Controversies and progress on standardization of large-scale brain network nomenclature

工作组 标准化 术语 神经信息学 数据科学 网络科学 分类学(生物学) 命名法 领域(数学) 计算机科学 复杂网络 万维网 生物 计算机网络 哲学 语言学 植物 数学 纯数学 操作系统
作者
Lucina Q. Uddin,Richard F. Betzel,Jessica R. Cohen,Jessica S. Damoiseaux,Felipe De Brigard,Simon B. Eickhoff,Alex Fornito,Caterina Gratton,Evan M. Gordon,Angela R. Laird,Linda Larson‐Prior,Anthony R. McIntosh,Lisa D. Nickerson,Luiz Pessoa,Ana Lúısa Pinho,Russell A. Poldrack,Adeel Razi,Sepideh Sadaghiani,James M. Shine,Anastasia Yendiki
出处
期刊:Network neuroscience [The MIT Press]
卷期号:7 (3): 864-905 被引量:65
标识
DOI:10.1162/netn_a_00323
摘要

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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