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Imaging methods such as magnetic resonance imaging (MRI), micro-computed tomography (microCT) and light-sheet microscopy (LSM) of cleared tissue samples can generate intact anatomic and molecular whole-brain data. However, each modality produces unique artifacts based on the physical principles of the technique, including intensity inhomogeneity due to magnetic field bias in MRI or microscope optics in LSM and beam hardening in microCT 1, 2, 3 . These artifacts and the size of the datasets generated pose a substantial challenge in data handling, cross-modal image registration, and analysis. Visualization and anatomically relevant analysis of high-resolution, multi-field-of-view (mFOV) datasets require preprocessing to remove artifacts, stitching into a complete volume, and registration to a reference atlas 3,4 .Each step presents specific challenges. First, stitching acquired fields of view (FOVs) into a complete volume is computation and time intensive. Second,

Quantifying terabyte-scale multi-modal human and animal imaging data requires scalable analysis tools. We developed CloudReg, an open-source, automatic, terabyte-scale, cloud-based image analysis pipeline that pre-processes and registers cross-modal volumetric datasets with artifacts via spatially-varying polynomial intensity transform. CloudReg accurately registers the following datasets to their respective atlases: in vivo human and ex vivo macaque brain magnetic resonance imaging, ex vivo mouse brain micro-computed tomography, and cleared murine brain light-sheet microscopy.

Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis routines require impactful decision making and significant computational capabilities. Mitigating these issues requires the development of low-resource, easy to use, and flexible pipelines which can be applied across data with variable collection parameters. In response to these challenges, we have developed the MRI to Graphs (m2g) pipeline. m2g leverages functional and diffusion datasets to estimate connectomes reliably. To illustrate, m2g was used to process MRI data from 35 different studies (≈6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that followed established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes generated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://neurodata.io/mri/.

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