv\n\t\t|---sub-xx_ses-movie_task-movie_run-xx_events.tsv\n\t\t|---sub-xx_ses-movie_task-movie_run-xx_ica.fif.gz\n\t\t|---sub-xx_ses-movie_task-movie_run-xx_meg.fif\n\t\t|---sub-xx_ses-movie_task-movie_run-xx_meg.json\n\t\t|---...\n\t\t|---sub-xx_ses-movie_task-movie_trans.fif\n\n\n*the pre-processed MRI data*\n\nThe preprocessed MRI volume, reconstructed surface, and other associations including transformation files are hosted here\n\n\t|---./derivatives/preproc_meg-mne_mri-fmriprep/sub-xx/ses-movie/anat/\n\t\t|---sub-xx_ses-movie_desc-preproc_T1w.nii.gz\n\t\t|---sub-xx_ses-movie_hemi-L_inflated.surf.gii\n\t\t|---sub-xx_ses-movie_hemi-L_midthickness.surf.gii\n\t\t|---sub-xx_ses-movie_hemi-L_pial.surf.gii\n\t\t|---sub-xx_ses-movie_hemi-L_smoothwm.surf.gii\n\t\t|---sub-xx_ses-movie_hemi-R_inflated.surf.gii\n\t\t|---sub-xx_ses-movie_hemi-R_midthickness.surf.gii\n\t\t|---sub-xx_ses-movie_hemi-R_pial.surf.gii\n\t\t|---sub-xx_ses-movie_hemi-R_smoothwm.surf.gii\n\t\t|---sub-xx_ses-movie_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz\n\t\t|---sub-xx_ses-movie_space-MNI152NLin6Asym_desc-preproc_T1w.nii.gz\n\t\t|---...\n\nthe FreeSurfer surface data, the high-resolution head surface and the MRI-fiducials are provided here\n\n\t|---./derivatives/preproc_meg-mne_mri-fmriprep/sourcedata/\n\t\t|---freesurfer\t\n\t\t    |---sub-xx\n\t\t    |---...\n\n\n*the raw data*\n\n\t|---./sub-xx/ses-movie/\t\n\t\t|---meg/\n\t\t|\t|---sub-xx_ses-movie_coordsystem.json\n\t\t|\t|---sub-xx_ses-movie_task-movie_run-xx_channels.tsv\n\t\t|\t|---sub-xx_ses-movie_task-movie_run-xx_events.tsv\n\t\t|\t|---sub-xx_ses-movie_task-movie_run-xx_meg.ds\n\t\t|\t|---sub-xx_ses-movie_task-movie_run-xx_meg.json\n\t\t|\t|---...\t\t\n\t\t|---anat/\n\t\t\t|---sub-xx_ses-movie_T1w.json\n\t\t\t|---sub-xx_ses-movie_T1w.nii.gz\n\n","bids_version":"1.4.0","sessions_count":8,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-08 11:23:30","zarr_store_count":96,"zarr_index_etag":"56e864f2d56e36f68312aa72d643d337","zarr_source_commit":"f0f7fb821c7ce4e7bcc07d5b0d52e37519f84111","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 154.6 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":2,"num_datapaper_citations":0,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":165749226695,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":158004.84400000007,"recording_duration_min":34.132,"recording_duration_max":930,"recording_count":254,"recordings_unavailable":69,"recordings_measured":185,"channel_count_min":378,"channel_count_max":409,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-22 08:26:10\",\"metadata_updated_at\":\"2026-06-22 08:26:18\",\"archive_checked_at\":\"2026-06-22 08:25:45\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-22 08:26:20\",\"citations_updated_at\":\"2026-09-08 03:00:51\",\"channel_montage_checked_at\":null,\"hed_checked_at\":\"2026-06-30 04:45:13\",\"data_checked_at\":\"2026-08-02 03:01:04\",\"availability_report_at\":\"2026-07-23 01:14:37\",\"signal_defaults_at\":\"2026-09-02 12:01:23\"}","participants":12,"num_citations":2,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"155 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on003633/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}