MEG ExperimentsMEG ExperimentsStimulation and Recording Setup,Stimulation and Recording Setup,

Overview on Data AnalysisOverview on Data Analysis

Educational Seminar Educational Seminar

Institute for Biomagnetism and Institute for Biomagnetism and BiosignalanalysisBiosignalanalysis

November 14November 14thth, 2006, 2006

Outlet Outlet Stimulation and Recording SetupStimulation and Recording Setup

general structure of an MEG / EEG general structure of an MEG / EEG experimentexperiment

TriggersTriggers, a way of cross-talk between , a way of cross-talk between stimulation and data recordingstimulation and data recording

types of stimulation (devices) and their types of stimulation (devices) and their setupsetup

data acquisition setupdata acquisition setup

General Structure of an General Structure of an MEG / EEG ExperimentMEG / EEG Experiment

Several subjects / patients

Session 1 Session 2 Session n

Run 1Data set 1

Run nData set n

Run 2Data set 2

Trial / Epoch 1 Trial / Epoch nTrial / Epoch 2

Event / Stimulus 1 Event / Stimulus nEvent / Stimulus 2

MRI Session(optional)

Single Trial(continuous recording)

All Events

Alternative

Structure of a Run Structure of a Run

• Stimulus Onset Asynchrony (SOA) and Inter Stimulus Interval (ISI) might be randomized • Trial duration has to fixed for acquisition setup

• recordings can be done in multiple or single trial mode (continuous)

multiple trials can be recorded with or without gap (semi continuous)

multiple trials can be recorded event related (sync) or in user defined blocks

Structure of a TrialStructure of a Trial

trial might contain more than one stimulus / event activating the same or different modalities

TriggersTriggers, a way of cross-talk , a way of cross-talk between Stimulation and Data between Stimulation and Data

RecordingRecording generated bygenerated by

stimulation control software (stimulation control software (PresentationPresentation)) stimulus detection devices (stimulus detection devices (photo diode photo diode / / sound (onset) sound (onset)

detectordetector)) subject response unit (subject response unit (response buttonsresponse buttons))

used toused to synchronize recording and analysing intervals (run / trial onset)synchronize recording and analysing intervals (run / trial onset) mark stimulus appearance (mark stimulus appearance (event timingevent timing)) define stimulus type, define stimulus type, conditionsconditions ( (event codingevent coding))

sent to and acquiredsent to and acquired by the MEG electronic by the MEG electronic stored in data set (stored in data set (trigger porttrigger port channels channels // Markerfile Markerfile))

Trigger Interface Unit Trigger Interface Unit ECCECCVSM Omega 2005 WC MEG SystemVSM Omega 2005 WC MEG System

• several ports having different connector types (parallel, serial, BNC etc.)

• parallel port 1

• 8 lines connected to the stimulation computer (controled by the Presentation Software)

• parallel port 2

• 8 lines connected to photo diode, sound onset detector and response button unit (6 lines)

• DAC channels

• can be used for trigger in- and output from resp. to external devices

Triggers / Markers Triggers / Markers Representation in the DatasetRepresentation in the Dataset

• time series of triggers are stored in the trigger port channels in the data set

(visible during recording)

• triggers are stored as markers in the Markerfile with a user defined label and time offset to trial synchronizing trigger

trigger 1 active at 0 ms after sync trigger entry for marker1, delay = 0.0 s

trigger 2 active at 16 ms after sync trigger entry for marker2, delay = 0.016 s

both triggers simultaneously active entry for both markers as described above

Types of StimulationTypes of Stimulation

1. auditory stimulation

2. visual stimulation

3. somato-sensory stimulation

• pneumato-tactile

• electric (Grass stimulator, media-nerve stimulation)

• vibro tactile

4. olfactoric stimulation

During an MEG recording all these types of stimulation can be applied simultaneously (multi-modal).

Auditory StimulationAuditory Stimulation

Auditory Stimulation (contAuditory Stimulation (cont´d)´d)

• trial and stimulus sequence controlled by Presentation software

• to be prepared: soundfiles (.wav), may contain trigger determination pulse to define real appearance time according to stimulus onset

recommendation: to perform a hearing threshold measurement prior to a recording and to set the intensity of auditory stimuli according to the individual sensation level (dBSL)

Visual StimulationVisual Stimulation

Visual Stimulation (cont´d)Visual Stimulation (cont´d)

• trial and stimulus sequence controlled by Presentation software

• to be prepared:

image files (.jpeg / .img) containing marker generation spot

• additionally to be adjusted:

• position, angle of the projection screen

• size of the projection (field of view)

• brightness of the signal

Somato-Sensory StimulationSomato-Sensory Stimulationpneumatopneumato tactile tactile

Somato-Sensory StimulationSomato-Sensory Stimulationpneumato tactile (cont´d)pneumato tactile (cont´d)

• trial and stimulus sequence controlled by Presentation software

Pulse length adjustable

Stimulation (channel) patterns selectable

Somato-Sensory StimulationSomato-Sensory Stimulationvibro tactilevibro tactile

4 Piezo driven modules

Somato-Sensory StimulationSomato-Sensory Stimulationvibro tactile (cont´d)vibro tactile (cont´d)

• trial and stimulus sequence controlled by a command line interface (not by Presentation, C++-Program)

• modules are controlled independantly from each other

• the activation time course (amplitude, frequency) can be individually defined for each pin (e.g. Amplitude modulated stimulation)

• features pattern stimulation

Olfactoric StimulationOlfactoric Stimulation

Data Acquisition (Data Acquisition (Run Time ProtocolRun Time Protocol) ) SetupSetup

General informations

Accession No

(Principal Investigator)

Subject / Patient

Information

Data Acquisition (Data Acquisition (Run Time ProtocolRun Time Protocol) ) SetupSetup

Channel / Collection SettingsChannel / Collection Settings

Channel selection

(MEG, MEG+EEG)

Head localization procedure

(before and/or after run, continuous during run)

Collection parameters

(recording mode:

trial mode or continuous)

Data Acquisition (Data Acquisition (Run Time ProtocolRun Time Protocol) ) SetupSetup

Filter SettingsFilter Settings

Online

Hardware Filter

(default: off)

Env. Noise Reduction

(gradiometer order,

default: 3rd order)

Data Acquisition (Data Acquisition (Run Time ProtocolRun Time Protocol) ) SetupSetup

Trigger Trigger SettingsSettings

PPort1: 8 lines

(default:

reserved for Presentation)

PPort2: 8 lines

Line1: sound onset detector

Line2: photo diode

Line3-8: response buttons

Optional:

Use one of the triggers to synchronize a multiiple trial recording

Data Acquisition SetupData Acquisition SetupEEG Recording Setup EEG Recording Setup

• select number of electrodes to be recorded (e.g. 64 channel cap)

(max. 120 uni- and 8 bipolar channels)

• define label and position of electrodes (e.g. from Polhemus scan)

• define online high pass filter (default: no filter)

• perform impedance check and null offset correction prior to recording

Data Acquisition Setup Data Acquisition Setup (cont‘d)(cont‘d)

prior to a MEG / EEG recording:prior to a MEG / EEG recording:

EEG cap setup (?)EEG cap setup (?) Polhemus scan (?)Polhemus scan (?)

seat subject in most confortable (and stable) position under dewarseat subject in most confortable (and stable) position under dewar

(online) noise measurement under experimental conditions (online) noise measurement under experimental conditions

attach the head localization coils to the subjectattach the head localization coils to the subject connectconnect subject to stimulation (and response) equipment subject to stimulation (and response) equipment

perform a stimulus perform a stimulus thresholdthreshold measurement ( measurement (hearing levelhearing level,, field of field of viewview))

Administration / Storage of Data

Visualization of MEG Data

MEG Data Processing

MRI Data Processing

Source Modeling

Source Analysis Results Processing

OutletOutletData AnalysisData Analysis

Data Administration / StorageData Administration / Storage

• recorded data have to be copied from acquisition computer to data storage server

megacq to megserver:/data/megserver1/PI/Experiment (normally done by the technicians)

• for further analysis a backup of the data should be copied to personal workspace computer

• daily backup of the recorded raw data to DVD (done by the technicians)

• all recorded data will get an entry in the database

• (personally) analysed data have to be backuped by the users

• after completing analysis of an experiment all belonging data have to be deleted from

the data storage server

Data PreparationData Preparation

most useful programs for data preparation:

newSingleTrialDs merge multiple trials (having no gap) to a continuous data set

newDs: separation of conditions, resampling, sensor type separation,

recording mode modification

addMarker: trigger decoding, trial / condition classification

changeDsName: rename data set

dshead, dsinfo: get information about data set

General Aspects on Dataset General Aspects on Dataset Analysis using the CTF Analysis using the CTF

softwaresoftware

CTF dataset are folders containing several files, e.g.

.meg4 MarkerFile.mrk BadChannels

processing.cfg

• processing parameters are applied online (data storage in raw format)

(!! BESA does not recognize processing parameters but takes noise reduction level into account)

• almost all analysis procedures can be performed either by command line

programs or by tools supported by a graphical user interface

• central toolbox (GUI): DataEditor

• (Single) channel waveform

• Channel overlay (butterfly plot)

• Sensor layout

• Field Distribution contour map

Data VisualizationData Visualization

Averaging

Artifact Rejection

Offset Removal, Filtering

Source Modelling

Additional Data Processing

MEG / (EEG)MEG / (EEG)

Converting

Creating a Head Model

MRIMRI

MEG / MRI Co registration

Artifact Rejection

MEG Data ProcessingMEG Data ProcessingOverviewOverview

• by threshold detection (GUI tool, ctf_PtP_detection)• Manually

Main artifacts in MEG data: • eye movements• muscle activity • MCG (heart activity)• (spontaneous) brain activity from areas not of interest (e.g. alpha waves)

• by template selection

Artifact (trial) rejection :

Exclude channels (-> BadChannels file)

MEG Data ProcessingMEG Data ProcessingArtifact RejectionArtifact Rejection

Averaging

Artifact Rejection

Offset Removal, Filtering

Source Modelling

Additional Data Processing

MEG / (EEG) MEG / (EEG)

Converting

Creating a Head Model

MRIMRI

MEG / MRI Coregistration

Averaging

MEG Data ProcessingMEG Data ProcessingAveraging (in time space)Averaging (in time space)

• increase signal to noise ratio

• seperate by conditions (selective averaging)

For evoked activity used to:

For induced activity a (time) frequency analysis (FFT, wavelet anal.) has

to be applied prior to averaging in frequency

space

Offset Removal, Filtering

Averaging

Artifact Rejection

Offset Removal, Filtering

Source Modelling

Additional Data Processing

MEG / (EEG) MEG / (EEG)

Converting

Creating a Head Model

MRIMRI

MEG / MRI Co registration

MEG Data ProcessingMEG Data ProcessingOffset Removal, temporal FilteringOffset Removal, temporal Filtering

Offset Removal:by subtracting mean of the pre trigger interval the whole trial a defined latency rangefrom each channel

Filter Options: low pass high pass band pass notch filter

Remove Power LineInterference

+ harmonics

Remove Linear Trend

Averaging

Artifact Rejection

Offset Removal, Filtering

Source Modelling

Additional Data Processing

MEG / (EEG) MEG / (EEG)

Converting

Creating a Head Model

MRIMRI

MEG / MRI Coregistration

Additional Data Processing

Frequency / Power spectrum (FFT)

Time Frequency Analysis Methods (Wavelet Transformation etc.)

Difference between / Sum of dataset

Event detecting / marking (condition linking)

RMS Power / Global Field Power

Grand average (several sessions, several subjects) !! not recommended in sensor domain !!

MEG Data ProcessingMEG Data ProcessingAdditional Data Processing StepsAdditional Data Processing Steps

• estimation of the signal power

• estimation of the signal to noise ratio

• detection of signal power peaks

• collapsing the channels to one curve

Bc = magnetic field of channel c,N = number of channels

MEG Data ProcessingMEG Data ProcessingAdditional Data ProcessingAdditional Data Processing

RMS ValueRMS Value

Averaging

Artifact Rejection

Offset Removal, Filtering

Source Modelling

Additional Data Processing

MEG / (EEG) MEG / (EEG)

Converting

Creating a Head Model

MRIMRI

MEG / MRI Coregistration

Converting

Supported MRI formats:

• Dicom3• ACR-NEMA• Siemens• GE Genesis• Analyse (default IBB)• MINC (MNI)• Generic

2-dimensional MRI slices

volumetric data set

MRI Data ProcessingMRI Data ProcessingConvertingConverting

Averaging

Artifact Rejection

Offset Removal, Filtering

Source Modelling

Additional Data Processing

MEG / (EEG) MEG / (EEG)

Converting

Creating a Head Model

MRIMRI

MEG / MRI CoregistrationMEG / MRI Coregistration

Define fiducial positions in MRI slices to match up MEG and MRI coordinate systems

MRI Data ProcessingMRI Data ProcessingMEG/MRI CoregistrationMEG/MRI Coregistration

Averaging

Artifact Rejection

Offset Removal, Filtering

Source Modelling

Additional Data Processing

MEG / (EEG) MEG / (EEG)

Converting

Creating a Head Model

MRIMRI

MEG / MRI Coregistration

Creating a Head Model

Define a sphere origin and radius

• Fit a sphere to manually defined markers or input headshape points from an external source (polhemus)

• Fit sphere to the computer extracted head surface

• Operator defined sphere parameters

Head Model for SourceModelling (all steps done by technicians, results stored in a general folder available on all computers)

Assumption:human head acts like spherical volume conductor for B-fields

MRI Data ProcessingMRI Data ProcessingCreating an (individual) head modelCreating an (individual) head model

Averaging

Artifact Rejection

Offset Removal, Filtering

Source Modelling

Additional Data Processing

MEG / (EEG) MEG / (EEG)

Converting

Creating a Head Model

MRIMRI

MEG / MRI Coregistration

Source Modelling

Goal: to estimate the source(s) underlying a

magnetic field distribution

Results: • location, orientation and strength (moment) of equivalent current dipole(s)• validation criterias: fit error, residual field, plausibility

Approach:• set some constraints (e.g. fit interval, set some constraints (e.g. fit interval, source model, relationship between source model, relationship between dipoles) dipoles) • perform the dipole fitperform the dipole fit

Assumption:• head as a spherical volume conductor• source as an equivalent current dipole

Source ModellingSource Modelling

• define a spatial filter• the measured magnetic field is multiplied with a weighting vector W• this vector depends as well on the channel configuration and their properties as on the localization and orientation of a chosen source underlying the field

profit of the virtual sensor:• collapsing the signal of all MEG channels to one single waveform• further processing of the source waveform• method to compare intra individual source analysis results (different components)• method to compare inter individual results (group analysis)

• built up of a virtual sensor• which responds maximal to the region of interest (spatial sensitive)• contribution of spontaneous brain activity from other regions is reduced• uncorrelated system noise is canceled out • output: timeseries of the source strength (dipole moment) (source waveform)

Source ModellingSource ModellingSignal Space ProjectionSignal Space Projection

Source ModellingSource ModellingSignal Space Projection (Performing)Signal Space Projection (Performing)

assume one or more dipole(s) with fixed position and orientation

calculate the source strength by adapting the dipole moment(s) to the measured field distribution

Proof of plausibility (matching anatomical structures ?)

Overlay of functional and anatomical data

Source Analysis ResultsSource Analysis ResultsIntegration MEG and MRI DataIntegration MEG and MRI Data

Average in source space, 9 subjects, confidence intervals of the mean (95 %)

Source Analysis ResultsSource Analysis ResultsGroup Analysis (localization)Group Analysis (localization)

Source Analysis ResultsSource Analysis ResultsGroup Analysis (dipole moment, source waveforms)Group Analysis (dipole moment, source waveforms)

Thank you for your attention!Thank you for your attention!

Are there further questions?Are there further questions?