Robust and reproducible oscillatory phenotypes of brain function from Optically Pumped Magnetometers

Secondary Supervisor(s): Dr Anna Kowalczyk

University of Registration: University of Birmingham

BBSRC Research Themes: Integrated Understanding of Health (Ageing)

Project Outline

Magnetoencephalography (MEG) is a safe, affordable and scalable measure of macro-scale synaptic dynamics. It can quantify healthy brain function and how it is modulated by cognitive context, ageing and clinical conditions. MEG has substantial potential for accurate assessment and tracking of brain health in individuals – a key global priority for modern medicine highlighted by the World Health Organisation in 2022.

The next phase of MEG research is to move from robust population-level effects towards personalised and targeted monitoring of brain health and function in individuals. A challenge is that between-subject variability is often wider than observed group differences, making assessment ineffective for a substantial minority of people and limiting effective clinical translation of MEG as a tool for personalised medicine.

This PhD project will reconcile robust group level effects with the wide variability in observed single subject oscillatory dynamics. We will undertake two related approaches to overcome these challenges using visual pathways of the human brain as a target neuronal system. The cortical regions of visual system are readily accessible non-invasive electrophysiology and are the source of two prominent neuronal oscillations that provide a real-time readout of synaptic dynamics (Buzsáki & Wang 2012; 10.1146/annurev-neuro-062111-150444).

We hypothesise that individual differences in neuronal function are masked by acquisition and anatomical factors can that this can be greatly optimised. Specifically, current OPM-MEG studies use generic acquisition protocols which are not tuned to individual anatomy. Moreover, analysis approaches rely on between-subject averaging across large groups to achieve statistically robust results.

Project 1 will utilise Optically Pumped Magnetometers (OPM) to develop and validate data acquisition protocols with optimised signal to noise level in individual participants. OPMs are a novel form of MEG which enables flexible sensor arrangement without the constraints of large and expensive cryogenic cooling system. We will use individual anatomical structure estimated from MRI scans to optimise OPM sensor placement and develop methods to compute the expected effect size (Chaumon et al 2021; 10.1016/j.neuroimage.2021.11710.1016) and statistical power of alpha and gamma band brain responses to visual stimulation at a single subject (N=1) level. This provides a principled method for computing optimal number sensor array positions and number experimental trails required for single participants, as well as the required number of individual participants required to observe robust group level effects. We will use the cutting edge OPM laboratory at the Centre for Human Brain health as well as existing OPM datasets.

Project 2 will develop an individual oscillatory phenotype to quantify the dynamics of individual participants cortical response to visual stimulation. This is a low-dimensional description of a single OPM-MEG recording that combines metrics such as individual oscillatory frequencies with network, dynamic and non-linear features (Quinn et al 2018; 10.3389/fnins.2018.00603). The IOP for the OPM data recorded from the visual system will be characterised by alpha and gamma band oscillations analysed with the Empirical Mode Decomposition. This approach will enable a signal-to-noise improvement with precise quantification of the morphology of each individual occurrence of an oscillation within visual cortex.

Finally, we will explore whether anatomical differences identified in project 1 underlie any of the critical oscillatory features estimated from project 2. We hypothesise that OPM-MEG has high sensitivity to functional differences in synaptic activity that cannot be accounted for by simple anatomical variability. Only by considering all these factors can we separate the contributions of acquisition, anatomy and synaptic function to the measured signal.