{"doi":"10.1101/2022.01.31.478189","title":"Micapipe: A Pipeline for Multimodal Neuroimaging and Connectome Analysis","abstract":"<jats:title>\n                  A\n                  <jats:sc>bstract</jats:sc>\n                </jats:title>\n                <jats:p>\n                  Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain structure, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and different spatial scales. Here, we present\n                  <jats:italic>micapipe</jats:italic>\n                  , an open processing pipeline for BIDS-conform multimodal MRI datasets.\n                  <jats:italic>micapipe</jats:italic>\n                  can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. These matrices are routinely generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level.\n                  <jats:italic>micapipe</jats:italic>\n                  was tested on several datasets and is available at\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/MICA-MNI/micapipe\">https://github.com/MICA-MNI/micapipe</jats:ext-link>\n                  , documented at\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://micapipe.readthedocs.io/\">https://micapipe.readthedocs.io/</jats:ext-link>\n                  , and containerized as a BIDS App\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"http://bids-apps.neuroimaging.io/apps/\">http://bids-apps.neuroimaging.io/apps/</jats:ext-link>\n                  . We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, and connectivity.\n                </jats:p>","journal":null,"year":null,"id":639104,"datarank":0.4636563680037475,"base_score":3.091042453358316,"endowment":3.091042453358316,"self_citation_contribution":0.4636563680037475,"citation_network_contribution":0.0,"self_endowment_contribution":0.4636563680037475,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":21,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":109386,"name":"Jessica Royer","orcid":"0000-0002-4448-8998","position":1,"is_corresponding":false},{"id":106638,"name":"Peer Herholz","orcid":"0000-0002-9840-6257","position":2,"is_corresponding":false},{"id":109385,"name":"Sara Larivière","orcid":"0000-0001-5701-1307","position":3,"is_corresponding":false},{"id":109382,"name":"Reinder Vos de Wael","orcid":"0000-0003-0574-6576","position":4,"is_corresponding":false},{"id":109384,"name":"Casey Paquola","orcid":"0000-0002-0190-4103","position":5,"is_corresponding":false},{"id":109383,"name":"Oualid Benkarim","orcid":"0000-0003-3922-7643","position":6,"is_corresponding":false},{"id":554369,"name":"Bo‐yong Park","orcid":"0000-0001-7096-337X","position":7,"is_corresponding":false},{"id":1660309,"name":"Janie Degré-Pelletier","orcid":null,"position":8,"is_corresponding":false},{"id":664989,"name":"Mark Nelson","orcid":"0000-0002-5944-6096","position":9,"is_corresponding":false},{"id":847580,"name":"Jordan DeKraker","orcid":"0000-0002-4093-0582","position":10,"is_corresponding":false},{"id":683918,"name":"Christine Tardif","orcid":"0000-0001-8356-6808","position":11,"is_corresponding":false},{"id":60906,"name":"Jean-Baptiste Poline","orcid":null,"position":12,"is_corresponding":false},{"id":241349,"name":"Luis Concha","orcid":"0000-0002-7842-3869","position":13,"is_corresponding":false},{"id":109394,"name":"Boris C. Bernhardt","orcid":"0000-0001-9256-6041","position":14,"is_corresponding":false},{"id":241369,"name":"Raúl Rodríguez‐Cruces","orcid":"0000-0002-2917-1212","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Micapipe: A Pipeline for Multimodal Neuroimaging and Connectome Analysis","abstract":"<jats:title>\n                  A\n                  <jats:sc>bstract</jats:sc>\n                </jats:title>\n                <jats:p>\n                  Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain structure, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and different spatial scales. Here, we present\n                  <jats:italic>micapipe</jats:italic>\n                  , an open processing pipeline for BIDS-conform multimodal MRI datasets.\n                  <jats:italic>micapipe</jats:italic>\n                  can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. These matrices are routinely generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level.\n                  <jats:italic>micapipe</jats:italic>\n                  was tested on several datasets and is available at\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/MICA-MNI/micapipe\">https://github.com/MICA-MNI/micapipe</jats:ext-link>\n                  , documented at\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://micapipe.readthedocs.io/\">https://micapipe.readthedocs.io/</jats:ext-link>\n                  , and containerized as a BIDS App\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"http://bids-apps.neuroimaging.io/apps/\">http://bids-apps.neuroimaging.io/apps/</jats:ext-link>\n                  . We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, and connectivity.\n                </jats:p>","is_dataset_classified":null,"base_score":3.091042453358316,"endowment":3.091042453358316,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4212986928","authors":[],"funders":[{"funder_name":"Canadian Institutes of Health Research","grant_id":"unidentified","title":"unidentified"},{"funder_name":"National Institutes of Health","grant_id":"3P41EB019936-07S1","title":"Enhancing neuroimaging reusability through semantic enrichment"},{"funder_name":"National Institutes of Health","grant_id":"1R01MH096906-01A1","title":"Large-scale Automated Synthesis of Functional Neuroimaging Data"}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2022,"count":9},{"year":2023,"count":3},{"year":2024,"count":6},{"year":2025,"count":3}],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/02/02/2022.01.31.478189.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/02/02/2022.01.31.478189.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2022.01.31.478189","host_type":"publisher"},{"url":"https://doi.org/10.1101/2022.01.31.478189","host_type":"repository"},{"url":"https://dx.doi.org/10.60692/hy1a8-4jv49","host_type":""},{"url":"https://dx.doi.org/10.60692/gcqt3-aay92","host_type":""}],"fields_of_study":["Advanced Neuroimaging Techniques and Applications","Functional Brain Connectivity Studies","Advanced MRI Techniques and Applications"],"mesh_terms":[],"keywords":["Human Connectome Project","Connectome","Neuroimaging","Tractography","Connectomics","Computer science","Diffusion MRI","Artificial intelligence","Neuroscience","Pipeline (software)","Functional connectivity","Pattern recognition (psychology)","Psychology","Magnetic resonance imaging","Medicine","Radiology, Nuclear Medicine and Imaging","Neuroimaging Data Analysis","Cognitive Neuroscience","Analysis of Brain Functional Connectivity Networks","Health Sciences","Functional MRI","Life Sciences","Programming language","FOS: Psychology","Brain Connectivity","Diffusion Magnetic Resonance Imaging","Radiology","Magnetic Resonance Imaging Applications in Medicine"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T22:29:26.205189Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}