A Recipe for Accurate Estimation of Lifespan Brain Trajectories, Distinguishing Longitudinal and Cohort Effects

07/27/2020
by   Øystein Sørensen, et al.
0

We address the problem of estimating how different parts of the brain develop and change throughout the lifespan, and how these trajectories are affected by genetic and environmental factors. Estimation of these lifespan trajectories is statistically challenging, since their shapes are typically highly nonlinear, and although true change can only be quantified by longitudinal examinations, as follow-up intervals in neuroimaging studies typically cover less than 10 of the lifespan, use of cross-sectional information is necessary. Linear mixed models (LMMs) and structural equation models (SEMs) commonly used in longitudinal analysis rely on assumptions which are typically not met with lifespan data, in particular when the data consist of observations combined from multiple studies. Generalized additive mixed models (GAMMs) offer an attractive alternative to LMMs and SEMs. In this paper, we propose various ways of formulating GAMMs for accurate estimation of lifespan trajectories of 12 brain regions, using a large longitudinal dataset and realistic simulation experiments. We show that GAMMs are able to accurately fit lifespan trajectories, distinguish longitudinal and cross-sectional effects, and estimate effects of genetic and environmental exposures. Finally, we discuss and contrast questions related to lifespan research which strictly require longitudinal data and questions which can be answered with purely cross-sectional data, and in the latter case, which simplifying assumptions that need to be made. The examples are accompanied with R code, providing a tutorial for researchers interested in using GAMMs.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/11/2023

Geodesic Mixed Effects Models for Repeatedly Observed/Longitudinal Random Objects

Mixed effect modeling for longitudinal data is challenging when the obse...
research
04/23/2020

Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data

We present a new modelling approach for longitudinal count data that is ...
research
06/28/2021

A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data

Normative aging trends of the brain can serve as an important reference ...
research
11/24/2020

A Bayesian semi-parametric approach for inference on the population partly conditional mean from longitudinal data with dropout

Studies of memory trajectories using longitudinal data often result in h...
research
09/11/2023

Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees

Growth curve models are popular tools for studying the development of a ...
research
10/31/2017

Functional data approaches for mixed longitudinal studies, with applications in midlife women's health

Motivated by applications of mixed longitudinal studies, where a group o...
research
02/10/2014

Genomic Prediction of Quantitative Traits using Sparse and Locally Epistatic Models

In plant and animal breeding studies a distinction is made between the g...

Please sign up or login with your details

Forgot password? Click here to reset