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HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography

Periodic Reporting for period 4 - HRMEG (HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography)

Periodo di rendicontazione: 2021-03-01 al 2022-08-31

Human brain function can be studied non-invasively with several methods. For example, using a variant of magnetic resonance imaging, one can monitor changes in the flow of oxygenated blood to get indirect information about brain activations. This method – functional magnetic resonance imaging or fMRI – is spatially accurate but unfortunately too slow to track neural events at the speed they occur. Measuring the electric activity of neurons directly gives high temporal resolution and enables monitoring neural processing at its native speed. Such measurements can be carried out by recording the neurally-generated electric potential differences on the scalp (electroencephalography or EEG) or the magnetic fields just outside of the head (magnetoencephalography or MEG). Due to the varying conductivities of the intervening tissues, the spatial resolution of EEG is inferior to that of MEG. Therefore, out of the non-invasive neuroimaging methods that can track neural events at the speed they occur, MEG is considered to have the best spatial resolution.

However, current MEG system are still limited in spatial resolution since the employed superconducting sensors must be kept several centimeters away from the scalp of the study subject, which reduces signal strength and decreases spatial resolution compared to having the sensors right on the scalp. Interestingly, recent advances in quantum optics have provided a novel magnetic sensing technology – optically-pumped magnetometer – that enables constructing non-superconducting yet sufficiently sensitive sensors for MEG. This high-resolution magnetoencephalography (HRMEG) project leverages these developments and aims at constructing a novel MEG whose spatial resolution outperforms that of current MEG systems. The higher spatial resolution enables non-invasive recordings of certain brain signals, particularly the so-called gamma-band signals, in unprecedented fidelity. Importantly, this higher resolution can be achieved in study subjects of all ages, unlike in conventional MEG where the sensor array cannot be adapted e.g. to the smaller heads of children.

The HRMEG project has encompassed the design and construction of a high-resolution MEG system employing optically-pumped magnetometers. Such a system comprises not only the sensors but subsystems to control the sensors, acquire the signals, suppress external magnetic interference and perform co-registration with MR-images. The project has also entailed the development and adaptation of data analysis techniques for such a system as well as the applications of the system for brain–computer interfaces and characterization of brain responses to continuous naturalistic sensory stimuli such as movies.

To conclude, the HRMEG approach and system has opened a new way to study the working human brain. It has been applied in basic neuroscientific research of sensory and cognitive functions and is now being translated also to clinical use to potentially improve e.g. the pre-surgical evaluation of epilepsy patients. We will also seek to improve the diagnostics of certain neurological disorders, such as mild traumatic brain injury, with the HRMEG technology.
The first half of the project has focused on the development of the measurement system. To this end, we have constructed a helmet that holds optically-pumped magnetometers at freely-selectable locations around the head. In this helmet, the sensors can be adjusted to just touch the scalp of the subject. To ensure proper operation of the sensors and to reduce external magnetic interference, we have also designed and constructed a compensation coil system around the sensor array; the currents in the coils are dynamically controlled by signals from the magnetometers such that the magnetic field is kept below 1 nT (nanotesla) (Iivanainen et al., 2019a). We have also developed a system that optically measures the positions and orientations of the sensors with respect to the head surface of the subject. By combining this information with the structural magnetic resonance images of the subject, we can accurately estimate the locations of the neural sources underlying the measured magnetic signals (Zetter et al. 2018; 2019).

We performed the first human measurements by presenting the subjects with dynamic visual stimuli. The HRMEG system detected clear visually-induced responses, including suppression of the alpha activity at around 10 Hz and elevation of both narrow-band (30-40 Hz) and broad-band gamma (above 50 Hz) activity. We compared the HRMEG results with those obtained with conventional MEG in the same subjects and found that HRMEG yielded better signal-to-noise ratio and was able to better separate activatios in the two gamma bands (Iivanainen et al., 2019b).

In anticipation of applying the HRMEG system to brain–computer interfacing, we have developed a convolutional neural network classifier that allows training across multiple subjects' MEG data and rapid adaptation to the invidual's MEG responses (Zubarev et al., 2019).

The main result of the first half of the project is a working HRMEG system, with which we have successfully performed initial human brain measurements.

The second half of the project was severely affected by the COVID restrictions as laboratory work and human measurements were restricted to various degrees for about a year. Yet, we were able to continue improving the measurement set-up, both in terms of hardware and software-based methods. We developed a system for better control of the ambient magnetic field (Mäkinen et al., 2020; Zetter et al., 2020; Iivanainen et al., 2021), which enabled us to prepare for recordings in epilepsy patients. We have also developed software-based interference suppression methods (Helle et al., 2021) and means for localizing and calibrating the sensors (Iivanainen et al., 2022).

We extended human measurements to more natural stimuli. First, we studied how well we can pick up the induced gamma-band responses to still pictures; all image types induced detectable gamma-band activity whose level depended on the image type (Grön, 2022). We then used a movie as a dynamic, naturalistic stimulus. We showed a 12-min clip of the Hollywood movie "Forrest Gump" to 10 participants while their brain activity was measured by the HRMEG system. We observed that brain signals significantly correlated across the viewers (Forsman, 2021).

We also tested recording animal brain activity with the HRMEG approach. Our pilot experiment in a domestic cat demonstrated that feline brain activity could be measured with high fidelity with this non-invasive technique.

In summary, the HRMEG project has yielded a versatile, high-resolution MEG system and that system has been successfully applied to study human and animal brain activity.
Within its limited scope, the constructed system performs better than current state-of-the-art MEG systems based on conventional technology: the HRMEG system yields better signal-to-noise ratio and spatial resolvability of neocortical brain sources and enables the sensor array to be adapted to the head size and shape of the subject, which is particularly important when studying children or even small animals as we have demonstrated for the first time. Our current HRMEG system only covers roughly one lobe of the brain but that has already turned out to be sufficient to demonstrate these advantages with respect to conventional technology.
HRMEG setup for measuring brain responses to visual stimuli (FIg. 1) and results (Fig. 2)