Differentiating Task-Based Functional Ultrasound Signals via Data-Driven Decompositions

Differentiating Task-Based Functional Ultrasound Signals via Data-Driven Decompositions

Enthoven, Maarten (TU Delft Electrical Engineering, Mathematics and Computer Science)

Leus, G.J.T. (mentor)
Hunyadi, Borbala (mentor)
Kruizinga, Pieter (graduation committee)

Delft University of Technology

Electrical Engineering | Circuits and Systems

2021-05-28

At the Center for Ultrasound and Brain imaging at Erasmus MC in Rotterdam, a mouse's visual cortex had been imaged using the fUS technique. The mouse had been exposed to different visual stimuli. The stimuli varied in position, size, and shape. We investigate how the measured task-based fUS signals differ depending on the visual stimuli presented to the mouse. For that purpose, we decompose the fUS data with four different methods giving different levels of sparsity. This thesis compares the performance of the four methods, and provides neurological insights obtained with these methods. For modeling the data, we consider four data-driven decomposition methods: ICA and three sDL variants. The methods decompose the data into better interpretable spatial maps and time courses. Every decomposition is further examined by training L1-regularized prediction models that optimize for sparsity. The goal is to predict the presented stimulus based on the decomposed data. Furthermore, the potential of group lasso regularization in prediction models is illustrated. The decompositions extract spatial maps anatomically linked to the visual cortex, superior colliculus and the hippocampus. All decompositions achieve considerable performance in the position prediction but have low success in the size and shape prediction. ICA outperformed the three sDL variants in all prediction tasks. Furthermore, group lasso regularization is found to be a useful tool to obtain discriminatory information in the time dynamics.

Functional Ultrasound
Brain Decoding
Explainable Machine Learning
Sparsity
Data-Driven Modelling

http://resolver.tudelft.nl/uuid:3d6811f9-208a-4c7e-826b-785e7a720986

Student theses

master thesis

© 2021 Maarten Enthoven