Prediction of Adolescents’ Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization

TL;DR

This study uses brain scans and deep learning to predict fluid intelligence scores in children aged 9–10, finding that autoencoders with regularization work best.

Contribution

The study introduces an autoencoder model with reconstruction regularization for predicting fluid intelligence in adolescents using brain imaging data.

Findings

Autoencoders with reconstruction regularization outperformed MLPs and classical models in predicting fluid intelligence scores.

The model achieved a PCC of 0.209 ± 0.02 and MSE of 105.212 ± 2.53 on the test set.

All models predicted fluid intelligence more accurately in female adolescents than in male adolescents.

Abstract

The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9–10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents.

We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms.

We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.

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