Bradley MacIntosh

At A Glance

Our primary desire is to utilize non-invasive brain imaging techniques to study chronic diseases through a vascular lens with the goal of identifying new therapies. Additionally, our focus entails using artificial intelligence and deep machine learning to guide neurosurgical applications for stroke treatment. In summary, the novel use of existing tools and the development of new techniques aim to improve our current understanding of brain health, treatment, and recovery.

Short Bio

Bradley MacIntosh is a Senior Scientist within the Hurvitz Brain Sciences program & Physical Sciences platform at Sunnybrook Research Institute and a core member of the Heart and Stroke Foundation Canadian Partnership for Stroke Recovery. He is appointed as a Senior Scientist within the Computational Radiology & Artificial Intelligence group at Oslo University Hospital in Norway. Brad holds an academic post as an Associate Professor in the Department of Medical Biophysics at the University of Toronto, which is also where he completed his PhD in 2006. His MSc was at the Robarts Research Institute at Western University, supervised by Ravi Menon. After completing his postdoctoral fellowship at the University of Oxford’s Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), under the supervision of Professor Peter Jezzard, Brad returned to Toronto to work at Sunnybrook Research Institute.

Research Synopsis

The MacIntosh lab focuses on developing and translating vascular imaging tools and measures to advance our understanding of human brain disease. Magnetic Resonance Imaging (MRI) is a versatile medical imaging modality, and we use this method for  functional imaging to study neurovascular function, cerebral blood flow, and related aspects of brain physiology and pathophysiology.

METHODS: Our primary method of Arterial Spin Labeling (ASL) magnetic resonance technique is predominant in our research activities. This technique produces non-invasive cerebral blood flow maps with spatial resolution that approaches the millimeter details of a conventional anatomical picture. Our ASL innovation includes image processing of individual scans, a pipeline to use these images in group analysis or multi-site trials, and additional ASL hemodynamic features (such as the arterial transit time and the spatial coefficient of variation). On-going efforts arousing deep learning to improve physiological MRI in radiology applications.

CLINICAL MOTIVATION: With our focus on physiological and pathophysiological imaging techniques, the desired outcome is to help patient populations in the areas of stroke, small vessel disease, Alzheimer’s disease, and diabetes. For instance, the MacIntosh lab works closely with the Sunnybrook Centre for Youth Bipolar Disorder (CYBD) as the non-invasive ASL technique makes it possible to map cerebral blood flow without ionizing radiation – a key component in protecting the developing brain while conducting such research. We also use aerobic exercise interventions to study brain adaption and changes with aerobic fitness.

COLLABORATION: The MacIntosh lab has a focus on clinical translation and work with Clinician-Scientist colleagues. The lab is involved in a variety of internal, national, and international initiatives which includes a national drug trial through the Canadian Partnership for Stroke Recovery, an international LeDucq Foundation international network to study perivascular spaces in small vessel disease, brain vascular changes in people with diabetes, as well as using deep learning tools to characterize brain diseases at the Computational Radiology & Artificial Intelligence unit (CRAI.no) at the Oslo Hospital University.

Recent Publications

• Qinghui, L, MacIntosh, B. J., Schellhorn, T., Skogen, K., Emblem, K., & Bjønerud, A. (in press). Voxels intersecting along orthogonal levels attention U-Net (viola-Unet) to segment intracerebral haemorrhage using computed tomography head scans. arXiv:2208.06313.
• Luciw, N. J., Shirzadi, Z., Black, S. E., Goubran, M., & MacIntosh, B. J. (2022, February 19). Automated generation of cerebral blood flow and arterial transit time maps from multiple delay arterial spin-labeled MRI. Magnetic Resonance in Medicine, 88(1), 406-417.
• MacIntosh, B. J., Ji, X., Chen, J. J., Gilboa, A., Roudala, E., Sekuler, A. B., Gao, F., Chad, J. A., Jegatheesan, A., Masellis, M., Goubran, M., Rabin, J., Lam, B., Cheng, I., Fowler, R., Heyn, C., Black, S. E., & Graham, S. J. (2021, November 30). Brain structure and function in people recovering from COVID-19 after hospital discharge or self-isolation: a longitudinal observational study protocol. Canadian Medical Association Journal Open, 9(4), E1114-E1119.
• Kim WSH, Luciw NJ, Atwi S, Shirzadi Z, Dolui S, Detre JA, Nasrallah IM, Swardfager W, Bryan RN, Launer LJ, MacIntosh BJ. Associations of white matter hyperintensities with networks of gray matter blood flow and volume in midlife adults: A coronary artery risk development in young adults magnetic resonance imaging substudy. Hum Brain Mapp. 2022 Aug 15;43(12):3680-3693. doi: 10.1002/hbm.25876. PMID: 35429100.
• Atwi S, Robertson AD, Theyers AE, Ramirez J, Swartz RH, Marzolini S, MacIntosh BJ. Cardiac-Related Pulsatility in the Insula Is Directly Associated With Middle Cerebral Artery Pulsatility Index. J Magn Reson Imaging. 2020 May;51(5):1454-1462. doi: 10.1002/jmri.26950. PMID: 31667941.

Visit the Google Scholar profile of Dr. Bradley MacIntosh.

Graduate Students

Ben Martin
Guocheng Jiang