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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: https://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.