English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  DetailsSummary

Released
Journal Article

Multi-modal ultra-high resolution structural 7-Tesla MRI data repository

MPS-Authors
/persons/resource/persons19963

Schäfer,  Andreas
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons23475

Bazin,  Pierre-Louis
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons20055

Turner,  Robert
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Forstmann_Keuken_2014.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Forstmann, B. U., Keuken, M. C., Schäfer, A., Bazin, P.-L., Alkemade, A., & Turner, R. (2014). Multi-modal ultra-high resolution structural 7-Tesla MRI data repository. Scientific Data, 1: 140050. doi:10.1038/sdata.2014.50.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0024-3F6A-F
Abstract
Structural brain data is key for the understanding of brain function and networks, i.e., connectomics. Here we present data sets available from the ‘atlasing of the basal ganglia (ATAG)’ project, which provides ultra-high resolution 7 Tesla (T) magnetic resonance imaging (MRI) scans from young, middle-aged, and elderly participants. The ATAG data set includes whole-brain and reduced field-of-view MP2RAGE and T2*-weighted scans of the subcortex and brainstem with ultra-high resolution at a sub-millimeter scale. The data can be used to develop new algorithms that help building high-resolution atlases both relevant for the basic and clinical neurosciences. Importantly, the present data repository may also be used to inform the exact positioning of electrodes used for deep-brain-stimulation in patients with Parkinson’s disease and neuropsychiatric diseases.