Literature DB >> 30840428

The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols

Glenda M. MacQueen1, Stefanie Hassel1, Stephen R. Arnott1, Addington Jean1, Christopher R. Bowie1, Signe L. Bray1, Andrew D. Davis1, Jonathan Downar1, Jane A. Foster1, Benicio N. Frey1, Benjamin I. Goldstein1, Geoffrey B. Hall1, Kate L. Harkness1, Jacqueline Harris1, Raymond W. Lam1, Catherine Lebel1, Roumen Milev1, Daniel J. Müller1, Sagar V. Parikh1, Sakina Rizvi1, Susan Rotzinger1, Gulshan B. Sharma1, Claudio N. Soares1, Gustavo Turecki1, Fidel Vila-Rodriguez1, Joanna Yu1, Mojdeh Zamyadi1, Stephen C. Strother1, Sidney H. Kennedy1.   

Abstract

Studies of clinical populations that combine MRI data generated at multiple sites are increasingly common. The Canadian Biomarker Integration Network in Depression (CAN-BIND; www.canbind.ca) is a national depression research program that includes multimodal neuroimaging collected at several sites across Canada. The purpose of the current paper is to provide detailed information on the imaging protocols used in a number of CAN-BIND studies. The CAN-BIND program implemented a series of platform-specific MRI protocols, including a suite of prescribed structural and functional MRI sequences supported by real-time monitoring for adherence and quality control. The imaging data are retained in an established informatics and databasing platform. Approximately 1300 participants are being recruited, including almost 1000 with depression. These include participants treated with antidepressant medications, transcranial magnetic stimulation, cognitive behavioural therapy and cognitive remediation therapy. Our ability to analyze the large number of imaging variables available may be limited by the sample size of the substudies. The CAN-BIND program includes a multimodal imaging database supported by extensive clinical, demographic, neuropsychological and biological data from people with major depression. It is a resource for Canadian investigators who are interested in understanding whether aspects of neuroimaging — alone or in combination with other variables — can predict the outcomes of various treatment modalities.
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Year:  2019        PMID: 30840428      PMCID: PMC6606427          DOI: 10.1503/jpn.180036

Source DB:  PubMed          Journal:  J Psychiatry Neurosci        ISSN: 1180-4882            Impact factor:   6.186


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