Altered brain chemistry and metabolism are key features of many neurological and neuropsychiatric conditions. These alterations can be assessed non-invasively using in vivo magnetic resonance spectroscopy (MRS), a technique closely related to MRI.
Dr. Near’s research is focused on:
- developing new MRS techniques for characterizing brain chemistry and metabolism, and
- applying these tools towards the study of mental health and brain disorders in both humans and preclinical models of disease.
The goal of Dr. Near’s research is to better understand brain chemistry and metabolism of brain health and disease, leading to improved monitoring and treatment of brain disorders.
Education
- B.Sc. Eng., 2004, Engineering Physics, Queen’s University, Canada
- PhD, 2009, medical biophysics, University of Western Ontario, Canada
- Postdoc, 2012, Oxford University, UK
Appointments & Affiliations
- Scientist, Physical Sciences, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute
- Associate Professor, Department of Medical Biophysics, University of Toronto
Research Foci
- -Magnetic Resonance Spectroscopy -Brain chemistry and metabolism -Alzheimer’s disease, aging
Publications
Affiliated Labs & Programs
Selected Publications
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Fowler CF, Goerzen D, Devenyi GA, Madularu D, Chakravarty MM, Near J. Neurochemical and cognitive changes precede structural abnormalities in the TgF344-AD rat model. Brain Communications 2022; 4 (2): fcac072.
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Dehghani M, Zhang S, Kumaragamage C, Rosa-Neto P, Near J. Dynamic 1 H-MRS for detection of 13 C-labeled glucose metabolism in the human brain at 3T. Magn reson med 2020; 84(3): 1140-1151.
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Near J, Harris AD, Juchem C, Kreis R, Marjańska M, Öz G, Slotboom J, Wilson M, Gasparovic C. Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations. NMR biomed. 2020; 34(5): e4257.
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Simpson R, Devenyi GA, Jezzard P, Hennessy TJ, Near J. Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit. Magnetic resonance in medicine 2017; 77(1): 23-33.