SEED-BASED ANALYSIS OF RESTING-STATE DATA USING SEEBARS at BRAMS, UdeM
This 90 minutes workshop presents the theoretical aspects of the analysis of resting-state data and the installation and usage of the SeeBARS software developed by Thomas Gisiger, research associate at the CRBLM.
MRI scanning of subjects at rest is quickly becoming a major tool for investigating brain function, connectivity and plasticity. However, due to the low signal of interest present in the BOLD (blood-oxygen-level dependent) signal, and because there is no task period to use as regressor (like in typical functional scan paradigm), resting-state data has to be processed and interpreted differently than typical functional MRI signal: BOLD signal needs to be filtered and cleaned using various confound regression, and maps quantifying correlation between the activity in brain regions need to be computed.
In order to accomplish these involved steps with a minimum amount of effort, the center has invested in the creation of a seed-based resting-state analysis pipeline (SeeBARS) that automatically performs all the necessary computations to analyze resting-state data. This pipeline can be used on any number of subjects, sessions and conditions, and also assists the user in verifying results computed at each step. It can be used both to compute functional connectivity maps at the single subject level, and to study changes in functional connectivity between groups.
The pipeline is written as a bash script that can run on any Mac and LINUX stations where FSL (free fMRI analysis software) and Matlab (equipped with the signal-processing toolbox) are installed. Only minimal knowledge of FSL (and none of Matlab) is required to use it and it is readily available, as well as initial technical support, upon request.
When: Wednesday, November 2nd, 2016, 10:00-12:00
Where: BRAMS, 1430 Mont Royal boul. | Suite 0-114 | Université de Montréal | Outremont, QC H2V 4P3
The workshop is free.
To register, please, fill in the form below. The workshop is limited to 20 participants.