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The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data. ► Construction of brain connectivity measures invariant to linear transformations. ► Development of a theoretical framework and derivation of new multivariate measures. ► Relationship to existing approaches in the literature. ► Application on EEG data and combined EEG/MEG measurements. ► MEG observed brain interactions that were not observable in EEG.

Due to its high temporal resolution, electroencephalography (EEG) is widely used to study functional and effective brain connectivity. Yet, there is currently a mismatch between the vastness of studies conducted and the degree to which the employed analyses are theoretically understood and empirically validated. We here provide a simulation framework that enables researchers to test their analysis pipelines on realistic pseudo-EEG data. We construct a minimal example of brain interaction, which we propose as a benchmark for assessing a methodology’s general eligibility for EEG-based connectivity estimation. We envision that this benchmark be extended in a collaborative effort to validate methods in more complex scenarios. Quantitative metrics are defined to assess a method’s performance in terms of source localization, connectivity detection and directionality estimation. All data and code needed for generating pseudo-EEG data, conducting source reconstruction and connectivity estimation using baseline methods from the literature, evaluating performance metrics, as well as plotting results, are made publicly available. While this article covers only EEG modeling, we will also provide a magnetoencephalography version of our framework online.

MUltiple SIgnal Classification (MUSIC) is a standard localization method which is based on the idea of dividing the vector space of the data into two subspaces: signal subspace and noise subspace. The brain, divided into several grid points, is scanned entirely and the grid point with the maximum consistency with the signal subspace is considered as the source location. In one of the MUSIC variants called Recursively Applied and Projected MUSIC (RAP-MUSIC), multiple iterations are proposed in order to decrease the location estimation uncertainties introduced by subspace estimation errors. In this paper, we suggest a new method called Self-Consistent MUSIC (SC-MUSIC) which extends RAP-MUSIC to a self-consistent algorithm. This method, SC-MUSIC, is based on the idea that the presence of several sources has a bias on the localization of each source. This bias can be reduced by projecting out all other sources mutually rather than iteratively. While the new method is applicable in all situations when MUSIC is applicable we will study here the localization of interacting sources using the imaginary part of the cross-spectrum due to the robustness of this measure to the artifacts of volume conduction. For an odd number of sources this matrix is rank deficient similar to covariance matrices of fully correlated sources. In such cases MUSIC and RAP-MUSIC fail completely while the new method accurately localizes all sources. We present results of the method using simulations of odd and even number of interacting sources in the presence of different noise levels. We compare the method with three other source localization methods: RAP-MUSIC, dipole fit and MOCA (combined with minimum norm estimate) through simulations. SC-MUSIC shows substantial improvement in the localization accuracy compared to these methods. We also show results for real MEG data of a single subject in the resting state. Four sources are localized in the sensorimotor area at f=11Hz which is the expected region for the idle rhythm. •We introduce a new method which extends RAP-MUSIC to a self-consistent algorithm.•The method is a solution to subspace deficiency of data in MUSIC based algorithms.•It improves MUSIC based algorithms localization's accuracy.•To localize interacting sources, imaginary part of the cross-spectrum is studied.

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.

We introduce a novel method to estimate bivariate synchronization, i.e. interacting brain sources at a specific frequency or band, from MEG or EEG data robust to artifacts of volume conduction. The data driven calculation is solely based on the imaginary part of the cross-spectrum as opposed to the imaginary part of coherency. In principle, the method quantifies how strong a synchronization between a distinct pair of brain sources is present in the data. As an input of the method all pairs of pre-defined locations inside the brain can be used which is computationally exhaustive. In contrast to that, reference sources can be used that have been identified by any source reconstruction technique in a prior analysis step. We introduce different variants of the method and evaluate the performance in simulations. As a particular advantage of the proposed methodology, we demonstrate that the novel approach is capable of investigating differences in brain source interactions between experimental conditions or with respect to a certain baseline. For measured data, we first show the application on resting state MEG data where we find locally synchronized sources in the motor-cortex based on the sensorimotor idle rhythms. Finally, we show an example on EEG motor imagery data where we contrast hand and foot movements. Here, we also find local interactions in the expected brain areas.

1. This account presents information on all aspects of the biology of Robinia pseudoacacia L. that are relevant to understanding its ecological characteristics and behaviour. The main topics are presented within the standard framework of the Biological Flora of the British Isles: distribution, habitat, communities, responses to biotic factors, responses to environment, structure and physiology, phenology, floral and seed characters, herbivores and disease, and history and conservation. 2. Robinia pseudoacacia, false acacia or black locust, is a deciduous, broad-leaved tree native to North America. The medium-sized, fast-growing tree is armed with spines, and extensively suckering. It has become naturalized in grassland, semi-natural woodlands and urban habitats. The tree is common in the south of the British Isles and in many other regions of Europe. 3. Robinia pseudoacacia is a light-demanding pioneer species, which occurs primarily in disturbed sites on fertile to poor soils. The tree does not tolerate wet or compacted soils. In contrast to its native range, where it rapidly colonizes forest gaps and is replaced after 15-30 years by more competitive tree species, populations in the secondary range can persist for a longer time, probably due to release from natural enemies. 4. Robinia pseudoacacia reproduces sexually, and asexually by underground runners. Disturbance favours clonal growth and leads to an increase in the number of ramets. Mechanical stem damage and fires also lead to increased clonal recruitment. 5. The tree benefits from di-nitrogen fixation associated with symbiotic rhizobia in root nodules. Estimated symbiotic nitrogen fixation rates range widely from 23 to 300 kg ha⁻¹ year⁻¹. The nitrogen becomes available to other plants mainly by the rapid decay of nitrogen-rich leaves. 6. Robinia pseudoacacia is host to a wide range of fungi both in the native and introduced ranges. Megaherbivores are of minor significance in Europe but browsing by ungulates occurs in the native range. Among insects, the North American black locust gall midge (Obolodiplosis robiniae) is specific to Robinia and is spreading rapidly throughout Europe. 7. In parts of Europe, Robinia pseudoacacia is considered an invasive non-indigenous plant and the tree is controlled. Negative impacts include shading and changes of soil conditions as a result of nitrogen fixation.

Severe acute respiratory syndrome coronovirus-2 (SARS-CoV-2) is the cause of the coronavirus disease 2019 (COVID-19) pandemic. Vaccination is considered the core approach to containing the pandemic. There is currently insufficient evidence on the efficacy of these vaccines in immunosuppressed inflammatory bowel disease (IBD) patients. The aim of this study was to investigate the humoral response in immunosuppressed IBD patients after COVID-19 mRNA vaccination. In this prospective study, IgG antibody levels (AB) against the SARS-CoV-2 receptor-binding domain (spike-protein) were quantitatively determined. For assessing the potential neutralizing capacity, a SARS-CoV-2 surrogate neutralization test (sVNT) was employed in IBD patients (n = 95) and healthy controls (n = 38). Sera were examined prior to the first/second vaccination and 3/6 months after second vaccination. Patients showed lower sVNT (%) and IgG-S (AU/mL) AB both before the second vaccination (sVNT p < 0.001; AB p < 0.001) and 3 (sVNT p = 0.002; AB p = 0.001) and 6 months (sVNT p = 0.062; AB p = 0.061) after the second vaccination. Although seroconversion rates (sVNT, IgG-S) did not differ between the two groups 3 months after second vaccination, a significant difference was seen 6 months after second vaccination (sVNT p = 0.045). Before and three months after the second vaccination, patients treated with anti-tumor necrosis factor (TNF) agents showed significantly lower AB than healthy subjects. In conclusion, an early booster shot vaccination should be discussed for IBD patients on anti-TNF therapy.

Cell Painting images offer valuable insights into the state of a cell and enable many biological applications, but publicly available arrayed datasets only include hundreds of genes perturbed. The JUMP (Joint Undertaking in Morphological Profiling) Cell Painting Consortium perturbed roughly 75% of the protein-coding genome in human U-2 OS cells, generating a rich resource of single-cell images and extracted features. These profiles capture the phenotypic impacts of perturbing 15,243 human genes, including overexpressing 12,609 genes (using open reading frames, ORFs) and knocking out 7,975 genes (using CRISPR-Cas9). We mitigated technical artifacts by rigorously evaluating data processing options and validated the robustness and biological relevance of the dataset. Analysis of phenotypic profiles revealed novel gene clusters and functional relationships, including those associated with mitochondrial function, cancer, and neural processes. The JUMP Cell Painting genetic dataset is a valuable resource for exploring gene relationships and uncovering novel functions.Competing Interest StatementThe Authors declare the following competing interests: S.S., B.A.C., and A.E.C. serve as scientific advisors for companies that use image-based profiling and Cell Painting (A.E.C: Recursion, SyzOnc, Quiver Bioscience, S.S.: Waypoint Bio, Dewpoint Therapeutics, Deepcell, B.A.C: Marble Therapeutics) and receive honoraria for occasional scientific visits to pharmaceutical and biotechnology companies. Authors with affiliations to the pharmaceutical, technology, and biotechnology companies listed are or have been employees of those companies and may have real or optional ownership therein. All other authors declare no competing interests.Footnotes* https://github.com/jump-cellpainting/2024_Chandrasekaran_Morphmap