* Wen, J., Tian, Y., (2024). The Genetic Architecture of Biological Age in Nine Human Organ Systems. Nature Aging (in press). Link

* Yang, Z., Wen, J. (Co-supervision & Genetic analysis), (2024). Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures. Nature Medicine (in press). Link

* Wen, J., Zhao, B., (2024). The Genetic Architecture of Multimodal Human Brain Age. Nature Communications. Link

* Wen, J., Antoniades M., (2024). Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. Biological Psychiatry. Link

* Yang, Z., Wen, J., (2024). Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nature Communications. Link

* Skampardoni, I., Nasrallah, IM., Abdulkadir, A., Wen, J., (2024). Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals. JAMA Pyschiatry. Link

* Wen, J., Skampardoni, I., (2024) Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability. medRxiv. Link

* Wen, J., Yang, Z., Nasrallah, I., (2024). Genetic, Clinical Underpinnings of Brain Change Along Two Neuroanatomical Dimensions of Clinically-defined Alzheimer’s Disease. bioRxiv. Link

* Wen, J., Nasrallah, I., Abdulkadir, A., (2023). Genomic loci influence patterns of structural covariance in the human brain. PNAS. Link

* Hwang, G., Wen, J. (co-first), Sotardi, S., (2023) Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population. JAMA Psychiatry. Link

* Wen, J., Varol, E., Yang, (2023). Subtyping brain diseases from imaging data. Machine Learning for Brain Disordersy Link

* Wen, J., Fu, C.H., Tosun, D., (2022). Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression. JAMA Psychiatry, 79(5), pp.464-474. Link

* Wen, J., Varol, E., Sotiras, A., (2022). Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes. Medical Image Analysis, 75, p.102304. Link

* Yang, Z., Wen, J., and Davatzikos, C., (2022). Surreal-GAN: Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns. ICLR. Link

* Wen, J., Samper-González, J., Bottani, S., (2021). Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer’s disease. Neuroinformatics, 19(1), pp.57-78. Link

* Yang, Z., Nasrallah, I.M., Shou, H., Wen, J., (2021). A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure. Nature Communications, pp.1-15. Link

* Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., (2020). Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Medical image analysis, 63, p.101694. Link

* Wen, J., Varol, E., Chand, G., (2020), October. MAGIC: multi-scale heterogeneity analysis and clustering for brain diseases. MICCAI. Springer, Cham. Link

* Bertrand, A., Wen, J. (co-first), Rinaldi, D., (2018). Early cognitive, structural, and microstructural changes in presymptomatic C9orf72 carriers younger than 40 years. JAMA neurology, 75(2), pp.236-245. Link

* Wen, J., Zhang, H., Alexander, D.C., (2019). Neurite density is reduced in the presymptomatic phase of C9orf72 disease. Journal of Neurology, Neurosurgery & Psychiatry 90(4), pp.387-394. Link