Detecting resting-state networks using scalable multi-subject spatial canonical 
correlation analysis

Dähne, Sven; Huntenburg, Julia M.; Babayan, Anahit; Erbey, Miray; Kumral, Deniz; Reinelt, Janis; Reiter, Andrea; Röbbig, Josefin; Schaare, Herma Lina; Margulies, Daniel S.; Müller, Klaus-Robert; Villringer, Arno; Gaebler, Michael

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Detecting resting-state networks using scalable multi-subject spatial canonical correlation analysis

Dähne, S., Huntenburg, J. M., Babayan, A., Erbey, M., Kumral, D., Reinelt, J., et al. (2016). Detecting resting-state networks using scalable multi-subject spatial canonical correlation analysis. Poster presented at 22nd Annual Meeting of the Organization for Human Brain Mapping (OHBM), Geneva, Switzerland.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-002B-5003-E Versions-Permalink: https://hdl.handle.net/21.11116/0000-0003-5050-8

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Urheber:
Dähne, Sven¹, Autor
Huntenburg, Julia M.², Autor
Babayan, Anahit³, Autor
Erbey, Miray³, Autor
Kumral, Deniz³, Autor
Reinelt, Janis³, Autor
Reiter, Andrea⁴, Autor
Röbbig, Josefin³, Autor
Schaare, Herma Lina³, Autor
Margulies, Daniel S.², Autor
Müller, Klaus-Robert¹, Autor
Villringer, Arno³, Autor
Gaebler, Michael³, Autor

Affiliations:
1External Organizations, ou_persistent22
2Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1356546
3Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549
4Max Planck Fellow Group Cognitive and Affective Control of Behavioural Adaptation, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1753350