Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

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Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Adams, HHH; Roshchupkin, GV; DeCarli, C; Franke, B; Grabe, HJ; Habes, M; Jahanshad, N; Medland, SE; Niessen, W; Satizabal, CL; Schmidt, R; Seshadri, S; Teumer, A; Thompson, PM; Vernooij, MW; Wittfeld, K; Ikram, MA.
Full exploitation of high dimensionality in brain imaging: The JPND working group statement and findings.
Alzheimers Dement (Amst). 2019; 11: 286-290. [OPEN ACCESS]
PubMed PUBMED Central FullText FullText_MUG


Autor/innen der Med Uni Graz:
Schmidt Reinhold

Dimensions Citations:

Plum Analytics:
Number of Figures: 2
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Advances in technology enable increasing amounts of data collection from individuals for biomedical research. Such technologies, for example, in genetics and medical imaging, have also led to important scientific discoveries about health and disease. The combination of multiple types of high-throughput data for complex analyses, however, has been limited by analytical and logistic resources to handle high-dimensional data sets. In our previous EU Joint Programme-Neurodegenerative Disease Research (JPND) Working Group, called HD-READY, we developed methods that allowed successful combination of omics data with neuroimaging. Still, several issues remained to fully leverage high-dimensional multimodality data. For instance, high-dimensional features, such as voxels and vertices, which are common in neuroimaging, remain difficult to harmonize. In this Full-HD Working Group, we focused on such harmonization of high-dimensional neuroimaging phenotypes in combination with other omics data and how to make the resulting ultra-high-dimensional data easily accessible in neurodegeneration research.

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