Association and prediction of phenotypic traits from neuroimaging data using a multi-component mixed model excluding the target vertex

Baptiste Couvy-Duchesne; Futao Zhang; Kathryn E. Kemper; Julia Sidorenko; Naomi R. Wray; Peter M. Visscher; Jian Yang; Olivier Colliot

doi:10.1117/12.2581022

15 February 2021 Association and prediction of phenotypic traits from neuroimaging data using a multi-component mixed model excluding the target vertex

Baptiste Couvy-Duchesne, Futao Zhang, Kathryn E. Kemper, Julia Sidorenko, Naomi R. Wray, Peter M. Visscher, Jian Yang, Olivier Colliot

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115960H (2021) https://doi.org/10.1117/12.2581022
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Mass-univariate association analyses aim at mapping the brain regions associated with a trait/disorder. The correlation between vertices may cause a true association to spread locally (cluster of association) or distally (false positive cluster). We previously showed that controlling for all vertices in the model (using a linear mixed model: LMM), could greatly reduce the probability of false positive and improve mapping precision. Here, we investigated a new LMM called MOMENT which reduces false positive rate in methylome-wide association studies. Compared to LMM, MOMENT had enhanced power and mapping precision but failed at reducing the rates of false positives clusters.The increasing sample sizes from neuroimaging studies should allow detection of image measures are associated with phenotypic traits with smaller effect sizes, which will advance progress in the mapping of the brain regions associated with traits and diseases [1]. The UK Biobank (UKB) is one of the best example of this new generation of samples [2]. Multimodal Brain MRI collection is currently ongoing, with tens of thousands of individuals already imaged out of a target of 100,000 [2]. The large sample size, together with the breadth of phenotyping (incl. self-reports, in lab assessments, prescription and medical history), should allow new insights into the factors contributing to brain differences between older adults.

Conference Presentation

Citation Download Citation

Baptiste Couvy-Duchesne, Futao Zhang, Kathryn E. Kemper, Julia Sidorenko, Naomi R. Wray, Peter M. Visscher, Jian Yang, and Olivier Colliot "Association and prediction of phenotypic traits from neuroimaging data using a multi-component mixed model excluding the target vertex", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115960H (15 February 2021); https://doi.org/10.1117/12.2581022

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available