OPTIMISED CHARACTERISATION OF DEEP BRAIN OSCILLATORY DYNAMICS IN HEALTH & DISEASE, USING NOVEL ACQUISITION/ANALYSIS OF SIMULTANEOUS MEG AND EEG
Lead Research Organisation:
Cardiff University
Department Name: Sch of Psychology
Abstract
The aim of this project is to better localise sources of oscillatory activity within the brain by developing and applying novel methods to combine concurrent MEG and EEG (MEEG). In particular, the combination of EEG with MEG will allow more sensitive characterisation of deeper brain structures such as the amygdala and medial temporal lobe - structures that are difficult to measure from with MEG alone. In this project I first will develop methods based on work from my previous Masters course in Physics at Nottingham. I will then demonstrate the application of these optimised methods in cognitive paradigms such as MEEG localisation of emotional responses in the amygdala to, for example, face stimuli. The methods will also be used to better characterise deep activity, such as hippocampal theta oscillations in spatial memory paradigms, in mesial temporal lobe epilepsy patients.
People |
ORCID iD |
| Krishna Devi Singh (Primary Supervisor) | |
| Megan Godfrey (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509449/1 | 30/09/2016 | 29/09/2021 | |||
| 1799542 | Studentship | EP/N509449/1 | 30/09/2016 | 30/05/2020 | Megan Godfrey |
| Description | A new method of measuring network connectivity in the brain has been developed, based on the correlation of 'disorder' of brain activity across a range of time scales. This has been shown to reveal network patterns that vary with time scale. The new method, called MRVE correlation, shows similar network patterns to the most robust conventionally used technique, but has been shown to give more consistent results across healthy participants, suggesting that this method may give less noisy results than the established measure. A paper has been drafted reporting these results. |
| Exploitation Route | As MRVE correlation has been shown to give more consistent network patterns across healthy participants than the most consistent established measure, it could therefore be helpful in detecting subtle alterations in brain activity due to disease. This could aid in understanding the mechanisms underlying mental disorders, and/or be developed as a method of detecting connectivity changes for early diagnosis. |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare |