Advanced imaging technology has provided scientists with unprecedented access to the living brain. The Center for Translational Research in Neuroimaging and Data Science (TReNDS) is a tri-institutional effort supported by Georgia State, the Georgia Institute of Technology and Emory University that is focused on making better use of complex brain imaging data through improved analysis, with a goal of identifying biomarkers that can help address brain health and disease.
Vince Calhoun, a Georgia Research Alliance Eminent Scholar in Brain Health and Image Analysis, is the founding director of the Center for Translational Research in Neuroimaging and Data Science (TReNDS). He is professor of psychology at Georgia State and also has appointments at the Georgia Institute of Technology in the School of Electrical and Computer Engineering and in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. He has developed algorithms that have strengthened understanding of brain function, structure and genomics, and how each is affected during various tasks or by mental or neurological illness. He also works to develop neuroinformatics tools that enable experts to use larger data sets and improve efficiency in data capture, management, analysis and sharing.
The brain is a highly complex organ and advanced analytic tools only scratch the surface of this complexity. The Center for Translational Research in Neuroimaging and Data Science (TReNDS) draws upon engineering and computer science principles to develop new algorithms to extract the maximal information possible from the available data, drawing upon signal and image processing, machine/deep learning and statistical signal processing. Its goal is to translate these approaches into biomarkers that can help address relevant areas of brain health and disease.
The center is also focused on increasing cooperation among Atlanta brain imaging researchers by developing collaborative tools to help make data capture, management, analysis and sharing easier. Large-scale data sharing and multimodal data fusion techniques are the underpinnings of its approach.