{"dataset":{"id":"54514","dataset_id":"on003922","name":"Multisensory Correlation Detector","description":"A magnetoencephalography dataset from 13 participants performing audiovisual multisensory tasks, including causality judgment, temporal order judgment, and unimodal localizer conditions. The dataset comprises 1500 trials of audiovisual sequences with simultaneous behavioral recordings and structural MRI, designed to investigate neural mechanisms of multisensory correlation computations using time-resolved encoding models.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on003922","concept_doi":"10.82901/nemar.on003922","latest_version_doi":"10.82901/nemar.on003922.v1.0.0","created_at":"2026-06-22 21:31:49","updated_at":"2026-07-10 22:22:20","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Multisensory Correlation Detector\",\n  \"description\": \"A magnetoencephalography dataset from 13 participants performing audiovisual multisensory tasks, including causality judgment, temporal order judgment, and unimodal localizer conditions. The dataset comprises 1500 trials of audiovisual sequences with simultaneous behavioral recordings and structural MRI, designed to investigate neural mechanisms of multisensory correlation computations using time-resolved encoding models.\",\n  \"methods_description\": \"MEG data were acquired using a 306-channel Neuromag Elekta system at 1 kHz sampling rate with 0.03-330 Hz bandpass filtering. Participants completed 10 blocks (3 causality, 3 temporal order, 2 auditory localizer, 2 visual localizer) presenting six audiovisual sequences. Head position was tracked with four HPI coils before each block. Anatomical T1-weighted MRI was acquired on a 3-T Siemens Trio scanner (1.0 × 1.0 × 1.1 mm voxels, TR=2300 ms, TE=2.98 ms). EOG and ECG were recorded simultaneously.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Pesnot Lerousseau, J.\": {\n      \"orcid\": \"0000-0003-3799-0602\"\n    },\n    \"Parise, C.\": {},\n    \"Ernst, MO.\": {},\n    \"van Wassenhove, V.\": {\n      \"orcid\": \"0000-0002-2569-5502\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"multisensory integration\"\n    },\n    {\n      \"term\": \"audiovisual perception\"\n    },\n    {\n      \"term\": \"temporal order judgment\"\n    },\n    {\n      \"term\": \"causality perception\"\n    },\n    {\n      \"term\": \"encoding model\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41467-022-29687-6\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on003922\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on003922\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/sdata.2018.110\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds003922.v1.0.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"European Research Council\",\n      \"award_number\": \"ERC-YStG-263584\"\n    },\n    {\n      \"funder_name\": \"Agence Nationale de la Recherche\",\n      \"award_number\": \"ANR-16-CE37-0004-04\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"meg\"\n  ],\n  \"sizes\": [\n    \"80.6 GB (171 files)\"\n  ],\n  \"formats\": [\n    \".csv\",\n    \".dat\",\n    \".fif\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".txt\",\n    \".yml\"\n  ],\n  \"source_hash\": \"a19419df3c891a0c197176aa5a9cc8eeee172720dc987db50f0798874395d7e2\"\n}","last_activity_at":"2026-06-22 21:31:49","source":"openneuro","source_id":"ds003922","subject_count":14,"modalities":"anat,meg","age_min":21,"age_max":88,"file_size":81286848531,"total_files":682,"tasks":"mcd,noise,rest","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Pesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V.","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003922-blue)](https://doi.org/10.82901/nemar.on003922)\n\n### DESCRIPTION\nMagnetoencephalography (MEG) dataset recorded during the presentation of audiovisual sequences with a causality judgment task and temporal order judgment task. This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al. 2019).\n\n### PUBLISHED IN\nPesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V. (2022). Multisensory correlation computations in the human brain identified by a time-resolved encoding model. *Nature Communications*. http://doi.org/10.1038/s41467-022-29687-6\n\n### PARTICIPANTS\nThe dataset contains 13 participants (Ab140232, Jl150443, Mm150194, Al150424, Mp110340, Rt160359, Cb140229, Cc160310, Lb160367, Mb160304, Mk150295, Sl160372, Mp150285). \n\n### EXPERIMENT\nThe experiment consisted of 10 consecutive recording blocks of 8 minutes each, whose order was counterbalanced across participants. Three blocks tested participants on a Causality judgement, and three blocks tested participants with a Temporal order judgement. Importantly, the same audiovisual sequences were used in both tasks in order to maintain a constant flow of feedforward multisensory inputs while manipulating the endogenous task requirements. Each block was composed of 25 repetitions of the 6 possible audiovisual sequences. A total of 75 presentations of each stimulus sequence were thus tested in each task. Four additional recording blocks consisted of participants passively hearing (auditory localizer, 2 blocks) or viewing (visual localizer, 2 blocks) one constitutive modality of the audiovisual sequence. Each localizer block was composed of 25 repetitions of the 6 possible stimuli (auditory or visual part of each stimuli), yielding a total of 50 presentations of each auditory and visual stimuli (2 tasks x 3 blocks x 25 repetitions x 6 sequences + 2 modalities x 25 repetitions x 2 blocks x 6 sequences = 1500 trials in total). \n\n### STIMULI\nSix audiovisual sequences were presented (DD, DC, CC, AA, AV, VV). \n\n### BLOCKS\nTen blocks were presented (3 Causality, 3 Temporal, 2 Auditory, 2 Visual). \n\n### EVENTS\n- 'Causality/DD':11\n- 'Causality/DC':12\n- 'Causality/CC':13\n- 'Causality/AA':14\n- 'Causality/AV':15\n- 'Causality/VV':16\n- 'Temporal/DD':21\n- 'Temporal/DC':22\n- 'Temporal/CC':23\n- 'Temporal/AA':24\n- 'Temporal/AV':25\n- 'Temporal/VV':26\n- 'Auditory/DD':41\n- 'Auditory/DC':42\n- 'Auditory/CC':43\n- 'Auditory/AA':44\n- 'Auditory/AV':45\n- 'Auditory/VV':46\n- 'Visual/DD':51\n- 'Visual/DC':52\n- 'Visual/CC':53\n- 'Visual/AA':54\n- 'Visual/AV':55\n- 'Visual/VV':56\n\n### MEG\nBrain magnetic fields were recorded in a MSR using a 306 MEG system (Neuromag Elekta LTD, Helsinki). MEG recordings were sampled at 1 kHz and band-pass filtered between 0.03 Hz and 330 Hz.\n\nFour head position coils (HPI) measured the head position of participants before each block; three fiducial markers (nasion and pre-auricular points) were used for digitization and anatomicalMRI (aMRI) immediately following MEG acquisition.\n\nElectrooculograms (EOG, horizontal and vertical eye movements) and electrocardiogram (ECG) were simultaneously recorded. Prior to the session, 2 min of empty room recordings was acquired for the computation of the noise covariance matrix.\n\nBad MEG channels were marked manually.\n\n### MRI\nThe T1 weighted aMRI was recorded using a 3-T Siemens Trio MRI scanner. Parameters of the sequence were: voxel size: 1.0 × 1.0 × 1.1 mm; acquisition time: 466 s; repetition time TR = 2300 ms; and echo time TE = 2.98 ms\n\n### BEHAVIOR\n\nFile sourcedata/behavioral_data.txt\n\n### REFERENCES\n\nPesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V. (2022). Multisensory correlation computations in the human brain identified by a time-resolved encoding model. Nature Communications. http://doi.org/10.1038/s41467-022-29687-6\n\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\n\nNiso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. 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