@inproceedings{PB-VRVis-2017-040,
author = {Haubensak, Wulf and Ganglberger, Florian and Kaczanowska, Joanna and Penninger, Josef M. and Hess, Andreas and B{\"u}hler, Katja},
title = {Predicting brain functional maps from genetic information},
year = {2017},
booktitle = {Society for Neuroscience (SFN) 2017 (Poster)},
editor = {Society for Neuroscience},
url = {https://www.vrvis.at/publications/PB-VRVis-2017-040},
publisher = {Society for Neuroscience},
abstract = {Linking genetic information to brain anatomy allows for the  computational exploration of molecular-to-systems level  organization of brain function. Here, we fused publicly  available brain gene expression maps and connectivity data with  functionally related gene sets. We find that the functional  genetic load accumulates in specific nodes in the brain and  associates neuronal networks with specific multigenic functions.  These maps recapture known functional anatomical annotations  from literature and functional MRI data. When applied to meta  data from mouse QTLs and human neuropsychiatric databases, our  method predicts functional maps underlying behavioral or  psychiatric traits. We show that it is possible to predict  functional neuroanatomy from mouse and human genetic meta data  and provide a discovery tool for high throughput functional  exploration of brain anatomy in silico.},
}