Micapipe: A pipeline for multimodal neuroimaging and connectome analysis

• DOI: 10.1016/j.neuroimage.2022.119612
• Publication Date: 2022-09-06T00:44:12Z
• Authors: Raúl R. Cruces; Jessica Royer; Peer Herholz; Sara Larivière; Reinder Vos de Wael; Casey Paquola; Oualid Benkarim; Bo-yong Park; Janie Degré-Pelle...; Mark C. Nelson; Jordan DeKraker; Ilana R. Leppert; Christine Tardif; Jean-Baptiste Poline; Luis Concha; Boris C. Bernhardt
• Keywords: Multiscale; 0301 basic medicine; Electronic Data Processing; Brain; Neurosciences. Biological psychiatry. Neuropsychiatry; Neuroimaging; Magnetic Resonance Imaging; BIDS; Article; Multimoda; 03 medical and health sciences; Diffusion Tensor Imaging; Connectome; Humans; Software; info:eu-repo/classification/ddc/610; MRI; RC321-571

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, 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 to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe 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. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. 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. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.