Vanderbilt University Institute of Imaging Science

Anirban Sengupta, Ph.D.

Anirban

Sengupta

Ph.D

Research Instructor

Radiology and Radiological Science

Sengupta A, Wang F, Mishra A, Reed JL, Chen LM, Gore JC. Detection and characterization of resting state functional networks in squirrel monkey brain. Cereb Cortex Commun. 2023

Sengupta A, Mishra A, Wang F, Li M, Yang PF, Chen LM, Gore JC. Functional networks in non-human primate spinal cord and the effects of injury. Neuroimage. 2021.

Gao Y, Sengupta A, Li M, Zu Z, Rogers BP, Anderson AW, Ding Z, Gore JC; Alzheimer's Disease Neuroimaging Initiative. Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer's disease. PLoS One. 2020

Sengupta A, Agarwal S, Gupta PK, Ahlawat S, Patir R, Gupta RK, Singh A. On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images. Eur J Radiol. 2018

Sengupta A, Ramaniharan AK, Gupta RK, Agarwal S, Singh A. Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components. J Magn Reson Imaging. 2019

anirban.sengupta@vumc.org

My research interest lies at the intersection of MRI and fMRI data analysis and machine learning techniques. I am currently interested in using resting state fMRI for probing various clinically relevant neuroscience questions in primate models. My PhD research work involved devising methodology for tumor segmentation and grading in glioma patients using a combination of DCE-MRI and conventional MRI parameters under a machine learning framework.

For the current research projects, I analyze the resting state functional connectivity in monkey brain and spinal cord injury models to investigate changes in their functional circuits. I use data driven techniques such as ICA for detecting characteristic features from the fMRI signal of brain and spinal cord.

Charlotte Sappo, Ph.D.

Charlotte

Sappo

Research Fellow

VUIIS, Biomedical Engineering

charlotte.r.sappo@vanderbilt.edu

Optimized and open-source hardware solutions for ultra-high and ultra-low field imaging

Lucas Sainburg

Lucas

Sainburg

Graduate Student

Biomedical Engineering

I currently work on methods to characterize and localize abnormal brain activity and connectivity in epilepsy with neuroimaging.

Lab Website

https://my.vanderbilt.edu/vlmorgan/

I currently work on methods to characterize and localize abnormal brain activity and connectivity in epilepsy with neuroimaging. Lab website: https://my.vanderbilt.edu/vlmorgan/

lucas.sainburg@vanderbilt.edu

I am interested in using neuroimaging (e.g., fMRI and diffusion MRI) to study and improve treatments for neurological disorders

Jordan Racca

Jordan

Racca

Ph.D

Postdoc at VUIIS 2/22 thru 8/22.

Current: Instructor and Resident Scientist, Vanderbilt Collaborative for STEM Education and Outreach

Tahsin Reasat

Tahsin

Reasat

Graduate Student

Biomedical Engineering

tahsin.reasat@vanderbilt.edu

Michael Pridmore, Ph.D

Michael

Pridmore

Ph.D

Research Fellow Trainee

Vanderbilt University Institute of Imaging Science

Phone

(615)936-2374

Pridmore M, Castoro R, McCollum MS, Kang H, Li J, Dortch R. Length-dependent MRI of hereditary neuropathy with liability to pressure palsies. Ann Clin Transl Neurol. 2020;7(1):15-25. doi:10.1002/acn3.50953

Pechman K, Davis L, Pridmore M, Elliot S, Gifford K, Hohman T, & Jefferson A L. Comparison of hippocampal segmentation methods to differentiate participants with mild cognitive impairment and normal cognition: The vanderbilt memory and aging project. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2016;12(7 Supplement), P549-P550. https://doi.org/10.1016/j.jalz.2016.06.1074

Foster P, Hubbard T, Campbell R, Poole J, Pridmore M, Bell C, & Harrison D. Spreading activation in emotional memory networks and the cumulative effects of somatic markers. Brain Informatics. 2016;1-9. https://doi.org/10.1007/s40708-016-0054-2

Jefferson, A., Liu, D., Gifford, K., Hohman, T., Rane, S., Pechman, K., Logan, L., Benson, E., Wisniewski, K., Wiggins, M., Samuels, L., Pridmore, M., & Acosta, L. (2015). Clinician staging of mild cognitive impairment severity yields neuropsychological, neuroimaging, and genetic susceptibility differences among subtypes: The vanderbilt memory and aging project. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 11(7), P241. https://doi.org/10.1016/j.jalz.2015.07.292

m.pridmore@vumc.org

I am interested in applying magnetic resonance imaging techniques in clinical scenarios to better patient outcomes and influence clinical decision-making. I have experience in research projects including peripheral nerve trauma, language cognition, and brain/cardiac imaging in neurodegenerative disease. I also have interest in data integrity and processing techniques.

My current projects in the Crescenzi laboratory include applying multi-nuclear MRI to patient populations with salt sensitivity, lipedema, and/or lymphedema. Current projects include comparing data acquisition methods, data analysis for sodium imaging, and working with datasets across multiple collaborations.