Yixue Feng | Notion

I'm currently a Neuroscience PhD candidate at the University of Southern California advised by Dr. Paul Thompson at the Imaging Genetics Center. My research interests are developing deep generative models for tract-based diffusion MRI analysis, to help understand macro- and micro-structural changes in white matter tracts in neurodegenerative diseases such as Alzheimer’s and Parkinson’s Disease. I have published significant first-author work on generative models for automated analysis of tractography data. I have extensive experience in applying machine learning and deep learning methods to large multi-site and multi-modal neuroimaging data.

Education

2021-Expected 2026

Ph.D., Neuroscience, GPA: 3.87/4 University of Southern California.📍Los Angeles, CA

2019-2021

MS.E., Computer and Information Science (CIS), GPA: 3.94/4 University of Pennsylvania.📍Philadelphia, PA

2015-2019

B.A., Computer Science and Cognitive Science, GPA: 3.77/4 University of Virginia.📍Charlottesville, VA

Publications

Journal

Feng, Y., Chandio, B. Q., Villalón-Reina, J. E., Thomopoulos, S. I., Nir, T. M., Benavidez, S., Laltoo, E., Chattopadhyay, T., Joshi, H., Venkatasubramanian. G., John, J. P., Jahanshad, N., Jack, C. R., Weiner M. W., Thompson, P. M., for the Alzheimer’s Disease Neuroimaging Initiative (2024). Microstructural Mapping of Neural Pathways in Alzheimer’s Disease using Macrostructure-Informed Normative Tractometry, submitted to Alzheimer’s & Dementia, Special Issue on the 20th Anniversary of ADNI, May 1, 2024.
Feng, Y., Kim, M., Yao, X., Liu, K., Long, Q., Shen, L., & for the Alzheimer’s Disease Neuroimaging Initiative. (2022). Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment. BMC Bioinformatics, 23(S3), 402. https://doi.org/10.1186/s12859-022-04946-x
Zhang, X., Feng, Y., Li, F., Ding, J., Tahseen, D., Hinojosa, E., Chen, Y., & Tao, C. (2024). Evaluating MedDRA-to-ICD terminology mappings. BMC Medical Informatics and Decision Making, 23(S4), 299. https://doi.org/10.1186/s12911-023-02375-1

Conference Proceedings

Feng, Y., Chandio, B. Q., Villalón-Reina, J. E., Thomopoulos, S. I., Joshi, H., Nair, G., Joshi, A. A., Venkatasubramanian, G., John, J. P., & Thompson, P. M. (2023). BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data. In M. Karaman, R. Mito, E. Powell, F. Rheault, & S. Winzeck (Eds.), Computational Diffusion MRI (Vol. 14328, pp. 152–164). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47292-3_14
Feng, Y., Chandio, B. Q., Thomopoulos, S. I., Chattopadhyay, T., & Thompson, P. M. (2023). Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 1–6. https://doi.org/10.1109/EMBC40787.2023.10340009
Feng, Y., Chandio, B. Q., Chattopadhyay, T., Thomopoulos, S. I., Owens-Walton, C., Jahanshad, N., Garyfallidis, E., & Thompson, P. M. (2023). Learning optimal white matter tract representations from tractography using a deep generative model for population analyses. In M. G. Linguraru, L. Rittner, N. Lepore, E. Romero Castro, J. Brieva, & P. Guevara (Eds.), 18th International Symposium on Medical Information Processing and Analysis (p. 48). SPIE. https://doi.org/10.1117/12.2670244
Feng, Y., Kim, M., Yao, X., Liu, K., Long, Q., & Shen, L. (2020). Deep Multiview Learning to Identify Population Structure with Multimodal Imaging. 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), 308–314. https://doi.org/10.1109/BIBE50027.2020.00057

Pre-print

Feng, Y., Chandio, B. Q., Villalon-Reina, J. E., Benavidez, S., Chattopadhyay, T., Chehrzadeh, S., Laltoo, E., Thomopoulos, S. I., Joshi, H., Venkatasubramanian, G., John, J. P., Jahanshad, N., & Thompson, P. M. (2024). Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer’s Disease [Preprint]. Neuroscience. https://doi.org/10.1101/2024.02.05.578943
Chandio, B. Q., Villalon-Reina, J. E., Nir, T. M., Thomopoulos, S. I., Feng, Y., Benavidez, S., Jahanshad, N., Harezlak, J., Garyfallidis, E., & Thompson, P. M. (2024). Bundle Analytics based Data Harmonization for Multi-Site Diffusion MRI Tractometry [Preprint]. Neuroscience. https://doi.org/10.1101/2024.02.03.578764
Chattopadhyay, T., Joshy, N. A., Ozarkar, S. S., Buwa, K., Feng, Y., Laltoo, E., Thomopoulos, S. I., Villalon, J. E., Joshi, H., Venkatasubramanian, G., John, J. P., & Thompson, P. M. (2024). Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts [Preprint]. Neuroscience. https://doi.org/10.1101/2024.02.04.578829
Homdee, N., Boukhechba, M., Feng, Y. W., Kramer, N., Lach, J., & Barnes, L. E. (2019). Enabling Smartphone-based Estimation of Heart Rate. ArXiv:1912.08910 [Cs, Eess, Stat]. http://arxiv.org/abs/1912.08910

Poster/Abstract

Feng Y, Chandio BQ, Villalón-Reina JE, Thomopolous SI, Nir TM, Benavidez S, Laltoo E, Chattopadhyay, Joshi H, Venkatasubramanian G, John JP, Jahanshad N, Reid RI, Jack CR, Weiner MW, Thompson PM, or the Alzheimer’s Disease Neuroimaging Initiative. Generative AI for Normative Tractometry: Mapping Microstructural Abnormalities in Dementia [abstract]. ISMRM Workshop on 40 Years of Diffusion: Past, Present & Future Perspectives. 2025 Feb 16-20; Kyoto, Japan.
Feng, Y., Chandio, B. Q., Villalon-Reina, J. E., Thomopoulos, S. I., Joshi, H., Venkatasubramanian, G., John, J. P., & Thompson, P. M. (2023). Alzheimer’s Disease Effects on White Matter Tracts Mapped using 3D Tractometry in an Indian Cohort. Presented at SfN 2023, Nov. 2023.
Feng ,Y., Chandio ,B. Q., Garyfallidis, E., Thompson, P.M. (2023) Detecting Structural Anomalies in Tractography using Deep Variational Autoencoders. Presented at OHBM 2023, Jul. 2023.
Feng, Y., Chandio, B. Q., Chattopadhyay, T., Thomopoulos, S. I., Owens‐Walton, C., Garyfallidis, E., Jahanshad, N., & Thompson, P. M. (2022). Learning Streamline Embeddings with Variational Autoencoder for Intersubject Bundle Comparison in Alzheimer’s Disease. Alzheimer’s & Dementia, 18(S5). https://doi.org/10.1002/alz.066204
Feng, Y., Chandio, B. Q., Chattopadhyay, T., Thomopoulos, S. I. , Owens-Walton, C., Jahanshad, N., Garyfallidis, E., Thompson, P.M. (2022). Deep generative model for learning tractography streamline embeddings based on Convolutional Variational Autoencoder. ISMRM 2022, London, England, UK, May 07-12, 2022.
Feng, Y., Kim, M., Liu, K., Saykin, A. J., Moore, J. H., Long, Q., & Shen, L. (2021). Identifying multimodal imaging‐driven subtypes in mild cognitive impairment using deep multiview learning. Alzheimer’s & Dementia, 17(S4). https://doi.org/10.1002/alz.052718

Presentations

Jun 2022

Learning meaningful white matter tract representations from tractography using deep generative model. Neuroscience Graduate Forum, University of Southern California. 📍Los Angeles, CA.

Aug 2021

Deep Multiview Learning to Identify Imaging-driven Subtypes in Mild Cognitive Impairment. International Conference on Intelligent Biology and Medicine (ICIBM 2021).📍 Virtual.

Mar 2021

Identifying Alzheimer's Disease Population Structure From Multimodal Imaging Using Deep Multiview Learning Framework. Third Annual DBEI & CCEB Research Day at University of Pennsylvania.📍 Virtual.

Aug 2018

Improving MedDRA to ICD Mapping Coverage: Current Gaps and Future Mapping Strategies. Cancer Prevention Research Institute of Texas (CPRIT) Undergraduate Summer Program.📍 Houston, TX.

Honor & Awards

Nov 2022

Aug 2021

Travel Award, International Conference on Intelligent Biology and Medicine (ICIBM) 📍 Philadelphia PA.