# Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1801.04013 · HTML: https://mlsanity.com/paper/1801.04013/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2018.

## Built on by (most cited first, 7 of 7)

- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)
- [Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss](https://mlsanity.com/paper/2106.03052/) (2021)
- [Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants](https://mlsanity.com/paper/2205.04871/) (2022)
- [An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis](https://mlsanity.com/paper/2404.00144/) (2024)
- [A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates](https://mlsanity.com/paper/2311.08176/) (2023)
- [Individual brain parcellation: Review of methods, validations and applications](https://mlsanity.com/paper/2407.00984/) (2024)
- [Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning](https://mlsanity.com/paper/2605.28990/) (2026)

## Abstract (arXiv)

Brain age prediction based on neuroimaging data could help characterize both the typical brain development and neuropsychiatric disorders. Pattern recognition models built upon functional connectivity (FC) measures derived from resting state fMRI (rsfMRI) data have been successfully used to predict the brain age. However, most existing studies focus on coarse-grained FC measures between brain regions or intrinsic connectivity networks (ICNs), which may sacrifice fine-grained FC information of the rsfMRI data. Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance. In this study, we develop a deep learning method to use convolutional neural networks (CNNs) to learn informative features from the fine-grained whole brain FC measures for the brain age prediction. Experimental results on a large dataset of resting-state fMRI demonstrate that the deep learning model with fine-grained FC measures could better predict the brain age.