A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

ABSTRACT:

Introduction

In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction.

Methods

We used a multicohort dataset of cognitively healthy individuals (age range = 32.

SUBMITTER: Dartora C 

PROVIDER: S-EPMC10800627 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature