A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds
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2024
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| author | Orlichenko, Anton Qu, Gang Zhou, Ziyu Liu, Anqi Deng, Hong-Wen Ding, Zhengming Stephen, Julia M. Wilson, Tony W. Calhoun, Vince D. Wang, Yu-Ping |
| author_facet | Orlichenko, Anton Qu, Gang Zhou, Ziyu Liu, Anqi Deng, Hong-Wen Ding, Zhengming Stephen, Julia M. Wilson, Tony W. Calhoun, Vince D. Wang, Yu-Ping |
| contents | Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_07977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds Orlichenko, Anton Qu, Gang Zhou, Ziyu Liu, Anqi Deng, Hong-Wen Ding, Zhengming Stephen, Julia M. Wilson, Tony W. Calhoun, Vince D. Wang, Yu-Ping Quantitative Methods Machine Learning Neurons and Cognition Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds. |
| title | A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds |
| topic | Quantitative Methods Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2405.07977 |