# Inverse problem

## Introduction

In this tutorial you can find information about how to fit dipole models and how to do source reconstruction using minimum-norm estimation to the somatosensory evoked potentials (SEPs) of a single subject from the preprocessing. We will be working on the dataset from the previous hands on sessions, and we will use the functional and anatomical data from these tutorials to deal with the inverse problem. As you already noticed we have prepared two different mathematical models from the forward problem. We will use both to solve the inverse problem and compare the results. You've either got the relevant data already processed yourself or can find in the data directory.

This tutorial will not show how to combine source-level data over multiple subjects. It will also not describe how to do source-localization of oscillatory activation. You can check the Localizing oscillatory sources using beamformer techniques tutorial if you are interested in the later.

## Background

### Dipole fit

In this tutorial we will use the dipole fitting approach (1) to localise the neuronal activity and (2) to estimate the time course of the activity. This approach is most suitable for relatively early cortical activity which is not spread over many or large cortical areas. Dipole fitting assumes that a small number of point-like equivalent current dipoles (ECDs) can describe the measured topography. It optimises the location, the orientation and the amplitude of the model dipoles in order to minimise the difference between the model and measured topography. A good introduction to dipole fitting is provided by Scherg (1990) ^{1)}.

### Minimum norm estimate

To calculate distributed neuronal activation we will use the minimum-norm estimation. This approach is favored for analyzing evoked responses and for tracking the wide-spread activation over time. It is a distributed inverse solution that discretizes the source space into locations on the cortical surface or in the brain volume using a large number of equivalent current dipoles. It estimates the amplitude of all modeled source locations simultaneously and recovers a source distribution with minimum overall energy that produces data consistent with the measurement ^{2)} ^{3)}. The reference for the implemented method is Dale et al. (2000).

## BEM

### Dipole fit

First we load the relevant data

load elec load sourcemodel load headmodel_bem load leadfield_bem load mri_resliced load EEG_avg

Then we do the dipole fit

% Dipole fit cfg = []; cfg.numdipoles = 1; %number of expected cfg.headmodel = headmodel_bem; %the head model cfg.grid = leadfield_bem; %the precomputed leadfield cfg.elec = elec; %the electrode model cfg.latency = 0.025; %the latency of interest dipfit_bem = ft_dipolefitting(cfg,EEG_avg);

dipfit_bem.dip ans = pos: [13.958237048680118 34.388465910583285 97.809684095994314] %dipole position mom: [3x1 double] %dipole moment pot: [74x1 double] %potential at the electrodes rv: 0.034549469532012 %residual variance unit: 'mm'

A quick look dipfit_bem.dip gives us information about the dipole fit. Especially a low residual variance (rv) shows us that the fitted dipole quite well explains the data.

And we visualize the dipole and see where it was localized in the brain.

%Visualise dipole fit ft_plot_mesh(headmodel_bem.bnd(3)); alpha 0.7; ft_plot_dipole(dipfit_bem.dip.pos(1,:), mean(dipfit_bem.dip.mom(1:3,:),2), 'color', 'b','unit','mm')

*Figure 1. Dipole computed with BEM model*

### Minimum norm estimate

cfg = []; cfg.method = 'mne'; %specify minimum norm estimate as method cfg.latency = [0.024 0.026]; %latency of interest cfg.grid = leadfield_bem; %the precomputed leadfield cfg.headmodel = headmodel_bem; %the head model cfg.mne.prewhiten = 'yes'; %prewhiten data cfg.mne.lambda = 3; %regularisation parameter cfg.mne.scalesourcecov = 'yes'; %scaling the source covariance matrix minimum_norm_bem = ft_sourceanalysis(cfg,EEG_avg);

For the purpose of visualisation we internet the MNE results onto the replaced anatomical MRI.

cfg = []; cfg.parameter = 'avg.pow'; interpolate = ft_sourceinterpolate(cfg, minimum_norm_bem , mri_resliced);

cfg = []; cfg.method = 'ortho'; cfg.funparameter = 'pow'; ft_sourceplot(cfg,interpolate);

#### Exercise 1

## FEM

%% FEM load elec load sourcemodel load headmodel_fem_tr load leadfield_fem load mri_resliced load EEG_avg

%% dipole fit cfg = []; cfg.numdipoles = 1; cfg.grid = sourcemodel; cfg.headmodel = headmodel_fem_tr; cfg.grid = leadfield_fem; cfg.elec = elec; cfg.latency = 0.025; dipfit_fem = ft_dipolefitting(cfg,EEG_avg);

ft_plot_mesh(headmodel_bem.bnd(3));alpha 0.7; ft_plot_dipole(dipfit_fem.dip.pos(1,:), mean(dipfit_fem.dip.mom(1:3,:),2), 'color', 'r','unit','mm')

*Figure 3. Dipole computed with FEM model*

% Minimum norm estimate cfg = []; cfg.method = 'mne'; cfg.latency = [0.024 0.026]; cfg.grid = leadfield_fem; cfg.headmodel = headmodel_fem_tr; cfg.mne.prewhiten = 'yes'; cfg.mne.lambda = 3; cfg.mne.scalesourcecov = 'yes'; minimum_norm = ft_sourceanalysis(cfg,EEG_avg);

cfg = []; cfg.parameter = 'avg.pow'; interpolate = ft_sourceinterpolate(cfg, minimum_norm , mri_resliced);

cfg = []; cfg.funparameter = 'pow'; cfg.method = 'ortho'; ft_sourceplot(cfg,interpolate);

## Comparison of BEM and FEM

ft_plot_mesh(headmodel_bem.bnd(3));alpha 0.7; ft_plot_dipole(dipfit_fem.dip.pos(1,:), mean(dipfit_fem.dip.mom(1:3,:),2), 'color', 'r','unit','mm') ft_plot_dipole(dipfit_bem.dip.pos(1,:), mean(dipfit_bem.dip.mom(1:3,:),2), 'color', 'b','unit','mm')

*Figure 5. Comparison of a BEM and FEM dipole fit*

#### Exercise 2

#### Exercise 3

## Summary and suggested further reading

In this tutorial we learned how solve the inverse problem. For this we used the preprocessed functional data and the forward model. The inverse techniques we used in this tutorial were “Dipole Fit” and “Minimum Norm Estimation”. We used both techniques with the different choices of for the forward model BEM and FEM.

This tutorial was last tested on 27-08-2017 by Simon Homölle on MacOs, Matlab 2015b.

^{1)}

^{2)}

^{3)}