↓ Skip to main content

Integration of ultra-high field MRI and histology for connectome based research of brain disorders

Overview of attention for article published in Frontiers in Neuroanatomy, January 2013
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
105 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Integration of ultra-high field MRI and histology for connectome based research of brain disorders
Published in
Frontiers in Neuroanatomy, January 2013
DOI 10.3389/fnana.2013.00031
Pubmed ID
Authors

Shan Yang, Zhengyi Yang, Karin Fischer, Kai Zhong, Jörg Stadler, Frank Godenschweger, Johann Steiner, Hans-Jochen Heinze, Hans-Gert Bernstein, Bernhard Bogerts, Christian Mawrin, David C. Reutens, Oliver Speck, Martin Walter

Abstract

Ultra-high field magnetic resonance imaging (MRI) became increasingly relevant for in vivo neuroscientific research because of improved spatial resolutions. However, this is still the unchallenged domain of histological studies, which long played an important role in the investigation of neuropsychiatric disorders. While the field of biological psychiatry strongly advanced on macroscopic levels, current developments are rediscovering the richness of immunohistological information when attempting a multi-level systematic approach to brain function and dysfunction. For most studies, histology sections lost information on three-dimensional reconstructions. Translating histological sections to 3D-volumes would thus not only allow for multi-stain and multi-subject alignment in post mortem data, but also provide a crucial step in big data initiatives involving the network analyses currently performed with in vivo MRI. We therefore investigated potential pitfalls during integration of MR and histological information where no additional blockface information is available. We demonstrated that strengths and requirements from both methods can be effectively combined at a spatial resolution of 200 μm. However, the success of this approach is heavily dependent on choices of hardware, sequence and reconstruction. We provide a fully automated pipeline that optimizes histological 3D reconstructions, providing a potentially powerful solution not only for primary human post mortem research institutions in neuropsychiatric research, but also to help alleviate the massive workloads in neuroanatomical atlas initiatives. We further demonstrate (for the first time) the feasibility and quality of ultra-high spatial resolution (150 μm isotopic) imaging of the entire human brain MRI at 7T, offering new opportunities for analyses on MR-derived information.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
France 2 2%
Netherlands 1 <1%
Switzerland 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
Czechia 1 <1%
Japan 1 <1%
Canada 1 <1%
Other 0 0%
Unknown 93 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 23%
Student > Ph. D. Student 23 22%
Student > Master 13 12%
Professor > Associate Professor 9 9%
Student > Postgraduate 6 6%
Other 19 18%
Unknown 11 10%
Readers by discipline Count As %
Medicine and Dentistry 23 22%
Neuroscience 16 15%
Engineering 10 10%
Psychology 9 9%
Agricultural and Biological Sciences 9 9%
Other 19 18%
Unknown 19 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 October 2013.
All research outputs
#18,349,805
of 22,725,280 outputs
Outputs from Frontiers in Neuroanatomy
#919
of 1,157 outputs
Outputs of similar age
#218,071
of 280,762 outputs
Outputs of similar age from Frontiers in Neuroanatomy
#21
of 31 outputs
Altmetric has tracked 22,725,280 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,157 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,762 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.