Objective 1.
As part of the project the necessary software implementations including the crucial tensor SVD MP-denoising has been completed and the denoising method had been incorporated into the eigenspace beamforming script in the Matlab-based Fieldtrip toolbox meeting the project schedule. Following the implementation the method’s performance on simulated and actual data has been carefully tested and assessed, and it was concluded that the gain of utilizing the method did not meet expectations. In effect only a small noise reduction was achieved on real data, complicated by additional drawbacks of the method which then made us reach the conclusion that the pursuit of a wider use is not warranted (see more details at WP1). This part of the project was perfectly on schedule up to the implementation and analysis stage. However, further planned measures i.e. publication of the method and tutorials of its use, were not taken and the objective was completed without further action. As an alternative measure to achieve improvements on the inverse model, I sought out to implement a Bayesian beamformer for use with magnetometers and planar gradiometers, which has the promise of overcoming one of the most significant problems of beamforming, degraded performance with simultaneous and correlated sources. This work started halfway through the project and is still under way. Improvement of the forward model was aimed to be achieved by obtaining better conductivity
estimates, an important component of accurate forward modelling. This was approached via two ways: (a) updating the currently used standards for white and grey matter conductivities through analysing conductivity measurements of live human tissue samples, (b) implementing a combinatorial estimation of optimal skull, scalp, grey matter, white matter and cerebrospinal fluid conductivity. The updating of currently used brain tissue conductivity estimates had to be abandoned at the very start of the project due to the sudden passing of a key collaborator, Prof Capogna, who was
essential and irreplaceable to the access to live tissue samples and the measurements of the samples. Since it was not feasible to acquire samples and measurements from other institutions, I took an alternative measure with the specific aim of improving the conductivity estimates through different means. I set out to run the cross-modal attention task - designed for MEG – also as an fMRI task. The rationale behind is that using the BOLD signal as an independent marker of brain regions primarily involved in processing the visual and the auditory stimuli, I can use the fMRI results to identify the most optimal conductivity estimates for MEG source analysis. Theoretically, the method could be used as a generic method, irrespective of the task by simply running short visual and auditory stimulation (i.e. localizer sequences) in both MEG/EEG and fMRI. To examine the feasibility of this method I implemented and ran the cross- modal attention task during fMRI scanning with the same participants who performed the identical task in the MEG. The analysis of the results is currently ongoing. Improving forward modelling via the optimization of conductivity parameters entered into computations was set out through combinatorial approximation of best conductivity estimates of layer conductivities (skull, scalp, white matter, grey matter, CSF). Essentially, the method suggests finding the most optimal conductivity estimates through systematically changing layer conductivities and assessing the source localization results. As part of the objective the method has been implemented with advanced forward modelling with Boundary Element Method and Finite Element Method , however further testing of the method on the full experimental study has been shifted due to delay of the experimental data collection. The assessment of the method’s performance is currently ongoing.
Objective 2.
SNR improvement was approached via developments of source localization through optimizing forward and inverse modelling (see Objective 1). The experiment crucial for Objective 2 has been implemented and was conducted, the recorded data have been preprocessed and analysis has been ongoing/partially completed. Even with standard source localization methods, thalamic activity has been detected at the visual stimulation frequency, but so far not at the auditory stimulation frequency. The mismatch is likely due to the much stronger brain response to visual stimulation than to auditory stimulation, at least in sensory cortical areas. To my knowledge this is the first clear demonstration of thalamic signals in response to visual (or any other) brain stimulation via non-invasive electrophysiological methods. If the ongoing optimization of forward modelling and modified beamforming will achieve SNR improvement and better detection of visual and auditory signals in the thalamus remains to be seen as analysis and optimization of the methods as discussed above (Objective 1) is largely ongoing. Note that the assessment of MP-denoising has been completed as the method did not show significant enough SNR improvement at sensor or source level, as well as, specifically detecting thalamic signals (see Objective 1).
Objective 3.
Achieving the objective required the collection of the above mentioned experimental data and the preprocessing and analysis of the collected data. This has been fully achieved and whilst no cross-modal attention effect was identified in the thalamus, a series of electrophysiological correlates have been detected in various (primarily posterior) brain areas. The writing up of the results of the MEG findings is underway currently aimed at being submitted to a high-impact open-access journal. In addition, the collected fMRI data proved to be highly important at identifying effects largely complementary to the MEG findings and thus contributing to refining our models of cross-modal attention filtering. The first draft of the planned paper disseminating these fMRI findings is being prepared. Currently, the MEG and the fMRI results are being prepared as separate publications due to the large volume of findings with both methods.
