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, 44 (4), 223-236

The Canadian Biomarker Integration Network in Depression (CAN-BIND): Magnetic Resonance Imaging Protocols

Glenda M. MacQueen  1 , Stefanie Hassel  1 , Stephen R. Arnott  1 , Addington Jean  1 , Christopher R. Bowie  1 , Signe L. Bray  1 , Andrew D. Davis  1 , Jonathan Downar  1 , Jane A. Foster  1 , Benicio N. Frey  1 , Benjamin I. Goldstein  1 , Geoffrey B. Hall  1 , Kate L. Harkness  1 , Jacqueline Harris  1 , Raymond W. Lam  1 , Catherine Lebel  1 , Roumen Milev  1 , Daniel J. Müller  1 , Sagar V. Parikh  1 , Sakina Rizvi  1 , Susan Rotzinger  1 , Gulshan B. Sharma  1 , Claudio N. Soares  1 , Gustavo Turecki  1 , Fidel Vila-Rodriguez  1 , Joanna Yu  1 , Mojdeh Zamyadi  1 , Stephen C. Strother  1 , Sidney H. Kennedy  1 , CAN-BIND Investigator Team
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The Canadian Biomarker Integration Network in Depression (CAN-BIND): Magnetic Resonance Imaging Protocols

Glenda M. MacQueen et al. J Psychiatry Neurosci.
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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.

Conflict of interest statement

G. MacQueen reports consultancy/speaker fees from Lundbeck, Pfizer, Johnson & Johnson and Janssen, outside the submitted work. B. Frey reports grants and personal fees from Pfizer and personal fees from Sunovion, outside the submitted work. R. Milev reports grants, nonfinancial support and honoraria from Lundbeck, Janssen and Pfizer; personal fees and honoraria from Sunovion, Shire, Allergan and Otsuka; grants from Boehringer Ingelheim; and grants from the Ontario Brain Institute, the Canadian Institutes for Health Research and CAN-BIND, outside the submitted work. F. Vila-Rodriguez reports nonfinancial support from Magventure during the conduct of the study; grants from the Canadian Institutes for Health Research, Brain Canada, the Michael Smith Foundation for Health Research, and the Vancouver Coastal Health Research Institute; and personal fees from Janssen, outside the submitted work. S. Rizvi reports grants from Pfizer Canada, outside the submitted work. S. Strother reports grants from Canadian Biomarker Integration Network in Depression during the conduct of the study and grants from Ontario Brain Institute, outside the submitted work. He is also the chief scientific officer of the neuroimaging data analysis company ADMdx, Inc (www. admdx.com), which specializes in brain image analysis to enable diagnosis, prognosis and drug effect detection for Alzheimer disease and various other forms of dementia. R. Lam reports grants from Canadian Institutes of Health Research during the conduct of the study; grants from Asia-Pacific Economic Cooperation, VGH-UBCH Foundation, BC LEading Edge Endowment Fund, Janssen, Lundbeck, Pfizer and St. Jude Medical, outside the submitted work; personal fees from Allergan, Akili, CME Institute, Canadian Network for Mood and Anxiety Treatments, Janssen, Lundbeck, Lundbeck Institute, Pfizer, Otsuka, Medscape and Hansoh, outside the submitted work; travel expenses from Asia-Pacific Economic Cooperation outside the submitted work; and stock options from Mind Mental Health Technologies.

Figures

Fig. 1
Fig. 1
Overview of the CAN-BIND neuroinformatics framework. Data from each site is uploaded to Brain-CODE, where specifically designed pipelines check the data for compliance with scan acquisition parameters, naming convention and completeness. Automatic messages are sent to initiate manual QC. The CAN-BIND neuroinformatics framework also includes pipelines for the analysis of phantom data. CAN-BIND = Canadian Biomarker Integration Network in Depression; fBIRN = Functional Biomedical Informatics Research Network; QC = quality control; SPReD = originally named the Stroke Patient Recovery Research Database; XNAT = Extensible Neuroimaging Archiving Toolkit.
Fig. 2
Fig. 2
Examples of data quality tracking and assessment pipelines. Phantom data are tracked longitudinally to monitor adherence and data quality of imaging protocols. Illustrated here is an example where spiking in the overall mean signal intensity across acquired images at one data acquisition site (light blue) was tracked to be related to its SNR and its SNFR. (A) Mean signal longitudinal: this metric tracks the average overall signal intensity across all voxels and images, per scanning session. (B) SNR longitudinal: this metric tracks the average overall SNR. The mean SNR is the static spatial noise × image across a 21 × 21 voxel region of interest centred on the image. The signal summary value is the average of the signal image across this same region of interest. Then, SNR = (signal summary value)/√(variance summary value/number of time points). (C) SFNR longitudinal: the SFNR is the voxel-wise ratio of the temporal variance standard deviation and temporal mean intensity of the 4-dimensional phantom image after quadratic detrending. The SFNR summary value is the mean SFNR value within the evaluation region of interest (a 21 × 21 voxel region in the centre of the image). SFNR = signal-to-fluctuation-noise ratio; SNR = signal-to-noise ratio.
Fig. 3
Fig. 3
Examples of data quality tracking and assessment pipelines. Phantom data are tracked longitudinally to monitor adherence and data quality of imaging protocols. Illustrated here are examples where the mean intensity of ghost-only voxels showed deviations; investigation and explanation of these anomalies are listed in 1, 2 and 3, below. Mean bright ghost longitudinal: ghost metrics are calculated for each volume by taking a dilated mask (“original mask”) of the data, and shifting it by N/2 voxels in the appropriate axis to create a “ghost mask.” Whereas the mean intensities of those voxels in the ghost mask and not in the original mask is the “mean ghost” value, the “mean bright ghost” is the mean intensity of the top 10% of ghost-only voxels. (1) Anomaly: investigation led to protocol adjustments. (2) Receiver coil failure: addressing failure resulted in data returning to the level seen previously. (3) Anomalies, investigation: corresponding human functional MRI scans acquired around this date appeared fine; subsequent phantom scans were fine.
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