Metoder och krav för noggrann lokalisering av sensorer i on-scalp magnetoencefalografi

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging method which is used both in neuroscientific research and clinical medicine. Current state-of-the-art MEG systems require cryogenic cooling as well as thermal insulation between the sensors and the head of subjects, leading to lower sensitivity due to the relatively large spatial separation. Recently, a new type of sensor has been developed that does not require cryogenic temperatures to operate and can thus be placed much closer to the scalp of subjects. In such an on-scalp MEG system, the sensors of the array could be freely moveable in relation to each other as to conform to the head shape and size of individual subjects. To properly estimate the location and extent of neural sources within the brain, one needs to accurately know the position of all sensors in relation to the head. In on-scalp MEG systems this seemingly mundane issue becomes important, as all sensors must be localised individually. Large errors in the sensor positions may result in considerable errors in source estimates. In this thesis, different sensor localisation methods to be used in co-registration of MEG data with structural magnetic resonance images were examined, and the performance requirements for such methods were determined through the use of simulations. We found that the maximum acceptable root-mean-square sensor position error is $\sim3$ mm, which is achievable for most localisation methods examined. Thus the choice of method depends less on the localisation accuracy and more on other parameters such as ease of use, cost and commercial availability.Magnetoencefalografi (MEG) är en noninvasiv metod för undersökning av hjärnfunktion. MEG används både inom neurovetenskaplig forskning och klinisk medicin. Nuvarande MEG-system kräver kryogen nedkylning och värmeisolering mellan sensorerna och försökspersonens huvud, vilket leder till nedsatt känslighet på grund av det relativt stora avståndet mellan sensorerna och hjärnan. Nyligen har en ny typ av sensorer utvecklats som inte kräver kryogen nedkylning, och kan därmed placeras mycket närmare huvudet. I ett så kallat "on-scalp" MEG-system kunde sensorerna vara fritt flyttbara i förhållande till varandra för att på bästa sätt passa försökspersonens huvudform och -storlek. För att kunna avgöra varifrån inuti hjärnan MEG-signaler härstammar bör man veta sensorernas exakta position i förhållande till huvudet. I ett on-scalp MEG-system blir detta synligtvis triviala problem viktigt, i och med att alla sensorer måste lokaliseras enskilt. Ifall det uppstår fel i deras positioner kan detta orsaka märkbara fel i var hjärnaktiviteten som givit upphov till MEG-signalen avgörs vara. I detta diplomarbete har olika metoder för att lokalisera sensorerna undersökts, och noggrannhetskraven för dessa metoder har fastställts genom flera olika typers simuleringar. Utgående från dessa fastställdes det maximala tolererbara kvadratiska medelvärdesfelet i sensorernas position till $\sim3$ mm. Denna noggrannhetsnivå är uppnåelig för de flesta av de undersökta lokaliseringsmetoderna. Därmed bör valet av lokaliseringsmetod grunda sig på andra variabler såsom användarvänlighet, bekostnad och kommersiell tillgänglighet

Miniature biplanar coils for alkali-metal-vapor magnetometry

Atomic spin sensors offer precision measurements using compact, microfabricated packages, placing them in a competitive position for both market and research applications. Performance of these sensors such as dynamic range may be enhanced through magnetic field control. In this work, we discuss the design of miniature coils for three-dimensional, localized field control by direct placement around the sensor, as a flexible and compact alternative to global approaches used previously. Coils are designed on biplanar surfaces using a stream-function approach and then fabricated using standard printed-circuit techniques. Application to a laboratory-scale optically pumped magnetometer of sensitivity $\sim$20 fT/Hz$^{1/2}$ is shown. We also demonstrate the performance of a coil set measuring $7 \times 17 \times 17$ mm$^3$ that is optimized specifically for magnetoencephalography, where multiple sensors are operated in proximity to one another. Characterization of the field profile using $^{87}$Rb free-induction spectroscopy and other techniques show $>$96% field homogeneity over the target volume of a MEMS vapor cell and a compact stray field contour of $\sim$1% at 20 mm from the center of the cell

Neural Substrate for Metacognitive Accuracy of Tactile Working Memory

The human prefrontal cortex (PFC) has been shown to be important for metacognition, the capacity to monitor and control one's own cognitive processes. Here we dissected the neural architecture of somatosensory metacognition using navigated single-pulse transcranial magnetic stimulation (TMS) to modulate tactile working memory (WM) processing. We asked subjects to perform tactile WM tasks and to give a confidence rating for their performance after each trial. We circumvented the challenge of interindividual variability in functional brain anatomy by applying TMS to two PFC areas that, according to tractography, were neurally connected with the primary somatosensory cortex (S1): one area in the superior frontal gyrus (SFG), another in the middle frontal gyrus (MFG). These two PFC locations and a control cortical area were stimulated during both spatial and temporal tactile WM tasks. We found that tractography-guided TMS of the SFG area selectively enhanced metacognitive accuracy of tactile temporal, but not spatial WM. Stimulation of the MFG area that was also neurally connected with the S1 had no such effect on metacognitive accuracy of either the temporal or spatial tactile WM. Our findings provide causal evidence that the PFC contains distinct neuroanatomical substrates for introspective accuracy of tactile WM.Peer reviewe

Optical Co-registration of MRI and On-scalp MEG

| openaire: EC/H2020/678578/EU//HRMEGTo estimate the neural generators of magnetoencephalographic (MEG) signals, MEG data have to be co-registered with an anatomical image, typically an MR image. Optically-pumped magnetometers (OPMs) enable the construction of on-scalp MEG systems providing higher sensitivity and spatial resolution than conventional SQUID-based MEG systems. We present a co-registration method that can be applied to on-scalp MEG systems, regardless of the number of sensors. We apply a structured-light scanner to create a surface mesh of the subject’s head and the sensor array, which we fit to the MR image. We quantified the reproducibility of the mesh and localised current dipoles with a phantom. Additionally, we measured somatosensory evoked fields (SEFs) to median nerve stimulation and compared the dipole positions between on-scalp and SQUID-based systems. The scanner reproduced the head surface with <1 mm error. Phantom dipoles were localised with 2.1 mm mean error. SEF dipoles corresponding to the P35m response for OPMs were well localised to the somatosensory cortex, while SQUID dipoles for two subjects were erroneously localised to the motor cortex. The developed co-registration method is inexpensive, fast and can easily be applied to on-scalp MEG. It is more convenient than traditional co-registration methods while also being more accurate.Peer reviewe

Requirements for Coregistration Accuracy in On-Scalp MEG

| openaire: EC/H2020/678578/EU//HRMEGRecent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual’s head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be (Formula presented.) and (Formula presented.), respectively.Peer reviewe

Adaptive neural network classifier for decoding MEG signals

| openaire: EC/H2020/678578/EU//HRMEGWe introduce two Convolutional Neural Network (CNN )classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain–computer interfaces (BCI).Peer reviewe