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Markers of Brain Resilience

Bhowmick, Anubrata (2021) Markers of Brain Resilience.

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Abstract:Developing psychopathology after a traumatic event has been sought-after research for some time, and most of it has focused on the detrimental causes of anxiety, depression, or post-traumatic stress disorder. Earlier research showed a high degree of intra-individual variation in how individuals respond to stress. While no attempt has been made to understand resiliency using the available data, some researchers have tried to understand the same using a medical perspective. In this thesis, we are developing methods to improve the estimation of functional brain connectivity using magnetic resonance imaging (MRI). This involves preprocessing and estimation of connectivity using state-of-the-art tools. It is then followed by the analysis of the correlation matrices, which is the baseline for understanding the significance of the connections. The analysis is followed by research and development of various Machine Learning algorithms to understand whether complex mathematical algorithms can make sense of the data, and the correlations between them. This also led to another question as to whether they can perform better when there is not enough data for the analysis. This was followed by experimenting with state-of-the-art neural networks for brain analysis for a comparison of the brain regions and was concluded with the development of a new feature-engineered multi-layer perceptron framework that not only dealt with the low data problem but was also able to find robust biomarkers of brain resilience. Our research resulted in finding biomarkers of brain resilience from various Machine Learning models and showing that feature-engineered Multi-Layer Perceptron models can conclude better results as compared to data-hungry graph models, with the fe-MLP model performing significantly better with around 64% classification accuracy as compared to 62% from the BrainGNN model. It also answers a significant question in research, pertaining to the fact that, if properly feature-engineered, multi-layer perceptron models can perform significantly better with less data, as compared to complex models.
Item Type:Essay (Master)
Clients:
Philips Research, Eindhoven, Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/87777
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