Associate Professor

Jean Chen

PhD, McGill University

The Rotman Research Institute Baycrest
3560 Bathurst Street, Toronto, Ontario Canada M6A 2E1
Research Interests
Biomedical Imaging, Data Science and Computational Biology, Image-Guided Therapy and Device Development, Neuroscience

At a Glance

  • My research is driven by the need to better understand age-related neurological diseases, by using MRI to observe the living brain in healthy aging and disease. 
  • We use resting-state functional MRI (fMRI), arterial-spin labeling, cerebrovascular-reactivity mapping and simultaneous EEG-fMRI to understand the link between brain metabolism and vascular health. 
  • We develop new techniques for MRI fingerprinting, diffusion MRI and resting-state fMRI, and integrate them to better understand neurovascular, metabolic and structural interactions. 
  • In the multidisciplinary environment at Baycrest, we translate our methods to the study of healthy aging and to patients with mild-cognitive impairment, late-life depression, hypertension, diabetes, and stroke.

Short Bio

Dr. Chen received her MSc (2004) in Electrical Engineering from the University of Calgary, and her PhD (2009) in Biomedical Engineering from McGill University. She completed her postdoctoral work on multimodal MRI of brain aging at the Martinos Center for Biomedical Imaging, Harvard Medical School (2011), and joined MBP as faculty in 2011. She is a Senior Scientist at the Rotman Research Institute and Tier II Canada Research Chair in Neuroimaging of Aging. She currents heads the Chen Lab (Research in Advanced Neuroimaging using MRI). Her research is funded by the CIHR, NSERC and the Heart and Stroke Foundation.

Research Synopsis

Demonstration of vascular and physiological effects on resting-state fMRI. We used state-of-the-art ultra-fast fMRI acquisition techniques with multivariate physiological monitoring to assess the effect of carbon dioxide (CO2) fluctuations on the resting-state fMRI signal, providing the first detailed assessment of its kind (Golestani et al., NeuroImage 2014). In addition, we demonstrate experimentally the modulation of fMRI-based functional network measurements by non-neural cerebrovascular reactivity (Golestani et al., NeuroImage 2015).

Demonstration of dynamic neurovascular coupling and vascular bias in resting-state functional MRI. The extent of neurovascular coupling is unknown in resting-state fMRI, much less the effect of vascular contributions to resting-state fMRI functional connectivity. Our work, which used a comprehensive set of vascular measures, demonstrated for the first time the spatial variability in resting-state neurovascular coupling as well as the relationship between functional connectivity measures and macrovascular presence (Tak et al., NeuroImage 2014), with critical implications for rs-fMRI data interpretation (Tak et al., Brain Connect 2015).

Demonstration of dissociation between neurovascular and structural variations in healthy brain aging. Structural changes in the brain have long been observed as part of aging and neurodegenerative diseases. While neuronal integrity is irrevocably tied to neurovascular health, the neurovascular mechanism underlying this structural decline has remained unknown. This work clearly demonstrated, for the first time, distinct patterns of vascular and structural changes in normal aging (Chen et al., NeuroImage 2011), and pioneered a new imaging processing methodology (Chen et al., PLoS ONE 2013) for multi-modality imaging in the community of aging.

Elucidation of the dynamic relationship between vascular and metabolic mechanisms of the BOLD (blood-oxygenation level-dependent) fMRI signal. The understanding of neurovascular interactions in the transient BOLD signal is critical to the understanding and interpretation of BOLD fMRI. For the first time, we obtained simultaneous measurement of BOLD-specific blood flow and volume measurements, which experimentally clarified the origins of the BOLD signal transients (Chen and Pike, NeuroImage 2009). 5. Elucidation of the relationship between vascular and metabolic mechanisms of the BOLD signal. We developed MRI techniques to measure venous cerebral blood volume changes (Chen and Pike, NMR Biomed 2009), which led to the quantification of the venous flow-volume relationship in humans (Chen and Pike, NeuroImage 2010). I also developed methodology to quantify the effect of hypercapnic calibration on cerebral metabolism (Chen and Pike, J Cereb Blood Flow 2010). These measurements are critical for the use of techniques such as calibrated BOLD. The methods associated with these publications have been widely discussed, and the results are being adopted by research labs around the world.

Recent Publications

  • Ragot D. M. and Chen J. J. Characterizing signal and noise origins of spin-echo BOLD fMRI at 3 Tesla. Magn Reson Imaging 2018; epub ahead of print PMID: 30439514.
  • Chen J. J.. Cerebrovascular reactivity mapping using MRI: Considerations for Alzheimer’s disease. Front Neurosci (Special Issue on Metabolic and Vascular Biomarkers for Imaging Aging and Alzheimer’s Disease) 2018; epub ahead of print; doi:10.3389/fnagi.2018.00170 (invited review).
  • Chen J. J.. Functional MRI of brain physiology in aging and neurodegenerative diseases. NeuroImage (Special Issue on Physiological and Quantitative fMRI), 2018; epub ahead of print; doi 10.1016/j.neuroimage.2018.05.050 (invited review).
  • Chad J., Pasternak O., Salat D. H. and Chen J. J. Re-examining age-related white-matter microstructural degeneration with free-water corrected diffusion-tensor imaging. Neurobiol Aging 2018; 71-167-170. PMID: 30145396.
  • Chu P. P. W., Golestani A. M., Kwinta J. B., Khatamian Y. B. and Chen J. J. Characterizing the modulation of resting-state fMRI metrics by baseline physiology. NeuroImage 2018;173: 72-87. PMID: 29452265.
  • Golestani A. M., Faraji-Dana Z., Kayvanrad M. A., Setsompop K., Graham S. J. and Chen J. J. Simultaneous multislice resting-state fMRI at 3 Tesla: Slice-acceleration related biases in physiological-noise effects. Brain Connect 2018; 8: 82-93. PMID: 29226689.
  • Golestani A. M., Kwinta J. B. and Chen J. J. The effect of low-frequency physiological correction on the reproducibility and specificity of resting-state fMRI metrics: Functional connectivity, ALFF and ReHo. Front Neurosci (Special Issue on Reproducibility) 2017; 11: 546. PMCID: PMC5833680.
  • Golestani, A. M., Wei L. L., Kwinta J. B. and Chen J. J. Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults. NeuroImage 2016; 138: 147-163. PMID:  27177763.
  • Khatamian Y. B., Ragot D. M, Golestani A. M. and Chen J. J. Spin-echo resting-state functional connectivity in high-susceptibility areas: Sensitivity, specificity and the role of physiological noise. Brain Connectivity 2016; 6: 283-297. PMID: 26842962.
  • Golestani A. M., Kwinta J. B., Strother S. C., Khatamian Y. B. and Chen J. J. The association between cerebrovascular reactivity on resting-state fMRI functional connectivity: The influence of basal carbon dioxide. NeuroImage 2016; 132: 301-313. PMID: 26908321.
  • Halani, S., Kwinta, J. B., Golestani A. M. and Chen J. J. Comparing cerebrovascular reactivity measured using BOLD and cerebral blood flow imaging: The effect of vascular tension on vasodilatory and vasoconstrictive reactivity. NeuroImage 2015; 110: 110-123. PMID: 25655446. 

Graduate Students

Xiaole Zhong
Yutong Sun