retinotopic mapping workshop

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

• 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/MrVista

Step 1 – The inplane viewAnatomical view

Step 1 – The inplane viewMean fMRI BOLD amplitude map

Step 1 – The inplane viewComputing the correlation analysis

Step 1 – The inplane viewFirst view of phase-encoded data

Step 1 – The inplane viewMultiple slices through the same dataset

Step 1 – The inplane viewDefining an ROI – use CTRL-R or the ‘ROI’ menu

Step 1 – The inplane viewAccessing the plotting tools

Step 1 – The inplane viewA sample fMRI time course

Step 1 – The inplane viewAverage of a single cycle

Step 1 – The inplane viewFFT of a mean time series

Step 1 – The inplane viewFFT of a mean time series after data averaging

Step 1 – The inplane viewPlot single cycle time courses in a set of ROIs down the calcarine sulcus

Step 1 – The inplane viewAveraging 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

Alignmenthttp://white.stanford.edu/newlm/index.php/RxAlign

Alignmenthttp://white.stanford.edu/newlm/index.php/RxAlign

Alignmenthttp://white.stanford.edu/newlm/index.php/RxAlign

Alignmenthttp://white.stanford.edu/newlm/index.php/RxAlign

Alignmenthttp://white.stanford.edu/newlm/index.php/RxAlign

Alignmenthttp://white.stanford.edu/newlm/index.php/RxAlign

Alignmenthttp://white.stanford.edu/newlm/index.php/RxAlign

Alignmenthttp://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