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Correcting for eddy currents, movement-induced distortion and gradient inhomogeneities is imperative when processing diffusion MRI (dMRI) data, but is highly computing resource-intensive. Recently, Compute Unified Device Architecture (CUDA) was implemented for the widely-used eddy-correction software, 'eddy', which reduces processing time and allows more comprehensive correction. We investigated processing speed, performance and compatibility of CUDA-enabled eddy-current correction processing compared to commonly-used non-CUDA implementations. Four representative dMRI datasets from the Human Connectome Project, Alzheimer's Disease Neuroimaging Initiative and Chronic Diseases Connectome Project were processed on high-specification and regular workstations through three different configurations of 'eddy'. Processing times and graphics processing unit (GPU) resources used were monitored and compared. Using CUDA reduced the 'eddy' processing time by a factor of up to five. The CUDA slice-to-volume correction method was also faster than non-CUDA eddy except when datasets were large. We make a series of recommendations for eddy configuration and hardware. We suggest that users of eddy-correction software for dMRI processing utilise CUDA and take advantage of the slice-to-volume correction option. We recommend that users run eddy on computers with at least 32GB motherboard random access memory (RAM), and a graphics card with at least 4.5GB RAM and 3750 cores to optimise processing time.

Background: Structural neuroimaging studies revealed a consistent pattern of volumetric reductions in both hippocampus (HC) and anterior cingulate cortex (ACC) of individuals with major depressive episode(s) (MDE). This study investigated HC and ACC volume differences in currently depressed individuals (n = 150), individuals with a past lifetime MDE history (n = 79) and healthy controls (n = 287)., Methods: Non-demented individuals were recruited from a cohort of community-dwelling older adults (ESPRIT study). T1-weighted magnetic resonance images and FreeSurfer Software (automated method) were used. Concerning HC, a manual method of measurement dividing HC into head, body, and tail was also used. General Linear Model was applied adjusting for covariates., Results: Current depression was associated with lower left posterior HC volume, using manual measurement, in comparison to healthy status. However, when we slightly changed sub-group inclusion criteria, results did not survive to correction for multiple comparisons., Conclusions: The finding of lower left posterior HC volume in currently depressed individuals but not in those with a past MDE compared to healthy controls could be related to brain neuroplasticity. Additionally, our results may suggest manual measures to be more sensitive than automated methods., (Copyright © 2018 Elsevier Ltd. All rights reserved.)