Seminar Series: Lexin Li

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Lexin Li
April 22, 2021
3:00PM - 4:00PM
Location
Virtual Presentation

Date Range
Add to Calendar 2021-04-22 15:00:00 2021-04-22 16:00:00 Seminar Series: Lexin Li Title Multimodal Neuroimaging Analysis Meeting Link Speaker Lexin Li - University of California at Berkeley, Department of Biostatistics and Epidemiology & Helen Wills Neuroscience Institute Abstract Multimodal neuroimaging is now becoming a norm in neuroscience research. It utilizes different physical and physiological sensitivities of imaging scanners and technologies, and acquires different types of brain images for a common set of experimental subjects. It measures distinct brain characteristics, ranging from brain structure and function to numerous chemical constituents, and produces a variety of imaging modalities, including anatomical magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), among many others. Multimodal neuroimaging analysis aggregates such diverse but often complementary information, and borrows strength across different modalities to render an integrated data resolution that would otherwise not be available with any single data type. In this talk, we examine a number of statistical problems and present two case studies in multimodal neuroimaging analysis. One is generalized liquid association analysis, and the other is integrative factor regression inference. Virtual Presentation Department of Statistics webmaster@stat.osu.edu America/New_York public
Description

Title

Multimodal Neuroimaging Analysis

Meeting Link

Speaker

Lexin Li - University of California at Berkeley, Department of Biostatistics and Epidemiology & Helen Wills Neuroscience Institute

Abstract

Multimodal neuroimaging is now becoming a norm in neuroscience research. It utilizes different physical and physiological sensitivities of imaging scanners and technologies, and acquires different types of brain images for a common set of experimental subjects. It measures distinct brain characteristics, ranging from brain structure and function to numerous chemical constituents, and produces a variety of imaging modalities, including anatomical magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), among many others. Multimodal neuroimaging analysis aggregates such diverse but often complementary information, and borrows strength across different modalities to render an integrated data resolution that would otherwise not be available with any single data type. In this talk, we examine a number of statistical problems and present two case studies in multimodal neuroimaging analysis. One is generalized liquid association analysis, and the other is integrative factor regression inference.