@article{PB-VRVis-2017-013,
author = {Ganglberger, Florian and Kaczanowska, Joanna and Penninger, Josef M. and Hess, Andreas and B{\"u}hler, Katja and Haubensak, Wulf},
title = {Predicting functional neuroanatomical maps from fusing brain
  networks with genetic information},
year = {2017},
journaltitle = {NeuroImage},
doi = {https://doi.org/10.1016/j.neuroimage.2017.08.070},
url = {https://www.vrvis.at/publications/PB-VRVis-2017-013},
issn = {1053-8119},
abstract = {Abstract Functional neuroanatomical maps provide a mesoscale  reference framework for studies from molecular to systems  neuroscience and psychiatry. The underlying structure-function  relationships are typically derived from functional  manipulations or imaging approaches. Although highly  informative, these are experimentally costly. The increasing  amount of publicly available brain and genetic data offers a  rich source that could be mined to address this problem  computationally. Here, we developed an algorithm that fuses  gene expression and connectivity data with functional genetic  meta data and exploits cumulative effects to derive  neuroanatomical maps related to multi-genic functions. We  validated the approach by using public available mouse and  human data. The generated neuroanatomical maps recapture  known functional anatomical annotations from literature and  functional MRI data. When applied to multi-genic meta data  from mouse quantitative trait loci (QTL) studies and human  neuropsychiatric databases, this method predicted known  functional maps underlying behavioral or psychiatric traits.  Taken together, genetically weighted connectivity analysis  (GWCA) allows for high throughput functional exploration of  brain anatomy in silico. It maps functional genetic  associations onto brain circuitry for refining functional  neuroanatomy, or identifying trait-associated brain  circuitry, from genetic data.},
}