3dBAR – a tool (and web service) for creation of 3d models of brain structures from 2d atlas data.

BrainSlices – an online repository of brain tissue images

In many neuroscience experiments one obtains brain tissue of animals. The tissue is sliced and stained for the analysis and when the experiment is over the slices are disposed of or covered by dust on one of countless laboratory shelves. If another scientist wants to access the data, it is often necessary to perform redundant experiments which means a waste of time, money and life of further animals. An improvement of the present situation would be a repository of images of brain tissue slices with controlled access to the data geared towards open sharing of data. This is the aim of project: to facilitate online sharing of slice images and related metadata.

An open server software for maintaining repositories of two-dimensional brain images of different modalities (stained sections, blockfaces, X-ray, MRI scan planes etc.) is being created. The repository functionality includes image upload, online browsing (in a manner similar to Googlemaps) and sharing. The software is designed to allow creation of private repositories by interested entities (after release) and will be used as a basis of a world-wide accessible repository.

Analyses of mice behaviour in Python

Several laboratories from our institute perform complex behavioural experiments with IntelliCage system, which produces abundant amount of data. To extract the most information from these data we are developing methodology and a dedicated Python library (PyMICE) for processing it.

We are also trying to quantitatively assess, how well we understand the mehanisms behind the data. To obtain that goal we propose formalised mathematical models of behaviour and measure how much of data variability can be explained by them.

Measurement physics and analysis of extracellular potentials

It is in our interest to understand the functioning of the brain. For this we need to make observations about it, this requires a means of quantifiable measurement of the neural activity. One of the most popular measures used to study the brain is the extracellular potential. The neural activity gives rises to transmembrane currents that can be measured in the extracellular medium as electrical potential. For example, when a sharp electrode is positioned close to the soma of a neuron, it picks the extracellular signature of an action potential or spike. When there are many neurons in the vicinity of this electrode, one can increase the number of electrodes, and perform spike sorting using triangulation. This spiking information dominates the high frequency component of the potential. The low frequency component is called Local Field Potential or LFP and is dominated by synaptic currents, its dendritic processing and the restoring currents in the neuron after an action potential. The LFP is not a local measure and has contributions from neurons located farther away. This can used to our advantage to measure the activity over longer spatial range and at a sub population resolution of neurons.

LFP gives a measure of the potentials for a large population of cells, but in order to have a meaningful understanding of the underlying phenomena, it is more useful to estimate the distribution of the current sources that generate the observed potential. For this, in our lab, we develop tools that can analyze the LFP and give us the current source densities distributions. This is referred to as the inverse problem. We developed a parameter free method, called Kernel Current Source Density, that can estimate the current source distribution.

In order to test these tools, it is essential that we have a full grasp of what the neurons are doing in selected situations. For this, we create ground truth testing data using simulations of neurons. Each of these neurons is modelled with biophysical and morphological realism, using compartmental modeling. The transmembrane currents generated by these neurons over the course of the simulation are tracked, and then used to generate LFP.  This approach is called forward modeling.

We also model LFP while considering the non-trivial electrical properties of the recording setup and the slice. We use Finite Element Methods, to model such details, and test how varying assumptions about the conductivity profile of the setup influence the signal recorded.