Using the connectome visualisation tool¶
The connectome tool bar in MRtrix3 has been designed from scratch, with the intention of providing a simple, data-driven mechanism for visually assessing individual connectomes as well as the results of network-based group statistics. The interface may therefore vary considerably from other connectome visualisation packages, and may be intimidating for new users who simply want to ‘see the connectome’. I hope I can convince you in this tutorial that the design of this tool allows you, the user, to dictate exactly how you want to visualise the connectome, rather than being forced to conform to a particular prior expectation of how such things should be visualised.
Initialising the tool¶
My suspicion is that new users will load the tool, and immediately
think: ‘Where do I load my connectome?’. Well, let’s take a step
backwards. If you were to give the software a connectome matrix, with no
other data, there would be no way to visualise that connectome in the
space of an MR image: the software has no information about the spatial
locations of the nodes upon which that connectome is based. So the first
step is actually to load an image to provide the tool with this
information, using the “Node image” button at the top of the toolbar.
The desired image is the output of the
labelconvert command, as
detailed in the Structural connectome construction guide: the
tool uses this image to localise each parcel in 3D space in preparation
for visualisation. Alternatively, you can load the relevant parcellation
image from the command-line when you first run
mrview, using the
If you still do not see anything in the
mrview main window, this is
likely because you have not yet opened a primary image in
is currently necessary for
mrview to correctly set up the camera
positioning. The easiest solution is to open your parcellation image not
only to initialise the connectome tool, but also as a standard image in
mrview; then simply hide the main image using the ‘View’ menu.
With the basis parcellation image loaded, the tool will display the location of each node; note however that all of the nodes are exactly the same colour, and exactly the same size, and there are no connections shown between them - it’s an entirely dis-connected network. This makes sense - we haven’t actually provided the tool with any information regarding which connections are present and which are absent. We can also do the opposite: change the “Edge visualisation” - “Visibility” from ‘None’ to ‘All’, and now the software shows every edge in the connectome non-discriminantly.
Therefore, we need some mechanism of informing the software of which
edges should be drawn, and which should not. Most logically, this could
be achieved by loading a structural connectome, and perhaps applying
some threshold. So now, for the “Edge visualisation” - “Visibility”
option, select “Matrix file”, and load your connectome. The software now
uses the data from this external file to threshold which edges are drawn
and which are not, and also allows you to vary that threshold
interactively. (You can also load a connectome matrix from the command
line using the
The connectome still however has a binary appearance; every edge in the connectome is either present or absent, and they all have the same size and the same colour. We know that our connectome contains weights distributed over a wide scale, and would like to be able to see this as part of our visualisation; for instance, we may decide that more dense connections should have a ‘hot’ colour appearance, whereas less dense connections should be darker. We can achieve this by changing the “Edge visualisation” - “Colour” from ‘Fixed’ to ‘Matrix file’, and selecting an appropriate matrix file (perhaps the same file as was used for the visibility threshold, perhaps not).
For most users, connectome data will be loaded using the
‘open’ button in the ‘connectome matrices’ section, or at the command-line
mrview is first run using the
Basis of connectome visualisation customisation¶
With the above steps completed, you should obtain a fairly rduimentary visualisation of the connectome you have loaded. The plethora of buttons and gadgets in the connectome tool user interface is however a clue regarding the scope of customisation available for precisely how the connectome data will be displayed.
As an example, consider the ‘Edge visualisation - Colour’ entry. These options control how the colour of each individual edge in the connectome will be determined, based on the data the tool is provided with. Clicking on the main combo box shows that there are a few options available:
- Fixed: Use the same fixed colour to display all visible edges.
- By direction: The XYZ spatial offset between the two nodes connected by
an edge is used to derive an RGB colour (much like the default streamlines colouring).
- Connectome: The colour of each edge will depend on the value for that
edge in the connectome you have loaded, based on some form of value -> colour mapping (a ‘colour map’).
- Matrix file: Operates similarly to the connectome option; except that
the value for each edge is drawn from a matrix file that is not the connectome matrix you have loaded (though it must be based on the same parcellation to have any meaning). So for instance: You could load a structural connectome file as your connectome matrix and show only those edges where the connection density is above a certain threshold, but then set the colour of each edge based on a different matrix file that contains functional connectivity values.
If the Connectome or Matrix file options are used, it is also possible to alter the colour map used, and modify the values at which the edges will reach the colours at either extreme of the colour map.
Hopefully, this simple demonstration will be enough to highlight the design principle of this tool, and therefore the frame of mind necessary to use it effectively:
What *data* do I want to determine a specific *visual property* of my connectome?
There is tremendous power in separating these two entities. For instance, consider a use case where I have performed network-based group statistics, and wish to visualise my result. I may choose to threshold the connectome edges based on statistical significance, but set the width of the connections based on the mean connection strength to get an idea of the density of connections in the detected network, but set the colour of each edge based on the effect size to see which components of the network are most affected. I can even automatically hide any nodes that are not involved in the detected network by selecting “Node visualisation” - “Visibility” - ‘Degree >= 1’.
Importing detailed node information¶
When the parcellation image is first loaded, the software has no
information regarding the designations of the underlying nodes, so it
simply labels them as “Node 1”, “Node 2” etc.. To show the anatomical
name of each node in the list, you must load the connectome
lookup table that was used as the target output in the
step during [structural connectome construction]. This file provides a
list of node indices and their corresponding names, so is perfect for
subsequent assessment of the resulting connectomes, whether using this
tool or in other contexts (e.g. Matlab). Such a lookup table may also
include a pre-defined colour for each node, which can then be used
during visualisation by selecting “Node Visualisation -> Colour -> LUT”.
There are a couple of neat tricks that can be used to produce impressive-looking visualisations, but need some pre-processing or careful consideration in order to achieve them.
Visualising edges as streamlines / streamtubes¶
Rather than drawing a straight line between connected nodes to represent an edge, it is possible with tractography-based connectome construction to instead represent each connection based on the structural trajectory by which those nodes are inter-connected. This can be achieved as follows:
- When generating the connectome using tck2connectome, use the
-assignmentsoption. This will produce a text file where each line contains the indices of the two nodes to which that particular streamline was assigned.
- Use the connectome2tck command to produce a single track file,
where every streamline represents the mean, or exemplar, trajectory
between two nodes. This is achieved using two command-line options:
-exemplarsto instruct the command to generate the exemplar trajectory for each edge, rather than keeping all streamlines (you will need to provide your parcellation image); and
-files singleto instruct the command to place all computed exemplars into a single output file.
- In the
mrviewconnectome toolbar, select “Edge visualisation” - “Geometry” - ‘Streamlines / Streamtubes’, and select the exemplar track file just generated.
Visualising nodes as triangulated meshes¶
Although the node parcellations are represented as volumetric segmentations, and we do not yet have support for importing mesh-based parcellations, it is still possible to visualise the conectome nodes using a mesh-based representation. This is done by explicitly converting the volume of each parcel to a triangulated mesh. The process is as follows:
- Compute a triangular mesh for each node, and store the results in a
single file. The command is called label2mesh. Note that the
output file must be in the
.objfile format: this is the only format currently supported that is capable of storing multiple mesh objects in a single file.
- (Optional) Smooth the meshes to make them more aesthetically pleasing
(the results of the conversion process used in
label2meshappear very ‘blocky’). Apply the meshfilter command, using the
smoothoperator. Again, the output must be in the
- In the
mrviewconnectome toolbar, select “Node visualisation” - “Geometry” - ‘Mesh’, and select the mesh file just generated.
Using node selection to highlight features of interest¶
The table in the connectome toolbar that lists the node names and colours can also be used to select and highlight particular nodes. In most cases, this will simply be an additional ‘toy’ for navigating the data; however it’s also possible that this capability will prove to be a powerful tool for demonstrating network features.
In any connectome visualisation software, when the user selects one or
more particular nodes of interest, some modification must be applied to
the visual features of the nodes in order to ‘highlight’ the nodes of
interest. In many cases, this may be hard-wired to behave in a
particular way. In the case of
mrview in MRtrix3, this highlighting
mechanism is entirely flexible: the user can control the visual modifications
applied to both those network elements selected and those not selected. For
instance, you may choose for nodes to become completely opaque when you
select them, while other un-selected nodes remain transparent; or they
may grow in size with respect to the rest of the connectome; or they may
change in colour to highlight them; or those nodes not selected may
disappear entirely. This flexibility is accessed via the “Selection
visualisation settings” button, which will open a dialog window
providing access to these settings.
As manual selection applies to nodes only, the behaviour for edges is as follows:
- When no nodes are selected, all edges are drawn according to their standard settings.
- If a single node is selected, all edges emanating from that node are considered to be ‘selected’, and the relevant visual modifiers will be applied.
- If two or more nodes are selected, only connections exclusively connecting between the nodes of interest are considered to be ‘selected’.
Node visualisation using matrices¶
When using external data files to control the visual properties of the connectome, most commonly vector files will be used to determine visual properties of nodes, and matrix files will be used to determine visual properties of edges. These provide precisely one scalar value per connectome element, and therefore provide a static visual configuration.
It is however also possible to set any visual property of the connectome nodes based on a matrix file. In this scenario, the values to be drawn from the matrix - and hence their influence on the relevant visual property of the nodes - depends on the current node selection. That is: once you select a node of interest, the software extracts the relevant row from the matrix, and uses only that row to influence the node visual property to which it has been assigned. In the case where multiple nodes of interest are selected, an additional drop-down menu is provided, that allows you to prescribe how those multiple rows of matrix data are combined in order to produce a single scalar value per node, which can then be used to influence its relevant visual property.