Retinotopic mapping workshop
COSMO 2012
Starting materials
• In the folder ‘COSMO’ you will find raw data
and toolboxes – as if you had just finished an
fMRI retino mapping scan
• Your mission is to process these data to the
stage where you can see phase-encoded
retinotopy on the cortical surface.
• How many visual areas can you delineate?
Stages I
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Examine raw (aligned) data
Average time series
Compute coherence / phase maps
Visualize data in the ‘Inplane’ view
Plot (and understand) time series from
different parts of the brain
Stages II
• Compute alignment between ‘Inplanes’ and a
high resolution reference anatomy
• Project data into this ‘Gray’ view
• Find the calcarine sulcus. Plot times series
along this anatomical feature
• Render the surface of the brain as a 3D mesh
• Project retinotopy data to this surface.
• Inflate the surface
Stages III
• Create flattened representations of the left
and right occipital cortex
• Project data to ‘Flat maps’
• Identify the borders of visual areas.
• Create and label ROIs around these areas
• Project these ROIs back to th Gray view
• Render them.
Help!
http://white.stanford.edu/Wiki.php
http://white.stanford.edu/newlm/index.php/Mr
Vista
Step 1 – The inplane view
Anatomical view
Step 1 – The inplane view
Mean fMRI BOLD amplitude map
Step 1 – The inplane view
Computing the correlation analysis
Step 1 – The inplane view
First view of phase-encoded data
Step 1 – The inplane view
Multiple slices through the same dataset
Step 1 – The inplane view
Defining an ROI – use CTRL-R or the ‘ROI’ menu
Step 1 – The inplane view
Accessing the plotting tools
Step 1 – The inplane view
A sample fMRI time course
Step 1 – The inplane view
Average of a single cycle
Step 1 – The inplane view
FFT of a mean time series
Step 1 – The inplane view
FFT of a mean time series after data averaging
Step 1 – The inplane view
Plot single cycle time courses in a set of ROIs down the calcarine sulcus
Step 1 – The inplane view
Averaging time series data across scans
Alignment
• Purpose: Compute an affine transformation
between the ‘Inplane’ anatomical data and a
high-resolution anatomical dataset.
• Segmentation, mesh generation has been
performed on the highres anatomy already
• If all functional datasets are transformed into
this space then data from different expt on
the same subject can be compared
Alignment
http://white.stanford.edu/newlm/index.php/RxAlign
Alignment
http://white.stanford.edu/newlm/index.php/RxAlign
Alignment
http://white.stanford.edu/newlm/index.php/RxAlign
Alignment
http://white.stanford.edu/newlm/index.php/RxAlign
Alignment
http://white.stanford.edu/newlm/index.php/RxAlign
Alignment
http://white.stanford.edu/newlm/index.php/RxAlign
Alignment
http://white.stanford.edu/newlm/index.php/RxAlign
Alignment
http://white.stanford.edu/newlm/index.php/RxAlign
Install segmentation
Install segmentation
Install segmentation
Install segmentation
View data in high-resolution anatomy
Rendering on flat map
Flattening…
Flattening…
Flattening…
Flattening…
This is the end…
• Or in fact the beginning
• How many visual areas can you find?
• Define ROIs around some of them (use the
polygon ROI tool)
• Plot some statistics (co vs phase?)
More
• Transform ROIs back into the Gray view.
• Build and render a 3D mesh in this view (you
will have to find and run mrmMeshSrv.exe
first)
• Look at data on the inflated mesh
• Extract voxel-level data from an ROI