Medical Image Analysis (MIA) Lab
The Medical Image Analysis (MIA) lab is headed by Dr. Vince Calhoun. With more than thirty researchers from diverse backgrounds (electrical engineering, computer science, physics, math and statistics) and with the help of multiple multimillion dollar grants, MIA lab’s main research focus is to develop methods to better understand brain structure, function and human behavior. The MIA lab has developed a suite of powerful software tools to help other researchers investigate different types of data sets: sMRI, fMRI, DTI, EEG, MEG, genetic data, among others. Below are a few of the research areas in which the MIA lab is involved.
Methods to identify brain imaging biomarkers: Each brain imaging modality reports on a different aspect of the brain (e.g. gray matter integrity, blood flow changes, electrical activity) and each has strengths and weaknesses. Many mental illnesses, such as schizophrenia, bipolar disorder, and depression, currently lack definitive biological markers and rely primarily on symptom assessments for diagnosis. Methods developed by the MIA lab help to find multimodal biomarkers to characterize and classify complex brain disorders such as schizophrenia. Novel features with high consistency are found applying multivariate methods which focus on higher order statistics such as independent component analysis (ICA). A major focus of the lab is incorporating available knowledge (such as spatial or temporal constraints) into these algorithms in order to improve performance.
Spatiotemporal fusion of imaging and genetic data: Each existing modality for imaging the living brain can only report upon a limited domain. For example, functional imaging provides information about dynamic blood flow changes in response to a stimulus, whereas electroencephalography (EEG) provides information about the electrical activity of the brain with centimeter spatial and millisecond temporal resolution. Finally, gene array imaging can assess specific differences at the chromosomal level that are present in individuals, some of which have functional consequences. Multiple imaging modalities are typically collected on the same set of individuals and methods to effectively combine these different types of information are developed by the MIA lab.
In addition to the above main research areas, MIA is also interested in the following areas: analysis of complex-valued fMRI data, a unified framework for flexible brain image analysis, genetic markers of white matter integrity, image based classification and machine learning tools, and others.
For further information about the MIA lab, to download software tools developed by MIA lab and for individual research interests of MIA lab’s researchers please visit the lab website.
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