Colleen A. Brenner

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

S. Strother, R. Lam, S. Kennedy, 2020, JAMA network open.

Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression

R. Lam, S. Kennedy, B. Frey, 2017, Scientific Reports.

Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

R. Lam, Z. Daskalakis, F. Farzan, 2018, Front. Pharmacol..

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort

Stephen C. Strother, Moyez Dharsee, Luciano Minuzzi, 2016, BMC Psychiatry.

The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: A pilot study.

R. Lam, S. Kennedy, G. MacQueen, 2018, Journal of affective disorders.

Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression

Arun Ravindran, Moyez Dharsee, Roumen Milev, 2017, Scientific Reports.