Tutorial 20: Head model

Authors: Francois Tadel, Elizabeth Bock, John C Mosher, Richard Leahy, Sylvain Baillet

The following tutorials describe how brain activity can be estimated from the MEG/EEG recordings we have processed so far. This step consists in solving two separate modeling problems: the modeling of the electromagnetic properties of the head and of the sensor array (a.k.a. head model or forward model) and the estimation of the brain sources which have produced the data. That second step is known as source modeling or solving an inverse problem. It requires that the forward modeling of the head tissues and sensor instrumentation is completed first. This tutorial explains how to compute a head model for the subject of the auditory oddball experiment. As far as source modeling is concerned, we focus the tutorial on linear estimates of distributed source models, which are popular and physiologically plausible approaches (there is no dipole fitting available in the software): you may want to refer to other sources for a complete description of source imaging with MEG and EEG.


Why estimating sources?

Reconstructing the activity of the brain from MEG or EEG recordings involves several sophisticated steps. Athough Brainstorm simplifies the procedures required, it is important to understand whether source modeling is essential to answer the neuroscience question which brought you to collect data in the first place.

If one of your primary objectives is to identify and map the regions of the brain involved in a specific stimulus response or behaviour, source estimation can help address this aspect. Empirical interpretations of sensor topographies can inform where about brain generators might be located: which hemisphere, what broad aspect of the anatomy (e.g., frontal vs. posterior regions). Source estimation methods improves anatomical resolution with respect to the interpreration of sensor patterns. Spatial resolution in MEG and EEG depends on source depth and orientation and overall SNR: still, one can expect to be able to source activations within the millimeter to centimeter range, especially relatively, when contrasts between conditions are implemented in the study design.

Source mapping is a form of spatial deconvolution of sensor data. In EEG in particular, scalp topographies are very smooth and it is common to that contributions from distant brain regions overalp over large clusters of electrodes. Moving to the source space can help separating the active regions.

More specifically in MEG, source maps can be a great assest to alleviate some specific issues with that modality. In MEG, the head of the subject is not fixed. Hence sensor topographies depend on the actual position of the subject under the rigid helmet. Therefore, between two acquisition runs, or between subjects with different head shapes and sizes, the same MEG sensors may pick up signals from different parts of the brain. This problem does not exist in EEG, where electrode montages are attached to the head and arranged according to standard positions.

Another important point to consider when interpreting MEG sensor maps is that every MEG manufacturer uses different types of sensor technology (e.g., magnetometers vs. gradiometers; axial vs. tangential gradiometers, etc. yielding different physical measures). This is not an issue with EEG, with essentially one sensor type (electrodes, dry or active, all measuring Volts): two EEG electrode caps with same number of electrodes only differ in how well they ensure good contact with the scalp. Working in source space alleviates all these aspects.

Nevertheless, if your neuroscience question can be solved by measuring signal latencies over broad regions, or other aspects which do not depend crucially on anatomical localization (such as global signal properties integrated over all or clusters of sensors), source modeling might not be a requirement. To sort out this question wil influence the time and hardware requirements to complete your data analysis (source analysis multiplies the needs in terms of disk storage and computational specifications).


The origins of MEG/EEG signals

To better understand how the forward model is elaborated, we need to have at least a basic understabnding of the physiological origins of MEG/EEG signals. Note that, as always when dealing with modeling, we need to deal with various degrees of approximation.

Overall, it is assumed that most of the MEG/EEG signals are generated by postsynaptic activity of ensembles of cortical pyramidal neurons of the cerebral cortex. The reason is essentially in the morphology and mass effect of these cells, which present elongated shapes and are oriented perpendicularly to the cortical surface. Mass effects of close-to-simulatenous changes in post-synaptic potentials pyramidal neural assemblies add up in time and space. These effects can conveniently be modeled at a mesoscopic spatial scale with electric dipoles distributed along the cortical mantle (green arrows in figure below). Note that there is growing evidence that MEG and EEG are also sensitive to deeper, cortical and subcortical structures, including brain nuclei and the cerebellum, where pyramidal cells are rare or absent. The emphasis in the present tutorial is on pyramidal cell assemblies for simplicity.

The primary and volume currents generated by these dipoles create differences in electrical potentials and magnetic fields that can be detected outside the head. We can record them with bipolar montages of electrodes placed on the skin (EEG) or very sensitive superconducting detectors (SQUIDs/MEG).


Source space

Dipole fitting vs distributed methods

The source estimation process consists in estimating the position and activity of a set of electric dipoles, to approximate the activity of the brain that produced the MEG/EEG data we recorded. Two families of solutions were explored in the past decades: the dipole fitting methods (we estimate the position and amplitude of a very limited number of dipoles over short time windows) and the distributed methods (we define a priori a dense grid of dipoles and then estimate their activity from the recordings).

The single dipole fitting approaches are very efficient in specific cases where we know in advance the number or regions involved and their latencies of activity. But they are difficult to generalize and automate, and not adapted for group analysis. With Brainstorm, we decided to work only with distributed source models, which require less manual tuning for getting acceptable results.

Location constraints

Our first step of modeling consists in defining the positions and orientations of the dipoles for which we want to estimate the activity. This set of dipoles is our source space. By default, we limit our analysis to the cortex envelope, based on this observation that most of the MEG/EEG signals is related with the synchronous activity of assemblies or cortical pyramidal cells. The simple solution we recommend is to directly use the vertices of the cortical surface we imported in the first tutorials (the nodes we can see in the grey mesh in the left image below).

In order to represent all the possible dipole orientations, we define three dipoles for each vertex of the cortex surface, corresponding to three orthogonal directions (X,Y,Z). When importing the anatomy of the subject, we downsampled the cortex surface to 15,000 vertices. This will correspond to a source space of 45,000 dipoles. We will compute a forward model that connects the activity of these 45,000 dipoles with the 275 MEG sensors we have in this dataset.

This default number of 15,000 vertices is empirical. Over the years, our experience seemed to show that it represents a good balance between the representation of the brain circumvolutions, the surface sampling and the amount of data that is generated. Using less vertices makes it difficult to preserve the shape of the brain, using more vertices produces more data without adding to the spatial resolution of the method and may lead to computational memory issues.

Orientation constraints

Additionally, we can impose constraints of orientation on the dipoles, to match the physiological observation that the pyramidal cells are mostly organized perpendicularly to the cortex surface. This has the advantage of limiting the number of dipoles to 15,000 (one per vertex) and making the results much easier to display and process. However, this constraint is most of the time too strong and distorts the reconstruction. This orientation constraint is an option of the inverse model and will be discussed in the following introduction tutorials.

Fully unconstrained

The spatial constraint of imposing all the dipoles to be on the cortical surface might also be too restrictive in some cases, because our model is then not able to correctly represent the activity in deeper brain structures or in the cerebellum. Therefore we also offer an option to use the entire brain volume as our source space (the green dots below represent dipoles locations in volume model). This produces results that can be better or worse depending on the data, but in all the cases much more difficult to review. Volume and mixed head volumes are discussed in the advanced tutorials about source modeling.


Forward problem

The first step of the source reconstruction consists in computing a model that explains how the electric currents or the magnetic fields flow from the electric generators in the brain (source space) through the different tissues of the head (mostly brain, CSF, skull and skin), to finally reach the sensors.

Available methods for MEG forward modeling


The forward models are related with the anatomy of the subject and the description of the sensors, therefore the menus associated to its computation are attached to the channel file.

Repeat the same operation for the other file. We have two different acquisition runs with two different relative position of the head and the sensors, therefore we need to compute two different head models.


Database explorer

Additional considerations about the management of the head model files.


On the hard drive

Right-click on any head model > File > View file contents:

Structure of the head model files

Gain matrix


Additional documentation

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Tutorials/HeadModel (last edited 2016-01-13 22:57:23 by SylvainBaillet)