Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG--MRI

With a hybrid MEG--MRI device that uses the same sensors for both modalities, the co-registration of MRI and MEG data can be replaced by an automatic calibration step. Based on the highly accurate signal model of ultra-low-field (ULF) MRI, we introduce a calibration method that eliminates the error sources of traditional co-registration. The signal model includes complex sensitivity profiles of the superconducting pickup coils. In ULF MRI, the profiles are independent of the sample and therefore well-defined. In the most basic form, the spatial information of the profiles, captured in parallel ULF-MR acquisitions, is used to find the exact coordinate transformation required. We assessed our calibration method by simulations assuming a helmet-shaped pickup-coil-array geometry. Using a carefully constructed objective function and sufficient approximations, even with low-SNR images, sub-voxel and sub-millimeter calibration accuracy was achieved. After the calibration, distortion-free MRI and high spatial accuracy for MEG source localization can be achieved. For an accurate sensor-array geometry, the co-registration and associated errors are eliminated, and the positional error can be reduced to a negligible level.Comment: 11 pages, 8 figures. This work is part of the BREAKBEN project and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 68686

Multi-locus transcranial magnetic stimulation—theory and implementation

Background: Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method: a magnetic field pulse from a TMS coil can excite neurons in a desired location of the cortex. Conventional TMS coils cause focal stimulation underneath the coil centre; to change the location of the stimulated spot, the coil must be moved over the new target. This physical movement is inherently slow, which limits, for example, feedback-controlled stimulation. Objective: To overcome the limitations of physical TMS-coil movement by introducing electronic targeting. Methods: We propose electronic stimulation targeting using a set of large overlapping coils and introduce a matrix-factorisation-based method to design such sets of coils. We built one such device and demonstrated the electronic stimulation targeting in vivo. Results: The demonstrated two-coil transducer allows translating the stimulated spot along a 30-mmlong line segment in the cortex; with five coils, a target can be selected from within a region of the cortex and stimulated in any direction. Thus, far fewer coils are required by our approach than by previously suggested ones, none of which have resulted in practical devices. Conclusion: Already with two coils, we can adjust the location of the induced electric field maximum along one dimension, which is sufficient to study, for example, the primary motor cortex. (C) 2018 The Author(s). Published by Elsevier Inc.Peer reviewe

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto-or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications.Peer reviewe

Automatic and robust noise suppression in EEG and MEG : The SOUND algorithm

Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise-and artifact-contaminated channels and trials. Conventionally, EEG and MEG data are inspected visually and cleaned accordingly, e.g., by identifying and rejecting the so-called "bad" channels. This approach has several shortcomings: data inspection is laborious, the rejection criteria are subjective, and the process does not fully utilize all the information in the collected data. Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data. These approaches offer objective, automatic, and robust removal of noise and disturbances by taking into account the sensor-or trial-specific signal-to-noise ratios. We introduce a method called the source-estimate-utilizing noise-discarding algorithm (the SOUND algorithm). SOUND employs anatomical information of the head to cross-validate the data between the sensors. As a result, we are able to identify and suppress noise and artifacts in EEG and MEG. Furthermore, we discuss the theoretical background of SOUND and show that it is a special case of the well-known Wiener estimators. We explain how a completely data-driven Wiener estimator (DDWiener) can be used when no anatomical information is available. DDWiener is easily applicable to any linear multivariate problem; as a demonstrative example, we show how DDWiener can be utilized when estimating event-related EEG/MEG responses. We validated the performance of SOUND with simulations and by applying SOUND to multiple EEG and MEG datasets. SOUND considerably improved the data quality, exceeding the performance of the widely used channel-rejection and interpolation scheme. SOUND also helped in localizing the underlying neural activity by preventing noise from contaminating the source estimates. SOUND can be used to detect and reject noise in functional brain data, enabling improved identification of active brain areas.Peer reviewe

Visual deviant stimuli produce mismatch responses in the amplitude dynamics of neuronal oscillations

Objectives: Auditory and visual deviant stimuli evoke mismatch negativity (MMN) responses, which can be recorded with electroencephalography (EEG) and magnetoencephalography (MEG). However, little is known about the role of neuronal oscillations in encoding of rare stimuli. We aimed at verifying the existence of a mechanism for the detection of deviant visual stimuli on the basis of oscillatory responses, so-called visual mismatch oscillatory response (vMOR). Methods: Peripheral visual stimuli in an oddball paradigm, standard vs. deviant (7: 1), were presented to twenty healthy subjects. The oscillatory responses to an infrequent change in the direction of moving peripheral stimuli were recorded with a 60-channel EEG system. In order to enhance the detection of oscillatory responses, we used the common spatial pattern (CSP) algorithm, designed for the optimal extraction of changes in the amplitude of oscillations. Results: Both standard and deviant visual stimuli produced Event-Related Desynchronization (ERD) and Synchronization (ERS) primarily in the occipito-parietal cortical areas. ERD and ERS had overlapping time-courses and peaked at about 500-730 ms. These oscillatory responses, however, were significantly stronger for the deviant than for the standard stimuli. A difference between the oscillatory responses to deviant and standard stimuli thus reflects the presence of vMOR. Conclusions: The present study shows that the detection of visual deviant stimuli can be reflected in both synchronization and desynchronization of neuronal oscillations. This broadens our knowledge about the brain mechanisms encoding deviant sensory stimuli. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe

Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain

Magnetoencephalography (MEG) is a noninvasive technique for investigating neuronal activity in the living human brain. The time resolution of the method is better than 1 ms and the spatial discrimination is, under favorable circumstances, 2-3 mm for sources in the cerebral cortex. In MEG studies, the weak 10 fT-1 pT magnetic fields produced by electric currents flowing in neurons are measured with multichannel SQUID (superconducting quantum interference device) gradiometers. The sites in the cerebral cortex that are activated by a stimulus can be found from the detected magnetic-field distribution, provided that appropriate assumptions about the source render the solution of the inverse problem unique. Many interesting properties of the working human brain can be studied, including spontaneous activity and signal processing following external stimuli. For clinical purposes, determination of the locations of epileptic foci is of interest. The authors begin with a general introduction and a short discussion of the neural basis of MEG. The mathematical theory of the method is then explained in detail, followed by a thorough description of MEG instrumentation, data analysis, and practical construction of multi-SQUID devices. Finally, several MEG experiments performed in the authors' laboratory are described, covering studies of evoked responses and of spontaneous activity in both healthy and diseased brains. Many MEG studies by other groups are discussed briefly as well.Peer reviewe

Preparation and execution of teeth clenching and foot muscle contraction influence on corticospinal hand-muscle excitability

Contraction of a muscle modulates not only the corticospinal excitability (CSE) of the contracting muscle but also that of different muscles. We investigated to what extent the CSE of a hand muscle is modulated during preparation and execution of teeth clenching and ipsilateral foot dorsiflexion either separately or in combination. Hand-muscle CSE was estimated based on motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) and recorded from the first dorsal interosseous (FDI) muscle. We found higher excitability during both preparation and execution of all the motor tasks than during mere observation of a fixation cross. As expected, the excitability was greater during the execution phase than the preparation one. Furthermore, both execution and preparation of combined motor tasks led to higher excitability than individual tasks. These results extend our current understanding of the neural interactions underlying simultaneous contraction of muscles in different body parts.Peer reviewe

EEG Artifact Removal in TMS Studies of Cortical Speech Areas

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is commonly applied for studying the effective connectivity of neuronal circuits. The stimulation excites neurons, and the resulting TMS-evoked potentials (TEPs) are recorded with EEG. A serious obstacle in this method is the generation of large muscle artifacts from scalp muscles, especially when frontolateral and temporoparietal, such as speech, areas are stimulated. Here, TMS–EEG data were processed with the signal-space projection and source-informed reconstruction (SSP–SIR) artifact-removal methods to suppress these artifacts. SSP–SIR suppressed muscle artifacts according to the difference in frequency contents of neuronal signals and muscle activity. The effectiveness of SSP–SIR in rejecting muscle artifacts and the degree of excessive attenuation of brain EEG signals were investigated by comparing the processed versions of the recorded TMS–EEG data with simulated data. The calculated individual lead-field matrix describing how the brain signals spread on the cortex were used as simulated data. We conclude that SSP–SIR was effective in suppressing artifacts also when frontolateral and temporoparietal cortical sites were stimulated, but it may have suppressed also the brain signals near the stimulation site. Effective connectivity originating from the speech-related areas may be studied even when speech areas are stimulated at least on the contralateral hemisphere where the signals were not suppressed that much.Peer reviewe

TMS-EEG: From basic research to clinical applications

Proceeding volume: 1626Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful technique for non-invasively studying cortical excitability and connectivity. The combination of TMS and EEG has widely been used to perform basic research and recently has gained importance in different clinical applications. In this paper, we will describe the physical and biological principles of TMS-EEG and different applications in basic research and clinical applications. We will present methods based on independent component analysis (ICA) for studying the TMS-evoked EEG responses. These methods have the capability to remove and suppress large artifacts, making it feasible, for instance, to study language areas with TMS-EEG. We will discuss the different applications and limitations of TMS and TMS-EEG in clinical applications. Potential applications of TMS are presented, for instance in neurosurgical planning, depression and other neurological disorders. Advantages and disadvantages of TMS-EEG and its variants such as repetitive TMS (rTMS) are discussed in comparison to other brain stimulation and neuroimaging techniques. Finally, challenges that researchers face when using this technique will be summarized.Peer reviewe