Corticospinal beta-band synchronization entails rhythmic gain modulation

Rhythmic synchronization of neurons in the beta or gamma band occurs almost ubiquitously, and this synchronization has been linked to numerous nervous system functions. Many respective studies make the implicit assumption that neuronal synchronization affects neuronal interactions. Indeed, when neurons synchronize, their output spikes reach postsynaptic neurons together, trigger coincidence detection mechanisms, and therefore have an enhanced impact. There is ample experimental evidence demonstrating this consequence of neuronal synchronization, but beyond this, beta/gamma-band synchronization within a group of neurons might also modulate the impact of synaptic input to that synchronized group. This would constitute a separate mechanism through which synchronization affects neuronal interactions, but direct in vivo evidence for this putative mechanism is lacking. Here, we demonstrate that synchronized beta-band activity of a neuronal group modulates the efficacy of synaptic input to that group in-phase with the beta rhythm. This response modulation was not an addition of rhythmic activity onto the average response but a rhythmic modulation of multiplicative input gain. Our results demonstrate that beta-rhythmic activity of a neuronal target group multiplexes input gain along the rhythm cycle. The actual gain of an input then depends on the precision and the phase of its rhythmic synchronization to this target, providing one mechanistic explanation for why synchronization modulates interactions

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.Peer reviewe

Prototyping open digital tools for urban commoning

The paper will discuss an experimental co-design approach to the development of a digital toolkit prototype and a resulting set of co-design principles, which are put forward as a way of infrastructuring future design of digital tools for urban commoning. Focus is placed on the case study of a commoning hub in a Parisian suburb where the toolkit was co-designed through a series of prototyping workshops, carried out with hub users and addressing key hub needs. The prototyping process explored possibilities for re-appropriating and re-framing existing digital technologies as open toolkits, which can be further re-purposed by users, here and beyond, after the design of an initial toolkit prototype

Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, WPPC and GCMI tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appears to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions

Periodogram Connectivity of EEG Signals for the Detection of Dyslexia

Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalent is estimated between 5% and 12% of the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the spectral density at each channel. This relationship is computed by means of different correlation measures, inferring connectivity-like markers that are eventually selected and classified by a linear support vector machine. The experiments performed shown AUC values up to 0.7, demonstrating the applicability of the proposed approach for objective DD diagnosis

Characterisation of two snake toxin-targeting human monoclonal immunoglobulin G antibodies expressed in tobacco plants.

Current snakebite antivenoms are based on polyclonal animal-derived antibodies, which can neutralize snake venom toxins in envenomed victims, but which are also associated with adverse reactions. Therefore, several efforts within antivenom research aim to explore the utility of recombinant monoclonal antibodies, such as human immunoglobulin G (IgG) antibodies, which are routinely used in the clinic for other indications. In this study, the feasibility of using tobacco plants as bioreactors for expressing full-length human monoclonal IgG antibodies against snake toxins was investigated. We show that the plant-produced antibodies perform similarly to their mammalian cell-expressed equivalents in terms of in vitro binding. Complete neutralization was achieved by both the plant and mammalian cell-produced anti-α-cobratoxin antibody. The feasibility of using plant-based expression systems may potentially make it easier for laboratories in resource-poor settings to work with human monoclonal IgG antibodies

Site-Specific Bioconjugation of a Murine Dihydrofolate Reductase Enzyme by Copper(I)-Catalyzed Azide-Alkyne Cycloaddition with Retained Activity

Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) is an efficient reaction linking an azido and an alkynyl group in the presence of copper catalyst. Incorporation of a non-natural amino acid (NAA) containing either an azido or an alkynyl group into a protein allows site-specific bioconjugation in mild conditions via CuAAC. Despite its great potential, bioconjugation of an enzyme has been hampered by several issues including low yield, poor solubility of a ligand, and protein structural/functional perturbation by CuAAC components. In the present study, we incorporated an alkyne-bearing NAA into an enzyme, murine dihydrofolate reductase (mDHFR), in high cell density cultivation of Escherichia coli, and performed CuAAC conjugation with fluorescent azide dyes to evaluate enzyme compatibility of various CuAAC conditions comprising combination of commercially available Cu(I)-chelating ligands and reductants. The condensed culture improves the protein yield 19-fold based on the same amount of non-natural amino acid, and the enzyme incubation under the optimized reaction condition did not lead to any activity loss but allowed a fast and high-yield bioconjugation. Using the established conditions, a biotin-azide spacer was efficiently conjugated to mDHFR with retained activity leading to the site-specific immobilization of the biotin-conjugated mDHFR on a streptavidin-coated plate. These results demonstrate that the combination of reactive non-natural amino acid incorporation and the optimized CuAAC can be used to bioconjugate enzymes with retained enzymatic activityope

The acute effects of a lunch containing capsaicin on energy and substrate utilisation, hormones, and satiety

BACKGROUND: Addition of capsaicin to the diet has been shown to increase satiety and thermogenesis. The effects of capsaicin on ghrelin, peptide YY (PYY) and glucagon-like peptide 1 (GLP-1), in relation to changes in hunger and satiety are unknown. AIM: To test the acute effects of a lunch containing capsaicin on gut derived hormones (GLP-1, ghrelin, and PYY), energy expenditure (EE), substrate oxidation and satiety at lunch in the postprandial state. METHODS: Thirty subjects (age: 31 +/- 14 years, BMI: 23.8 +/- 2.8 kg/m(2)) were studied twice in a crossover design. After 30 min resting on a bed, resting metabolic rate was measured by a ventilated hood system. Subsequently lunch (35% of daily energy intake) was served. The two lunch conditions were: (1) lunch without capsaicin and (2) lunch with capsaicin (CAPS). The macronutrient composition (energy percentage) of the lunches was 60% carbohydrates, 10% protein and 30% fat. During 3 h after the lunch diet-induced thermogenesis was measured. Furthermore, anchored 100 mm visual analogue scales on the appetite profile were collected (t = 0, 30, 60, 120, 150, 180 and 240) and blood samples were taken for analysis of GLP-1, PYY, and ghrelin concentrations (t = 0, 45, 60, 120, and 180). RESULTS: Satiety and EE were not different after CAPS lunch as compared to the control lunch. Fifteen minutes after lunch CAPS lunch increased GLP-1 (p < 0.05) and tended to decrease ghrelin (p = 0.07) as compared to the control lunch. PYY responses were not different between the CAPS lunch and the control lunch. CONCLUSIONS: An acute lunch containing capsaicin had no effect on satiety, EE, and PYY, but increased GLP-1 and tended to decrease ghrelin