Abstract
Motivation: Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. Results: We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results. Availability and implementation: BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat. Supplementary information: Supplementary data are available at Bioinformatics online.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3771-3778 |
| Number of pages | 8 |
| Journal | Bioinformatics |
| Volume | 35 |
| Issue number | 19 |
| DOIs | |
| State | Published - Oct 1 2019 |
Bibliographical note
Funding Information:J.F. is partially supported by the key project of Shanghai Science and Technology Innovation Plan [number 15JC1400101 and 16JC1420402], Shanghai Municipal Science and Technology Major Project [number 2018SHZDZX01] and the National Natural Science Foundation of China [number 71661167002 and 91630314]. J.Z. is supported by the National Science Foundation of China [number 61573107], the Special Funds for Major State Basic Research Projects of China [number 2015CB856003], the Shanghai Natural Science Foundation [number 17ZR1444200] and the National Basic Research Program of China (Precision Psychiatry Program) [number 2016YFC0906402]. W.C. is supported by grants from the National Natural Sciences Foundation of China [number 81701773 and 11771010], the Shanghai Sailing Program [number 17YF1426200] and the Natural Science Foundation of Shanghai [number 18ZR1404400]. Z.L. is supported in part by the Key Research and Development Plan of Shandong Province [number 2017CXGC1503 and 2018GSF118228]. H.W. is supposed by the Shanghai Natural Science Foundation [number 17ZR1401600] The research was also partially supported by the Shanghai AI Platform for Diagnosis and Treatment of Brain Diseases, the Projects of Zhangjiang Hi-Tech District Management Committee, Shanghai [number 2016–17], the Base for Introducing Talents of Discipline to Universities [number B18015] and the Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, PR China.
Publisher Copyright:
© 2019 The Author(s). Published by Oxford University Press. All rights reserved.