Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
Neuroimage. 2022 Nov;263:119612. doi: 10.1016/j.neuroimage.2022.119612. Epub 2022 Sep 5.
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.
多模态磁共振成像（MRI）通过促进大脑微观结构、几何形状、功能和连接性的多尺度和活体分析，加速了人类神经科学的发展。然而，多模态神经影像学的丰富性和复杂性要求处理方法能够整合跨模态信息，并整合不同空间尺度的发现。在这里，我们提出了 micapipe，这是一个用于多模态 MRI 数据集的开放处理管道。基于符合 BIDS 标准的输入数据，micapipe 可以生成 i）来自扩散轨迹的结构连接组图，ii）来自静息态信号相关性的功能连接组图，iii）用于量化皮质间接近度的测地距离矩阵，以及 iv）用于评估皮质髓鞘替代物的区域间相似性的微结构谱协方差矩阵。上述矩阵可以在已建立的 18 个皮质分割（100-1000 个分割）中自动生成，此外还可以在皮质下和小脑分割中生成，从而允许研究人员轻松地在不同的空间尺度上复制发现。结果在三个不同的表面空间（原生、conte69、fsaverage5）上表示，输出符合 BIDS 标准。处理后的输出可以在个体和组水平上进行质量控制。micapipe 在多个数据集上进行了测试，可在 https://github.com/MICA-MNI/micapipe 上获得，在 https://micapipe.readthedocs.io/ 上有文档记录，并作为 BIDS App http://bids-apps.neuroimaging.io/apps/ 进行了容器化。我们希望 micapipe 将促进对人类大脑微观结构、形态、功能和连接性的稳健和综合研究。
