Investigating human aging and disease at scale via AI/ML, imaging, genetics, and beyond the brain

[Grigory Bronevetsky]

Modeling Talks

Investigating human aging and disease at scale via AI/ML, imaging, genetics, and beyond the brain Dr. Junhao (Hao) Wen, Columbia U Tues, Dec 2, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/investigating-human-aging-and-disease-at-scale-via-aiml-imaging-genetics More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: This talk encompasses three intertwined yet progressive perspectives: i) scrutinizing the reproducibility of AI/ML in neuroimaging research; ii) depicting the neuroanatomical heterogeneity of brain disorders using AI/ML and imaging; and iii) embracing multi-scale (organs and omics) approaches to investigate brain aging and disease beyond the brain. Integrating AI-driven decision support systems into clinical settings to identify potential genetic, proteomic, metabolomics, and imaging biomarkers for future therapeutic interventions is central to his research interests.   Bio:  Junhao (Hao) Wen, PhD, is a computational neuroscientist with expertise in medical image computing, artificial intelligence/machine learning, multi-omics, and multi-organ bioinformatics. He is the director of imaging genetics research at Columbia's Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID). He also holds affiliated appointments at Columbia's Department of Biomedical Engineering (BME), the New York Genome Center (NYGC), and Columbia's Data Science Institute (DSI). He is also a visiting faculty member at the Center for AI and Data Science for Integrated Diagnostics (AI2D) at the University of Pennsylvania and the founding co-chair of the Brain Imaging Genetics workgroup of the International Society for Advancing Alzheimer's Disease Research and Treatment, the largest international Alzheimer's research community. Dr. Wen’s research focuses on developing and applying artificial intelligence/machine learning techniques to analyze multi-organ and multi-omics biomedical data in the context of human aging and disease, with a particular emphasis on clinical and computational neuroscience to advance precision medicine. His work aims to leverage AI’s capabilities to uncover insights beyond human perception, encompassing several key areas. First, he uses artificial intelligence/machine learning to explore the genetic underpinnings of disease-related neuroanatomical variations (imaging genetics), enabling personalized diagnostic and prognostic approaches. Second, his research adopts a holistic multi-organ and multi-omics framework, recognizing the interconnected nature of organ systems to unravel the complexities of brain structure and function. Furthermore, he leads and contributes to initiatives aimed at consolidating and harmonizing large-scale biomedical datasets, such as the MULTI consortium, which integrates multi-organ and multi-omics data to advance holistic human aging and disease modeling. A central objective of Dr. Wen’s research is to integrate AI-driven decision-support systems into clinical practice, identifying genetic, proteomic, and imaging biomarkers that will inform future therapeutic strategies.

[Grigory Bronevetsky]

Video Recording: https://youtube.com/live/g0J1vaqXgzU

Summary

• Focus: AI/ML for medical applications using imaging and multi-omics

• Project 1: Can ML cause a reproducibility crisis in science?

  • Analyzed prior modeling research on Alzeheimer’s disease

  • Identified many cases of data leakage in their analysis

  • Trained CNNs on the imaging data, 

    • Separated:

      • training/validation from 

      • independent test, which was only used after the 2nd round of peer review

    • Compared 3D CNN and showed that the accuracy is similar to a simpler linear SVM

  • Evaluated extrapolation accuracy across different datasets, showing reduced accuracy when the data distribution shifts

  • Demonstrated that 

    • Splitting data across images but not across patients results in overfitting; 

    • Robust models require splitting across patients: all images from one patient are either train or validation, not split across both

• Project 2: Heterogeneous dynamics of disease

  • Using multiple data modalities: MRI, genetics

  • Clustering disease trajectory signatures

  • MAGIC: Multi-scale  heterogeneity analysis and clustering

    • Clustering of brain images to identify disease subtypes

    • Spatially breaking down brain into feature clusters, with different degrees of spatial resolution

  • Looking at late-life depression

    • Projecting measurements into a low-dimensional subspace

    • Tracking patients’ disease progress over time

  • GAN-based methods for mapping disease heterogeneity

    • The discriminator compresses imaging data into a low-dimensional latent space

    • Clustered these latent vectors, which encode the propagation of dementia into 5 subtypes based on brain imaging and symptoms

    • Subtypes have distinct genetic markers

• Project 3:

  • Link imaging with genetics

  • MuSIC: Multi-scale structural imaging covariance atlas

  • Genome-wide association between genetic features and spatial clusters in images

  • Genetic architecture of multi-modal brain age

• Project 4: multi-omics and multi-organ modeling of human aging and disease

  • Associating 9 phenotype-base aging clocks

  • Relating phenotypic and generic correlation between different disease phenotypes

    • Often related

    • However, where environmental factors are dominant these correlations may have different directionalities

  • Biological aging clocks

    • Idea: 

      • Train model to predict chronological age from some type of feature

      • Look at which features are predictive

    • 11 proteome-based ProBAGs

    • Plasma proteomics data

    • Can do age-bias correction

      • Models tends to regress towards mean age (~45 yo)

      • Messes up results for diseased patients (e.g. predicted age of diseased patients is younger than chronological)

      • Correction methods undo this bias by explicitly conditioning on diseased/healthy populations

      • Organ-specific aging clocks

    • Observation: prediction of clinical age is not that useful; need to predict actual disease state/diagnosis

• https://labs-laboratory.com/medicine