Research Themes By Clinical Application
The Section for Biomedical Image Analysis (SBIA), part of the Center of Biomedical Image Computing and Analytics — CBICA, is devoted to the development of computer-based image analysis methods, and their application to a wide variety of clinical research studies. Image analysis methodologies include functional and structural connectomics, radiomics and radiogenomics, machine learning in imaging, image registration, segmentation, population-based statistical analysis. Clinical research studies span a variety of clinical areas and organs, and are performed within a wide network of collaborations from within and outside Penn. They include brain diseases such as Alzheimer's, schizophrenia, autism, and TBI, evaluation of treatment effects in large clinical trials, and precision diagnostics and predictive modeling in breast and brain cancer.
Aging and AD
Since its original inception in 1994, the lab has been involved in brain aging work, as well as in using imaging analytics to derive early markers of Alzheimer’s Disease. More recently, the lab’s emphasis has been on machine learning methods for deriving brain aging and AD individualized indices.
— Brain Aging and Neurodegenerative Diseases — Aging
In collaboration with the Department of Psychiatry, the lab has been using advanced analytics to characterize structural and functional brain development, as well as to flag deviations from normative brain development that relate to subsequent development of neuropsychiatric disorders.
— Network Structures of the Brain — Network
— Patterns of coordinated development — Patterns
— Resting-state Functional MRI — rs-fMRI
- Related Software: GraSP
Mouse Brain Maturation Atlas via DTI
In this project, we employ a computational neuroanatomical approach to quantify postnatal developmental patterns of C57BL/6J mouse brain via Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). Goal is to develop a normative atlas against which neurogenetic mice may be compared, thereby facilitating genotype-phenotype studies.
Mouse Brain Maturation Atlas via DTI — Mouse
- Mouse Datasets
The lab has been involved in various types of analyses aiming to characterize the complex and often subtle effects of neuropsychiatric disorders on brain structure and function. Emphasis has been on schizophrenia, ADHD, ASD, and depression.
— Multi-modal brain MRI pattern analysis of childhood-onset ADHD in never-treated adults — ADHD
— Autism spectrum disorder — ASD
— Machine Learning and Pattern Analysis of MRI in Neuropsychiatric Disorders — Schizophrenia— Facial Expression Analysis in Neuropsychiatric Disorders — Facial Expressions
The lab has a great deal of activity in the field of computational neurooncology. Initial work focused on segmentation and registration and atlasing. Since 2012, the main emphasis has been on radiomics and radiogenomics, as well as brain connectomics. Our focus is to provide optimized, personalized neurosurgical plans utilizing predictive maps of tumor infiltration and recurrence, radiogenomic estimates of molecular characteristics, and robust estimates of brain connectivity.
— Imaging Genomics (Radiogenomics)
— Predictive Modeling and Biomarkers Using Machine Learning and Radiomics (Predictive Modeling)
We have investigated systematic (targeted) biopsy methods that obtain tissue samples from locations that, together, maximize the probability of detecting cancer
— Optimized Prostate Cancer Detection using a Statistical Atlas of Cancer Distribution — Prostate
Traumatic Brain Injury
We are investigating various aspects of TBI using diffusion imaging, that include but are not limited to imaging markers of diffuse axonal injury, connectomic markers of TBI and differences in longitudinally followed TBI populations and designing global markers of injury. We are also developing multimodal connectomic methods that will help address the question of rewiring in the brain in the course of recovery and treatment.
- We are developing novel measures to assess connectivity related injury burden. One such measure is Disruption Index of the Structural Connectome (DISC).
— Identification of structural subnetworks of the brain — Structural Subnetworks
- We develop advanced subnetwork detection algorithms that can identify subsystems of the brain network with distinct connectivity patterns or distinct biological or behavioral correlates, using machine learning and probabilistic inference techniques
Sex differences in human behavior show adaptive complementarity: Males have better motor and spatial abilities, whereas females have superior memory and social cognition skills. Studies also show sex differences in human brains but do not explain this complementarity. Recent years have witnessed an increased attention to studies of sex differences, partly because such differences offer important considerations for personalized medicine. Using a large sample of healthy young individuals, each assessed with diffusion MRI and a computerized neurocognitive battery, we conducted a comprehensive set of experiments examining sex-related differences in the structural connectome (connection wise and subnetwork differences) and elucidated how these differences may relate to sex differences at the level of behavior. This has led to on going studies investigating sex differences in autism spectrum disorder and traumatic brain injury.
— Sex differences in human behavior — Sex Differences