Tuesday, December 6, 2011

data dict

Global var [scannerdata]

  • Attributes of each trial: 
    • [iArtifacts]: if the current trial is considered as a "bad" trial due to artifacts, ∈ (0: good trial; 1: bad)
    • [iTrialType]: trial type {1,2,3,4,-1}
    • [iInstructionLength]: length of the instruction period, ∈(100:1000 ms), source: presentation log 5xxx.
    • [iOffIndex]: the block index number for current trials's light off in eegData02, ∈(1:191)
    • [iOffOffset]: the offset index number for current trials's light off  in each block in eegData02, ∈ (1:12500)
    • [iOnIndex]: the block index number for current trials's light on in eegData02, ∈(1:191)
    • [iOnOffset]: the offset index number for current trials's light on  in each block in eegData02, ∈ (1:12500)
  • Attributes with respect to the raw markers (markerData(1, x).trial & time)
    • [lightOnTimeClean]: clean events of light on events (with double trigger events manually removed)
    • [lightOnCleanIndex]: index of clean events in the originally markers -- gData.markerData(1,1).trial
    • [lightOffTimeClean]: clean events of light off events (with double trigger events manually removed)
    • [lightOffCleanIndex]: index of clean events in the originally markers -- gData.markerData(1,2).trial
  • EEG channel working data
    • [eegData02]: EEG working data. {Column 1: left&Right. Column2: up&down. Column3: info channel}
      • Column3 info channel data format: [condition].[iTrialtype]  e.g. 54.1 = light off for proleft, 52.3 = light on for antileft

Sunday, December 4, 2011

MEGANTI - Preprocessing

[Presentation sce file]
In total 14 blocks, each block has 80 trials, each trial has following four events recorded in the log file:

  1. [Start]: duration 1000 ms, code 0, port_code 1
  2. [Pro] or [Anti] initiation: LightOn, (green pro, red anti) duration 100 ms, code 54, port_code 54, 
  3. [pro5xxx] or [anti5xxx]: randomized length, code: 5+ [100:50:1000], e.g. 5100, 5250..., port_code [101:1:117]
  4. [GO cue ∈ 1 | 2 | 3 | 4] :  duration 20 ms, LightOff
    • 1: greyboxproleft, port_code 201 -- prostim left
    • 2 :greyboxproright, port_code 202  -- prostim right
    • 3: greyboxantileft, port_code 203 -- antistim left
    • 4: greyboxantiright, port_code 204 - -- antistim right
Therefore(in preprocessing_subject.m), the duration of instruction (green or red box) = 100 ms (initiation) + x ms (x is indicated by event2 in matlab code).  The actual difference between light on and off is around 117-119 ms with some trials had more than 135 ms lag (var: timeLag).


Subjects
SW - [2010-08-11] - Old system no parallel code
raw: S02_MEG54_VEF_02.ds
stim log (raw): sw32-110810-SO2-Pro+Anti-saccade task.log
Problems in the data:
  1. The stim log has 195 missing event condition, all replaced with -1.
  2. The lightOn marker has 130 extra double triggers - fixed (lightOnTimeClean:1121)
  3. The lightOff marker has 3 extra  double triggers - fixed (lightOffTimeClean:1120)
  4. Verify by comparing the time diff between on and off with the event code, 121.40 

Saturday, October 15, 2011

re: Thesis Overview

TASK RELATED NEUROMAGNETIC ACTIVITY UNDERLYING THE VISUAL PERCEPTION OF VELOCITY CHANGE: A MEG STUDY

Supervisor: Dr. JFX DeSouza (link)
MEG Analyses Mentor: Dr. P Ferrari


Research questions

Part 1: Motion perception (fast & slow moving dots)
  • Is there different cortical representation for fast & slow motion?
  • Are there different temporal dynamics for fast & slow motion?
Part 2: Decision-making
  • Subjects need to detect a change in the motion velocity and respond with button press
  • When and where in the brain is a visual perception (velocity change) transformed into the neural signals for action?

Experiment
  • Prior to the imaging sessions, I conducted a perceptual thresholds test (N=22) to acquire the minimal detectable increase and decrease in motion velocity for the subjects
  • I set up the experimental environment and collected MEG & MRI data (N=12) with the helps from S. Bells and M. Lalancette at Toronto Sickkids hospital.
    • Velocity change in the experiment was set according to each subject's perceptual thresholds, so the correct and incorrect responses could be compared
    • A delayed motor response paradigm was used to separate decision-making signals from motor related signals.


Analyses

Results

Part 1 - Motion Perception
No difference in the MT+ locations (Talairach) for fast and slow motion was found (Hotelling's T2 for two multivariate independent samples)
Fig - MT+ locations
Fig - comparing the MT+ locations with previous studies

No temporal dynamics for source peak amplitude and latency found
  • Velocity and visual display had no effects (3-way ANOVA)
  • The amplitude and latency btw the three ROIs were different (P<.05)
  • Multiple-comparison showed cuneus had higher amplitude & earlier latency than V3A & MT+

 
Part 2 - Decision-making
Comparing the grand average event-related beamformer images: 
  • In the correct responses, the frontoparietal sources were observed at various time points after the velocity change onset (left column, perm-test, P<.05)
  • No significant frontoparietal activations were observed in the incorrect responses (right column)


Comparing the time-frequency plots for correct and incorrect responses from IPL and SMC sources:
  • IPL source showed a beta power difference between correct and incorrect responses from 200 - 400 ms (area encircled in dotted line)
  • The initiation of IPL beta ERS was aligned with the high beta ERD increase in SMC after 400 ms (dotted line)

Friday, May 27, 2011

MT+ location


Left MT+



 Right MT+

Problem: # 12 subject

sub#12 has noisy sensor data, but the beamformer reported good MT activation.




(-30    -68    1)



(-35    -66    -3)



Other subjects' RMS can be found here



Scatter plot: outlier sub#12 is in the black box






When sub#12 is excluded :




Individual virtual sensor time course at MT+, please look at sub#12:

Left Fast
Left Slow
 
Right Fast
 Right Slow



Cuneus 

Left Fast

 Left Slow

Right Fast

Right Fast







Sunday, May 22, 2011

Current

D:\MEG\joe\backup\MEG_progs\matlab_backup\matlab2\meg_utils\topoplot

Monday, May 16, 2011

Glassbrain movies

[A001] Aligned to [motion stimulus] onset
All movies are made with neurological orientation in Tal coordinates from onset (0 ms) of visual stim to 500 ms after onset.

AB

AK

CM
Left_Fast: weak MT+


DA
Left_Fast: interesting, contraL cuneus -> contraL V3A -> ipsiL MT+ -> contraL MT+


ES

JD
Left_Fast:contraL MT+ then moved to ipsiL MT+

PD
Left_Fast: V3A prior to V1 activation

SL

ST (this subject had no strong V3A activation)

SW
Right_Slow

SX

WW
Left_Fast: V3A prior to cuneus



[B002] Group avg for velocity change onset


[A002] Group avg for GO cue onset

[C] Group avg for button press
Incorrect response

Saturday, May 14, 2011

Individual RMS plots for 9 out of the total 12 subjects

Bandpass: 1-30Hz
Motion stim onset: 0 s

In each subject, the first plot is left visual display and second plot is for right. 

Subject: AB



Subject: AK


Subject: CM

 Subject: DA



 Subject: ES



Subject: JD


Subject: PD


Subject: SW 


Subject: SX



There are two markers for the visual stim: (1) one is synchronized with the onset of the motion stim, (2) The second is synched with the onset of the velocity change, which is not shown here.

Tuesday, May 3, 2011

Wednesday, April 20, 2011

MT+ localization

A001_FastSlow_GetRawData(1, aSubjects, [1 1 1 1], 1, 4, 7)
A001_FastSlow_GetRawData(1, aSubjects, [1 1 1 1], 1, 8, 12)
A001_FastSlow_GetRawData(1, aSubjects, [1 1 1 1], 1, 15, 30)
A001_FastSlow_GetRawData(1, aSubjects, [1 1 1 1], 1, 30, 60)




April 24
A01_FastSlow_assembleRawData()
New function for read data from .mat file and assemble matrix for analysis.
  aSubjects = {'AB', 'AK', 'CM', 'DA', 'ES', 'JD', 'PD', 'SL', 'ST', 'SW','SX', 'WW'};
A001_FastSlow_assembleRawData(1, aSubjects)

April 23
New function readTrialsFromRaw()
Added external RMS function: rms.m

Motion localizer figs:
/Users/joseph/data/data/7_SPM2/05_MT_localization/jpg/



function A001_FastSlow_GetRawData(onJoeServer, aSubjects, toDo, doBeamformer)
New function for searching peaks in ctf and do beamformer


aSubjects = {'AB', 'AK', 'CM', 'DA', 'ES', 'JD', 'PD', 'SL', 'ST', 'SW', 'SX', 'WW'};
 A001_FastSlow_GetRawData(1, aSubjects, [1 1 1 1], 1)


function A001_FastSlow_MT(aSubjects, toDo, dataHome)
New function for permutation and calculate glass brain for three regions:
primary visual (V1), V3A,and MT+

output:
/Users/joseph/data/data/7_SPM2/04_permutation/A001_MT/

aSubjects = {'AB', 'AK', 'CM', 'DA', 'ES', 'JD', 'PD', 'SL', 'ST', 'SW', 'SX', 'WW'};
A001_FastSlow_MT(aSubjects, [1 0 0 0])
A001_FastSlow_MT(aSubjects, [0 0 1 0])
A001_FastSlow_MT(aSubjects, [0 0 0 1])
A001_FastSlow_MT(aSubjects, [0 1 0 0])

Monday, March 7, 2011

A001 Stimulus onset - Left, right, fast, slow -- collapsed

2010/03/04

(1)Create $Datahome/02_script/03_A001_Combined_ERB_2.5cm run beamformers
IMPORTANT CHANGE!
In dataset that is aligned to the onset of stimulus, there are two folders:
ANALYSIS_A001: stimulus onset
ANALYSIS_A002: velocity onset

(2)normalization for the ERB (A001 correct and err), in matlab run:
Combo_A001_Correct_normlization
Combo_A001_Err_normlization

(3) scripts for ERB permutation
New function, changed the parameters, plot_permutation_tfr_sheng_v2.m


Combo_A001_permute_batch(1,50)  % start time, end time, unit 10 ms for correct and error permunation


!!! Check log file after running !!!

(4) permutation threshold, plotting glass brains
    D:\MEG\joe\data\7_SPM2\02_averaging\A001_Correct (local)
       Saved threshold and max scale in 
       tCorrectA001.xls ==> tCorrectA001.mat
       Server: /Users/joseph/data/data/7_SPM2/02_averaging/A001_Correct/

    D:\MEG\joe\data\7_SPM2\02_averaging\A001_Err  
       tErrA001.xls ==> tErrA001.mat  
       Server: /Users/joseph/data/data/7_SPM2/02_averaging/A001_Err/

  Combo_A001_Correct_glassBrain(1, 50, 1, 1)
  Combo_A001_Err_glassBrain(1, 50, 1, 1)

MT+

Gitelman, D. R., A. C. Nobre, et al. (1999). "A large-scale distributed network for covert spatial attention." Brain 122(6): 1093-1106.
 


















Fig. 6 Foci of activations in the temporo-occipital region. Filled circles (d) denote the locations found in the current study. The foci of activations in MT (E, G, C) and an area subserving movement-related knowledge (e) are taken from several previous studies (Zeki et al., 1991; Martin et al., 1995; Beauchamp et al., 1997; Dupont et al., 1997; Chawla et al., 1998).





 Spatiotemporal Activity of a Cortical Network for Processing VisualMotion Revealed by MEG and fMRIAHLFORS

Wednesday, January 26, 2011

Clarification of the events and baseline

Four events of interest and baseline windows


TFR data

http://dl.dropbox.com/u/14828654/motionMEG/doc/manuscript/figs.xls
http://dl.dropbox.com/u/14828654/motionMEG/doc/manuscript/ROI_memo_combo.xls
http://dl.dropbox.com/u/14828654/motionMEG/doc/manuscript/ROI_TFR_compare.xls
http://dl.dropbox.com/u/14828654/motionMEG/doc/manuscript/ROI_TFR.xls



L_ACC/medial frontal G.


Cohen, M. X., K. R. Ridderinkhof, et al. (2008). "Medial frontal cortex and response conflict: Evidence from human intracranial EEG and medial frontal cortex lesion." Brain Research 1238: 127-142.



stimulus on at 0 s, GO cue at 2.2 s,
speed change onset at somewhere around 1 s



key pressed at 0 s



Peri-response, we also observed enhancements in lower band power(delta) following the desynchronization in the beta band. The pre-response beta and post-response theta changes look related to each other -- need to statistically verify this.






L_SMC


Beta oscillation suppressions around motor and supplementary motor regions have been linked to motor preparatory processes (Miller et al., 2007; Neuper et al., 2006; Pfurtscheller et al., 2003) and are thought to reflect decreased global neural coherence during the processing and planning of movements. 


L_SMC

Beta band power difference in preCun pre-stimulu