EEG functional connectivity after perinatal stroke.

Impaired cognitive functioning after perinatal stroke has been associated with long-term functional brain network changes. We explored brain functional connectivity using a 64-channel resting-state electroencephalogram in 12 participants, aged 5-14 years with a history of unilateral perinatal arterial ischemic or haemorrhagic stroke. A control group of 16 neurologically healthy subjects was also included-each test subject was compared with multiple control subjects, matched by sex and age. Functional connectomes from the alpha frequency band were calculated for each subject and the differences in network graph metrics between the 2 groups were analyzed. Our results suggest that the functional brain networks of children with perinatal stroke show evidence of disruption even years after the insult and that the scale of changes appears to be influenced by the lesion volume. The networks remain more segregated and show a higher synchronization at both whole-brain and intrahemispheric level. Total interhemispheric strength was higher in children with perinatal stroke compared with healthy controls.

The effects of transcutaneous auricular vagal nerve stimulation on cortical GABAergic and cholinergic circuits: A transcranial magnetic stimulation study.

Neurophysiological evidence that transcutaneous auricular vagal nerve stimulation (taVNS) affects neuronal signalling at the cortical level is sparse. We used transcranial magnetic stimulation to assess the effect of taVNS on the excitability of intracortical GABAergic and cholinergic circuits. In this within-subject, double-blind study on 30 healthy participants, we used TMS paradigms to assess the effect of a single session of taVNS at 100 Hz and sham earlobe VNS (sVNS) on short-interval intracortical inhibition (SICI) curve and short-latency afferent inhibition (SAI). Control experiment was performed on additional 15 participants using the same experimental settings, but delivering no stimulation (xVNS). Bayesian statistics were used to assess the differences, producing % values that reflect the certainty that the values of interest were decreased during or after stimulation compared with baseline. taVNS increased SICI (96.3%), whereas sVNS decreased SICI (1.2%). SAI was not affected by taVNS, although it was decreased during sVNS (1.34% and 9.1%, for interstimulus intervals 20 and 24 ms, respectively). The changes in TMS parameters detected during sVNS were present in the same direction in the control experiment with no stimulation. Our study provides evidence that taVNS increases the activity of cortical GABAAergic system, leaving cortical cholinergic circuits unaffected. Changes in intracortical cortical excitability during sVNS, which were also observed in the control experiment with no stimulation were likely the effect of expectation related to participation in an interventional study.

Possible Eliashberg-Type Superconductivity Enhancement Effects in a Two-Band Superconductor MgB_{2} Driven by Narrow-Band THz Pulses.

We study THz-driven condensate dynamics in epitaxial thin films of MgB_{2}, a prototype two-band superconductor (SC) with weak interband coupling. The temperature and excitation density dependent dynamics follow the behavior predicted by the phenomenological bottleneck model for the single-gap SC, implying adiabatic coupling between the two condensates on the ps timescale. The amplitude of the THz-driven suppression of condensate density reveals an unexpected decrease in pair-breaking efficiency with increasing temperature-unlike in the case of optical excitation. The reduced pair-breaking efficiency of narrow-band THz pulses, displaying minimum near ≈0.7 T_{c}, is attributed to THz-driven, long-lived, nonthermal quasiparticle distribution, resulting in Eliashberg-type enhancement of superconductivity, competing with pair breaking.

Tracking the Time Evolution of the Electron Distribution Function in Copper by Femtosecond Broadband Optical Spectroscopy.

Multitemperature models are nowadays often used to quantify the ultrafast electron-phonon (boson) relaxations and coupling strengths in advanced quantum solids. To test their applicability and limitations, we perform systematic studies of carrier relaxation dynamics in copper, a prototype system for which the two-temperature model (TTM) was initially considered. Using broadband time-resolved optical spectroscopy, we study the time evolution of the electron distribution function, f(E), over a large range of excitation densities. Following intraband optical excitation, f(E) is found to be athermal over several 100 fs, with a substantial part of the absorbed energy already being transferred to the lattice. We show, however, that the electron-phonon coupling constant can still be obtained using the TTM analysis, provided that the data are analyzed over the time window where the electrons are already quasithermal, and the electronic temperature is determined experimentally.

Synaptic Scaling Improves the Stability of Neural Mass Models Capable of Simulating Brain Plasticity.

Neural mass models offer a way of studying the development and behavior of large-scale brain networks through computer simulations. Such simulations are currently mainly research tools, but as they improve, they could soon play a role in understanding, predicting, and optimizing patient treatments, particularly in relation to effects and outcomes of brain injury. To bring us closer to this goal, we took an existing state-of-the-art neural mass model capable of simulating connection growth through simulated plasticity processes. We identified and addressed some of the model's limitations by implementing biologically plausible mechanisms. The main limitation of the original model was its instability, which we addressed by incorporating a representation of the mechanism of synaptic scaling and examining the effects of optimizing parameters in the model. We show that the updated model retains all the merits of the original model, while being more stable and capable of generating networks that are in several aspects similar to those found in real brains.

Energy scales and dynamics of electronic excitations in functionalized gold nanoparticles measured at the single particle level.

The knowledge of the electronic structure and dynamics of nanoparticles is a prerequisite to develop miniaturized single-electron devices based on nanoparticles. Low-temperature transport measurements of individual stable metallic nanoparticles enable unravelling the system specific electronic structure while ultrafast optical spectroscopy gives access to the electron dynamics. In this work, we investigate bare and thiol-functionalized gold nanoparticles. For the latter, we employ a fast and low-cost fabrication technique which yields nanoparticles with narrow size distribution. Using relatively long thiol-ended alkane chains for the functionalization modifies the electronic density of states of the nanoparticles. The study of decay dynamics of surface-plasmon-related hot electrons reveals the presence of electronic states at the interface which serve as a fast decay channel for electronic relaxation. By low-temperature scanning tunnelling microscopy we precisely investigate the energy scales and electronic interactions relevant for the tunnel charge transport through this system. We observe that the interaction between the functional ligand and the substrate on which the nanoparticles reside also influences the electronic transport. The procedure that we employ can be easily adapted to other metallic nanoparticles. Our findings are therefore important for incorporating them into single-electron devices.

Ultrafast Metamorphosis of a Complex Charge-Density Wave.

Modulated phases, commensurate or incommensurate with the host crystal lattice, are ubiquitous in solids. The transition between such phases involves formation and rearrangement of domain walls and is generally slow. Using ultrafast electron diffraction, we directly record the photoinduced transformation between a nearly commensurate and an incommensurate charge-density-wave phase in 1T-TaS(2). The transformation takes place on the picosecond time scale, orders of magnitude faster than previously observed for commensurate-to-incommensurate transitions. The transition speed and mechanism can be linked to the peculiar nanoscale structure of the photoexcited nearly commensurate phase.

Snapshots of cooperative atomic motions in the optical suppression of charge density waves.

Macroscopic quantum phenomena such as high-temperature superconductivity, colossal magnetoresistance, ferrimagnetism and ferromagnetism arise from a delicate balance of different interactions among electrons, phonons and spins on the nanoscale. The study of the interplay among these various degrees of freedom in strongly coupled electron-lattice systems is thus crucial to their understanding and for optimizing their properties. Charge-density-wave (CDW) materials, with their inherent modulation of the electron density and associated periodic lattice distortion, represent ideal model systems for the study of such highly cooperative phenomena. With femtosecond time-resolved techniques, it is possible to observe these interactions directly by abruptly perturbing the electronic distribution while keeping track of energy relaxation pathways and coupling strengths among the different subsystems. Numerous time-resolved experiments have been performed on CDWs, probing the dynamics of the electronic subsystem. However, the dynamics of the periodic lattice distortion have been only indirectly inferred. Here we provide direct atomic-level information on the structural dynamics by using femtosecond electron diffraction to study the quasi two-dimensional CDW system 1T-TaS(2). Effectively, we have directly observed the atomic motions that result from the optically induced change in the electronic spatial distribution. The periodic lattice distortion, which has an amplitude of ∼0.1 Å, is suppressed by about 20% on a timescale (∼250 femtoseconds) comparable to half the period of the corresponding collective mode. These highly cooperative, electronically driven atomic motions are accompanied by a rapid electron-phonon energy transfer (∼350 femtoseconds) and are followed by fast recovery of the CDW (∼4 picoseconds). The degree of cooperativity in the observed structural dynamics is remarkable and illustrates the importance of obtaining atomic-level perspectives of the processes directing the physics of strongly correlated systems.

A Meta-Analysis of Sampled Maximal Aerobic Capacity Data for Boys Aged 11 Years Old or Less Obtained by Cycle Ergometry.

The aim of this study was to develop distributions of VO2max based on measured values that exist in the literature in prepubertal boys using cycle ergometry. PRISMA guidelines were followed in conducting this research. One database was searched for peak and maximal VO2 values in healthy boys with mean age under 11 years old. Data were split into articles reporting absolute and relative VO2max values and analyzed accordingly. Multilevel models grounded in Bayesian principles were used. We investigated associations between VO2max and body mass, year of the study, and country of origin. Differences in "peak" and "maximal" VO2 were assessed. Absolute VO2max (Lmin-1) increases with age (P ~100%) but mean relative VO2max does not change (P ~100%). Absolute VO2max is higher in more recent studies (P = 95.7 ± 0.3%) and mean relative VO2max is lower (P = 99.6 ± 0.1%). Relative VO2max in the USA is lower compared with boys from other countries (P = 98.8 ± 0.2%), but there are no differences in absolute values. Mean aerobic capacity estimates presented as "peak" values are higher than "maximal" values on an absolute basis (P = 97.5 ± 0.3%) but not on a relative basis (P = 99.6 ± 0.1%). Heavier boys have lower cardiorespiratory fitness (P ≈ 100%), and body mass seems to be increasing faster with age in the USA compared with other countries (P = 92.3 ± 0.3%). New reference values for cardiorespiratory fitness are presented for prepubertal boys obtained with cycle ergometry. This is new, as no reference values have been determined so far based on actual measured values in prepubertal boys. Aerobic capacity normalized to body weight does not change with age. Cardiorespiratory fitness in prepubertal boys is declining, which is associated with increasing body mass over the last few decades. Lastly, this study did not find any statistically significant difference in the sample's mean aerobic capacity estimates using the "peak" and "maximum" distinctions identified in the literature.

Corrigendum: autohrf-an R package for generating data-informed event models for general linear modeling of task-based fMRI data.

[This corrects the article DOI: 10.3389/fnimg.2022.983324.].

Age related changes and sex related differences of functional brain networks in childhood: A high-density EEG study.

OBJECTIVE: The aim of this study was to explore functional network age-related changes and sex-related differences during the early lifespan with a high-density resting state electroencephalography (rs-EEG). METHODS: We analyzed two data sets of high-density rs-EEG in healthy children and adolescents. We recorded a 64-channel EEG and calculated functional connectomes in 27 participants aged 5-18 years. To validate our results, we used publicly available data and calculated functional connectomes in another 86 participants aged 6-18 years from a 128-channel rs-EEG. We were primarily interested in alpha frequency band, but we also analyzed theta and beta frequency bands. RESULTS: We observed age-related increase of characteristic path, clustering coefficient and interhemispheric strength in the alpha frequency band of both data sets and in the beta frequency band of the larger validation data set. Age-related increase of global efficiency was seen in the theta band of the validation data set and in the alpha band of the test data set. Increase in small worldness was observed only in the alpha frequency band of the test data set. We also observed an increase of individual peak alpha frequency with age in both data sets. Sex-related differences were only observed in the beta frequency band of the larger validation data set, with females having higher values than same aged males. CONCLUSIONS: Functional brain networks show indices of higher segregation, but also increasing global integration with maturation. Age-related changes are most prominent in the alpha frequency band. SIGNIFICANCE: To the best of our knowledge, our study was the first to analyze maturation related changes and sex-related differences of functional brain networks with a high-density EEG and to compare functional connectomes generated from two diverse high-density EEG data sets. Understanding the age-related changes and sex-related differences of functional brain networks in healthy children and adolescents is crucial for identifying network abnormalities in different neurologic and psychiatric conditions, with the aim to identify possible markers for prognosis and treatment.

A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns.

Importance: Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective: To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment. Design: Double blind randomized controlled trial. Setting: Multicenter. Participants: Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Interventions: Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2). Main Outcomes and Measures: To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group. Results: 192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group. Conclusions and Relevance: Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

QuNex-An integrative platform for reproducible neuroimaging analytics.

Introduction: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods: To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results: The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion: Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

autohrf-an R package for generating data-informed event models for general linear modeling of task-based fMRI data.

The analysis of task-related fMRI data at the level of individual participants is commonly based on general linear modeling (GLM), which allows us to estimate the extent to which the BOLD signal can be explained by the task response predictors specified in the event model. The predictors are constructed by convolving the hypothesized time course of neural activity with an assumed hemodynamic response function (HRF). However, our assumptions about the components of brain activity, including their onset and duration, may be incorrect. Their timing may also differ across brain regions or from person to person, leading to inappropriate or suboptimal models, poor fit of the model to actual data, and invalid estimates of brain activity. Here, we present an approach that uses theoretically driven models of task response to define constraints on which the final model is computationally derived using actual fMRI data. Specifically, we developed autohrf-an R package that enables the evaluation and data-driven estimation of event models for GLM analysis. The highlight of the package is the automated parameter search that uses genetic algorithms to find the onset and duration of task predictors that result in the highest fitness of GLM based on the fMRI signal under predefined constraints. We evaluated the usefulness of the autohrf package on two original datasets of task-related fMRI activity, a slow event-related spatial working memory study and a mixed state-item study using the flanker task, and on a simulated slow event-related working memory data. Our results suggest that autohrf can be used to efficiently construct and evaluate better task-related brain activity models to gain a deeper understanding of BOLD task response and improve the validity of model estimates. Our study also highlights the sensitivity of fMRI analysis with GLM to precise event model specification and the need for model evaluation, especially in complex and overlapping event designs.

Evaluation and comparison of most prevalent artifact reduction methods for EEG acquired simultaneously with fMRI.

Multimodal neuroimaging using EEG and fMRI provides deeper insights into brain function by improving the spatial and temporal resolution of the acquired data. However, simultaneous EEG-fMRI inevitably compromises the quality of the EEG and fMRI signals due to the high degree of interaction between the two systems. Fluctuations in the magnetic flux flowing through the participant and the EEG system, whether due to movement within the magnetic field of the scanner or to changes in magnetic field strength, induce electrical potentials in the EEG recordings that mask the much weaker electrical activity of the neuronal populations. A number of different methods have been proposed to reduce MR artifacts. We present an overview of the most commonly used methods and an evaluation of the methods using three sets of diverse EEG data. We limited the evaluation to open-access and easy-to-use methods and a reference signal regression method using a set of six carbon-wire loops (CWL), which allowed evaluation of their added value. The evaluation was performed by comparing EEG signals recorded outside the MRI scanner with artifact-corrected EEG signals recorded simultaneously with fMRI. To quantify and evaluate the quality of artifact reduction methods in terms of the spectral content of the signal, we analyzed changes in oscillatory activity during a resting-state and a finger tapping motor task. The quality of artifact reduction in the time domain was assessed using data collected during a visual stimulation task. In the study we utilized hierarchical Bayesian probabilistic modeling for statistical inference and observed significant differences between the evaluated methods in the success of artifact reduction and associated signal quality in both the frequency and time domains. In particular, the CWL system proved superior to the other methods evaluated in improving spectral contrast in the alpha and beta bands and in recovering visual evoked responses. Based on the results of the evaluation study, we proposed guidelines for selecting the optimal method for MR artifact reduction.

Optically Triggered Néel Vector Manipulation of a Metallic Antiferromagnet Mn2Au under Strain.

The absence of stray fields, their insensitivity to external magnetic fields, and ultrafast dynamics make antiferromagnets promising candidates for active elements in spintronic devices. Here, we demonstrate manipulation of the Néel vector in the metallic collinear antiferromagnet Mn2Au by combining strain and femtosecond laser excitation. Applying tensile strain along either of the two in-plane easy axes and locally exciting the sample by a train of femtosecond pulses, we align the Néel vector along the direction controlled by the applied strain. The dependence on the laser fluence and strain suggests the alignment is a result of optically triggered depinning of 90° domain walls and their motion in the direction of the free energy gradient, governed by the magneto-elastic coupling. The resulting, switchable state is stable at room temperature and insensitive to magnetic fields. Such an approach may provide ways to realize robust high-density memory device with switching time scales in the picosecond range.

Subpicosecond metamagnetic phase transition in FeRh driven by non-equilibrium electron dynamics.

Femtosecond light-induced phase transitions between different macroscopic orders provide the possibility to tune the functional properties of condensed matter on ultrafast timescales. In first-order phase transitions, transient non-equilibrium phases and inherent phase coexistence often preclude non-ambiguous detection of transition precursors and their temporal onset. Here, we present a study combining time-resolved photoelectron spectroscopy and ab-initio electron dynamics calculations elucidating the transient subpicosecond processes governing the photoinduced generation of ferromagnetic order in antiferromagnetic FeRh. The transient photoemission spectra are accounted for by assuming that not only the occupation of electronic states is modified during the photoexcitation process. Instead, the photo-generated non-thermal distribution of electrons modifies the electronic band structure. The ferromagnetic phase of FeRh, characterized by a minority band near the Fermi energy, is established 350 ± 30 fs after the laser excitation. Ab-initio calculations indicate that the phase transition is initiated by a photoinduced Rh-to-Fe charge transfer.

Mapping brain-behavior space relationships along the psychosis spectrum.

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 PSD patients spanning several diagnoses, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results with current sample sizes. However, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and neural gene expression maps derived from the Allen Human Brain Atlas. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.

bayes4psy-An Open Source R Package for Bayesian Statistics in Psychology.

Research in psychology generates complex data and often requires unique statistical analyses. These tasks are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to researchers and students that have the technical and statistical fundamentals that are required for probabilistic programming. Such knowledge is not part of the typical psychology curriculum and is a difficult obstacle for psychology students and researchers to overcome. The goal of the bayes4psy package is to bridge this gap and offer a collection of models and methods to be used for analysing data that arises from psychological experiments and as a teaching tool for Bayesian statistics in psychology. The package contains the Bayesian t-test and bootstrapping along with models for analysing reaction times, success rates, and tasks utilizing colors as a response. It also provides the diagnostic, analytic and visualization tools for the modern Bayesian data analysis workflow.

An open-source, end-to-end workflow for multidimensional photoemission spectroscopy.

Characterization of the electronic band structure of solid state materials is routinely performed using photoemission spectroscopy. Recent advancements in short-wavelength light sources and electron detectors give rise to multidimensional photoemission spectroscopy, allowing parallel measurements of the electron spectral function simultaneously in energy, two momentum components and additional physical parameters with single-event detection capability. Efficient processing of the photoelectron event streams at a rate of up to tens of megabytes per second will enable rapid band mapping for materials characterization. We describe an open-source workflow that allows user interaction with billion-count single-electron events in photoemission band mapping experiments, compatible with beamlines at 3rd and 4rd generation light sources and table-top laser-based setups. The workflow offers an end-to-end recipe from distributed operations on single-event data to structured formats for downstream scientific tasks and storage to materials science database integration. Both the workflow and processed data can be archived for reuse, providing the infrastructure for documenting the provenance and lineage of photoemission data for future high-throughput experiments.