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

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Huntenburg,  Julia M.
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Babayan,  Anahit
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Erbey,  Miray
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Kumral,  Deniz
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Reinelt,  Janis
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Reiter,  Andrea
Max Planck Fellow Group Cognitive and Affective Control of Behavioural Adaptation, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Röbbig,  Josefin
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schaare,  Herma Lina
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Margulies,  Daniel S.
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Villringer,  Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Gaebler,  Michael
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002B-5003-E
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