Source localization MfD 2010, 17th Feb 2010 Diana Omigie and Stjepana Kovac

Source localization: I. Aim / Application II. Theory Source localization: I Aim / Application II Theory a) What is recorded (EEG / MEG) b) Forward problem Forward solutions c) Inverse problem Inverse solutions d) Inverse solutions: discrete vs. distributed III The buttons in SPM

I Aim To find a focus of brain activity by analysing the electrical activity recorded from surface electrodes (EEG) or SQUID (Superconductive Quantum Interference Device; MEG)

I Application: - focal epilepsy: spikes seizures - evoked potentials: auditory evoked potentials somatosensory evoked potentials cognitive event related potentials -

- IIa What is recorded EPSP Layer IV radial tangential Lopez daSilva, 2004

IIb Forward problem Forward solution How to model the surfaces i.e. the area between recording electrode and cortical generator? Skin, CSF, skull, brain Realistic shape – (BEM isotropic, FEM anisotropic) Plummer, 2008

IIc Inverse problem Inverse solutions Discrete: Equivalent current dipole Distributed (differ in side constraint): Minimum norm (Halmalainen & Ilmoniemi 1984) LORETA (Pascual-Marqui, 1994) MSP – multiple sparse priors (Friston, 2008) ........... + -

IIc Inverse problem Inverse solutions Discrete source analysis Distributed source analysis Current dipole represents an extended brain area Each current dipole represents one small brain segment Number of sources < number of sensors Number of sources >> number of sensors The leadfieldmatrix has more rows (number of sensors) than colums (number of sources) The leadfieldmatrix has more colums than rows Result: Source model and source waveforms 3D Volume image for each timepoint

SPM source analysis Two aspects of source analysis are original in SPM: Based on Bayesian formalism: generic inversion it can incorporate and estimate the relevance of multiple constraints (data driven relevance estimation – Baysian model comparison) The subjects specific anatomy incorporated in the generative model of the data

III The buttons in SPM : Graphical user interface for 3D source localisation

III EEG/MEG imaging pipeline 0) Load the file Source space modeling Data co-registration Forward computation Inverse reconstruction Summarizing the results of the inverse reconstruction as an image

0) Load the file

1) Source space modeling MRI – individual head meshes (boundaries of different head compartments) based on the subject’s structural scan Template – SPM’s template head model based on the MNI brain MRI template

1) Source space modeling Select mesh size: - coarse normal fine

2) Data co-registration Co-register Fiducials – landmark based coregistration Surface matching

2) Data co-registration Methods to co-register “select” from default locations “type” MNI coordinates directory “click” manually each fiducial point from MRI images

3) Forward computation Recommendation: Forward Model Recommendation: Single shell for MEG BEM for EEG

3) Forward computation

4) Inverse reconstruction Invert Imaging VB-ECD Beamforming

4) Inverse reconstruction Default – click “Standard”: “MSP” method will be used. MSP : Multiple Sparse Priors (Friston et al. 2008a) Alternatives: GS (greedy search: default): iteratively add constraints (priors) ARD (automatic relevance determination): iteratively remove irrelevant constraints COH (coherence): LORETA-like smooth prior …

4) Inverse reconstruction TIME Time course of the region with maximal activity SPACE Maximal intensity projection (MIP)

5) Summarizing the results of inverse reconstruction as an image ? Timewindow of interest (ms peri-stimulus time) ? Frequency band of interest (default 0) ? Evoked/ induced inversion applied either to each trial (induced) and then averaged or inversion applied to the averaged trials (evoked) Window

5) Summarizing the results of inverse reconstruction as an image 3D NIfTI images allow GLM based statistical analysis (Random field theory)

Sources indicated under figures - Stavroula Kousta / Martin Chadwick (2007, MfD) - Maro Machizawa / Himn Sabir (2008, MfD) - SPM 8 manual BESA tutorials (http://www.besa.de), M. Scherg