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IRUS-ANZ has moved. From January 2022, new monthly statistics will only be added to the IRUS R5 service. This site is archived. All statistics up to December 2021 will remain available on this site.

Item Statistics

7T-fMRI: Faster temporal resolution yields optimal BOLD sensitivity for functional network imaging specifically at high spatial resolution

RepositoryUniversity of Melbourne [Minerva Access]
URLhttp://hdl.handle.net/11343/129287
Author(s)orcid:0000-0003-3780-2210; orcid:0000-0001-8836-9934; orcid:0000-0002-5497-7234; orcid:0000-0002-1005-3654; orcid:0000-0002-0342-9457; orcid:0000-0002-7198-8621; orcid:0000-0002-5569-3612; orcid:0000-0002-0278-4801; Yoo, PE; John, SE; Farquharson, S; Clea
Item typeArticle
JournalNeuroImage, Print_ISSN:1053-8119
DOIhttps://doi.org/10.1016/j.neuroimage.2017.03.002

Usage Date Range

Note: Statistics broken down by country are only available from Apr 2019 onwards.

     

Country Reporting Period Total Apr-2019 May-2019 Jun-2019 Jul-2019 Aug-2019 Sep-2019 Oct-2019 Nov-2019 Dec-2019 Jan-2020 Feb-2020 Mar-2020 Apr-2020 May-2020 Jun-2020 Jul-2020 Aug-2020 Sep-2020 Oct-2020 Nov-2020 Dec-2020 Jan-2021 Feb-2021 Mar-2021 Apr-2021 May-2021 Jun-2021 Jul-2021 Aug-2021 Sep-2021 Oct-2021 Nov-2021 Dec-2021
Overall totals 283 2 13 9 10 6 9 6 1 10 11 9 12 8 16 9 8 5 7 9 1 0 0 7 7 4 14 7 8 11 9 22 17 16
United States 85 1 1 5 4 2 3 0 0 1 3 2 2 1 3 3 1 1 1 5 0 0 0 3 1 1 1 1 1 4 5 16 9 4
Australia 42 0 0 1 2 3 2 2 1 0 0 1 2 1 1 2 2 3 2 1 0 0 0 1 1 1 1 1 3 1 2 0 3 2
United Kingdom 28 0 3 0 0 0 0 1 0 1 3 0 0 0 2 2 0 0 2 0 0 0 0 1 1 0 9 1 0 1 0 0 1 0
China 21 1 2 0 0 0 0 0 0 2 1 1 0 1 3 1 3 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 4
Unknown 20 0 3 3 3 1 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 2 0
Canada 12 0 2 0 0 0 1 0 0 1 1 0 0 0 2 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 2
France 10 0 0 0 0 0 0 1 0 0 2 0 0 0 3 0 0 0 0 0 0 0 0 1 1 2 0 0 0 0 0 0 0 0
Germany 9 0 1 0 0 0 2 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 2 0 0 0 1 0
Italy 6 0 0 0 0 0 0 0 0 1 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0
Belgium 5 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Japan 5 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0
Chile 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
Hong Kong 4 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Israel 4 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0
Iran, Islamic Republic of 3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
Korea, Republic of 3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0
Norway 3 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sweden 3 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Russian Federation 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
Singapore 2 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Spain 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bangladesh 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Finland 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Greece 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
India 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ireland 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Lithuania 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Netherlands 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
New Zealand 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Portugal 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Taiwan 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
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