A practical guide to linking brain-wide gene expression and neuroimaging data.
Allen human brain atlas
Connectome
Gene expression
Genetics
Genome
MRI
Transcriptome
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 04 2019
01 04 2019
Historique:
received:
30
07
2018
revised:
03
01
2019
accepted:
05
01
2019
pubmed:
17
1
2019
medline:
24
1
2020
entrez:
17
1
2019
Statut:
ppublish
Résumé
The recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using the AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.
Identifiants
pubmed: 30648605
pii: S1053-8119(19)30011-4
doi: 10.1016/j.neuroimage.2019.01.011
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
353-367Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.