32 channel nova medical coil

During visit 2, MRI data were obtained from 59/78 individuals who completed visit 1. Of the other 19 individuals, 4 dropped out prior to visit 2; 5 declined the MRI scan due to claustrophobia during a simulated scan; 2 failed MRI safety screening on the day of the scan; and 8 were unable to complete MRI visits due to the COVID-19 pandemic.
MRI data were acquired on a GE MR750 3 T scanner with a 32-channel Nova Medical coil using parallel imaging. MPnRAGE (Kecskemeti et al., 2016 (link)) with retrospective motion correction was used to obtain motion-corrected, 1.0 mm isotropic T1-weighted images (TR = 4.9 ms, TE = 1.8 ms, flip angles = 4°/8° [first 304/remaining 82 views], 200 axial slices, acquisition time = 9:01). We used a modified version of the “high-resolution in-plane thick-slab” approach described previously (Ekstrom et al., 2009 (link); Yushkevich et al., 2015b (link)) to acquire a T2-weighted sequence, with oblique coronal images spanning the length of the hippocampus (TR = 7200 ms, TE = 76 ms, flip angle = 150°, 30 slices, 0.4 mm × 0.4 mm in-plane, 2.0 mm slice thickness, acquisition time = 6:29). Because anatomical changes unfold slowly along the long axis relative to other axes, this approach allows for the identification of distinct hippocampal subfields with a relatively brief acquisition time.

Neuroimaging in Kingston will take place on the 3T Siemens Magnetom Prisma Fit scanner located in the Centre for Neuroscience Studies at Queen’s University. The protocol employs sequences adapted from the Human Connectome Project (http://www.humanconnectomeproject.org/) and diffusion imaging is based on recommendations from DSI Studio (http://dsi-studio.labsolver.org/Manual/b-table-for-qbi-dsi-and-gqi-scans). The established protocol includes the following acquisitions:
A harmonised sequence will be implemented on the 3T GE MR750 scanner located in Halifax with a 32-channel Nova Medical coil and be validated with two human volunteers scanned at both sites. Image quality will be assessed using MRIQC (poldracklab.github.io/mriqc/). Any biases in quantitative metrics between the two sites will be corrected using ComBat, an algorithm first described in genomics that derives a batch-specific transformation to express all data in a common space removing any batch effects using an empirical Bayes framework.45 (link) In this case, the ‘batches’ are the two centres. This approach has been validated in neuroimaging studies46 47 (link) and used in prior multicentre epilepsy neuroimaging studies as part of the ENIGMA Consortium.48 (link)