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

06/28/2021
by   Alphin J. Thottupattu, et al.
0

Normative aging trends of the brain can serve as an important reference in the assessment of neurological structural disorders. Such models are typically developed from longitudinal brain image data – follow-up data of the same subject over different time points. In practice, obtaining such longitudinal data is difficult. We propose a method to develop an aging model for a given population, in the absence of longitudinal data, by using images from different subjects at different time points, the so-called cross-sectional data. We define an aging model as a diffeomorphic deformation on a structural template derived from the data and propose a method that develops topology preserving aging model close to natural aging. The proposed model is successfully validated on two public cross-sectional datasets which provide templates constructed from different sets of subjects at different age points.

READ FULL TEXT

page 7

page 10

page 13

page 14

page 15

page 16

page 17

research
09/14/2022

Continuous longitudinal fetus brain atlas construction via implicit neural representation

Longitudinal fetal brain atlas is a powerful tool for understanding and ...
research
02/23/2021

Inferring temporal dynamics from cross-sectional data using Langevin dynamics

Cross-sectional studies are widely prevalent since they are more feasibl...
research
12/03/2020

brolgar: An R package to BRowse Over Longitudinal Data Graphically and Analytically in R

Longitudinal (panel) data provide the opportunity to examine temporal pa...
research
07/27/2020

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

We address the problem of estimating how different parts of the brain de...
research
12/04/2019

Learning to synthesise the ageing brain without longitudinal data

Brain ageing is a continuous process that is affected by many factors in...
research
03/27/2018

Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms

We propose a method to learn a distribution of shape trajectories from l...
research
08/15/2019

Learning Signal Subgraphs from Longitudinal Brain Networks with Symmetric Bilinear Logistic Regression

Modern neuroimaging technologies, combined with state-of-the-art data pr...

Please sign up or login with your details

Forgot password? Click here to reset