Explorative Analysis and Data Mining in Big Neural Data

Most motor actions are carried out by many neurons acting together in different areas of the brain. Recently, techniques have been developed and increasingly used that allow simultaneous recordings of the activity from a large number of neurons over time ranges as long as several weeks. At Neuronano Research Center (NRC) at Lund University, neural activity in rodents has been recorded using chronically implanted 128-channel electrodes. The targets are within areas that play a prominent role in the planning, monitoring and execution of movements and, consequently, are strongly affected by motor diseases such as Parkinson’s disease. The experiments conducted at NRC naturally produces a large amount of neural data. This master thesis work aims to perform feature extraction from that data, i.e., find a way to mathematically describe the disease states of the rodents without a priori knowledge of the states. This is done by extracting features from the recordings, whose values later are used to analyse and cluster the data. The results show that a clear majority of the neurons exhibit a significant difference in feature values between the disease states. This means that it is possible to mathematically describe the different disease states. Moreover, the results show that different neurons behave differently: Some, e.g., exhibit increased activity going from one state to another, while others exhibit decreased activity. Adding to this, some does not exhibit any change while others exhibit a significant change. That it is possible to mathematically describe the different disease states, with a timescale of hours, indicate that the same may be possible for states with smaller timescales. These states, with a smaller timescale, do not have to be connected to Parkinson’s disease but could rather be normal, healthy states such as locomotion or reaching.Parkinson’s – a mathematical description In search for answers to how the brains of Parkinson patients function there are not many tools available. One of them proves to be mathematics. Another is rodent experiments, which play an important and necessary role in the process of finding new treatments for the disease. Research indicate that, in rodents, it is possible to distinguish between different states of health and disease by using mathematical analysis. This can be done by analysing the brain signals of the animal, and this without knowing what state the animal is in at that time. It seems possible to use mathematics to describe different states of health and disease, opening up new possibilities on the road to finding new treatments. But, how does this actually work? Parkinson’s disease is the second most common degenerative disease. It damages the brain with consequences such as impaired movements, tremors at rest, dementia and, finally, 10 to 20 years later, death. The knowledge of exactly how the area of the brain affected by Parkinson’s works is unfortunately still limited. Better knowledge could lead to the development of new treatments, hopefully with less side effects than those currently used. The brain is the part of our body that controls our thoughts, feelings and actions. It interprets, helping us to make sense of the world, and controls actions such as breathing, talking and moving (to mention very few). Our actions, including movements, are controlled by the use of nerve cells, called neurons, connected to each other in a complicated network. The neurons use this network to communicate with each other by sending and receiving electrical signals: action potentials. At Neuronano Research Center (NRC), Lund University, a group of scientists has found a way to perform the very challenging task of recording the signals from a lot of these tiny neurons. The brain signals recorded are from rats and, specifically, from neurons in an area of their brain that controls movements. The reason that this area has been chosen is that it is strongly affected by diseases that impair our movements, such as Parkinson’s disease. The rats used in the experiments at NRC have received drug injections infecting half of their brains with Parkinson’s disease. In this way, the healthy half of the brain can be used as a reference when analysing what happens in the other, diseased half. During the experiments, action potentials from neurons in both the healthy and diseased part of the brain are recorded and saved, resulting in a very large dataset. The amount of action potentials, as well as the pattern that they are fired in affects the message being sent. Let us look at a simple example: A sequence of action potentials, sent from a neuron, can be represented by ones and zeros. A one is an action potential and a zero means that nothing happened at that time: 1 1 0 1 1 0 1 1 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0 1 The first sequence consists of two ones followed by a zero whereas the second does not follow any obvious pattern. Also, the first one has more action potentials (eight) than the other has (five). These properties, among many others, can easily be expressed mathematically. It has been found that the sequences of action potentials change when the behaviour changes: The meaning of a certain sequence from a certain neuron is not yet known, but maybe it will be in the future! But, the fact that sequences change when the behaviour of the animal changes is a strong motivation to continue the search: Answers to how our brains function and new treatments for diseases can be uncovered by means of mathematics

Design of a high-density multi-channel electrode for multi-structure parallel recordings in rodents

In neurophysiology, investigating brain connectivity within and between different brain structures is of fundamental importance for understanding nervous system function and its relation to behavior. Yet, parallel recordings in multiple brain structures is highly challenging, especially in rodents, which are most commonly employed in neurophysiological research but rather small in size. In this study, the design and manufacturing of a high-density multi-channel electrode for chronic, multi-structure parallel recordings in rats is presented and exemplified with functional neuronal recordings from 128 recording channels, placed bilaterally in eight different brain structures, in an awake, freely moving animal

Circuit-level analyses of cortico-basal ganglia-thalamic networks. Effects of dopamine dysregulation and experience dependent plasticity.

The cortico-basal ganglia-thalamic (CBT) circuit is thought to be involved in control of voluntary and goal-directed movements and action selection. Dopamine is known to play a crucial role in this circuit and regulating its activity. The important role of dopamine is particularly evident in Parkinson’s patients, where dopaminergic cells are dying and motor impairments follow. While dopamine replacement is an effective therapy, satisfactory alleviation only lasts for a limited number of years, after which patients frequently develop side-effects in the form of levodopa-induced dyskinesia. In order to clarify the neurophysiological consequences of dopamine dysregulation we have here investigated the electrophysiological activity of each part of the CBT-loop in rats during different experimental conditions, using custom made multi-channel electrodes. Neuronal activity changes in 16 CBT structures were characterized upon acute pharmacological dopaminergic manipulations and firing rate changes of subgroup of cells within different structures in the CBT circuit were shown to potentially be responsible for the severe akinesia induced by the drugs. We have also developed a novel method to monitor the global state of the CBT circuit in a rat model of levodopa-induced dyskinesia and showed how this approach can be used to help developing new pharmacological therapies. Lastly, to investigate how somatosensory input is affecting motor circuits, we have recorded activity of the whole CBT-loop in rats before and after extensive skilled forelimb reaching and grasping training. Preliminary results show that only the motor cortex display experience-dependent changes due to the reaching training