BIOGRAPHICAL SKETCH
NAME: Guo, Jia
eRA COMMONS USER NAME (credential, e.g., agency login): JIAGUO514
POSITION TITLE: Assistant Professor of Neurobiology
A. Personal Statement
Influenced by my doctoral research experience in Dr. Scott Small’s lab, and my training with Dr. Douglas Rothman. I have devoted myself to pursuing an academic, translational-research career to studying neurological and neuropsychiatric disorders using MRI. I have multidiscipline training in data science as a biomedical engineer. My expertise lies in computational modeling of physiology, advanced statistical analysis, and machine learning. I also have extensive experience in the development of preclinical and translational MRI/MRS techniques, including pulse sequence programming, imaging protocol design, data processing tools and image reconstruction algorithms.
The expertise needed to carry out the proposed studies began to develop while pursuing research in digital image processing under the mentorship of Dr. Gustavo Rohde in the Center for Bioimage and Informatics (CBI) at the Carnegie Mellon University. During my master program at CBI in BME, I have acquired the knowledge and skills to build intelligent systems based on mathematical modeling of signal and image data, with applications in biomedicine and beyond. During my doctoral program in BME at Columbia University, my training in research continued under the mentorship of Dr. Scott Small and co-mentorship of Dr. Douglas Rothman. I started to apply signal/image processing and mathematical modeling to the neuroimaging field with a combination of MR physics. Research is focused on measuring slow functional changes with MRI/MRS in normal and abnormal brains. I have adapted and optimized MRI-based tools in mouse models at 9.4T designed to measure ‘slow functional’ changes in the brain - slow changes in synaptic density or slow changes in functional neurochemistry. I have demonstrated their clinical utilities in cognitive aging, Alzheimer’s disease and schizophrenia.
In 2018, I assumed the directorship of the newly formed 9.4T small animal imaging lab (SAIL) at the Zuckerman Institute at Columbia University, with the goal to build an interdepartmental and interdisciplinary high field MR research platform that provides state-of-the-art MR equipment, dedicated wet lab space, Linux computing infrastructure and end-to-end expertise for the development and application of MRI and MRS methodology in brain research. As a key component of Columbia University MR Research Center, the SAIL has provided me the opportunity to expand my research focus, to broaden my role in education and fostering graduate students. As PI or co-investigator, I have lead independent research projects in the field of neuroimaging using preclinical 9.4T and clinical 3T scanners, with a specific focus on developing innovative and translational technologies applicable to the brain.
My team at SAIL is organized around the principle of computational neuroimaging. Research is focused on the study of intact biological systems by developing methods for obtaining structural, functional, physiological and biochemical information by in vivo MRI and MRS. Accordingly, I have constructed my team to have two divisions that work together toward this common goal. The first is a neuroimaging division in which we perform machine learning/deep learning algorithms on functional and structural MRI studies in animal models and human, designed to understand the spatial and temporal pattern in brain development and dysfunctions. The second is a neurobiochemical division in which we try to identify the underlying metabolic profiles using in vivo MRS to determine the underpinning mechanism and for the rapid discovery of drug target engagement.
B. Positions
2013-2018 Graduate Research Assistant, Columbia University, New York, NY
2015-2016 Visiting Graduate Research Associate, Yale University, New Haven, CT
2018-2018 Postdoctoral Research Scientist, Columbia University, New York, NY
2018-Current Director of Small Animal Imaging Laboratory, Columbia University, New York, NY
2018-Current Assistant Professor of Neurobiology, Department of Psychiatry, Columbia University, New York, NY
C. Contribution to Science
1. My early publications mainly addressed how to build intelligent algorithms/systems based on mathematical modeling of signal and image data, with applications in bioinformatics and signal sensing. I served as the first author or key contributor to these published works.
a. Huang H, Tosun AB, Guo J, Chen C, Wang W, Ozolek JA, Rohde GK. Cancer diagnosis by nuclear morphometry using spatial information. Pattern Recognit Lett. 2014 Jun 1;42:115-121. PubMed PMID: 24910485; PubMed Central PMCID: PMC4043190.
b. Guo J, Huang H, Chen C, Rohde GK. Optimized Nonlinear Discriminant Analysis (ONDA) for Supervised Pixel Classification. IEEE Signal Processing Letters. 2013; 20(12):1155-1158.
c. Chen S, Cerda F, Guo J, Harley JB, Shi Q, Rizzo P, Bielak J, Garrett JH, Kovačević J.
Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. 2013; :3412-3416.
2. In addition to the contributions described above, I started to apply mathematical modeling to the neuroimaging field with a combination of MR physics. Functional brain changes occur rapidly by alterations in neuronal activity, or more slowly, typified by changes in neuronal basal metabolism. Functional MRI has focused more on the prior than the latter, even slow brain changes are important for normal brain function and for many brain disorders. With this in mind, I have adapted and optimized MRI-based tools in mice designed to measure ‘slow functional’ changes in the brain - slow changes in synaptic or vasculature density or slow changes in neurotransmitter levels.
To model slow changes in synaptic or vasculature density, I developed and optimized a series of tools that can map cerebral blood volume (CBV) across the cortical mantle and within cortical layers. I showed that this reflects the known functional architecture of the mouse brain, and use a whisker-cutting paradigm to show that this approach is indeed sensitive to slow changes driven by synaptic pruning. Second, I demonstrated the utility of this approach for mapping slow changes in the brain associated with the disease, by pinpointing changes in vasculature density in mouse model of Alzheimer’s disease and human cohort of cognitive aging. However, translational CBV protocol in human studies commonly requires an intravenous injection of exogenous gadolinium based contrast agent (GBCA) and recent studies suggest that the GBCA can accumulate in the brain after frequent use. I hypothesized that endogenous sources of contrast might exist within the most conventional and commonly acquired structural MRI, potentially obviating the need for exogenous contrast in brain MRI. I first tested this hypothesis by developing and optimizing a deep learning algorithm, which we call DeepContrast, in mice, and modified the same approach for human studies. In both mouse and human studies, DeepContrast performs equally well as exogenous GBCA in mapping CBV of both the normal brain and brain with lesions.
To model slow changes in neurotransmitter levels, I first developed and optimized in mice an MR spectroscopy sequence and processing tools designed to measure changes in two neurotransmitters, GABA and glutamate. Moreover, I demonstrated the translational capabilities of this approach by identifying glutamate abnormalities in the brains of patients in the prodromal stages of schizophrenia. In our recent publication in Biological Psychiatry, we hypothesized that glutamate hyperactivity reflected by 1H-MRS derived hippocampal levels of Glx (glutamate/glutamine) represents early hippocampal dysfunction in CHR. I served as the first/leading author in the following researches.
a. Sun H, Liu X, Feng X, Liu C, Zhu N, Gjerswold-Selleck SJ, Wei H, Upadhyayula PS, Mela A, Wu C, Canoll P, Laine AF, Vaughan JT, Small SA, Guo J. Substituting gadolinium in brain MRI using DeepContrast. Biomedical Engineering Society Annual Meeting. Abstract # 2891. 2019 October 16-10.
b. Guo J, Feng X, Hannah S , Provenzano F, Small SA. MouseStream: A Software Suite for Mapping and Analyzing Mouse Cortical Functional Architecture In Vivo Using Magnetic Resonance Microscopy. ISMRM Annual Meeting Proceedings. 2018 August 22.
c. Feng X, Hamberger MJ, Hannah S, Guo J, Small SA, Provenzano F. Temporal lobe epilepsy lateralization using retrospective cerebral blood volume MRI. Neuroimage Clin. 2018;19:911-917. PubMed PMID: 30003028; PubMed Central PMCID: PMC6039834.
d. Provenzan F, Guo J (co-1st), Melanie MW, Feng,Xinyang, Hannah S, Brucato G, First MB, Rothman DL, Girgis R, Lieberman J, Small SA. Hippocampal Pathology in Clinical High-Risk Patients and the Onset of Schizophrenia. Biological Psychiatry. 2019. ISSN 0006-3223.
e. Wood E, Cummings K, O"Neill J, Guo J, Dapretto M, Bookheimer S, Green S. Thalamic GABA:Glu Ratio is Related to Thalamic Connectivity and Sensory Over-Responsivity in ASD. OHBM Proceedings. 2019 June 10.
f. Guo J, Gang Z, Sun Y, Laine AF, Small SA, Rothman DL. In vivo detection and automatic analysis of GABA in the mouse brain with MEGA-PRESS at 9.4 T. NMR in Biomedicine. 2018 January 01; 31(1): e3837.
3. Besides major contributions in MRI neuroimaging, I also contributed to MRI lung studies as a collaborator. I help develop a series of analytic tools to process 3He MRI lung scans and derive quantitative apparent diffusion coefficient (ADC) maps. Results have shown that participants with an accessory subsuperior airway had higher ADCs than participants with standard anatomy. This result has been included in a recent publication in PNAS.
a. Sun Y, Guo J, Balte PP, Dashnaw SM, Prince MR, Oelsner EC, Cascio CM, Albert MS, Wild J, Hughes EW, Barr R. Examination of Lung Function among Older Smokers with and without COPD by Apparent Diffusion Coefficient (ADC) of 3He MRI. ISMRM Annual Meeting Proceedings. 2018 August 22.
Complete List of Published Work in My Bibliography: https://www.ncbi.nlm.nih.gov/myncbi/1v9dyoU7d7QggJ/bibliography/public/