Primary Labs

Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL)

The Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) focuses on development and application of machine learning and image analysis methods towards establishing imaging signatures of various diseases and disorders. These imaging signatures, based on both conventional machine learning and deep learning methods, aim to serve as individualized biomarkers for precision diagnosis and predictive modeling. The lab’s long standing record on use of machine learning in neuroimaging includes imaging signatures of brain aging, Alzheimer’s Disease, schizophrenia, and brain cancer, as well as of functional connectivity. Some of the current challenges and targets include dissecting disease heterogeneity using semi-supervised learning methods, establishing radiogenomic markers of genetic mutations, and relating imaging and pathology data.

Center for Neuroimaging in Psychiatry (CNIP)

The Center for Neuroimaging In Psychiatry (CNIP) at the University of Pennsylvania is at the forefront of brain imaging in psychiatric research. The CNIP team acquires, processes and analyzes data in numerous modalities including: high (3T) and ultra high (7T) field Blood Oxygen Level Dependent functional Magnetic Resonance Imaging (BOLD fMRI), Diffusion Tensor Imaging (DTI), perfusion, Magnetic Resonance Spectroscopy (MRS), and Positron Emission Tomography (PET). This neuroimaging data is complemented with additional behavioral, phsyiological, and genetic information. All neuroimaging data is validated and organized into a customized XNAT database that archives and facilitates data processing. The CNIP is led by expert investigators collaborating with leading psychiatric researchers across the world with the goal of developing and implementing multi-modal techniques to advance the field of neuroimaging.

Diffusion & Connectomics In Precision Healthcare Research (DiCIPHR)

The DiCIPHR  lab focuses on understanding the structural and functional connectivity in the brain when it is healthy, and the ways the connectivity is altered as a result of disease. This is achieved by developing advanced mathematical and computational tools for various aspects of diffusion MRI analysis, as well as multimodal connectomics with applications in the clinic.

Penn Statistical Imaging and Visualization Endeavor (PennSIVE)

The Penn Statistical Imaging and Visualization Endeavor (PennSIVE) consists of a group of statisticians studying etiology and clinical practice through medical imaging. Based in the Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine, we work closely with collaborators at the University of Pennsylvania and nationally in medical specialties including neurology, neurosurgery, and radiology. Our primary goals include 1)Developing robust and generalizable statistical methods for the analysis of multimodal biomedical imaging data. 2)Integrating complex medical imaging data and other biomarkers to study health. 3)Building clinical tools for the assessment of disease diagnosis, progression, and prognosis through cross-sectional and longitudinal imaging studies.

Wang Lab

The Wang lab focuses on Alzheimer’s disease and other neurodegenerative disorders, aging, and psychiatric disorders including autism and bipolar disorder. Ongoing projects in the lab can be divided into the following three main directions: 1)Genetics and genomics of Alzheimer’s disease and other neurodegenerative disorders 2)Informatics and algorithm development for genome-scale experiments 3)Biomarker development for aging and neurodegenerative disorders. The Wang lab developed tools for the analysis of several large-scale genome-wide association (GWA) studies, which led to findings of new risk genes for frontotemporal dementia, progressive supranuclear palsy (PSP), and late-onset Alzheimer’s disease. The lab actively develops novel algorithms and computer programs that analyze GWA, DNA-seq and RNA-seq studies. The lab is also involved in biomarker development for aging and for Alzheimer's disease.