Analysis Core
Any proposal submitted for use of BIRC imaging facilities must include a section of planned analysis methods and describe the ability to bring such analyses to completion. If an investigator needs assistance with image analysis, the BIRC Director helps to match the investigator to an appropriate subset of BIRC Analysis Core members prior to approval of any proposed project. This is to ensure that projects supported by BIRC facilities are completed in a high-quality fashion and lead to either publications or submission of grants. A match between data collection and analysis via consultation between the Scientific Review Committee, BIRC Director, and relevant BIRC Analysis Core faculty must be demonstrated prior to project approval. Analysis Core faculty are remunerated for their involvement in any project via a combination of publication credit where appropriate and appropriate percent effort from the investigator using the investigator’s available research funds when available and appropriate. The following are the current members of the BIRC Analysis Core. They cover wide expertise and all have numerous students who can be supervised to work on any number of image analysis projects:
Nicole Lazar, PhD (Professor of Statistics)
Expertise and many years of experience in statistical methods to analyze complex time series data, including fMRI and EEG. Co-directs a statistical imaging group with Drs. McDowell and Park that focuses on analytical methods for imaging and functional connectivity. Helped to develop the FIASCO software for the analysis of fMRI data, and wrote a book on fMRI statistical methods. Focus of neuroimaging research is on corrections for multiple testing, methods for combining information across subjects, and methods for group comparisons.
Tianming Liu, PhD (Distinguished Research Professor of Computer Science)
Expertise in structural and functional brain mapping and image analysis. Developed novel computational algorithms and software systems for structural and functional connectivity analysis, and published over 200 papers on these topics.
Jennifer McDowell, PhD (Professor of Psychology)
Expert in multimodal brain imaging using structural and functional MRI, DTI, EEG, and MEG. PI or co-PI on several NIH and NSF grants. Co-directs the statistical imaging group with Drs. Lazar and Park. Has maintained a long-standing collaboration with the UGA Statistics Department that has advanced analytical strategies for neuroimaging data.
Cheolwoo Park, PhD (Professor of Statistics)
Three main areas of analytical expertise: (i) interdisciplinary research on functional magnetic resonance imaging (fMRI); (ii) multi-scale analysis; and (iii) statistical learning theory. Co-directs the statistical imaging group with Drs. Lazar and McDowell. Apply wavelets and scale-space approaches to address multiple problems in fMRI applications. Has developed novel, reliable, and efficient statistical/machine learning methods for massive and complex data.
Dean Sabatinelli, PhD (Associate Professor of Psychology)
Expert in multimodal brain imaging using fMRI and EEG. Analytical expertise in causal connectivity and noninvasive brain stimulation techniques, with methodological expertise in across GE, Siemens, and Philips MR scanners, as well as multiple EEG data collection systems and analysis packages.
Lawrence Sweet, PhD (Professor of Psychology)
Expert in multimodal brain imaging including functional, structural, and perfusion magnetic resonance imaging. Extensive background in experimental design and data analysis methods; provides neuroimaging expertise on numerous NIH-funded projects.
Qun Zhao, PhD (Associate Professor of Physics)
Expertise in structural and function MRI and MRS. Broad background in magnetic resonance imaging physics and engineering for various applications, including neural and regenerative medicine. Developed approaches for contrast-enhanced MRI with iron-oxide nanoparticles in both in vitro and in vivo application, including tracking of stem cells labeled with magnetic nanoparticles. Developed approaches to enhance detection of brain lesions, liver diseases, and improve image contrast. Developed machine learning models, including convolutional neural networks for detection of functional connectivity in human and animal brains using rs-fMRI data, classification of tissues, and segmentation of brain.