Analysis and visualization of the effect of multiple sclerosis on biological brain age.

ABSTRACT:

Introduction

The rate of neurodegeneration in multiple sclerosis (MS) is an important biomarker for disease progression but can be challenging to quantify. The brain age gap, which quantifies the difference between a patient's chronological and their estimated biological brain age, might be a valuable biomarker of neurodegeneration in patients with MS. Thus, the aim of this study was to investigate the value of an image-based prediction of the brain age gap using a deep learning model and compare brain age gap values between healthy individuals and patients with MS.

Methods

A multi-center dataset consisting of 5,294 T1-weighted magnetic resonance images of the brain from healthy individuals aged between 19 and 89 years was used to train a convolutional neural network (CNN) f

SUBMITTER: Romme CJA 

PROVIDER: S-EPMC11499186 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature