Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.

Even though pre-attentive cognition (MMN) was not affected, processing at the early perceptual level (N100) and in higher-order cognition (P300) was significantly disrupted by psilocybin. Our results have implications for the role of 5-HT receptors in altered information processing in psychosis and schizophrenia.

In this paper we report a new rat head phantom developed for testing electroencephalogram source localization techniques. The phantom is composed of an agar based mixture mimicking the rat brain, six dipoles and fourteen electrodes for modeling and monitoring of neural activity of the brain, respectively, and a 3D printed skull based on a computed tomography scan of a rat skull. In order to fabricate the phantom with currently available conventional techniques, the phantom is 1.8 times enlarged. To allow scaling, we performed an extensive study of electric properties of the agar based mixture, including electric conductivity, permittivity, and applied voltage, to ensure a linear operating regime. The new phantom facilitates testing of existing and the development of new cortical electrode implants as well as studying the quality of various source localization techniques.

Serotonergic psychedelics are recently gaining a lot of attention as a potential treatment of several neuropsychiatric disorders. Broadband desynchronization of EEG activity and disconnection in humans have been repeatedly shown; however, translational data from animals are completely lacking. Therefore, the main aim of our study was to assess the effects of tryptamine and phenethylamine psychedelics (psilocin 4 mg/kg, LSD 0.2 mg/kg, mescaline 100 mg/kg, and DOB 5 mg/kg) on EEG in freely moving rats. A system consisting of 14 cortical EEG electrodes, co-registration of behavioral activity of animals with subsequent analysis only in segments corresponding to behavioral inactivity (resting-state-like EEG) was used in order to reach a high level of translational validity. Analyses of the mean power, topographic brain-mapping, and functional connectivity revealed that all of the psychedelics irrespective of the structural family induced overall and time-dependent global decrease/desynchronization of EEG activity and disconnection within 1–40 Hz. Major changes in activity were localized on the large areas of the frontal and sensorimotor cortex showing some subtle spatial patterns characterizing each substance. A rebound of occipital theta (4–8 Hz) activity was detected at later stages after treatment with mescaline and LSD. Connectivity analyses showed an overall decrease in global connectivity for both the components of cross-spectral and phase-lagged coherence. Since our results show almost identical effects to those known from human EEG/MEG studies, we conclude that our method has robust translational validity.

Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI.