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Tourette syndrome (TS) is a neuropsychiatric disorder characterized by motor and phonic tics, which several different theories, such as basal ganglia-thalamo-cortical loop dysfunction and amygdala hypersensitivity, have sought to explain. Previous research has shown dynamic changes in the brain prior to tic onset leading to tics, and this study aims to investigate the contribution of network dynamics to them. For this, we have employed three methods of functional connectivity to resting-state fMRI data - namely the static, the sliding window dynamic and the ICA based estimated dynamic; followed by an examination of the static and dynamic network topological properties. A leave-one-out (LOO-) validated regression model with LASSO regularization was used to identify the key predictors. Most relevant predictors were related to regions belonging to the amygdala-mediated visual social processing network. This is in line with a recently proposed social decision-making dysfunction hypothesis, opening new horizons in understanding tic pathophysiology.

Journal of autism and developmental disorders (2022). doi:10.1007/s10803-022-05844-0, Published by Springer Science + Business Media B.V., Dordrecht [u.a.]

The paper describes a multidisciplinary work that uses a model-based systems engineering method for developing real-time magnetoencephalography (MEG) signal processing. We introduce a requirement-driven, model-based development methodology (RDD and MBD) to provide a high-level environment and efficiently handle the complexity of computation and control systems. The proposed development methodology focuses on the use of System Modeling Language to define high-level model-based design descriptions for later implementation in heterogeneous hardware/software systems. The proposed approach was applied to the implementation of a real-time artifact rejection unit in MEG signal processing and demonstrated high efficiency in designing complex high-performance embedded systems. In MEG signal processing, biological artifacts in particular have a signal strength that overtop the signal of interest by orders of magnitude and must be removed from the measurement to achieve high-quality source reconstructions with minimal error contributions. However, many existing brain–computer interface studies overlook real-time artifact removal because of the demanding computational process. In this work, an automated real-time artifact rejection method is introduced, which is based on the recently presented method “ocular and cardiac artifact rejection for real-time analysis in MEG” (OCARTA). The method has been implemented using the RDD and MBD approach and successfully verified on a Virtex-6 field-programmable gate array.

Simultaneous trimodal positron emission tomography/magnetic resonance imaging/electroencephalography (PET/MRI/EEG) resting state (rs) brain data were acquired from 10 healthy male volunteers. The rs‐functional MRI (fMRI) metrics, such as regional homogeneity (ReHo), degree centrality (DC) and fractional amplitude of low‐frequency fluctuations (fALFFs), as well as 2‐[18F]fluoro‐2‐desoxy‐d‐glucose (FDG)‐PET standardised uptake value (SUV), were calculated and the measures were extracted from the default mode network (DMN) regions of the brain. Similarly, four microstates for each subject, showing the diverse functional states of the whole brain via topographical variations due to global field power (GFP), were estimated from artefact‐corrected EEG signals. In this exploratory analysis, the GFP of microstates was nonparametrically compared to rs‐fMRI metrics and FDG‐PET SUV measured in the DMN of the brain. The rs‐fMRI metrics (ReHO, fALFF) and FDG‐PET SUV did not show any significant correlations with any of the microstates. The DC metric showed a significant positive correlation with microstate C (r (s) = 0.73, p = .01). FDG‐PET SUVs indicate a trend for a negative correlation with microstates A, B and C. The positive correlation of microstate C with DC metrics suggests a functional relationship between cortical hubs in the frontal and occipital lobes. The results of this study suggest further exploration of this method in a larger sample and in patients with neuropsychiatric disorders. The aim of this exploratory pilot study is to lay the foundation for the development of such multimodal measures to be applied as biomarkers for diagnosis, disease staging, treatment response and monitoring of neuropsychiatric disorders.

Gamma-aminobutyric acid (GABA) and glutamate are believed to have inhibitory and exhibitory neuromodulatory effects that regulate the brain's response to sensory perception. Furthermore, frequency-specific synchronization of neuronal excitability within the gamma band (30-80 Hz) is attributable to a homeostatic balance between excitation and inhibition. However, our understanding of the physiological mechanism underlying gamma rhythms is based on animal models. Investigations of the relationship between GABA concentrations, glutamate concentrations, and gamma band activity in humans were mostly restricted to the visual cortex and are conflicting. Here, we performed a multimodal imaging study combining magnetic resonance spectroscopy (MRS) with electroencephalography (EEG) in the auditory cortex. In 14 healthy subjects, we investigated the impact of individual differences in GABA and glutamate concentration on gamma band response (GBR) following auditory stimulus presentation. We explored the effects of bulk GABA on the GBR across frequency (30-200 Hz) and time (-200 to 600 ms) and found no significant relationship. Furthermore, no correlations were found between gamma peak frequency or power measures and metabolite concentrations (GABA, glutamate, and GABA/glutamate ratio). These findings suggest that, according to MRS measurements, and given the auditory stimuli used in this study, GABA and glutamate concentrations are unlikely to play a significant role in the inhibitory and excitatory drive in the generation of gamma band activity in the auditory cortex. Hum Brain Mapp 38:3975-3987, 2017. © 2017 Wiley Periodicals, Inc.

IEEE transactions on radiation and plasma medical sciences : ITRPFI 3(3), 377-385 (2019). doi:10.1109/TRPMS.2018.2886525, Published by IEEE, New York, NY

We have analyzed the possibility to construct multichannel magnetoencephalography (MEG) systems based on high-Tc direct current superconducting quantum interference devices (DC SQUIDs) with graphoepitaxial step edge Josephson junctions. A new layout of multilayer high-Tc superconducting flux transformers was tested and a new type of high-Tc DC SQUID magnetometer intended for MEG systems was realized. ameter 24 mm and a magnetic field resolution of ~4 fT/√Hz These magnetometers have a vacuum-tight capsule of outer diat 77 K. Crosstalk between adjacent sensors was estimated and measured for in-plane and axial configurations. The vibration-free cooling of sensors, minimization of the sensor-to-object distance and optimization of the sensor positions as well as the gantry design are discussed. Our findings may have implications for the next generation of noninvasive imaging techniques that will be used to understand human brain function.

Loudness dependence of auditory evoked potentials (LDAEP) evaluates loudness processing in the human auditory system and is often altered in patients with psychiatric disorders. Previous research has suggested that this measure may be used as an indicator of the central serotonergic system through the highly serotonergic innervation of the auditory cortex. However, differences among the commonly used analysis approaches (such as source analysis and single electrode estimation) may lead to different results. Putatively due to discrepancies of the underlying structures being measured. Therefore, it is important to learn more about how and where in the brain loudness variation is processed. We conducted a detailed investigation of the LDAEP generators and their temporal dynamics by means of multichannel magnetoencephalography (MEG). Evoked responses to brief tones of five different intensities were recorded from 19 healthy participants. We used magnetic field tomography in order to appropriately localize superficial as well as deep source generators of which we conducted a time series analysis. The results showed that apart from the auditory cortex other cortical sources exhibited activation during the N1/P2 time window. Analysis of time courses in the regions of interest revealed a sequential cortical activation from primary sensory areas, particularly the auditory and somatosensory cortex to posterior cingulate cortex (PCC) and to premotor cortex (PMC). The additional activation within the PCC and PMC has implications on the analysis approaches used in LDAEP research.

g r a p h i c a l a b s t r a c t a b s t r a c t Background: Polarized light imaging (PLI) has evolved into a powerful neuroimaging tool to analyze fiber tracts with submillimeter resolution in microtome sections of postmortem human brain tissue. In PLI polarized light changes its polarization state while passing through birefringent tissue, i.e., myelinated axons, which results in sinusoidal signals that characterize different fiber orientations. Noise, light scatter and filter inhomogeneities of the polarimeter interfere with the original sinusoidal PLI signals, which have direct effects on the accuracy of subsequent fiber modeling. New method: In our recent publications we have shown that the sinusoidal signal at each pixel location in PLI images can be restored utilizing independent component analysis (ICA). We now have further improved the signal separation quality by introducing a new constrained ICA algorithm (cICAP) where the component selection is directly included. In cICAP an analytical expression of the expected signal of interest is implemented as a priori information. Results: The algorithm precisely decomposes the deteriorated PLI signals into its underlying source signals. As such, the approach enhances sinusoidal basis functions and is therefore optimal for the extraction of independent spatial maps from PLI images. Comparison with existing methods: The new algorithm performs better and is faster compared to other well-known ICA algorithms. Conclusion: The decomposition in cICAP is optimal with respect to separation and identification of the sinu- soidal nature of the PLI signal. In this way the identification of the relevant components is automatically included and does not require any further component selection tool.

Gamma‐aminobutyric acid (GABA) and glutamate are believed to have inhibitory and exhibitory neuromodulatory effects that regulate the brain's response to sensory perception. Furthermore, frequency‐specific synchronization of neuronal excitability within the gamma band (30–80 Hz) is attributable to a homeostatic balance between excitation and inhibition. However, our understanding of the physiological mechanism underlying gamma rhythms is based on animal models. Investigations of the relationship between GABA concentrations, glutamate concentrations, and gamma band activity in humans were mostly restricted to the visual cortex and are conflicting. Here, we performed a multimodal imaging study combining magnetic resonance spectroscopy (MRS) with electroencephalography (EEG) in the auditory cortex. In 14 healthy subjects, we investigated the impact of individual differences in GABA and glutamate concentration on gamma band response (GBR) following auditory stimulus presentation. We explored the effects of bulk GABA on the GBR across frequency (30–200 Hz) and time (−200 to 600 ms) and found no significant relationship. Furthermore, no correlations were found between gamma peak frequency or power measures and metabolite concentrations (GABA, glutamate, and GABA/glutamate ratio). These findings suggest that, according to MRS measurements, and given the auditory stimuli used in this study, GABA and glutamate concentrations are unlikely to play a significant role in the inhibitory and excitatory drive in the generation of gamma band activity in the auditory cortex. Hum Brain Mapp 38:3975–3987, 2017. © 2017 Wiley Periodicals, Inc.

Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.

Higher association cortices as well as unisensory areas can support multisensory integration [D. Senkowski et al. (2008) Trends Neurosci., 31, 401-409]. The present study investigated whether audiovisual integration of emotional information emerges early at unisensory or later at higher association cortices. Emotional stimuli were presented in three blocks: audiovisual (AV), auditory (A) and visual (V). Eighteen participants performed a delayed emotional recognition task (happy, angry or neutral prosody and/or facial expression) while whole-brain magnetoencephalography (MEG) data were obtained. Time-frequency evoked and total power analyses were performed on the sensor data, and source localization of the frequencies of interest performed via a synthetic aperture magnetometry beamformer. To examine crossmodal integration between bimodal and unimodal conditions, two contrasts were specified: AV > A and AV > V. In the AV > A contrast, early effects were observed on both the temporal and the occipital evoked responses. However, at the source level, early alpha suppression was limited to the occipital sources without changes in temporal cortices. In the AV > V contrast, sensor and source findings revealed increased alpha suppression only in temporal cortices, with no changes in visual cortex. Thus, no crossmodal effect in unisensory areas emerged. Instead, increased frontal alpha activity in both the AV > A and AV > V contrasts supports the view that affective information from face and prosody converges at higher association cortices.

By attaching arrows to a line's ends, the Muller-Lyer illusion can be used to modulate perceived line length. In the present study, we investigated the dynamics of the brain processes underlying this illusion using magnetoencephalography. Subjects were presented with a horizontal line with arrows attached to its ends. Across trials, the angles formed by the arrows were repeatedly changed such that 2 variants of the Muller-Lyer length illusion were either induced or not. The onset of both variants of the illusion revealed consistent activations in visual areas between 85 and 130 ms after stimulus onset, as well as strong and longer lasting activations along the ventral visual processing stream including inferior occipital, inferior temporal, and fusiform gyrus within the range of 195--220 ms. Subsequent neural activation was observed in the right superior temporal cortex, as well as in the right inferior parietal and the right inferior frontal cortex. The time course and the location of the activations suggest that the mechanisms involved in generating the Muller-Lyer illusion are closely linked to the ones associated with object perception, consistent with theories considering a relevant contribution of higher visual areas to the generation of the Muller-Lyer illusion.

Data processing techniques in electroencephalography (EEG) and magnetoencephalography (MEG) need user interactions. However, particularly in clinical applications, fast and objective data processing is important. Here we present an observer-independent method for EEG and MEG analysis of mismatch negativity (MMN) that allows reliable estimation of source activity based on objective anatomical references. The procedure integrates several steps including artifact rejection, source estimation and statistical analysis. It enables the evaluation of source activity in a fully automatic and unsupervised manner. To test its feasibility we obtained EEG and MEG responses in an auditory oddball paradigm in 12 healthy volunteers. The automatized method of EEG and MEG data analysis estimated source activity. The automatically detected MMN was closely comparable with the results obtained by a user-controlled method based on the dipole fitting. The presented workflow can be performed easily, rapidly, and reliably. This development may open new fields in research and clinical applications of source-based EEG and MEG.

Mismatch negativity (MMN) and its neuromagnetic analog (MMNm) are event-related brain responses elicited by changes in a sequence of auditory events and indexes early cognitive processing. It consistently detects neural processing deficits in schizophrenia. So far MMN is assessed with different methods (electroencephalography, EEG; magnetoencephalography, MEG) and with different paradigms: the "traditional" oddball design with rare deviants (20%) or the "optimum" design with 50% deviants varying in one of five parameters each. These MMN measures may not reflect one unitary mechanism which is equally affected in schizophrenia. We compared both designs in 12 patients with schizophrenia and controls using MEG and EEG. Automated, observer-independent data analysis rendered the procedures suitable for clinical applications. The optimum design was fastest to detect MMN and MEG had the best signal-to-noise ratio. In addition MMN was mostly reduced in schizophrenia if measured with MEG in the optimum paradigm. Optimized paradigms improve sensitivity and speed for the detection of schizophrenia endophenotypes. Dysfunctions in this disorder may lie primarily in the fast and automatic encoding of stimulus features at the auditory cortex.

Background Combining both high temporal and spatial resolution by means of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is of relevance to neuroscientists. This combination, however, leads to a distortion of the EEG signal by the so-called cardio-ballistic artefacts. The aim of the present study was developing an approach to restore meaningful physiological EEG data from recordings at different magnetic fields. New methods The distortions introduced by the magnetic field were corrected using a combination of concepts from independent component analysis (ICA) and mutual information (MI). Thus, the components were classified as either related to the cardio-ballistic artefacts or to the signals of interest. EEG data from two experimental paradigms recorded at different magnetic field strengths up to 9.4 T were analyzed: (i) spontaneous activity using an eyes-open/eyes-closed alternation, and (ii) responses to auditory stimuli, i.e. auditory evoked potentials. Results Even at ultra-high magnetic fields up to 9.4 T the proposed artefact rejection approach restored the physiological time-frequency information contained in the signal of interest and the data were suitable for subsequent analyses. Comparison with existing methods Blind source separation (BSS) has been used to retrieve information from EEG data recorded inside the MR scanner in previous studies. After applying the presented method on EEG data recorded at 4 T, 7 T, and 9.4 T, we could retrieve more information than from data cleaned with the BSS method. Conclusions The present work demonstrates that EEG data recorded at ultra-high magnetic fields can be used for studying neuroscientific research question related to oscillatory activity.

In current clinical practice the degree of paraplegia or quadriplegia is objectively determined with transcranial magnetic stimulation (TMS) and somatosensory-evoked potentials (SSEP). We measured the MEG signal following electrical stimulation of upper and lower limbs in two normal and three clinically complete paraplegic subjects. From the MEG signal we computed distributed estimates of brain activity and identified foci just behind the central sulcus consistent in location with primary somatosensory (SI) for arm and foot and secondary somatosensory (SII) areas. Activation curves were computed from regions of interest defined around these areas. Activation of the SI foot area was observed in normal and paraplegic subjects when the upper limb was stimulated. Surprisingly, for each paraplegic subject, stimulation below the lesion was followed by cortical activations. These activations were weak, only loosely time-locked to the stimulus and were seen intermittently behind the central sulcus and nearby cortical areas. Statistical analysis of tomographic solutions and activation curves showed consistent responses following foot stimulation in one paraplegic (PS1) and intermittently in another paraplegic subject. We repeated the same experiment for PS1 in a different laboratory and the results from the analysis of foot stimulation from both laboratories revealed statistically significant focal cortical response only in the contralateral SI foot area.

Background Recently, magnetoencephalography (MEG) based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods for neuroscience research. It is well known that artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming process. New method The method (referred to as ocular and cardiac artifact rejection for real-time analysis, OCARTA) is based on constrained independent component analysis (cICA), where a priori information of the underlying source signals is used to optimize and accelerate signal decomposition. Thereby, prior information is incorporated by using the subject's individual cardiac and ocular activity. The algorithm automatically uses different separation strategies depending on the underlying source activity. Results OCARTA was tested and applied to data from three different but most commonly used MEG systems (4D-Neuroimaging, VSM MedTech Inc. and Elekta Neuromag). Ocular and cardiac artifacts were effectively reduced within one iteration at a time delay of 1 ms performed on a standard PC (Intel Core i5-2410M). Comparison with existing methods The artifact rejection results achieved with OCARTA are in line with the results reported for offline ICA-based artifact rejection methods. Conclusion Due to the fast and subject-specific signal decomposition the new approach introduced here is capable of real-time ocular and cardiac artifact rejection.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.

To detect extremely small magnetic fields generated by the human brain, currently all commercial magnetoencephalography (MEG) systems are equipped with low-temperature (low-Tc) superconducting quantum interference device (SQUID) sensors that use liquid helium for cooling. The limited and increasingly expensive supply of helium, which has seen dramatic price increases recently, has become a real problem for such systems and the situation shows no signs of abating. MEG research in the long run is now endangered. In this study, we report a MEG source localization utilizing a single, highly sensitive SQUID cooled with liquid nitrogen only. Our findings confirm that localization of neuromagnetic activity is indeed possible using high-Tc SQUIDs. We believe that our findings secure the future of this exquisitely sensitive technique and have major implications for brain research and the developments of cost-effective multi-channel, high-Tc SQUID-based MEG systems.

Background The feasibility of recording electroencephalography (EEG) at ultra-high static magnetic fields up to 9.4 T was recently demonstrated and is expected to be incorporated into functional magnetic resonance imaging (fMRI) studies at 9.4 T. Correction of the pulse artefact (PA) is a significant challenge since its amplitude is proportional to the strength of the magnetic field in which EEG is recorded. New method We conducted a study in which different PA correction methods were applied to EEG data recorded inside a 9.4 T scanner in order to retrieve visual P100 and auditory P300 evoked potentials. We explored different PA reduction methods, including the optimal basis set (OBS) method as well as objective and subjective component rejection using independent component analysis (ICA). Results ICA followed by objective rejection of components is optimal for retrieving visual P100 and auditory P300 from EEG data recorded inside the scanner. Comparison with existing methods Previous studies suggest that OBS or OBS followed by ICA are optimal for retrieving evoked potentials at 3 T. In our EEG data recorded at 9.4 T OBS performed alone was not fully optimal for the identification of evoked potentials. OBS followed by ICA was partially effective. Conclusions In this study ICA has been shown to be an important tool for correcting the PA in EEG data recorded at 9.4 T, particularly when automated rejection of components is performed.

Contingent magnetic variation (CMV) data were recorded in three healthy male subjects using a 2 x 37 biomagnetometer system. The experiment was repeated for one of the subjects using a 151 whole-head biomagnetometer; the same auditory GO/NOGO choice reaction time paradigm as in the first experiment was used, extended to include repetitions of identical runs and additional control conditions. Magnetic field tomography was applied to the averaged data of each subject, for each run and condition (e.g., GO/NOGO). An independent estimate of the current density in the brain was obtained every few milliseconds. The slow components were emphasized by integrating the square of the current density vector, pixel by pixel, revealing in each subject activity in the auditory cortex, sensorimotor cortex, inferior prefrontal area, and posterior inferior parietal area, The intersubject variability was large, but looking across subjects the auditory and sensorimotor cortex (which were best covered by the two probes) were consistently identified in each subject as contributing to the generation of the early and late slow CMV components. These findings were confirmed by the whole-head single-subject experiment, in which slow activity was also identified in the supplementary motor area (SMA) and posterior cingulate cortex (PCC), areas very likely missed in the first experiment because of the limited view of the twin system. The PCC and particularly the SMA activations were substantially reduced when identical runs were repeated, (C) 2000 Academic Press.

The magnetoencephalography (MEG) signal was recorded while subjects watched a video containing separate blocks of affective and cognitive advertisements and recalled slides extracted from the video a day later. An earlier behavioural study using the same video material showed that the affective advertisements were better recalled and that administration of propranolol (a beta-adrenergic blocker) abolished this effect. Magnetic field tomography (MFT) was used to extract tomographic estimates of activity millisecond by millisecond from the continuous MEG signal. Statistically significant differences between affective and cognitive blocks were identified in posterior and prefrontal areas. Cognitive blocks produced stronger activity in posterior parietal areas and superior prefrontal cortex in all three subjects. Affective blocks modulated activity in orbitofrontal and retrosplenial cortex, amygdala and brainstem. Individual contributions to the statistical maps were traced in real time from milliseconds to many seconds. Time-locked responses from the recall session were used to compare average and single trial MFT solutions and to combine activations from all subjects into a common anatomical space. The last step produced statistically significant increases in occipital and inferior ventral cortex between 100 and 200 ms compared to a prestimulus baseline.