Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for any atlas when raw data is not available

Author:

Dadashkarimi JavidORCID,Karbasi Amin,Liang Qinghao,Rosenblatt Matthew,Noble Stephanie,Foster Maya,Rodriguez Raimundo,Adkinson Brendan,Ye Jean,Sun Huili,Camp Chris,Farruggia Michael,Tejavibulya Link,Dai Wei,Jiang Rongtao,Pollatou Angeliki,Scheinost DustinORCID

Abstract

AbstractOpen-source, publicly available neuroimaging datasets—whether from large-scale data collection efforts or pooled from multiple smaller studies—offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Due to patient privacy and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases—without ever needing the raw data—allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).

Publisher

Cold Spring Harbor Laboratory

Reference58 articles.

1. Near-linear time approximation algorithms for optimal transport via sinkhorn iteration;arXiv preprint,2017

2. Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex

3. Small-world brain networks;The Neuroscientist,2006

4. Robust prediction of individual creative ability from brain functional connectivity

5. Bertsimas, D. , Tsitsiklis, J. ,. Introduction to linear optimization, athena scientific, 1997.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3