Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer's disease (AD), the most common form of dementia. Previous studies have shown that AD is closely related to alternation in the functional brain network, i.e., the functional connectivity among different brain regions. In this paper, we consider the problem of learning functional brain connectivity from neuroimaging, which holds great promise for identifying image-based markers used to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. More specifically, we study sparse inverse covariance estimation (SICE), also known as exploratory Gaussian graphical models, for brain connectivity modeling. In particular, we apply SICE to learn and analyze functional brain connectivity patterns from different subject groups, based on a key property of SICE, called the "monotone property" we established in this paper.

Shuiwang Ji, associate professor in the Department of Computer Science and Engineering at Texas A&M University, is one of the principal investigators on a $6 million grant from the National Institutes of Health to develop artificial intelligence-driven methods to automate the process of finding subtle telltale signs of Alzheimer's disease in neuroimaging data. Ji will lead the research team tasked with developing advanced deep-learning methods for finding relevant neural signatures lurking within neuroimages taken using different techniques, such as PET scans and MRIs. "I feel very excited with this collaborative opportunity to make scientific discoveries in medical domains using deep learning and artificial intelligence," said Ji, who has extensive expertise in machine learning, deep learning and medical image analysis. Alzheimer's disease affects 5.6 million Americans over the age of 65, and its symptoms are most noticeably the progressive impairment of cognitive and memory functions. It is also currently the most common form of dementia in the elderly.

Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer's disease (AD), the most common form of dementia. Previous studies have shown that AD is closely related to alternation in the functional brain network, i.e., the functional connectivity among different brain regions. In this paper, we consider the problem of learning functional brain connectivity from neuroimaging, which holds great promise for identifying image-based markers used to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. More specifically, we study sparse inverse covariance estimation (SICE), also known as exploratory Gaussian graphical models, for brain connectivity modeling. In particular, we apply SICE to learn and analyze functional brain connectivity patterns from different subject groups, based on a key property of SICE, called the "monotone property" we established in this paper.

This part will cover a basic introduction to machine learning with focus on working with neuroimaging data and include some hands-on exercises. To attend, no prior knowledge of the topic is required. Free lunch will be provided.

Depression affects more than 15 million American adults, or about 6.7 percent of the U.S. population, each year. It is the leading cause of disability for those between the ages of 15 and 44. Is it possible to detect who might be vulnerable to the illness before its onset using brain imaging? David Schnyer, a cognitive neuroscientist and professor of psychology at The University of Texas at Austin, believes it may be. But identifying its tell-tale signs is no simpler matter.