{"dataset":{"id":"61125","dataset_id":"on005795","name":"MULTI-CLARID (Multimodal Category Learning and Resting-state Imaging Data)","description":"This dataset presents multimodal simultaneous fMRI/EEG data collected from participants performing an auditory category learning task, alongside resting-state fMRI scans. Each dataset includes 63-channel EEG with additional ECG, EOG, facial EMG, and skin conductance recordings, a physiological file capturing respiration and finger-pulse signals, and structural MRI (T1-weighted and PD-weighted UTE) scans for anatomical reference and electrode localization. Collected at the Combinatorial NeuroImaging core facility of the Leibniz Institute for Neurobiology, the data support research into multi-dimensional auditory category learning and its neural correlates.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on005795","concept_doi":"10.82901/nemar.on005795","latest_version_doi":"10.82901/nemar.on005795.v1.0.0","created_at":"2026-06-27 19:01:03","updated_at":"2026-08-19 01:17:33","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"MULTI-CLARID (Multimodal Category Learning and Resting-state Imaging Data)\",\n  \"description\": \"This dataset presents multimodal simultaneous fMRI/EEG data collected from participants performing an auditory category learning task, alongside resting-state fMRI scans. Each dataset includes 63-channel EEG with additional ECG, EOG, facial EMG, and skin conductance recordings, a physiological file capturing respiration and finger-pulse signals, and structural MRI (T1-weighted and PD-weighted UTE) scans for anatomical reference and electrode localization. Collected at the Combinatorial NeuroImaging core facility of the Leibniz Institute for Neurobiology, the data support research into multi-dimensional auditory category learning and its neural correlates.\",\n  \"methods_description\": \"The auditory category learning experiment comprised 180 trials in which 160 frequency-modulated sounds, varying across five binary features (duration, modulation direction, intensity, modulation speed, frequency range), were presented in pseudo-randomized order with jittered inter-trial intervals. Participants indicated category membership via button press and received auditory feedback. MR data were acquired with a 3T Philips Achieva dStream scanner using a 32-channel head coil, synchronized via scanner trigger signals with stimulus presentation software (Presentation, Neurobehavioral Systems). Auditory stimuli were delivered through MR-compatible headphones with noise attenuation. Button responses were recorded via a Covilex ResponseBox 2.0. Respiration and heart rate were recorded with Invivo MRI sensors. 64-channel EEG (5 kHz sampling) was recorded using BrainAmp MRplus amplifiers, with additional bipolar channels for EOG, EMG, carbon wire loops, and skin conductance, all synchronized to the MR trigger. Preprocessing included MR-artifact correction, bandpass filtering (0.3–125 Hz), downsampling to 500 Hz, and carbon-wire-loop correction using BrainVision Analyzer 2.3.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Jörg Stadler\": {},\n    \"Torsten Stöter\": {},\n    \"Nicole Angenstein\": {},\n    \"Andreas Fügner\": {},\n    \"Marcel Lommerzheim\": {},\n    \"Artur Mathysiak\": {},\n    \"Anke Michalsky\": {},\n    \"Gabriele Schöps\": {},\n    \"Johann van der Meer\": {},\n    \"Susann Wolff\": {},\n    \"André Brechmann\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"simultaneous EEG-fMRI\"\n    },\n    {\n      \"term\": \"multimodal imaging\"\n    },\n    {\n      \"term\": \"auditory category learning\"\n    },\n    {\n      \"term\": \"resting-state fMRI\"\n    },\n    {\n      \"term\": \"category learning\"\n    },\n    {\n      \"term\": \"structural MRI\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on005795\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on005795\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds005795\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds005795.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"German Science Foundation\",\n      \"award_number\": \"BR2267/9-1\"\n    }\n  ],\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"eeg\",\n    \"func\"\n  ],\n  \"sizes\": [\n    \"6.9 GB (294 files)\"\n  ],\n  \"formats\": [\n    \".eeg\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".vhdr\",\n    \".vmrk\",\n    \".yml\"\n  ],\n  \"source_hash\": \"66a1d8084f11deaf090f139dee3db65d739d312cfbae5d536c8db01b94ff1f9b\"\n}","last_activity_at":"2026-06-27 19:01:03","source":"openneuro","source_id":"ds005795","subject_count":34,"modalities":"anat,eeg,func","age_min":18,"age_max":36,"file_size":6902192180,"total_files":671,"tasks":"learning,rest","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Jörg Stadler, Torsten Stöter, Nicole Angenstein, Andreas Fügner, Marcel Lommerzheim, Artur Mathysiak, Anke Michalsky, Gabriele Schöps, Johann van der Meer, Susann Wolff, André Brechmann","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005795-blue)](https://doi.org/10.82901/nemar.on005795)\n\nOverview\n\nThe study comprises data of a combined fMRI/EEG experiment. The EEG files contain 63 head channels, ECG, EOG, facial EMG and skin conductance data. A physio file contains respiration and finger-pulse data. In addition, a T1 weighted whole-brain anatomical MR scan, a PD weighted (UTE) scan for electrode localization is provided (defacing was performed using https://github.com/cbinyu/pydeface). Additional data of the participants (T2 weighted images, button press dynamics, hearing threshold, hearing abilities, and personality traits (NEO-FFI, BIS/BAS, SVF, ERQ, MMG) are available on request.\nThe study was conducted at the Combinatorial NeuroImaging (CNI) core facility of the Leibniz Institute for Neurobiology (LIN) Magdeburg and was approved by the ethics committee of the University of Magdeburg, Germany. All participants gave written informed consent.\nCurrently you will only find 5 data-sets that include the multi-dimensional category learning experiment (cf. Wolff & Brechmann, Cerebral Cortex, 2023) because of the copyright policy of OpenNeuro (i.e. CC0). If you are interested in the remaining data-sets, please contact brechmann@lin-magdeburg.de. Collaboration is highly welcome!\n\nDetails of the learning task\n\nThe auditory category learning experiment comprised 180 trials for which 160 different frequency modulated sounds were presented in pseudo-randomized order with a jittered inter-trial interval of 6, 8, or 10 s plus 19-95 ms in steps of 19 ms in order to ensure a pseudo-random jitter of the sound onset with the onset of the acquisition of an MR volume. Each sound had five different binary features, i.e. duration (short: 400 ms, long 800 ms), direction of the frequency modulation (rising, falling), intensity (soft: 76–81 dB, loud: 86–91 dB), speed of the frequency modulation (slow: 0.25 octaves/s, fast: 0.5 octaves/s), and frequency range (low: 500–831 Hz, high: 1630–2639 Hz with 5 different ranges each). Participants had to learn a target category defined by a combination of the features duration and direction (i.e. long/rising, long/falling, short/rising, or short/falling) by trial and error. In each trial, participants had to indicate via button press whether they thought a sound belonged to the target category (right index finger) or not (right middle finger). They received feedback about the correctness of the response by a prerecorded, female voice in standard German; e.g., \"ja\" (yes) or \"richtig\" (right) following correct responses, \"nein\" (no) or \"falsch\" (wrong) following incorrect responses. In 90% of the trials the feedback immediately followed the button press, in 10% it was delayed by 1500 ms. If participants failed to respond within 2 seconds after FM tone onset, a timeout feedback (\"zu spät\", too late) was presented. During the ~27 min learning experiment, participants were asked to fixate a white cross on grey background and avoid any movements. For the 10 min rs-fMRI, they were asked to close their eyes.\n\nTechnical details\n\nMR data were acquired with a 3 Tesla MRI scanner (Philips Achieva dStream) equipped with a 32-channel head coil. The MR scanner generates a trigger signal used to synchronize the multimodal data acquisition. The timing of stimulus events and the participants' responses were controlled by the software Presentation (Neurobehavioral Systems) running on a Windows stimulation-PC. \nAuditory stimuli were presented via a Mark II+ (MR-Confon, Magdeburg, Germany) audio control unit to MR compatible electrodynamic headphones with integrated ear muffs that provide passive damping of ambient scanner noise by ~24 dB. Earplugs (Bilsom 303) further reduce the noise by ~29 dB (SNR).\nButton presses of the participants were recorded with the ResponseBox 2.0 by Covilex (Magdeburg, Germany) that includes a response pad with two buttons. The device delivers continuous 8-bit data at a sampling rate of 500 Hz. The Teensy converts left and right button presses that exceed a defined threshold into USB keyboard events handled by the stimulation-PC.\nRespiration and heart rate was recorded with Invivo MRI Sensors at a sampling rate of 100 Hz and stored on the MRI acquisition PC at 496 Hz sampling rate.\n64-channel EEG (including ECG) was recorded at 5 kHz using two 32-channel amplifiers BrainAmp MRplus (Brain Products GmbH, Gilching, Germany). The amplifier's discriminative resolution was set to 0.5 µV/bit (range of +/-16.38 mV) and the signals were hardware-filtered in the frequency band between 0.01 Hz and 250 Hz. A bipolar 16-channel amplifier BrainAmp ExG MR was used to record 2 EOG, 4 EMG (Corrugator, Zygomaticus) channels as well as signals from 4 carbon wire loops (CWL) for correcting pulse and motion related artifacts. Another BrainAmp ExG MR amplifier with an ExG AUX box was used to record the skin conductance (GSR) at the index finger of the participant's non-dominant hand. All signals are synchronized with the MR trigger via a Sync box and two USB2 adapter. All data were recorded and stored with the BrainVision Recorder software. Preprocessing (MR-artifact correction, bandpass filtering between 0.3 and 125 Hz, downsampling to 500 Hz with subsequent CWL correction) and export of the EEG-data was performed in BrainVision Analyzer 2.3. Raw data for optimized artifact correction are available upon request. \n","bids_version":"1.10.0","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-08-26 17:46:14","zarr_store_count":34,"zarr_index_etag":"5ab3a1f80208378cab8d6e4bae9d3dc6","zarr_source_commit":"6d92f770048550dfcdc1962658edf12bcba70974","archive_status":"ready","archive_size":6725803749,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":5,"zarr_failure_count":5,"zarr_deterministic":0,"zarr_failed_at":"2026-08-26 17:46:14","num_dataset_citations":9,"num_datapaper_citations":0,"n_channels":72,"electrode_system":"10-10","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":6900455570,"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":20399.943999999996,"recording_duration_min":599.992,"recording_duration_max":600,"recording_count":39,"recordings_unavailable":5,"recordings_measured":34,"channel_count_min":72,"channel_count_max":72,"sampling_frequency":500,"power_line_frequency":50,"eeg_reference":"Unknown","placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-19 01:17:19\",\"metadata_updated_at\":\"2026-08-19 01:17:31\",\"archive_checked_at\":\"2026-06-27 19:14:28\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-27 19:13:09\",\"citations_updated_at\":\"2026-09-08 03:00:50\",\"channel_montage_checked_at\":\"2026-06-28 23:50:36\",\"hed_checked_at\":\"2026-06-30 05:24:18\",\"data_checked_at\":\"2026-08-24 03:00:12\",\"availability_report_at\":\"2026-07-23 01:26:29\",\"recording_stats_at\":\"2026-09-02 11:33:28\",\"signal_defaults_at\":\"2026-09-02 12:40:33\"}","participants":34,"num_citations":9,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"6.43 GB","zarr_data_failures":{"count":5,"detail_ref":"zarr/index.json","compacted_by":"migration_0074"},"zarr_index_url":"https://zarr.nemar.org/on005795/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}}