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Dr. Zee’s research focuses on developing and applying methods for observational and survival data analysis and identifying novel biomarkers of kidney disease progression. She collaborates on clinical research projects in […]

Dr. Shinohara’s methodological research centers on the assessment of structural and functional changes in the brain throughout the lifespan and in neurological, psychiatric, and developmental disorders. He is interested in […]

Dr. Shou’s methodological research focuses mainly on developing novel functional data analysis and machine learning models for multimodal and longitudinal imaging measures, wearable computing sensor data, and assessing biomarker reproducibility. […]

Dr. Linn’s methodological research addresses barriers to incorporating complex, high-dimensional data into models for personalized medicine and biomarker development using neuroimaging data. As a co-investigator, she provides biostatistical leadership on […]

Dr. Guo joined the Biostatistics faculty at the University of Pennsylvania in 1998, and was promoted to Professor of Biostatistics in 2010. He was elected as ASA fellow in 2010 […]

Dr. Cao has established himself as a leader in the field of collaborative analytics, particularly in high-dimensional and imaging science. He has worked on problems in the study of the […]

Dr. Mingyao Li's article in Nature Methods discusses how artificial intelligence is revolutionizing spatial omics, enhancing integration of diverse data and accelerating biological discoveries for improved health outcomes in biomedical research.

Russell “Taki” Shinohara, PhD was selected as the 2023 Mortimer Spiegelman Award recipient by the Applied Public Health Statistics Section of the American Public Health Association (APHA). Each year, the APHA […]

The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence […]

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple […]

While many findings in neuroimaging studies pertain to multiple imaging modalities, statistical methods underlying intermodal comparisons have varied. In our recently published paper in Human Brain Mapping, we propose the […]

Although mammography reduces breast cancer mortality by 15 to 20 percent, the diagnosis in many cases — approximately 15 percent of all breast cancers — occurs after a patient has […]

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce […]

Total brain white matter lesion volume is the most widely established MRI outcome measure in studies of multiple sclerosis. To estimate white matter lesion volume, there are a number of […]

A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes […]

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using […]

Spatial extent inference (SEI) is widely used across neuroimaging modalities to adjust for multiple comparisons when studying brain-phenotype associations that inform our understanding of disease. Recent studies have shown that […]

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis (MS). The most widely established MRI outcome measure is the […]

In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most […]

The central vein sign is a promising MR imaging diagnostic biomarker for multiple sclerosis. Recent studies have demonstrated that patients with MS have higher proportions of white matter lesions with […]

As the field of neuroimaging grows, it can be difficult for scientists within the field to gain and maintain a detailed understanding of its ever-changing landscape. While collaboration and citation […]

In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of […]

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis. While WML have been studied for over two decades using […]

This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data, and demonstrates its ability to recover obscured longitudinal information. The proposed count works by incorporating information […]

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, […]

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer […]

MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American […]

PREVAIL: In this work, our lab developed a model for predicting the degree and spatial pattern of MS lesion tissue recovery. The model is based solely on lesion presentation at […]