TY - JOUR
T1 - Integration of human whole-brain transcriptome and neuroimaging data
T2 - Practical considerations of current available methods
AU - Selvaggi, Pierluigi
AU - Rizzo, Gaia
AU - Mehta, Mitul A
AU - Turkheimer, Federico E
AU - Veronese, Mattia
N1 - Funding Information:
GR is a current employee of Invicro. In the past 3 years MAM has acted as an advisory board member for Lundbeck and Forum Pharmaceuticals. He also holds research funding from Lundbeck, Takeda and Johnson & Johnson. MV has received consulting honoraria from GSK. No other conflict of interested are disclosed.
Funding Information:
This paper represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London , UK that support PS, MV and MAM. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. PS is supported by a PhD studentship jointly funded by the NIHR-BRC at SLaM and the Department of Neuroimaging, King’s College London .
Publisher Copyright:
© 2021
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - The Allen Human Brain Atlas (AHBA) is the first example of human brain transcriptomic mappings and detailed anatomical annotations which, for the first time, has allowed the integration of human brain transcriptomics with neuroimaging. This has been made possible because the AHBA offered an original dataset that contains mRNA expression measures for >20,000 genes covering the whole brain and, critically, in a standard stereotaxic space. In recent years many different methods have been used to integrate this data set with brain imaging data, although this endeavour has lacked harmony in terms of the workflow of data processing and subsequent analyses. In this work we discuss five main issues that experience has highlighted as in need of thorough consideration when integrating the AHBA with neuroimaging. These concerns are corroborated by comparing the performance of three different publicly available methods in correlating the same measures of serotonin receptors density with the correspondent AHBA mRNA maps. In this representative case, we were able to show how these methods can lead to discrepant results, suggesting that processing options are not neutral. We believe that the field should take into serious consideration these issues as they could undermine reproducibility and, in the end, the intrinsic value of the AHBA. We also advise on possible strategies to overcome these discrepancies. Finally, we encourage authors towards practices that will improve reproducibility such as transparency in reporting, algorithm and data sharing, collaboration.
AB - The Allen Human Brain Atlas (AHBA) is the first example of human brain transcriptomic mappings and detailed anatomical annotations which, for the first time, has allowed the integration of human brain transcriptomics with neuroimaging. This has been made possible because the AHBA offered an original dataset that contains mRNA expression measures for >20,000 genes covering the whole brain and, critically, in a standard stereotaxic space. In recent years many different methods have been used to integrate this data set with brain imaging data, although this endeavour has lacked harmony in terms of the workflow of data processing and subsequent analyses. In this work we discuss five main issues that experience has highlighted as in need of thorough consideration when integrating the AHBA with neuroimaging. These concerns are corroborated by comparing the performance of three different publicly available methods in correlating the same measures of serotonin receptors density with the correspondent AHBA mRNA maps. In this representative case, we were able to show how these methods can lead to discrepant results, suggesting that processing options are not neutral. We believe that the field should take into serious consideration these issues as they could undermine reproducibility and, in the end, the intrinsic value of the AHBA. We also advise on possible strategies to overcome these discrepancies. Finally, we encourage authors towards practices that will improve reproducibility such as transparency in reporting, algorithm and data sharing, collaboration.
UR - http://www.scopus.com/inward/record.url?scp=85102612093&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2021.109128
DO - 10.1016/j.jneumeth.2021.109128
M3 - Article
C2 - 33722642
SN - 0165-0270
VL - 355
SP - 109128
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
M1 - 109128
ER -