Computational Methods and Analyses for Assessing Cerebellar Electrophysiology with Magneto- and Electroencephalography

The cerebellum contains almost 80 % of all neurons in the human brain and is now recognized as a critical node in the distributed neural circuits underlying autonomic, sensorimotor, cognitive, and emotional functions. Cerebellar dysfunction has furthermore been implicated in some of the most prevalent neuropsychiatric diseases including autism and schizophrenia, with new information implicating the cerebellum in neurodegenerative dementias and Parkinson's disease as well. Despite advances in our understanding of cerebellar structure and function on the microscopic scale, and an increasing number of hemodynamic functional imaging studies, the macroscopic scale electrophysiology of the cerebellum remains poorly characterized. Magneto- and electroencephalography (M/EEG) can non-invasively and directly measure neural activity at sub-millisecond temporal resolution and therefore hold promise to bridge this gap. M/EEG has, however, so far mainly been employed to study the cerebral cortex and its use in the study of cerebellar electrophysiology is largely unexplored. This thesis presents a comprehensive investigation on assessing cerebellar neural activity with M/EEG. First, a new technique that allows for reconstruction of the cerebellar cortex from standard-resolution in-vivo MRI data is presented. This technique is used to create a surface source space and quantify the detectability of neural activity in the cerebellum with M/EEG. We then develop a novel analytical framework to compare the performance of source estimators and quantify the spatial fidelity of the cerebellar source estimates that are attainable using our new cerebellar source space tools. The proposed methods and analyses assume only standard MRI and M/EEG data. They are therefore readily applicable and thus enable a new and efficient way of studying cerebellar electrophysiology in health and disease.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Harvard-MIT Program in Health Sciences and Technology

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Samuelsson, John Gustaf Wilhelm

Advisor dc:contributor.advisor

• Hämäläinen, Matti

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/138977

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/138977