Vogelstein, J. | Search bioRxiv

bioRxiv2018

Improving Corticostriatal Parcellation Through Multilevel Bagging with PyBASC

Increasing the reproducibility of neuroimaging measurement addresses a central impediment to the advancement of human neuroscience and its clinical applications. Recent efforts demonstrating variance in functional brain organization within and between individuals shows a need for improving reproducibility of functional parcellations without long scan times. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two large datasets to demonstrate that compared to a standard clustering framework, bagging improves the reproducibility and test-retest reliability of both cortical and subcortical functional parcellations across a range of sites, scanners, samples, scan lengths, clustering algorithms, and clustering parameters (e.g., number of clusters, spatial constraints). With as little as six minutes of scan time, bagging creates more reproducible group and individual level parcellations than standard approaches with twice as much data. This suggests that regardless of the specific parcellation strategy employed, bagging may be a key method for improving functional parcellation and bringing functional neuroimaging-based measurement closer to clinical impact.

neuroscience↗

bioRxiv2017

A Comprehensive Cloud Framework for Accurate and Reliable Human Connectome Estimation and Meganalysis

Modern scientific discovery depends on collecting large heterogeneous datasets with many sources of variability, and applying domain-specific pipelines from which one can draw insight or clinical utility. For example, macroscale connectomics studies require complex pipelines to process raw functional or diffusion data and estimate connectomes. Individual studies tend to customize pipelines to their needs, raising concerns about their reproducibility, which add to a longer list of factors that may differ across studies and result in failures to replicate (including sampling, experimental design, and data acquisition protocols). Mitigating these issues requires multi-study datasets and the development of pipelines that can be applied across them. We developed NeuroDatas MRI to Graphs (NDMG) pipeline using several functional and diffusion studies, including the Consortium for Reliability and Reproducability, to estimate connectomes. Without any manual intervention or parameter tuning, NDMG ran on 25 different studies ({approx}6,000 scans) from 19 sites, with each scan resulting in a biologically plausible connectome (as assessed by multiple quality assurance metrics at each processing stage). For each study, the connectomes from NDMG are more similar within than across individuals, indicating that NDMG is preserving biological variability. Moreover, the connectomes exhibit near perfect consistency for certain connectional properties across every scan, individual, study, site and modality; these include stronger ipsilateral than contralateral connections and stronger homotopic than heterotopic connections. Yet, the magnitude of the differences varied across individuals and studies--much more so when pooling data across sites, even after controlling for study, site, and basic demographic variables (i.e., age, sex, and ethnicity). This indicates that other experimental variables (possibly those not measured or reported) are contributing to this variability, which if not accounted for can limit the value of aggregate datasets, as well as expectations regarding the accuracy of findings and likelihood of replication. We therefore provide a set of principles to guide the development of pipelines capable of pooling data across studies while maintaining biological variability and minimizing measurement error. This open science approach provides us with an opportunity to understand and eventually mitigate spurious results for both past and future studies.

neuroscience↗