Sidney Kennedy



Research Programs


Sidney H. Kennedy is Professor of Psychiatry and Arthur Sommer Rotenberg Chair in Depression and Suicide Studies at the University of Toronto, Director of the Centre for Suicide and Depression Studies at St. Michael’s Hospital, and a Scientist at Li Ka Shing Knowledge Institute and Krembil Research Institute in Toronto. Dr. Kennedy is the lead investigator for the Canadian Biomarker Integration Network in Depression (CAN-BIND), a national depression biomarker initiative. He has published extensively on new drug evaluation, neuroimaging and neurostimulation therapies, personality factors in depression, antidepressant effects on sexual function, and treatment guidelines for Major Depressive Disorder and Bipolar Disorder.

Dr. Kennedy is the Immediate Past President of the International Society for Affective Disorders, former President of the Canadian College of Neuropsychopharmacology, and the founding chair of the Canadian Network for Mood and Anxiety Treatments (CANMAT).

Dr. Kennedy has authored close to 500 peer-reviewed publications and 11 books on depression and related topics. He is a Fellow of the Canadian Academy of Health Sciences and the Royal Society of Canada.

Recent Publications

  1. Schwartzmann, B, Quilty, LC, Dhami, P, Uher, R, Allen, TA, Kloiber, S et al.. Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study. Sci Rep. 2023;13 (1):8418. doi: 10.1038/s41598-023-35179-4. PubMed PMID:37225718 PubMed Central PMC10209049.
  2. Husain, MI, Foster, JA, Mason, BL, Chen, S, Zhao, H, Wang, W et al.. Pro-inflammatory markers are associated with response to sequential pharmacotherapy in major depressive disorder: a CAN-BIND-1 report. CNS Spectr. 2023; :1-28. doi: 10.1017/S109285292300233X. PubMed PMID:37218291 .
  3. Demchenko, I, Desai, N, Iwasa, SN, Gholamali Nezhad, F, Zariffa, J, Kennedy, SH et al.. Manipulating facial musculature with functional electrical stimulation as an intervention for major depressive disorder: a focused search of literature for a proposal. J Neuroeng Rehabil. 2023;20 (1):64. doi: 10.1186/s12984-023-01187-8. PubMed PMID:37193985 PubMed Central PMC10190013.
  4. Ashorn, P, Ashorn, U, Muthiani, Y, Aboubaker, S, Askari, S, Bahl, R et al.. Small vulnerable newborns-big potential for impact. Lancet. 2023;401 (10389):1692-1706. doi: 10.1016/S0140-6736(23)00354-9. PubMed PMID:37167991 .
  5. Demchenko, I, Tassone, VK, Dunnett, S, Balachandar, A, Li, S, Anderson, M et al.. Impact of COVID-19 on electroconvulsive therapy practice across Canadian provinces during the first wave of the pandemic. BMC Psychiatry. 2023;23 (1):327. doi: 10.1186/s12888-023-04832-7. PubMed PMID:37165333 PubMed Central PMC10170445.
  6. Dama, M, Wu, M, Tassone, VK, Demchenko, I, Frey, BN, Milev, RV et al.. The course of insomnia symptoms during the acute treatment of major depressive disorder: A CAN-BIND-1 report. Psychiatry Res. 2023;325 :115222. doi: 10.1016/j.psychres.2023.115222. PubMed PMID:37163883 .
  7. Kennedy, SH, Bekele, M, Berlin, NL, Ranganathan, K, Hamill, JB, Haileselassie, E et al.. A Prospective Evaluation of the Quality of Life and Mental Health Implications of Mastectomy Alone on Women in sub-Saharan Africa. Ann Surg. 2023; :. doi: 10.1097/SLA.0000000000005891. PubMed PMID:37144388 .
  8. Chin Fatt, CR, Asbury, S, Jha, MK, Minhajuddin, A, Sethuram, S, Mayes, T et al.. Leveraging the microbiome to understand clinical heterogeneity in depression: findings from the T-RAD study. Transl Psychiatry. 2023;13 (1):139. doi: 10.1038/s41398-023-02416-3. PubMed PMID:37117195 PubMed Central PMC10147668.
  9. Rahmioglu, N, Mortlock, S, Ghiasi, M, Møller, PL, Stefansdottir, L, Galarneau, G et al.. The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions. Nat Genet. 2023;55 (3):423-436. doi: 10.1038/s41588-023-01323-z. PubMed PMID:36914876 PubMed Central PMC10042257.
  10. Lee, LH, Bradburn, E, Craik, R, Yaqub, M, Norris, SA, Ismail, LC et al.. Machine learning for accurate estimation of fetal gestational age based on ultrasound images. NPJ Digit Med. 2023;6 (1):36. doi: 10.1038/s41746-023-00774-2. PubMed PMID:36894653 PubMed Central PMC9998590.
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Affiliations & Other Activities

  • Staff Physician, Department of Mental Health, St. Michael’s Hospital
  • Professor, Department of Psychiatry, University of Toronto
  • Research Scientist, Krembil Neurosciences, University Health Network