Neuronal Reconstruction Workshop

1. Neuronal Reconstruction Workshop Darren R. Myatt*, Slawomir J. Nasuto, Giorgio A. Ascoli. *d.r.myatt@reading.ac.uk, http://www.rdg.ac.uk/neuromantic

2. More Acknowledgements • Thanks also go to • Tye Hadlington • Nathan Skene • Kerry Brown (GMU) • Thanks specifically do not go to the Heathrow Airport Security team

3. 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

4. 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

5. 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)

6. 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.

7. Flavours of Reconstruction • Reconstruction methods may be split into 4 (or possibly 5) broad classes • Manual • Semi-manual • Semi-automatic • Automatic • So automatic that you don’t even need to turn up to work any more

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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.

14. 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!

15. 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

16. 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

17. Basic Interface Mode Buttons Mode options Overlaid Reconstruction Image Stack Stack Bar Image Processing

18. 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

19. 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)

20. 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

21. 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

22. 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.

23. 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

24. Semi-automatic Reconstruction • Newly added to the application • Still a bit of a Work In Progress, as it is not as intuitive as I would like yet • Employs an extension to 3D of the semi-automatic algorithm used in NeuronJ • Includes estimate of dendritic radius • Additional post-processing to improve accuracy

25. 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

26. 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.

27. 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!