2021 Investigators Awards recipients, topics, and abstracts
Read more about the award recipients.
Anouck Girard, PhD, Associate Professor of Aerospace Engineering; Josephine Kasa-Vubu, MD, Clinical Professor of Pediatrics; Michael DiPietro, MD, Professor Emeritus of Radiology:
“Using Artificial Intelligence to Broaden and Diversify Outdated Standards for the Determination of Skeletal Maturation in Growing Children”
Todd Hollon, MD, Assistant Professor of Neurosurgery; Honglak Lee, PhD, Associate Professor of Computer Science; Sandra Camelo-Piragua, MD, Associate Professor of Pathology:
“Rapid Intraoperative Molecular Diagnosis of Diffuse Gliomas Using Stimulated Raman Histology and Deep Neural Networks”
Hui Jiang, PhD, Associate Professor of Biostatistics:
“Statistical and Computational Methods for Asymmetric Integration of Datasets from Different Cancers for the Identification of Cancer-related Genes and Biomarkers in Case-control Analyses”
Collaborators:
J. Chad Brenner, PhD, Associate Professor of Otolaryngology-Head and Neck Surgery; Kevin (Zhi) He, PhD, Research Associate Professor of Biostatistics
Michael Mathis, MD, Assistant Professor of Anesthesiology:
“Predicting Cardiac Surgery-Associated Acute Kidney Injury Using Federated Learning”
Collaborators:
Karandeep Singh, MD, MMSc, Assistant Professor of Learning Health Sciences, Internal Medicine, Urology, and Information; Donald Likosky, PhD, Professor of Cardiac Surgery; Rahul Ladhania, PhD, Assistant Professor of Health Informatics and Biostatistics; Paramveer Dhillon, PhD, Assistant Professor of Information
Amy Pasternak, PharmD, Clinical Assistant Professor of Pharmacy; Vaibhav Sahai, MBBS, MS, Associate Professor of Medical Oncology and Hematology:
“Assessing the Impact of Germline Pharmacogenetics (PGx) on Medication Outcomes and Clinician Prescribing Decisions in Patients with Cancer”
Collaborators:
Daniel Hertz, PharmD, PhD, Assistant Professor of Pharmacy; Valerie Gunchick, MS, Clinical Research Project Manager
Scott Peltier, PhD, Research Scientist of Biomedical Engineering/Functional MRI Laboratory; Zhongming Liu, PhD, Associate Professor of Biomedical Engineering and Electrical Engineering and Computer Science:
“Deep Learning for Prediction of Mild Cognitive Impairment and Dementia of the Alzheimer’s type”
Collaborators:
Benjamin Hampstead, PhD, ABPP/CN, Professor of Psychology and Psychiatry; Jeffrey Fessler, PhD, Professor of Electrical Engineering and Computer Science; Douglas Noll, PhD, Professor of Biomedical Engineering
Deep learning approaches have been increasingly applied to functional magnetic resonance imaging (fMRI) data but face a common challenge on generalizability and explainability. When models learn to optimize the performance for a specific goal or dataset, they often fail to generalize to other or future goals and datasets. The models also tend to be too complex to understand or act as a “black box” unexplainable in terms of neural circuits and dynamics. Despite its increasing application to fMRI, deep learning remains inadequate to advance mechanistic understanding of brain functions or inform treatment of brain diseases. To address these issues, we propose to use an explainable and generalizable system of artificial intelligence. This system represents, model and predict fMRI activity for decoding and modulating neural circuits linked to behavior. To be explainable, the system learns representations that can be decoded and interpreted as spatial patterns and temporal dynamics of brain networks. The system is generalizable to different patients, brain states, behavioral tasks, and disease conditions, including MCI and DAT to be studied in the project.
Aim 1:Characterize latent representations of brain activity with task-free resting state fMRI. We will train the model with resting state fMRI data from the Human Connectome Project (HCP) and Alzheimer’s Disease Network Initiative (ADNI). The model will learn to disentangle the generative factors of spontaneous brain activity, to represent activity and connectivity patterns in terms of independent latent variables, and to decode the representations back to brain networks and dynamics. We hypothesize that the model-learned representations will be separable across subjects in different cognitive conditions (e.g. cognitively normal, MCI, DAT) and be usable to explain and predict behavioral measures.
Aim 2: Characterize latent representations of brain activity with pathology-specific task fMRI.
We will transfer the learned model from the large-scale resting state data to local data acquiring tasks relevant to Alzheimer’s Disease (spatial navigation, object location). We will apply the general linear model analysis to the time series of the latent representation, localize the task-related functions to specific regions in the latent space, and map the so-localized functions from the latent space back to the brain. We hypothesize that the latent space learned from the resting state data is generalizable to task conditions and it can be divided into subspaces or regions associated with specific task-related functions. By focusing on tasks related to MCI- and DAT-pathology, we can characterize individual responses and behavioral phenotypes.
Xu Shi, PhD, Assistant Professor of Biostatistics:
“Automated Harmonization of Multi-institutional Electronic Health Records Data”
Collaborator:
V.G. Vinod Vydiswaran, PhD, Associate Professor of Learning Health Sciences and Information