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

Teller N., Chad J. A., Wong A., Gunraj H., Gilboa A., Roudaia E., Sekuler A., Gao F., Jegatheesan A., Masellis M., Goubran M., Rabin J. S., Lam B., Cheng I., Fowler R., Heyn C., Black S. E., MacIntosh B. J., Ji X., Graham S. J. and Chen J. J. Feasibility comparison of diffusion-tensor and correlated-diffusion imaging for studying white-matter abnormalities: application to COVID-19.Hum Brain Mapp. 2023; 44: 3998-4010. 

Han A., Chad J. A., Dhollander T., Chen J. J. Fixel-based analysis of aging white matter reveals selective fibre-specific degeneration. Neurobiol Aging 2023. In press. (Senior responsible author).

69        Srisaikaew P., Chad J. A., Mahakkanukrauh P., Anderson N. and Chen J. J. Effect of sex on the APOE4-aging interaction in the white matter microstructure of cognitively normal older adults using the novel orthogonal-tensor decomposition DTI.  Front Neurosci 2023; 17: 1049609.(Senior responsible author)

 Agrawal V., Zhong X. Z. and Chen J. J.  Generating dynamic carbon dioxide traces from respiratory-volume recordings: Feasibility using neural networks and application in functional magnetic resonance imaging. Front Neuroimaging 2023. Epub ahead of print doi: 10.3389/fnimg.2023.1119539 (Senior responsible author)

Patel A., Chad J. A., Chen J. J. Is adiposity associated with white matter health and intelligence equally in men and women? J Obesity 2023; 31: 1011-1023. Selected as Editor’s Choice. 

Shams S., Prokopiou P., Esmaelbeigi A., Mitsis G, Chen J. J. Model-based deconvolution methods for estimating the carbon-dioxide response function in fMRI. Neuroimage 2022; 265: 119758.  

Tan J. L., Ragot D. M. and Chen J. J. Characterization of the echo-time dependence of spin-echo EPI BOLD contrast at 3 Tesla in grey and white matter. J Neuromethods 2022. 381: 109691

Taha H., Chad J. A. and Chen J. J. DKI enhances the sensitivity and interpretability of age-related DTI patterns in the white matter of UK Biobank participants. Neurobiol Aging 2022; 115: 35-45. PMID: 35468551. 

Zhong X. Z., and Chen J. J. Resting-state fMRI signal variations in aging: The role of neural activity. Hum Brain Mapp 2022; 43: 2880-2897. PMID: 35293656 (Senior responsible author)

Attarpour A., Ward J. and Chen J. J.Vascular origins of low-frequency oscillations in the cerebrospinal fluid signal in resting-state fMRI: validation using photoplethysmography. Hum Brain Mapp 2021; 42: 2606-2622. PMID: 33638224. 

Chad J. A., Pasternak O. and Chen J. J. Orthogonal diffusion tensor decomposition reveals age-related degeneration patterns in complex fibre architecture.  Neurobiol Aging 2021; 101: 151-159. PMID: 33610963. 

Shams S. M., Levan P. and Chen J. J.Neuronal associations of respiratory-volume variations.NeuroImage 2021; 230: 117783. PMID: 33516896.

Hussein A., Matthews J. L., Symes C., Macgowan, C, MacIntosh B. J., Shirzadi Z., Pausova Z., Paus T. and Chen J. J.The association between aortic pulse-wave velocity and resting-state fMRI. Hum Brain Mapp 2020; doi:10.1002/hbm.24934. PMID: 32034832. 

Yuen N. H., Osachoff N. and Chen J. J.. Intrinsic frequencies of the resting-state fMRI signal: the frequency dependence of functional connectivity and the effect of mode mixing. Front Neurosci 2019; 4: 900. PMID: PMC6738198.

Graduate Students

Xiaole Zhong
Yutong Sun