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Spatial normalization of ultrahigh resolution 7 T magnetic resonance imaging data of the postmortem human subthalamic nucleus: A multistage approach

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Weiss,  Marcel
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Cognitive Science Center Amsterdam, University of Amsterdam, the Netherlands;

Keuken,  Max C.
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Cognitive Science Center Amsterdam, University of Amsterdam, the Netherlands;

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Geyer,  Stefan
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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

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Forstmann,  Birte U.
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Cognitive Science Center Amsterdam, University of Amsterdam, the Netherlands;

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Citation

Weiss, M., Alkemade, A., Keuken, M. C., Müller-Axt, C., Geyer, S., Turner, R., et al. (2015). Spatial normalization of ultrahigh resolution 7 T magnetic resonance imaging data of the postmortem human subthalamic nucleus: A multistage approach. Brain Structure & Function, 220(3), 1695-1703. doi:10.1007/s00429-014-0754-4.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0018-6CD4-3
Abstract
In this paper, we describe a novel processing strategy for the spatial normalization of ultrahigh resolution magnetic resonance imaging (MRI) data of small ex vivo samples into MNI standard space. We present a multistage scanning and registration method for data of the subthalamic nucleus (STN) obtained using ultrahigh 7 T MRI on four human postmortem brain samples. Four whole brains were obtained and subjected to multistage MRI scanning, corresponding to four different brain dissection stages. Data sets were acquired with an isotropic resolution of 100 lm enabling accurate manual segmentation of the STN. Spatial normalization to MNI reference space was performed, probability maps were calculated, and results were cross-checked with an independent in vivo dataset showing significant overlay. Normalization of results obtained from small tissue samples into MNI standard space will facilitate comparison between individual subjects, as well as between studies. When combining ultrahigh resolution MRI of ex vivo samples with histological studies via blockface imaging, our method enables further insight and inference as multimodal data can be compared within the same reference space. This novel technique may be of value for research purposes using functional MRI techniques, and in the future may be of assistance for anatomical orientation in clinical practice.