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001016530 0247_ $$2doi$$a10.1162/netn_a_00323
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001016530 1001_ $$0P:(DE-HGF)0$$aUddin, Lucina Q.$$b0$$eCorresponding author
001016530 245__ $$aControversies and progress on standardization of large-scale brain network nomenclature
001016530 260__ $$aCambridge, MA$$bThe MIT Press$$c2023
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001016530 520__ $$aProgress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)–endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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001016530 7001_ $$0P:(DE-HGF)0$$aBetzel, Richard F.$$b1
001016530 7001_ $$0P:(DE-HGF)0$$aCohen, Jessica R.$$b2
001016530 7001_ $$0P:(DE-HGF)0$$aDamoiseaux, Jessica S.$$b3
001016530 7001_ $$0P:(DE-HGF)0$$aDe Brigard, Felipe$$b4
001016530 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b5$$ufzj
001016530 7001_ $$0P:(DE-HGF)0$$aFornito, Alex$$b6
001016530 7001_ $$0P:(DE-HGF)0$$aGratton, Caterina$$b7
001016530 7001_ $$0P:(DE-HGF)0$$aGordon, Evan M.$$b8
001016530 7001_ $$0P:(DE-HGF)0$$aLaird, Angela R.$$b9
001016530 7001_ $$0P:(DE-HGF)0$$aLarson-Prior, Linda$$b10
001016530 7001_ $$0P:(DE-HGF)0$$aMcIntosh, A. Randal$$b11
001016530 7001_ $$0P:(DE-HGF)0$$aNickerson, Lisa D.$$b12
001016530 7001_ $$0P:(DE-HGF)0$$aPessoa, Luiz$$b13
001016530 7001_ $$0P:(DE-HGF)0$$aPinho, Ana Luísa$$b14
001016530 7001_ $$0P:(DE-HGF)0$$aPoldrack, Russell A.$$b15
001016530 7001_ $$0P:(DE-HGF)0$$aRazi, Adeel$$b16
001016530 7001_ $$0P:(DE-HGF)0$$aSadaghiani, Sepideh$$b17
001016530 7001_ $$0P:(DE-HGF)0$$aShine, James M.$$b18
001016530 7001_ $$0P:(DE-HGF)0$$aYendiki, Anastasia$$b19
001016530 7001_ $$0P:(DE-HGF)0$$aYeo, B. T. Thomas$$b20
001016530 7001_ $$0P:(DE-HGF)0$$aSpreng, R. Nathan$$b21
001016530 773__ $$0PERI:(DE-600)2900481-0$$a10.1162/netn_a_00323$$gVol. 7, no. 3, p. 864 - 905$$n3$$p864 - 905$$tNetwork neuroscience$$v7$$x2472-1751$$y2023
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