From OpenWetWare
Revision as of 15:59, 11 November 2008 by Michael S Beauchamp (talk | contribs) (Processing Subject Data)
Jump to: navigation, search
Brain picture
Beauchamp Lab Notebook

Old Protocols

September 2008 E-Phys Protocol

9/22/2008 Setup and Surgery
1. Identify potential electrodes with combined fMRI/CT
2. Create log file on log computer
3. Record resting activity and assess recording setup (Test signal-to-noise, etc.)

Microelectrode array experiments
If the new electrodes are ready, it will be a top priority to investigate their properties.
Some things to look at:

1. Stimulus selectivity
How does the stimulus-selectivity of microelectrodes differ from large electrodes?
How does stimulus-selectivity vary within a single microelectrode array (MEA)?
2. Response latency/duration
Within an object-selective visual area, do different patches of cortex depolarize at different latencies or for different durations?
3. Receptive Field Size
Do small electrodes have different RFs than big electrodes?
How does RF size vary within and between object-selective visual areas?
4. Gamma oscillation
Are oscillations in the gamma band more correlated among nearby electrodes than far electrodes?
5. Adaptation
Do microelectrodes demonstrate different adaptation behavior than large electrodes?
Do different microelectrodes within an ME array adapt differently to the same repeated stimuli?
6. Choice probability
Does the pattern of activity in object-selective cortical areas (as measured by MEAs) correlate in any way with the subject's behavior?


1. Perception thresholds
Perception thresholds may be lower with microelectrodes because of high current density.
2. Percepts in higher visual areas
If the microelectrodes have smaller RFs or are more feature-selective than the large electrodes, it could mean that sending current through them would activate a more functionally-segregated group of neurons than has previously been possible. If so, microstimulation with microelectrodes may be more likely to evoke percepts in higher-level visual areas than with the large electrodes.

Ryan's Adaptation Experiments
1) Target-detection Selectivity – 20 minutes

• 80 stimuli (+5 target stimuli), 20 blocks
• Stop-sign task (patient reports stop-signs)

2) Repeat-detection Selectivity – 20 minutes

• 80 stimuli, 20 blocks
• One-back task (patient reports repetitions of images)

3) Multiple-repetitions adaptation - 5 minutes

• 20 selective stimuli, 20 nonselective stimuli, 2 Hz presentation, patient is told to press button when he/she sees the stopsign.
• trial structure: 8 nonselective stimuli, 8 of the same selective stimulus, 8 nonselective stimuli, 8 different selective stimuli. Stopsign appear at random or at the end of each run.
• A rudimentary way to approximate this would be to have several copies of the same image in the image folder, and selecting all of them in the selectivity plugin.


• Now that we can record from all the electrodes simultaneously, it will be less important to identify selective categories. By showing equal amount of all the categories, we can get good sample sizes for several differently-selective electrodes.
•It would be nice to have a modified version of the current Selectivity plugin to satisfy these conditions:
1. the patient is supposed to press the button after stop-signs instead of after repeats
2. Selective stimuli are directly repeated every 8-12 images
3. The stimulus preceding the repeat is always a nonselective stimulus

Electrophysiology Protocols

Presurgical Scanning

After analysing fMRI data, upload the entire contents of the AFNI and SUMA directories to Xfiles. This can be simplfied by Apple-K (Connect to Server) in Finder and choosing XFiles; (

then the folders can be dragged from the server to Xfiles, or copied in the command line, easily (without using the Web-based GUI interface).

In the EMU

Setup Apparatus

Receptive Field Mapping

Electrical Stimulation


Perceptual Biasing


Making Grayscale From Color Images

It is also good to collect 10 minutes of resting data (no stimulation) from as many visual electrodes as possible for later analyses.

Todo list

Decide on screening stimuli i.e. pick 20 from each category faces, houses, bodies, scenes, tools, scrambles
Get rid of bad looking stimuli; make detailed protocol
Install matlab in EMU to allow image scrambling; install scrambling program.

1/16/2008 Add peak deflection (either -,+ or ABS) to RMS power measurement when ranking stimuli.

Processing Subject Data

After obtaining the CD containing the patient CT data from St. Luke's, use OsiriX to export all images (using the export to DICOM option, and the hierarchical, uncompress options).

CT scans have voxel size 0.488x0.488x1 mm; this may need to be adjusted manually with

 3drefit -zdel 1.000 DE_CTSDE+orig

(If the CTs look distorted in AFNI, then the voxel size must be adjusted). Next, the CTs must be registered with the hi-res presurgical MRI anatomy. This may fail because the CT has a coordinate system with a very different origin than the MRI. Registration routines will not work if the input datasets are not in rough alignment. To check this, type

 3dinfo DE_CTSDE+orig


 R-to-L extent:  -124.756 [R] -to-   124.756 [L] -step-     0.488 mm [512 voxels]
 A-to-P extent:  -124.756 [A] -to-   124.756 [P] -step-     0.488 mm [512 voxels]
 I-to-S extent:  -258.000 [I] -to-   -86.000 [I] -step-     1.000 mm [173 voxels]

We want the center of the dataset to be roughly at (0,0,0). For this example, this is true for (x,y) but not for z. First, create a copy of the dataset

 3dcopy DE_CTSDE+orig DE_CTSDEshift

Then, recenter the z-axis

 3drefit -zorigin 80 DE_CTSDEshift+orig

3dinfo returns

 R-to-L extent:  -124.756 [R] -to-   124.756 [L] -step-     0.488 mm [512 voxels]
 A-to-P extent:  -124.756 [A] -to-   124.756 [P] -step-     0.488 mm [512 voxels]
 I-to-S extent:   -80.000 [I] -to-    92.000 [S] -step-     1.000 mm [173 voxels]

The z-axis is now roughly centered around 0. In AFNI, examine the MR and the shifted CT to make sure they are in rough alignment. Next, use 3dAllineate to align the two datasets.

 3dAllineate -base {$ec}anatavg+orig -source DE_CTSDEshift+orig -prefix {$ec}CTSDE_REGtoanatV4 -verb -warp shift_rotate -cost mutualinfo -1Dfile {$ec}CTSDE_REGtoanatXformV4

Check in AFNI to make sure that they alignment is correct. NB: It is also possible to crop the MRI before Allineating since the MR coverage is typically greater than the CT coverage. In a test case, this did not have a big effect.

Next, the electrodes positions are manually located and saved as Tags. Then, these positions can be used to create an Electrodes file for display as spheres in SUMA. To label the electrodes, a separate file can be created with the label for each electrode.

 Sample file electrodes.1D
 -20.43       81.53       4.254  0.0 0.0 0.0 1.0  1.1   
 -28.43       76.84       4.254  0.0 0.0 0.0 1.0  1.1           
 -36.43       70.28       4.254  0.0 0.0 0.0 1.0  1.1             
 -42.43       61.84       4.254  0.0 0.0 0.0 1.0  1.1           
 -48.43       54.34       4.254  0.0 0.0 0.0 1.0  1.1            
 -52.43       44.03       4.254  0.0 0.0 0.0 1.0  1.1            
 Sample file
 coord_type = "mobile"
 default_SO_label = "CURRENT"
 bond = "surface"
   default_color = '1.0 1.0 1.0'
 default_font = 'he14'
 coord = "-20.43       81.53       4.254"
 col = "0.1 0.9 0.1"
 text = "RPIT1"


Things to do


Can stimuli be vector-based rather than pixel based, so as not to lose resolution with scaling? POSSIBLE if original file is vector-based
Enable online scrambling LOOKING INTO IT
Enable online color to black and white conversion LOOKING INTO IT


Analyze data from LR to see where the RFs are


First, quit Knot. Then, unplug one ITC from the USB port, wait for the power light to go off, plug it back in. Repeat this, one at a time, for all ITCs. Before beginning experiment, always make sure you get nice traces in Channel Data window, not just flat lines (even if looks nice on oscilloscope).

1-AFC task: change feedback so that it is not always correct.

can we extend stimulation into the response window?