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The locus cœruleus (LC) is a small noradrenergic nucleus of high interest in neuroscience. As the main source of noradrenaline in the brain, it is involved in numerous cognitive functions such as arousal, attention and memory. In addition, previous studies have emphasised its relationship with the progression of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. The visualisation of the LC in magnetic resonance imaging (MRI) is realised through so-called neuromelanin-imaging in which magnetisation transfer (MT) effects are thought to be the main source of contrast. However, current researches insufficiently characterise the underlying contrast generation mechanisms given the poor understanding in the LC tissue properties. Efficiently visualising the locus coe ruleus is therefore a challenging process that requires further investigation.

The aim of this thesis is to provide an efficient tool for better understanding the underlying contrast mechanisms in the LC and thereby optimising its visualisation through multi-compartment spoiled gradient echo (SPGR) imaging. Firstly, the simulation of such a sequence through the extended phase graph with exchange (EPG-X) framework is performed in order to determine optimal sequence parameter values. After an experimentation phase, the outcomes are validated against the developed model. Simulation suggests that an optimal contrast can be achieved by maximising the power of the excitation pulse. It is expected that the use of optimal sequences compared to the one currently played out at the Wellcome Centre for Human Neuroimaging (WCHN) would improve the LC visualisation. Nonetheless, the lack of robust validation prevent the generalisation of these observations. Secondly, the SPGR model is extended by including a saturation pre-pulse for amplifying the MT effects. The contrast optimisation is realised through simulation according to the same formalism as previously while accounting for the MT pre-pulse. Optimal parameter values suggest that an improvement is achievable regarding the MT-weighted sequence currently played out through the maximisation of the power and the off-resonance frequency of the MT pulse as well as the time delay between the saturation and excitation pulses. Unfortunately, no validation was achievable for this configuration which should be investigated in future researches.

Because of the lack of knowledge about the contrast mechanisms in the LC, the optimisation of an MRI sequence effectively targeting the LC is complex. Therefore, due to the numerous parameters involved and the poor confidence regarding their impact, future studies should focus on a better characterisation of this structure and the inherent contrast mechanisms.

MRI --- Locus cœruleus --- Magnetisation transfer --- SPGR --- Contrast --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres

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One of the promises of autonomous cars is that these will allow drivers to become passengers, and therefore to be engaged in many tasks other than driving such as working, reading or relaxing. However, there exists an increased risk of motion sickness incidence in self-driving cars, thus preventing people suffering from this state from devoting themselves to these tasks. As a consequence, the user acceptance and uptake of autonomous cars could be negatively affected, limiting the benefits this emerging technology may provide. To avoid the negative impact motion sickness could have on autonomous car adoption, this problem has to be investigated and appropriate countermeasures have to be developed. 

A first step in the development of a solution consists in detecting early signs of motion sickness, allowing so to initiate the triggering of various processes intended to alleviate the symptoms associated with motion sickness. The aim of this thesis is to identify physiological parameters that are indicative of motion sickness, and to determine the relevance of ocular parameters for predicting this state. Indeed, ocular data could be easily recorded in autonomous cars through integrated high precision cameras. 

A protocol to acquire data in context is first designed. This protocol aims at inducing motion sickness in 2 different ways. The first one consists in driving in a fixed-base driving simulator. The second one consists in performing some tasks on paper while being a passenger in a moving car. Twenty subjects took part to the protocol. Severe motion sickness was reported by 3 and 9 participants, during the session in simulator and the session in car respectively. The analysis of the collected data shows that heart rate, electrodermal and gastric activities increase with motion sickness. Machine learning models are then trained with ocular data as inputs, and a 3-level score, reflecting the severity of motion sickness, as ground truth. The results suggest that ocular data alone cannot predict motion sickness, but that it may be appropriate to combine it with other physiological data in order to predict motion sickness.

motion sickness --- data acquisition --- physiological data analysis --- autonomous cars --- ocular parameters --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres

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Finding a treatment for cancer is a major challenge of our time. In the ongoing research, combination therapies (the use of several drugs together) are of high interest. In comparison with the use of a single drug, combinations of synergistic drugs (i.e. drugs that are more effective together than alone) can be as effective while allowing to overcome the drug resistance, to reduce the doses at which the drugs are used, and consequently decrease their toxic effect and multiply their targets. However, the space of all potentially effective combinations is too large to experimentally test all of them and assess their effectiveness, this is known as the combinatorial explosion problem. To overcome that, the identification of interesting combinations requires the help of computational tools. In recent years, machine learning models have been successfully used in biomedical applications. They are typically used in order to determine which combinations would be interesting to be experimentally tested. Since some models aiming at predicting the responses of pairwise combinations already exist, there are only a few machine learning models able to predict responses of higher order drug combinations (the order of a drug combination is defined as the number of drugs in the combination). In addition to the response of a drug combination (typically expressed as a growth percentage), the synergy score of this combination is of high interest. The synergy score allow to answer the question: how much are those drugs more effective together than individually? This work is a step towards the use of machine learning to predict the effect of higher order (order larger than two) cancer drug combinations. It has been made in collaboration with Aalto University (Finland), where a machine learning tool called ComboFM has been developed. ComboFM is able to efficiently predict pairwise responses of cancer drugs. The goal of this work is to extend the use of ComboFM to the predictions of higher order drug combinations. To that end, we propose to combine ComboFM with another model, called the Dose model. The Dose model computes the responses of any order drug combinations, based on all the pairwise responses existing in the combination. This work investigates how those two models can be combined together in order to predict responses of higher order drug combinations while decreasing the amount of required experimental data (pairwise responses). This combination of models gives rise to several issues that are tackled and investigated. The experiments made in this thesis showed that ComboFM and the Dose model can efficiently be combined, as long as the parameters of both models are optimized specifically for this application.

Combination therapy --- Cancer treatment --- Machine learning --- Higher order drug combination --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres

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It has been shown that a single neuron can encounter different firing rates during the sleep and the awake states. Those rhythms directly have an impact on the synaptic weight between the neurons. Moreover, recent evidence shows that spindle oscillations encountered during sleep influence the calcium levels in the post-synaptic spine that trigger synaptic plasticity changes. There exists a large number of synaptic plasticity rules. In particular, this thesis focuses on calcium-induced synaptic plasticity. However, the little number of calcium-based models do not take into account the calcium dynamics in much detail. Indeed, to reproduce protocols and obtain results that are consistent with experimental data, a great number of simplifications are often considered. A review of the existing calcium-based models is made in order to categorize those models in a systematic way: ‘How do they implement the calcium flow into the neuron?’, ‘What is the equation governing synaptic plasticity depending on the calcium concentration?’, etc. The thesis focuses on the calcium-dependent synaptic plasticity model developed by Graupner et al. (2016). This model has made simplifications to implement the calcium dynamics while being consistent with data obtained experimentally. The contribution of this thesis is first to integrate this abstract model into a conductance-based model which allows switching from a tonic pattern to a bursting pattern, encountered during the switch to the sleep state. This allows observing what are the consequences of this switch on the calcium-dependent synaptic plasticity. The second main contribution of the thesis is
to integrate a more detailed calcium dynamics into the abstract calcium dynamics model from Graupner et al. (2016). The key message is the fact that integrating a detailed calcium dynamics into an abstract one represents a major challenge to tackle because of the large number of assumptions that have been made to construct this abstract model. This leads to the prospect that starting from a more physiological calcium dynamics then integrating a calcium-dependent synaptic plasticity rule to this model may be a more suitable way of doing.

Calcium --- Synaptic plasticity --- Mathematical modeling --- Sleep --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres