Sensor Space Analysis · OAT - OSL’s Analysis Tool • Task-based analysis in: sensor space, or...

Sensor Space Analysis

OHBA MEG Analysis Workshop

• Induced analysis of the decision making period:

• source reconstruction• epoching: time-locked to when the response is given• compute the average evoked power (the induced response, ERD/ERS) from 1-12Hz • group averaged over 30 subjects

25% 40%O

Response locked

Hunt et al., Nature Neuroscience, 2012.

superior parietal lobule (SPL) premotor

cortex

primary motor cortex

Talk Outline

• Analysing epoched task data in sensor space

• OSL (OHBA’s Software Library):• OAT (OSL’s Analysis Tool)

Epoched Data Example

• Faces versus motorbikes

➡ 240 trials (epochs) of presenting pictures of faces

➡ 120 trials (epochs) of presenting pictures of motorbikes

• We want to compare the responses time-locked to stimulus presentation (i.e. the Event-Related Fields (ERFs))

AFRICA: remove eyeblinks/cardiac etc.

Time-frequency decomposition

Source reconstruction

Convert into SPM format

Compute statistics on contrasts

oslview: remove bad epochs

Fit General Linear Model (GLM)

AFRICA: remove eyeblinks/cardiac etc.

Time-frequency decomposition

Source reconstruction

Convert into SPM format

Compute statistics on contrasts

oslview: remove bad epochs

Fit General Linear Model (GLM)

1s Local field potential (LFP)

1pT pick-up coils

What we’ve got

What we want

location 1

location 2

Epoching and ERFsEpoching takes in source or sensor data:

locations x timepoints

1pT pick-up coils

What we’ve got

What we want

location 1

1. Identify when trial events occurred (e.g. the time of stimulus presentation in each trial)

location 2

1pT pick-up coils

What we’ve got

What we want

location 1

timetr

Epoching takes in source or sensor data:locations x timepoints

Epoching and ERFs

1pT pick-up coils

What we’ve got

What we want

location 1

1pT pick-up coils

What we’ve got

What we want

1pT pick-up coils

What we’ve got

What we want

trial 1

trial 2

3. Average over all trials to compute an average stimulus response, known

as an ERF (Event Related Field)

2. Extract time-locked “epochs” of data

time-within-trial (s)

trials

Motorbikescondition

Face condition

Data at a sensor and at a timepoint within trial

ERFs can be also computed using separate multiple regressions at each sensor and timepoint

Trial-wise multiple regression

Y! X! e b =! +!

Design Matrix

Motorbikescondition

Face condition error

trials

More formally we fit a separate General Linear Model (GLM) at each sensor and timepoint

Trial-wise GLM

Y! X! e b =! +!

Design Matrix

DependentVariable

Regressors

Motorbikescondition

Face condition

trials

Trial-wise GLM

Regressionparameters

Y! X! e b =! +!

Design Matrix

DependentVariable

Regressors

UNKNOWN

Motorbikescondition

Face condition

trials

Trial-wise GLM

Y! X! e b =! +!

Design Matrix

Motorbikescondition

Faces condition error

trials

Y=Xb+e

Repeat for all timepoints within-trial at a sensor

Motorbikes (B2) at a sensor near the visual cortex (motorbike ERF)

Data at a sensor and timepoint-within-trial

trials

time within trials (s)

B are the Parameter Estimates (PEs) of b

Fitting over time = ERF

Y! X! e b =! +!

Design Matrix

Motorbikescondition

Faces condition

Data at a sensor and timepoint-within-trial error

trials

Y=Xb+e

Repeat for all sensors at a time-within-trial

Motorbikes (B2) at 100ms post-stimulus

Fitting over sensors

trials

B are the Parameter Estimates (PEs) of b

Sensor data at a voxel and at a timepoint

within trialerror

trials

Note that the GLM is a general framework, e.g. in which we can also fit continuous variables:

e.g. total value of the two options

Hunt, Nature Neuroscience, 2012

25% 40%OR

Other stuff the GLM can do• Continuous (e.g. behavioural) variables

• Time-frequency (induced response) analysis

• Linear, and higher order, trends between conditions

• Factorial designs (interaction effects)

• F-tests (combined explanatory power over multiple contrasts)

• Subject-wise GLMs at the group level (e.g. patients vs controls)

• See the FSL course FEAT/FMRI Preprocessing and Model-Based slides at:

http://www.fmrib.ox.ac.uk/fslcourse

Contrasts

Contrast [1 0] gives a COPE =1xB1+0xB2

Contrast [1 -1] gives a COPE =1xB1-1xB2

= B1 - B2

Y! X! e b =! +!

Design Matrix

Motorbikescondition

trials

A COntrast of Parameter Estimates (COPE) is a linear combination of the regression parameter estimates, e.g.

Contrasts

Contrast [1 0] gives a COPE =1xB1+0xB2

= B1 - B2

Y! X! e b =! +!

Design Matrix

Motorbikescondition

trials

Use a t-test to test the null hypothesis that COPE=0:

t-statistic:

t =COPE

std(COPE)

t =COPE

std(COPE)

t =COPE

std(COPE)

t =COPE

std(COPE)

A COntrast of Parameter Estimates (COPE) is a linear combination of the regression parameter estimates, e.g.

Contrasts

A COntrast of Parameter Estimates (COPE) is a linear combination of parameter estimates, e.g.

Contrast [0 1] gives a COPE = 0xB1+1xB2

= B2=!

Y! X! e b =! +!

Design Matrix

Motorbikescondition

trials

t =COPE

std(COPE)

t =COPE

std(COPE)

t =COPE

std(COPE)

Test the null hypothesis that B2=0e.g. where in time and space is there significant positive* activity in response to the motorbike condition?

* as we are doing a one-tailed t-test

Contrasts

= B1 - B2 =!

Y! X! e b =! +!

Design Matrix

Motorbikescondition

trials

t =COPE

std(COPE)

t =COPE

std(COPE)

t =COPE

std(COPE)

Test the null hypothesis that B1-B2=0e.g. where in time and space is there more* activity in response to the faces than the motorbike condition?

* as we are doing a one-tailed t-test

A COntrast of Parameter Estimates (COPE) is a linear combination of parameter estimates, e.g.

OAT - OSL’s Analysis Tool

• Task-based analysis in:

➡ sensor space, or

➡ source space (e.g. via beamforming)

• In:

➡ time domain (e.g. ERF-style), or

➡ in time-frequency domain (e.g. induced responses)

• In:

➡ time domain (e.g. ERF-style), or

➡ in time-frequency domain (e.g. induced responses)

• First-level (within-session) analysis, using:

➡ trial-wise GLM on epoched data

➡ time-wise GLM on continuous data

• Group-level (between-subject) subject-wise GLM analysis

OAT Pipeline Stages

• 4 distinct pipeline stages:

SPM MEEG object

subject COPES and t-statistics

Source Reconstruction

First-level within-session GLM

Subject-level session averaging

Design matrix

Contrasts

session COPEsand t-statistics

group COPES and t-statistics

Group-level subject-wise GLM

Design matrix

Contrasts

(Note: the source recon stage always gets run even for a

sensor space analysis)

OAT Setup

• Set some mandatory fields, and then use osl_check_oat call to setup an OAT struct:

➡ oat= osl_check_oat(oat);

• Set some mandatory fields, and then use osl_check_oat call to setup an OAT struct:

➡ oat= osl_check_oat(oat);

• Settings are organised by the 4 distinct stages of the pipeline:➡ oat.source_recon, e.g.

➡ oat.first_level (GLM within-session analysis)

➡ oat.subject_level (within-subject averaging)

➡ oat.group_level (GLM subject-wise analysis)

OAT Setup

• The oat gets stored in the directory specified in oat.source_recon.dirname, with a ‘.oat’ suffix

• A previously setup/run oat can be loaded into Matlab with:

• oat.source_recon.dirname=‘/path/oatname’;

• oat=osl_load_oat(oat);

Some oat.first_level settings

• Set time range and freq range using:

• oat.first_level.time_range = [-1 2] % secs around stimulus onset

• oat.first_level.tf_freq_range = [1 45] % Hz

Some oat.first_level settings

• To do an ERF analysis set oat.first_level.tf_method=‘none’

• To do a Time-Frequency (TF) induced response analysis set oat.first_level.tf_method=‘hilbert’ % or ‘morlet’

• Set time range and freq range using:

• oat.first_level.time_range = [-1 2] % secs around stimulus onset

• oat.first_level.tf_freq_range = [1 45] % Hz

Running OAT

• Use osl_run_oat to run an OAT:

➡ oat=osl_run_oat(oat);

Running OAT

• Use osl_run_oat to run an OAT:

➡ oat=osl_run_oat(oat);

• This only runs the stages specified in oat.to_do, e.g.:

➡ oat.to_do=[1 1 0 0]; only runs source_recon and first-level stages

OAT output

• After running, the oat struct contains filenames of the outputs for each stage of the pipeline:

➡ oat.source_recon.results_fnames

➡ oat.first_level.results_fnames

➡ oat.subject_level.results_fnames

➡ oat.group_level.results_fnames

• These can be loaded into Matlab, e.g. to load session 2’s first level results use the call:

➡ res=osl_load_oat_results(oat, oat.first_level.results_fnames{2})

Viewing OAT output

• It is highly recommended that you inspect oat.results.report (an HTML page), to ensure that OAT has run successfully (See the practical)

• In sensor space, use:

• Use osl_stats_multiplotER and osl_stats_multiplotTFR to call Fieldtrip interactive topoplots

• The two orientations of the MEGPLANARs are combined (in the first_level stage) by rectifying and adding

Practical

Sensor space trial-wise GLM using OAT on epoched task data:

a) Time-domain (ERF) analysis

b) Time-frequency (induced response) analysis

Sensor Space Analysis · OAT - OSL’s Analysis Tool • Task-based analysis in: sensor space, or...

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