Alexandre Boutet

At A Glance

• Develops neuroimaging biomarkers combined with machine learning to personalize neurosurgical therapies: moving from disease-specific to feature-specific treatment.

• Uses functional MRI to predict and optimize deep brain stimulation (DBS) outcomes in Parkinson's disease, with translation to treatment-resistant psychiatric and pain disorders.

• Establishes the safety of MRI in patients with implanted neuromodulation devices, enabling imaging across field strengths from 0.5T to 7T to directly probe brain function.

• Maps the brain networks engaged by neuromodulation (DBS and MR-guided focused ultrasound) using functional connectomics across movement and psychiatric disorders.

• Bridges diagnostic neuroradiology, neurosurgery, and neurology to translate advanced imaging into individualized clinical decision-making and improved treatment access.

Short Bio

Originally from Quebec city, Alex completed his medical school at McGill University and developed his passion for neuroscience at the Montreal Neurological Institute. He pursued a residency in radiology and a PhD in brain modulation at the University of Toronto followed by a neuroradiology fellowship. He now works as a neuroradiologist at the University Health Network in Toronto. His main interest is to use neuroimaging to improve patient care. Outside of work, he enjoys travelling and social gathering with friends. 

Research Synopsis

Dr. Boutet's research program applies advanced neuroimaging and machine learning toward individualized medicine in neurological and psychiatric disease. The central premise is that a patient's unique brain features should guide therapy. By characterizing individual functional and structural brain signatures, his work aims to enable feature-specific rather than disease-specific treatment, with deep brain stimulation (DBS) for Parkinson's disease serving as the main proof-of-concept. His group has shown that functional MRI acquired during active stimulation, coupled with machine-learning models, can predict optimal DBS parameters and reveal symptom-specific brain networks, and has advanced these methods from proof-of-concept into blinded, crossover clinical trials of fMRI-guided DBS programming.

A second pillar of the program addresses a long-standing barrier to imaging these patients: MRI safety in the presence of implanted neuromodulation devices. Dr. Boutet has led phantom and patient studies defining safe MRI practices for conventional, and directional DBS systems, and is now extending this work to low-field (0.5T) and ultra-high-field (7T) strengths. Establishing safety across this field range opens an unprecedented window for directly probing brain function in implanted patients, and connects naturally to his parallel interest in MR-guided focused ultrasound and other MRI-compatible neuromodulation technologies. Throughout, functional connectomics are used to map how neuromodulation engages distributed brain networks, informing both target selection and outcome prediction.

This work is translational and highly collaborative, sitting at the interface of diagnostic neuroradiology, neurosurgery, neurology, and medical physics. As a clinician investigator at the Krembil Research Institute and Slaight Neuroradiology Research Lead at UHN's Slaight Family Centre for Advanced MRI, Dr. Boutet leads a portfolio of peer-reviewed grants, including CIHR, Michael J. Fox Foundation, and NIH, spanning precision-medicine biomarkers for DBS, dual-field MRI methods for brain mapping, and MRI-compatible innovations for neuromodulation. The overarching goal is to convert cutting-edge imaging into practical tools that improve outcomes, patient selection, and access for people undergoing neuromodulation therapies.

Recent Publications

1. Au HCT, Louka A, Kashyap S, et al. A systematic review exploring the safety of deep brain stimulation at MRI field strengths beyond conventional field strengths. AJNR Am J Neuroradiol. 2026.

2. Santyr B, Boutet A, Germann J, et al. fMRI-based deep brain stimulation programming for Parkinson's disease: a blinded, crossover clinical trial. Brain Stimulation. 2026;19(2).

3. Santyr B, Boutet A, Ajala A, et al. Functional network differences between unilateral and bilateral deep brain stimulation of the subthalamic nucleus. npj Parkinsons Dis. 2026;11(1):215.

4. Santyr B, Loh A, Germann J, Boutet A, et al. The pattern of fMRI activation with STN DBS reveals symptom-specific networks. npj Parkinson's Disease. 2026.

5. Boone L, Shafie M, Naeem A, et al. Neuroimaging in lesioning therapy for obsessive-compulsive disorder: region-based and network analysis of preoperative outcome predictors and postoperative effects. NeuroImage: Clinical. 2026.

6. Loh A, Matossian GA, Santyr B, et al. Functional MRI of DBS using monopolar stimulation: contextualizing recent phantom findings. J Neurosurg. 2026.

7. Patel Y, Zou R, Nino ACV, et al. Magnetic resonance imaging use in pediatric deep brain stimulation: a systematic review and call for standardization. Neuroradiology. 2026;68:1861-9.

8. Hu CK, Mohammed WB, Bai Y, et al. Limited predictive value of preoperative nigrosome integrity for motor outcomes in Parkinson's disease deep brain stimulation. npj Parkinsons Dis. 2025;11(1):343.

9. Pai V, Boutet A, Malik M, et al. Differentiating CSF flow artifacts from pathology: an educational review. Insights Imaging. 2025;16(1):288.

10. Boutet A, Germann J, Fasano A. Imaging and neuromodulation in Parkinson's disease. Curr Opin Neurol. 2025;38(4):322-327.

11. Santyr B, Boutet A, Ajala A, et al. Emerging Techniques for the Personalization of Deep Brain Stimulation Programming. Can J Neurol Sci. 2025.

12. Ludovichetti R, Chow CT, Kashyap S, et al. Phantom Safety Assessment of 3 Tesla Magnetic Resonance Imaging in Directional and Sensing Deep Brain Stimulation Devices. Stereotact Funct Neurosurg. 2025.

13. Buongermini R, Bichsel O, Schmidt FA, et al. MR-guided focused ultrasound thalamotomy in a patient with thrombocytopenia: illustrative case. J Neurosurg Case Lessons. 2025;10(3).

14. Yang AZ, Boutet A, Pai V, et al. Imaging Findings of Intracerebral Infection after Deep Brain Stimulation: Pediatric Case Series and Literature Review. Mov Disord Clin Pract. 2025;12:242-5.

15. McKee H, Rohringer T, Yang AZ, et al. Temporomandibular joint lesions with intracranial extension: illustrative cases from a systematic review. AJNR Am J Neuroradiol. 2025.

16. Boutet A, Malik M, Yang AZ, et al. Focal leptomeningeal vascular anomalies on brain MRI: A mimic of leptomeningeal metastatic disease. Neurooncol Pract. 2024.

17. Boutet A, Haile SS, Yang AZ, et al. Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology. AJNR Am J Neuroradiol. 2024.

18. Ozkara BB, Boutet A, Comstock BA, et al. Artificial Intelligence-Generated Editorials in Radiology: Can Expert Editors Detect Them? AJNR Am J Neuroradiol. 2024.

19. Elias GJB, Germann J, Joel SE, Li N, Horn A, Boutet A, Lozano AM. A large normative connectome for exploring the tractographic correlates of focal brain interventions. Sci Data. 2024;11(1):353.

Landmark publications

1. Boutet A, Madhavan R, Elias GJ, et al. Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning. Nat Commun. 2021;12(1):1-13.

2. Elias GJ, Boutet A, Joel SE, et al. Probabilistic mapping of deep brain stimulation: insights from 15 years of therapy. Ann Neurol. 2021;89(3):426-43.

3. Boutet A, Chow CT, Narang K, et al. Improving Safety of MRI in Patients with Deep Brain Stimulation Devices. Radiology. 2020;296(2):250-62.

4. Boutet A, Rashid T, Hancu I, et al. Functional MRI safety and artifacts during deep brain stimulation: experience in 102 patients. Radiology. 2019;293(1):174-83.

Graduate Students

TBD