A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis

Abstract

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/.

PhiPipe: A multi‐modal MRI data processing pipeline with test–retest reliability and predicative validity assessments

“…However, currently there is no consensus on the criterion of optimum. A variety of metrics were used in previous pipeline validations, to just name a few, spatial smoothness (Esteban et al, 2019 ), consistency within and between datasets (Cruces et al, 2022 ), between‐group difference detection (Cui et al, 2013 ; Xu et al, 2018 ), discriminability (Lawrence et al, 2021 ), inter‐pipeline agreement (Li et al, 2021 ), and age predication/correlation (Alfaro‐Almagro et al, 2018 ; Tustison et al, 2014 ; Yan et al, 2016 ). However, these previous validations were usually not systematic by considering only one brain feature in one dataset or the metrics used were not informative for end‐users in experiment design and statistical analysis.…”

PhiPipe: a multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments

“…However, currently there is no consensus on the criterion of optimum. A variety of metrics were used in previous pipeline validations, to just name a few, spatial smoothness (Esteban et al, 2019), consistency within and between datasets (Cruces et al, 2022), between-group difference detection (Cui et al, 2013; Xu et al, 2018), discriminability (Lawrence et al, 2021), inter-pipeline agreement (Li et al, 2021), and age predication/correlation (Tustison et al, 2014; Yan et al, 2016; Alfaro-Almagro et al, 2018). However, these previous validations were usually not systematic by considering only one brain feature in one dataset or the metrics used were not informative for end-users in experiment design and statistical analysis.…”