Lecture 11: Source Localization in MEG Flashcards
So far in the module, we have discussed (sensor space) - (2)
activity in brain produces magnetic field which is picked up from sensors outside the head
We can plot activity across the sensors like this:
In sensor space, we detect the brain activity at sensors outside of the
head
In sensor space, we can perform assessments of
time and frequency of responses
In sensor space we can also look at approximately where the effects are happening - example
So yellow region show strongest effect happens towards the back of head but don’t have greater sense of ‘where’ it specifically is
Sometimes we want to know instead of sensor space , we want to know
where the activity is changing in the brain
What is source localization?
Tries to work out where the activity is changing in the brain by using a plausible model of the head and a set of assumptions about how signals propogate
Diagram of sensor space to (estimate) source space - (3)
No longer looking at…
In source space get better at…
This is an estimate source space meaning…
No longer looking at data per sensor but looking data due to positions within brain than the head
In source space, talk a bit better where responses are coming from - get this brain map like in MRI.
Its estimate of source space as difficult to transform sensor to source space so estimating based on what we know how signals move around and what we know about the anatomy of the participants head
Source localization gives our results in
source space
What does this diagram show? - (3)
Image things called dipoles in the brain
Has a positive charge on one side and a negative charge on other
This dipole produces our brain activity
We can model the sources in the brain as..
when the dipole turns on.. - (2)
dipoles in brain
When the dipole turns on it produces some brain activity that will be picked up at the sensors
Since we can model sources in the brain as dipoles we can consider the brain to include many
dipoles
Consider the brain to include many dipoles that are potential sources of
brain activity
Consider the brain to include many dipoles which are all potential sources of brain activity
what question are we then asking?
Which of these dipoles are turning on?
Consider the brain to include many dipoles which are potential sources of activity and
each dipole have a
location and an orientation
What does this image show? - (2)
The black dot shows the location of the dipole
Red arrow shows the orientation of dipole - which way is negative and which way is positive
Consider brain to have many dipoles and all potential sources of brain activity
To know where the possible sources of the brain activity (i.e., in diapoles) should be and likely orientation we need to know
the anatomy/structure of participants’ brain to see the possible sources of brain activity and likely orientation in cortex of grey matter
Participants have individual differences in how their
brain is structured
To know where the possible sources of the brain activity (i.e., in diapoles) should be and likely orientation we need to know the anatomy/structure of participants’ brain
how do we know the anatomy of the participants’ brain? - (2)
using their fMRI structural scan
or if we can’t then use average brain (e.g., MNI152 brain template)
Diagram of structural MRI and MNI
MNI is not as good as technique as structural MRI as..
but why is it used? - (2)
won’t know exact cortical folds and but some sense where their grey matter is
Used if some participants don’t show up for MRI session after EEG/MEG
From structural scan and MNI, we try to
produce 3D model of the brain using brain mesh
T1 Structural scans will take a series of
if we see an edge of scans then - (2)
2D slices that shows us the anatomy of the participants’ brain
compute a 3D model of brain
Intensity differences in 2D T1 anatomcial scan and T2 functional scans lets us work out the
3D shape of the brain , head and skull
From, Intensity differences in the 2D T1 and T2 scans let us work out the 3D shape of the brain, head and skull.
we can then build the
3D meshes of the brain
Intensity differences in the 2D T1 and T2 scans let us work out the 3D shape of the brain, head and skull
we can then build 3D meshes , one for brain, skull and head
This is typically created by
Freesurfer software
What are meshes?
meshes are made up of little triangles and each point of these triangles connect is called a vertx
Diagram of brain mesh and vertices
In each vertex of brain mesh, we can place a
dipole (potential source of brain activity) and decide (or estimate its orientation)
Putting dipoles in each vertex is essentially putting
use results of meshes for.. - (2)
dipoles in source space
use results of brain activity on meshes for data visualisation
Diagram of dipole in source space
+ where the dipoles placed in each vertex and why… - (2)
15,000 dipoles placed in vertexes on the cortex as we think MEG signal is coming from - surface of grey matter
dots are dipoles and covering cortical surface of a person’s brain
In each vertex of brain mesh, we can place a dipole (potential source of brain activity) and decide (or estimate its orientation)
OR we can allow dipole to have all 3 possible orientations
how to decide orientation or all orientations? - (2)
orientations
get software to learn all orientations and estimate it OR make simplified assumption that should be oriented perpendicular to the cortex (e.g., pyramidal cells)
Putting dipoles in each vertex of brain mesh allows the
analyses to let us look at the activity in each vertex (i.e., treat them like fMRI voxels)
Can understand how the current of diapole would propogate from a source based on (2)
shape of the brain and head
physics of the current flow
What does this diagram show in terms how MEG signals disperse from source (diapole)? - (2)
The diapole’s electric signal moves around the brain because in CSF and salty water has projections of electrical signal throughout the brain
The diapole has strong inital current but dispersal of signal electrically throughout brain
The MEG picks up the
stronger primary current and dispered volume currents (summed together)
What is volume currents which is hard to source localise
dispered currents in the photo
MEG picks up the stronger primary current and the dispersed volume currents (summed together) which is a bit harder for source localisation but
MEG is better for source localisation than
EEG
To estimate how MEG signal dispere from its source we - (3)
model the brain as one large sphere underneath each sensor (ignoring its detailed anatomy - like cortical folds as physics is hard). This makes many highly overlapping spheres centered on the brain.
Can model the brain as a simple sphere for MEG as magnetic fields are not strongly affected by the materials they are passing through.
Thinking how MEG dispere from source and reach sensor
Can model the brain as a simple sphere for MEG as magnetic fields are not strongly affected by the materials they are passing through.
Major advantage for MEG over EEG as - (2)
Electrical signals in EEG as its cares if its going through salty water or bones or metal
But magnetic signal disperes on the basis that it will be weaker the further away it gets but does not care what material it has gone through - reason why source localisation is better for MEG over EEG
Diagram of modelling the brain as simple sphere for MEG
More complicated and sophiscated model of how MEG signals dispere from its source (source localisation) takes..
skull and various tissues into account (known as Boundary Element or Finite Element Models) into Freesufer output
Individuals tend to not use much of the sophiscated models known as .. for MEG as they won’t work well - doesnt vary by materials
Boundary Element or Finite Elements Model
Whether we have overlapping spheres model or more sophiscated models of MEG (Boundary Element or Finite Element) of source localisation (how does signal dispere from source) is known as
head model - how we are thinking of anatomy of head so see how MEG signals move through
We can use more sophsicated models of source localisation for EEG - Boundary Element or Finite Element Models
which is needed for EEG as - (2)
as electrical signals are spread out more by the tissue they pass through and how much varies by the type of tissue (e.g., CSF and skin lets a lot through, bones don;t)
(although EEG source localisation will never be as good anyway due to this dispersal and the greater effect of the volume currents).
Additional step of producing head model for source localisation is to
know exactly where the brain is compared to MEG sensors
In co-registration in MEG, we can determine brain’s location in MEG helmet by
co-register (align) the head mesh (i.e., 3D meshes of the head and brain from MRI structural scan) to the MEG data (i.e., In MEG session, outline of the participants’ head is digitlsed)
Diagram of MRI structural session and MEG session where outline of head taken with lasers
Determining the brain’s location in the MEG helmet
MEG data also includes, beside outline, is - (2)
The sensor locations are recorded relative to reference coils placed at fiducial points on the head
Therefore, can see where the sensors are in relation to the head and brain
Diagram of forward vs inverse problem
we want inverse problem but its harder
The different sensors are differentially sensitive to in forward modelling
different sources and different sources more or less obvious at sensors (e.g,., orange one has big impact of sensor compared to others - blue isn’t perpendicular orientation)
The leadfield in forward model is the prediction of - (2)
what each sensor would show for a given source of activity
Gives matrix of each sensor x sources
What does this leadfield show? - (3)
orange one has big impact of sensor compared to others - blue isn’t perpendicular orientation
Numbers next to them are weights saying if we have activity at that source how much activity at this possible source gets to the sensor
If we do for sensory we get a matrix
Example of asking questions in forward model
Boto et al., (2016) did a forward model by looking what effect a simulated source would have with a sensor close to head (OPM) vs sensor far away from head (SQUIDs)
They compared on scalp OPM sensors and helmet-based SQUIDs which showed..
OPM sensors being closer to the brain than SQUIDs could result in a 5-fold increase in sensitivity!
Another diagram of forward vs inverse problem - (3)
not simply forward and backward
Forward problem is from true source to MEG measurement
Inverse is from MEG measurement to estimated source
Inverse modelling is like
looking at shadow of activity rather than activity in brain itself
Example of inverse problem being an ill-posed problem - money
Example of inverse problem being an ill-posed problem - equation
Example of inverse problem being ill-posed problem - equation is not just an analology as - (2)
Not just an analogy – literally like this except not just x and y but ~15000 values you can weight by different amounts to get the value at one sensor
The problem is not constrained enough for there to just be one possible answer
With inverse problem we also can not distinguish -
Kinda like vision as.. (2)
a distributed source near the surface from a deep surface that is more forcal (e.g., deep source could be weak but source near surface could be weak as wlel)
Kinda like vision if something is weak and nearby it is similar to being strong and far away - can’t tell the difference
Example of dipole fitting which says - (2)
- Wr got 15,000 places result could be coming from
- Which one is most likely?
Example of dipole fitting outcome - (2)
- On right, somtatosensory response map across sensors at particular time point
- On left we look at brain and one point and arrow (orientation)
The images of whole brain activity at each time point (current density map in distributed methods ) images can make a
video showing where the active over time - across different conditions
How does inverse model work in distributed methods? - (2)
Apply this transformation matrix to the activity in sensor space at each time point, to estimate the activity in all the dipoles over time - getting into source space - number of each sensor to number of each dipoles (source)
can apply one inverse model at sensor activity at any time and at any condition which gives different brain maps
