Beauchamp:Electrophysiology

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If the new electrodes are ready, it will be a top priority to investigate their properties. A good start will be to compare small electrode LFPs with nearby big electrode LFPs.<br>
If the new electrodes are ready, it will be a top priority to investigate their properties. A good start will be to compare small electrode LFPs with nearby big electrode LFPs.<br>
<br>
<br>
-
'''A. Recording questions:'''<br>
 
-
 
1) LFP selectivity <br>
1) LFP selectivity <br>
-
:Are small electrodes more selective within-category than large electrodes? <br>
+
:Are small electrodes more selective within-category than large electrodes? <br>
2) Response amplitude/latency/duration <br>
2) Response amplitude/latency/duration <br>
-
:Within an object-selective visual area, do different patches of cortex depolarize at different latencies or durations? <br>
+
:Within an object-selective visual area, do different patches of cortex depolarize at different latencies or durations? <br>
-
:Probably not, but we may have to adjust the pre-amp or amplifier settings.<br>
+
:Probably not, but we may have to adjust the pre-amp or amplifier settings.<br>
3) Receptive Field Size <br>
3) Receptive Field Size <br>
-
:do small electrodes have smaller RFs than big electrodes? <br>
+
:• Do small electrodes have smaller RFs than big electrodes? <br>
4) Gamma oscillation <br>
4) Gamma oscillation <br>
-
:Are oscillations in the gamma band more correlated among nearby electrodes than far electrodes?<br>
+
:Are oscillations in the gamma band more correlated among nearby electrodes than far electrodes?<br>
5) Adaptation
5) Adaptation
-
: Do  
+
:Do  
 +
 
 +
6) Choice probability
 +
:• Does the pattern of activity in object-selective cortical areas correlate in anyway with the subject's behavior?
'''B. Stimulation''' <br>
'''B. Stimulation''' <br>
If would be really interesting to compare perception thresholds. We might predict thresholds would be lower with small electrodes because of high current density. <br>
If would be really interesting to compare perception thresholds. We might predict thresholds would be lower with small electrodes because of high current density. <br>

Revision as of 18:31, 22 September 2008

Brain picture
Beauchamp Lab Notebook





Old Protocols

Contents

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
4. Record resting activity to make sure system is working

Microelectrode array experiments
If the new electrodes are ready, it will be a top priority to investigate their properties. A good start will be to compare small electrode LFPs with nearby big electrode LFPs.

1) LFP selectivity

• Are small electrodes more selective within-category than large electrodes?

2) Response amplitude/latency/duration

• Within an object-selective visual area, do different patches of cortex depolarize at different latencies or durations?
• Probably not, but we may have to adjust the pre-amp or amplifier settings.

3) Receptive Field Size

• Do small electrodes have smaller RFs than big electrodes?

4) Gamma oscillation

• Are oscillations in the gamma band more correlated among nearby electrodes than far electrodes?

5) Adaptation

• Do

6) Choice probability

• Does the pattern of activity in object-selective cortical areas correlate in anyway with the subject's behavior?

B. Stimulation
If would be really interesting to compare perception thresholds. We might predict thresholds would be lower with small electrodes because of high current density.
If the RFs are more selective (#1 above) then we might evoke percept with a small electrodes (because we are stimulating neurons with similar selectivity) where we could not with a big electrode.


Regular electrode experiments
0) Stimulate the big electrodes overlying visually responsive/identified areas.
1) General Selectivity – 10 minutes

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

--Identify selective electrodes
--Choose selective category
2) "Stop-sign" Repeat Adaptation – 10 minutes

• 20 selective stimuli, 20 nonselective stimuli, 20 blocks
• Stop-sign task (patient reports stop-signs)
• Need to code 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

3) "One-back" Repeat Adaptation – 10 minutes

• 20 stims from selective category, 5 stims from each nonselective category
• One-back task
• 2 Hz presentation rate
• Get enough trials such that we can measure choice probability
• Need the plugin to be modified such that:
1. Selective stimuli are directly repeated every 8-12 images
2. The stimulus preceding the repeat is always a nonselective stimulus

4) "One-back" Cross Adaptation – 10 minutes

• 20 stims from selective category, 5 stims from each nonselective category
• Altered one-back task - subject is told to respond to two selective-category stimuli in a row

5) 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.
• 8 nonselective stimuli (including stop-sign), 8 presentations of the same selective stimulus, 8 nonselective stimuli (w/ stopsign), 8 different selective stimuli.

6) "Stop-Sign" Adaptation – 20 minutes

• Adapting stimuli A and B
• 20 blocks -> 20 direct repeats
• 2 Hz presentation rate
• 8-12 intervening stimuli


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;

 xfiles.hsc.uth.tmc.edu (129.106.148.217)

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

Selectivity

Perceptual Biasing

Adaptation

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

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:  -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.

Things to do

HumanImageDetection

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

HumanLetterDetection

Analyze data from LR to see where the RFs are


DEBUGGING KNOT PROBLEMS

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?

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