Public defence in Biomedical Engineering, M.Sc. Amit Jaiswal

Title of the thesis: Improving the neuromagnetic source-imaging workflow

Doctoral Student: Amit Jaiswal
Opponent: Associate professor Seppo P. Ahlfors, Harvard Medical School, USA
Custos: Professor Lauri Parkkonen, Aalto University School of Science, Department of Neuroscience and Biomedical Engineering

Magnetoencephalography (MEG) is a cutting-edge, non-invasive technique to measure brain activity. It is employed in neuroscience research and clinically, e.g. for localizing epileptic foci and eloquent cortical areas around a brain tumor. It records signals at hundreds of locations around the head and provides time-series data, which are often integrated with patient’s magnetic resonance images (MRI) to estimate the underlying neural sources through a process referred to as source imaging. Such a mapping of the underlying brain activity from recorded signals requires several steps to be performed precisely. This thesis improves some of these steps to make the process faster, more accurate, and more economical. 

A major focus of the thesis is on how we can accurately digitize the head shape to improve the integration of MEG with MRI for enhancing the accuracy of MEG maps. By examining the digitization systems and suggesting best-practices guidelines, this research aims to improve the accuracy of MEG source imaging. The thesis also focuses on assessing the consistency of source-imaging tools by looking at different software packages used for analyzing MEG signals. By comparing results from these tools, the study revealed inconsistencies in underlying steps that could affect the output. This insight uplifts researchers' understanding of the analysis steps and helps optimize the analysis approaches. Finally, the thesis presents a robust tool that creates a virtual MRI – a pseudo-MRI – from a digitized head shape, eliminating the need for an actual MRI scan in many MEG applications.This could simplify the imaging process and make it more accessible while maintaining sufficient accuracy for many applications. 

Overall, this thesis improves the MEG source-imaging workflow and provides an additional tool to make the process more economic. The findings could have far-reaching implications for both scientific research and clinical practice.

Key words: MEG, EEG, source imaging, head digitization, pseudo-MRI, beamformers, open-source analysis software.

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/ 

Contact information:

Doctoral theses at the School of Science: https://aaltodoc.aalto.fi/handle/123456789/52 

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