MEG Experiments Stimulation and Recording Setup, Overview on Data Analysis Educational Seminar Institute for Biomagnetism and Biosignalanalysis November 14 th, 2006

Outlet Stimulation and Recording Setup general structure of an MEG / EEG experiment general structure of an MEG / EEG experiment Triggers, a way of cross-talk between stimulation and data recording Triggers, a way of cross-talk between stimulation and data recording types of stimulation (devices) and their setup types of stimulation (devices) and their setup data acquisition setup data acquisition setup

General Structure of an MEG / EEG Experiment Several subjects / patients Session 1Session 2 Run 1 Data set 1 Run 2 Data set 2 Trial / Epoch 1Trial / Epoch 2 Event / Stimulus 1 Event / Stimulus 2 Event / Stimulus n Trial / Epoch n Run n Data set n Session n MRI Session (optional) Alternative

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

Triggers, a way of cross-talk between Stimulation and Data Recording generated by generated by stimulation control software (Presentation) stimulation control software (Presentation) stimulus detection devices (photo diode / sound (onset) detector) stimulus detection devices (photo diode / sound (onset) detector) subject response unit (response buttons) subject response unit (response buttons) used to used to synchronize recording and analysing intervals (run / trial onset) synchronize recording and analysing intervals (run / trial onset) mark stimulus appearance (event timing) mark stimulus appearance (event timing) define stimulus type, conditions (event coding) define stimulus type, conditions (event coding) sent to and acquired by the MEG electronic sent to and acquired by the MEG electronic stored in data set (trigger port channels / Markerfile) stored in data set (trigger port channels / Markerfile)

Trigger Interface Unit ECC VSM 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 Representation 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 = s both triggers simultaneously active  entry for both markers as described above

Specific Aspects on Trigger Setup with the Presentation Software The triggers (port codes) can be set to (integer) values between 1 and 255 independing from the total number of triggers. HINT: There are some specifities for control of the pneumato tactile Stimulator

Types 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 Stimulation

Auditory Stimulation (cont´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 Stimulation

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 Stimulation pneumato tactile

Somato-Sensory Stimulation pneumato tactile (cont´d) trial and stimulus sequence controlled by Presentation software Pulse length adjustable Stimulation (channel) patterns selectable

Somato-Sensory Stimulation vibro tactile 4 Piezo driven modules

Somato-Sensory Stimulation 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 Stimulation

Data Acquisition (Run Time Protocol) Setup General informations Accession No (Principal Investigator) Subject / Patient Information

Data Acquisition (Run Time Protocol) Setup Channel / 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 (Run Time Protocol) Setup Filter Settings Online Hardware Filter (default: off) Env. Noise Reduction (gradiometer order, default: 3rd order)

Data Acquisition (Run Time Protocol) Setup Trigger Settings 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 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 (cont‘d) 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 dewar seat 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 subject attach the head localization coils to the subject connect subject to stimulation (and response) equipment connect subject to stimulation (and response) equipment perform a stimulus threshold measurement (hearing level, field of view) perform a stimulus threshold measurement (hearing level, field of view)

Administration / Storage of Data Visualization of MEG Data MEG Data Processing MRI Data Processing Source Modeling Source Analysis Results Processing Outlet Data Analysis

Data 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 Preparation most useful programs for data preparation: newSingleTrialDsmerge 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 Analysis using the CTF software CTF dataset are folders containing several files, e.g..meg4MarkerFile.mrkBadChannelsprocessing.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 Visualization

Averaging Artifact RejectionOffset Removal, Filtering Source Modelling Additional Data Processing MEG / (EEG) Converting Creating a Head Model MRI MEG / MRI Co registration Artifact Rejection MEG Data Processing Overview

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 Processing Artifact Rejection

Averaging Artifact RejectionOffset Removal, Filtering Source Modelling Additional Data Processing MEG / (EEG) Converting Creating a Head Model MRI MEG / MRI Coregistration Averaging

MEG Data Processing 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 RejectionOffset Removal, Filtering Source Modelling Additional Data Processing MEG / (EEG) Converting Creating a Head Model MRI MEG / MRI Co registration

MEG Data Processing Offset Removal, temporal Filtering Offset Removal: by subtracting mean of the pre trigger interval the whole trial a defined latency range from each channel Filter Options: low pass high pass band pass notch filter Remove Power Line Interference + harmonics Remove Linear Trend

Averaging Artifact RejectionOffset Removal, Filtering Source Modelling Additional Data Processing MEG / (EEG) Converting Creating a Head Model MRI 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 Processing Additional 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 B c = magnetic field of channel c, N = number of channels MEG Data Processing Additional Data Processing RMS Value

Averaging Artifact RejectionOffset Removal, Filtering Source Modelling Additional Data Processing MEG / (EEG) Converting Creating a Head Model MRI 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 Processing Converting

Averaging Artifact RejectionOffset Removal, Filtering Source Modelling Additional Data Processing MEG / (EEG) Converting Creating a Head Model MRI MEG / MRI Coregistration

Define fiducial positions in MRI slices to match up MEG and MRI coordinate systems MRI Data Processing MEG/MRI Coregistration

Averaging Artifact RejectionOffset Removal, Filtering Source Modelling Additional Data Processing MEG / (EEG) Converting Creating a Head Model MRI 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 Processing Creating an (individual) head model

Averaging Artifact RejectionOffset Removal, Filtering Source Modelling Additional Data Processing MEG / (EEG) Converting Creating a Head Model MRI 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, source model, relationship between dipoles) perform the dipole fit perform the dipole fit Assumption: head as a spherical volume conductor source as an equivalent current dipole Source 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 Modelling Signal Space Projection

Source Modelling 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 Results Integration MEG and MRI Data

Average in source space, 9 subjects, confidence intervals of the mean (95 %) Source Analysis Results Group Analysis (localization)

Source Analysis Results Group Analysis (dipole moment, source waveforms)

Thank you for your attention! Are there further questions?