Neuronal Reconstruction Workshop

Darren R. Myatt*,Slawomir J. Nasuto,Giorgio A. Ascoli.

d.r.myatt@reading.ac.uk, http://www.rdg.ac.uk/neuromantic

More Acknowledgements

Thanks also go to Tye Hadlington Nathan Skene Kerry Brown (GMU)

Thanks specifically do not go to the Heathrow Airport Security team

Requirements for this workshop

Laptop running/emulating Windows WINE should be ok, except for possibly the 3D display

A reasonable amount of RAM 1 Gig recommended, although 512M will be OK – less is

possible, but not great A standard 3 button mouse/trackball with mouse wheel

Not strictly necessary but strongly preferable – I have a few spares to hand out

Either a working CD-ROM drive or USB port that will recognise a flash drive If you have neither of these, then I will begin to suspect that

you are in league with the Heathrow airport security team in making my life more difficult than it needs to be

Workshop Aims

Provide participants with direct experience of reconstructing neurons and the challenges involved in resolving ambiguities Give a tutorial with the freeware Neuromantic application

Semi-manual reconstruction Semi-automatic reconstruction

To generate discussion about best practice for reconstructing dendritic trees Consistency remains a problem

Gather feedback and recommendations on improvement for the Neuromantic tool

The workshop length is not set in stone but will probably last for around two hours

Why Reconstruct Neurons?

Allows the validation and refinement of simulations of neuronal behaviour Compare between simulation (via NEURON or GENESIS)

and electrophysiological testing Gaining large enough populations of reconstructed

neurons allows insight into the morphological variation observed in each class.

Facilitates the identification of dendritic abnormalities associated with brain disease Epilepsy, Alzheimer’s disease, some forms of retardation etc. Compare statistical properties of trees between control and

experimental conditions (via L-Measure, for example)

Is it Live or is it Memorex?

Two main options for reconstruction… Live imaging (NeuroLucida)

Advantages: no real memory requirement, no discretisation in Z.

Disadvantages: specimen degradation over time and Z drift on stage

Reconstruction from an image stack Advantages: minimal specimen degradation and Z

drift Disadvantages: can require large amounts of

storage and Z values are usually discretised. A motorised stage is strongly preferred.

Flavours of Reconstruction

Reconstruction methods may be split into 4 (or possibly 5) broad classesManualSemi-manualSemi-automaticAutomaticSo automatic that you don’t even need to

turn up to work any more

Manual Reconstruction

User has to do define every neurite compartment with very little or no assistance

Incredibly laborious and time consuming Camera Lucida

Pencil and paper tracing via a system of prisms (it still exists!)

Neuron_Morpho Freeware plug-in for ImageJ Original inspiration for Neuromantic

Semi-manual Reconstruction

Each segment is still added manually by the user

Application gives some assistance in some elements of the task to reduce effort e.g. auto focussing, useful visualisation

NeuroLucida (without AutoNeuron), Neuromantic on manual mode

Generally considered to be the most accurate method of reconstruction, but still highly time consuming

Semi-automatic Reconstruction

Application requires constant user-interaction, but the application requires mainly topological information. Define beginning and end points of a dendrite, and

the neurite is traced out automatically NeuronJ

Freeware plug-in for ImageJ (single image only) Derived from the robust LiveWire algorithm

Neuromantic Semi-auto tracing is a 3D extension of the NeuronJ

algorithm with post-processing Also includes radius estimation

Automatic Reconstruction

What everybody really wants… Current automatic techniques are generally

limited to high quality microscopy data (e.g. confocal fluorescence)

AutoNeuron for NeuroLucida, NeuronStudio Numerous skeletonisation techniques, and

also the Rayburst algorithm. The outputs frequently require cleaning up to

bring reconstruction accuracy up to the required standard

Which flavour to choose?

t(Automatic)+t(Clean Up)<t(Manual)? Realistically, the clean up time will always be

non-zero, except in trivial cases With noisy data, fully automatic reconstruction

is unlikely to be possible A good reconstruction application should

make it as easy as possible to spot errors have good manual editing capabilities to facilitate

clean up

Issues with reconstruction

Interuser/Intrauser variation… Different users on the same system The same user on different systems Even the same user reconstructing the same

neuron on the same system! Thin dendrites (relative to image resolution)

are a particular problem, as errors in radius estimation can have a large impact on surface area and cross-sectional area.

Increased automation should increase consistency, but accuracy may still be a problem.

Example from Jaeger, 2001

•These reconstructions were performed in NeuroLucida by experienced users

•Surface area range shows over 20% variation, which has a lot of implications for behavioural simulations

•and this is just variation over individual dendrites, not a whole dendritic tree!

Pyramidal Neuron Example

•All 10 participants were complete novices at neuronal reconstruction

•Interquartile range of surface area shows around 15% variation

•Interquartile range of volume is around 30% variation

• Includes thicker neurites as well as thin

Neuromantic

Freeware application for making 3D reconstructions of neurons from serial image stacks

Programmed in C++ Builder Can function on any form of microscopy data from

non-deconvolved widefield stacks upwards. Semi-manual tracing

Manually position new compartments, which may then be edited afterwards as necessary

Semi-automatic tracing Longer neurite sections can be traced out automatically, and

the radius is calculated at each point The neuron can also be visualised in 3D to help

identify and correct errors

Basic Interface

Image Stack

Mode optionsMode ButtonsMode Buttons

Image Processing

Stack Bar

Overlaid ReconstructionOverlaid Reconstruction

Installation Time!

CD/Flash drive contains Neuromantic directory Stack containing basal tree of a pyramidal neuron

Simply copy the Neuromantic directory onto your computer somewhere, and it should be fine (hopefully!)

Copy the stack to a directory nearby Run the Neuromantic executable V1.4.1 to

make sure everything is working

Getting Started

An updated manual may be found in Manual.pdf in the Neuromantic directory

Load in the stack by pressing F2 or File->Load Stack and selecting the first image

Wait for a while under the stack loads (it’s 387 Megabytes in total with 86 images) – the status bar shows the current progress

Halve stack size if you are forced to use virtual RAM otherwise (Options->Stack->Halve Stack Size)

Stack Navigation

Most functionality is always present on the mouse for speed

Drag the stack around with the right button Zoom in/out by rolling the mouse wheel (or -/+ keys for

those without) Use the stack bar or hold down the middle mouse

button and move vertically to scroll through the different images (z axis)

Middle clicking the mouse button auto-focuses at that position (+/- 5 slices) Hold SHIFT while middle clicking to auto-focus over all

images

Semi-manual Reconstruction

Each compartment is added by dragging a line from one edge of the dendrite to the other, thus providing an estimate of the radius

The compartment added is of the type defined by the radio buttons in the Manual panel to the right

Every time a new compartment is added its parent is set to the currently selected compartment

So add a compartment, then auto-focus on the next position down the dendrite, then add the next etc.

In order to create a branch point, select the desired compartment with a left mouse click, then carry on as before

Selecting Compartments

As you move the cursor towards the centre of a compartment it will change, indicating that you can manipulate that segment

Left click a compartment to select it SHIFT whilst selecting to add to the current selection CTRL whilst selecting to select an entire branch ALT to select all the compartments of the same type CTRL+I inverts the current selection CTRL+D deselects all compartments Using these controls it is possible to efficiently select

any set of compartments, such as a subtree.

Editing Compartments

Selected compartments can be dragged around in the x/y plane using the left mouse button

The Z value is altered by selecting a compartment, navigating to the new desired image slice, and then pressing CTRL+C (or Edit->Set Z To Current Slice)

The radius of a compartment is altered by holding down CTRL, and dragging with the middle button

Press DELETE to delete all selected compartment

Semi-automatic Reconstruction

Newly added to the applicationStill a bit of a Work In Progress, as it is not

as intuitive as I would like yetEmploys an extension to 3D of the semi-

automatic algorithm used in NeuronJ Includes estimate of dendritic radiusAdditional post-processing to improve

accuracy

Semi-automatic Reconstruction

Employs Steerable Gaussian Filters to perform the image processing Efficiently yields information on the position of

neurites and flow direction from eigen analysis of the Hessian matrix

The standard deviation of the Gaussian determines the radius of the neurites detected

A graph search (via Djikstra’s algorithm) is then performed to calculate the optimal route via the defined cost function

Patchwork Method

Pre-processing on the entire image stack is expensive in both time and space.

For the basal stack used in this workshop, around 10Gigabytes of RAM would be required

Therefore, to avoid this issue, only the necessary patches of the image are image processed and routed.

Conclusions

Discussed reconstruction in general and some of the challenges associated with it

Given participants experience of the Neuromantic application, in terms of both its semi-manual and semi-automatic capabilities

I hope you have enjoyed yourselves!