Search

Midbrain dopamine neurons impact neural processing in the prefrontal cortex (PFC) through mesocortical projections. However, the signals conveyed by dopamine projections to the PFC remain unclear, particularly at the single-axon level. Here, we investigated dopaminergic axonal activity in the medial PFC (mPFC) during reward and aversive processing. By optimizing microprism-mediated two-photon calcium imaging of dopamine axon terminals, we found diverse activity in dopamine axons responsive to both reward and aversive stimuli. Some axons exhibited a preference for reward, while others favored aversive stimuli, and there was a strong bias for the latter at the population level. Long-term longitudinal imaging revealed that the preference was maintained in reward- and aversive-preferring axons throughout classical conditioning in which rewarding and aversive stimuli were paired with preceding auditory cues. However, as mice learned to discriminate reward or aversive cues, a cue activity preference gradually developed only in aversive-preferring axons. We inferred the trial-by-trial cue discrimination based on machine learning using anticipatory licking or facial expressions, and found that successful discrimination was accompanied by sharper selectivity for the aversive cue in aversive-preferring axons. Our findings indicate that a group of mesocortical dopamine axons encodes aversive-related signals, which are modulated by both classical conditioning across days and trial-by-trial discrimination within a day.

Background: Many students struggle with mathematics difficulties, such as arithmetic problem-solving, intuitive geometry concepts and learning disabilities. Currently, there is an increasingly interesting in applying neuroscientific research paradigms to elucidate mathematical thinking and neural mechanisms that underlie academic achievement. On this matter, eye-tracking technology has been a valuable option for educational research. It provides a non-invasive and real-time measurement of participants’ eye movements and pupil sizes during cognitive tasks. Moreover, the eye-tracker device is portable, allowing more ecological educational experimentations. Aim: Our main goals are to provide an overview and different opportunities for educational eye-tracking research to investigate mathematical thinking at schools. Setting: This study was conducted in Sao Bernardo do Campo, Sao Paulo, Brazil. Methods: This is a perspective article that briefly introduces the eye-tracking technique and describes its possible use in educational research. Results: We present the popular measures and the trends of this technology that could enable educational practitioners and scientists to apply the eye-tracking system to benefit teaching and learning mathematics in naturalistic research. Conclusion: The eye-tracking provides insights for innovative approaches to promote evidence-based practices and new interventions through self-directed learning and metacognition skills that could be helpful in mathematics education. Contribution: This article provides insight into eye-tracking system utility in educational research regarding the mathematics teaching–learning process.

Abstract We present a measurement of the cosmic-ray spectrum above 100 PeV using the part of the surface detector of the Pierre Auger Observatory that has a spacing of 750 m. An inflection of the spectrum is observed, confirming the presence of the so-called second-knee feature. The spectrum is then combined with that of the 1500 m array to produce a single measurement of the flux, linking this spectral feature with the three additional breaks at the highest energies. The combined spectrum, with an energy scale set calorimetrically via fluorescence telescopes and using a single detector type, results in the most statistically and systematically precise measurement of spectral breaks yet obtained. These measurements are critical for furthering our understanding of the highest energy cosmic rays.

RNA interference-based gene silencing drugs are attracting attention for treating various diseases. Lipid nanoparticles (LNPs) are carriers that efficiently deliver small interfering RNA (siRNA) to the cytoplasm of target cells. Recently, we developed potent and well-tolerated biodegradable LNPs with asymmetric ionizable lipids. Here, we evaluated the effect of LNPs on immune cells in mice. After intravenous administration, LNPs were efficiently incorporated into several tissue-resident macrophages, including liver macrophages, through an apolipoprotein E (ApoE)-independent mechanism. Administration of LNP-encapsulated siRNA against Irf5, encoding the transcription factor critical for inflammatory responses, sharply reduced its expression in macrophages in vivo, and persisted for as long as 7 days. The therapeutic potential of Irf5 siRNA-loaded LNPs in inflammatory diseases was tested in a concanavalin A (Con A)-induced hepatitis model, whose pathogenic mechanisms are dependent on cytokine secretion from macrophages. We found that Con A-induced liver injury was significantly attenuated after LNP injection. Serum aspartate transaminase, alanine aminotransferase, and inflammatory cytokine levels were significantly reduced in mice injected with Irf5 siRNA-loaded LNPs compared to those injected with control siRNA-loaded LNPs. Our results suggest that administering biodegradable LNPs to deliver siRNA is a promising strategy for treating inflammatory disorders.

Neurofeedback has been suggested as a potential complementary therapy to different psychiatric disorders. Of interest for this approach is the prediction of individual performance and outcomes. In this study, we applied functional connectivity-based modeling using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) modalities to (i) investigate whether resting-state connectivity predicts performance during an affective neurofeedback task and (ii) evaluate the extent to which predictive connectivity profiles are correlated across EEG and fNIRS techniques. The fNIRS oxyhemoglobin and deoxyhemoglobin concentrations and the EEG beta and gamma bands modulated by the alpha frequency band (beta-m-alpha and gamma-m-alpha, respectively) recorded over the frontal cortex of healthy subjects were used to estimate functional connectivity from each neuroimaging modality. For each connectivity matrix, relevant edges were selected in a leave-one-subject-out procedure, summed into “connectivity summary scores” (CSS), and submitted as inputs to a support vector regressor (SVR). Then, the performance of the left-out-subject was predicted using the trained SVR model. Linear relationships between the CSS across both modalities were evaluated using Pearson’s correlation. The predictive model showed a mean absolute error smaller than 20%, and the fNIRS oxyhemoglobin CSS was significantly correlated with the EEG gamma-m-alpha CSS (r = −0.456, p = 0.030). These results support that pre-task electrophysiological and hemodynamic resting-state connectivity are potential predictors of neurofeedback performance and are meaningfully coupled. This investigation motivates the use of joint EEG-fNIRS connectivity as outcome predictors, as well as a tool for functional connectivity coupling investigation.

Abstract Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer’s disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community.

IRF5 is a potential target for therapy in systemic lupus erythematosus (SLE). Here the authors show using mouse SLE-like models that genetic or chemical inhibition of IRF5 after SLE onset could be more effective than, or an add on for, currently utilised type I interferon inhibition.

AbstractRecent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks.

Abstract The hybrid design of the Pierre Auger Observatory allows for the measurement of the properties of extensive air showers initiated by ultra-high energy cosmic rays with unprecedented precision. By using an array of prototype underground muon detectors, we have performed the first direct measurement, by the Auger Collaboration, of the muon content of air showers between $$2\times 10^{17}$$ 2×1017 and $$2\times 10^{18}$$ 2×1018 eV. We have studied the energy evolution of the attenuation-corrected muon density, and compared it to predictions from air shower simulations. The observed densities are found to be larger than those predicted by models. We quantify this discrepancy by combining the measurements from the muon detector with those from the Auger fluorescence detector at $$10^{{17.5}}\, {\mathrm{eV}} $$ 1017.5eV and $$10^{{18}}\, {\mathrm{eV}} $$ 1018eV . We find that, for the models to explain the data, an increase in the muon density of $$38\%$$ 38% $$\pm 4\% (12\%)$$ ±4%(12%) $$\pm {}^{21\%}_{18\%}$$ ±18%21% for EPOS-LHC, and of $$50\% (53\%)$$ 50%(53%) $$\pm 4\% (13\%)$$ ±4%(13%) $$\pm {}^{23\%}_{20\%}$$ ±20%23% for QGSJetII-04, is respectively needed.

A search for time-directional coincidences of ultra-high-energy (UHE) photons above 10 EeV with gravitational wave (GW) events from the LIGO/Virgo runs O1 to O3 is conducted with the Pierre Auger Observatory. Due to the distinctive properties of photon interactions and to the background expected from hadronic showers, a subset of the most interesting GW events is selected based on their localization quality and distance. Time periods of 1000 s around and 1 day after the GW events are analyzed. No coincidences are observed. Upper limits on the UHE photon fluence from a GW event are derived that are typically at ∼7 MeV cm ^−2 (time period 1000 s) and ∼35 MeV cm ^−2 (time period 1 day). Due to the proximity of the binary neutron star merger GW170817, the energy of the source transferred into UHE photons above 40 EeV is constrained to be less than 20% of its total GW energy. These are the first limits on UHE photons from GW sources.

A catalog containing details of the highest-energy cosmic rays recorded through the detection of extensive air showers at the Pierre Auger Observatory is presented with the aim of opening the data to detailed examination. Descriptions of the 100 showers created by the highest-energy particles recorded between 2004 January 1 and 2020 December 31 are given for cosmic rays that have energies in the range 78–166 EeV. Details are also given on a further nine very energetic events that have been used in the calibration procedure adopted to determine the energy of each primary. A sky plot of the arrival directions of the most energetic particles is shown. No interpretations of the data are offered.

Goal-directed behavior often involves temporal separation and flexible context-dependent association between sensory input and motor output. The control of goal-directed behavior is proposed to lie in the frontoparietal network, but the computational architecture of this network remains elusive. Based on recent rodent studies that measured and manipulated projection neurons in the frontoparietal network together with findings from earlier primate studies, we propose a canonical scheme of information flows in this network. The parietofrontal pathway transmits the spatial information of a sensory stimulus or internal motor bias to drive motor programs in the frontal areas. This pathway might consist of multiple parallel connections, each controlling distinct motor effectors. The frontoparietal pathway sends the spatial information of cognitively processed motor plans through multiple parallel connections. Each of these connections could support distinct spatial functions that use the motor target information, including attention allocation, multi-body part coordination, and forward estimation of movement state (i.e., forward models). The parallel pathways in the frontoparietal network enable dynamic interactions between regions that are tuned for specific goal-directed behaviors. This scheme offers a promising framework within which the computational architecture of the frontoparietal network and the underlying circuit mechanisms can be delineated in a systematic way, providing a holistic understanding of information processing in this network. Clarifying this network may also improve the diagnosis and treatment of behavioral deficits associated with dysfunctional frontoparietal connectivity in various neurological disorders including Alzheimer’s disease.

Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients. Here, we investigated if this subject-independent design of affective decoding can be achieved using functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas. For this purpose, a linear discriminant analysis classifier was first trained in a dataset (49 participants, 24.65±3.23 years) and then tested in a completely independent one (20 participants, 24.00±3.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 ± 12.03%, p

Resting-state functional MRI activity is organized as a complex network. However, this coordinated brain activity changes with time, raising questions about its evolving temporal arrangement. Does the brain visit different configurations through time in a random or ordered way? Advances in this area depend on developing novel paradigms that would allow us to shed light on these issues. We here propose to study the temporal changes in the functional connectome by looking at transition graphs of network activity. Nodes of these graphs correspond to brief whole-brain connectivity patterns (or meta-states), and directed links to the temporal transition between consecutive meta-states. We applied this method to two datasets of healthy subjects (160 subjects and a replication sample of 54), and found that transition networks had several non-trivial properties, such as a heavy-tailed degree distribution, high clustering, and a modular organization. This organization was implemented at a low biological cost with a high cost-efficiency of the dynamics. Furthermore, characteristics of the subjects’ transition graphs, including global efficiency, local efficiency and their transition cost, were correlated with cognition and motor functioning. All these results were replicated in both datasets. We conclude that time-varying functional connectivity patterns of the brain in health progress in time in a highly organized and complex order, which is related to behavior.

Abstract Elves are a class of transient luminous events, with a radial extent typically greater than 250 km, that occur in the lower ionosphere above strong electrical storms. We report the observation of 1,598 elves, from 2014 to 2016, recorded with unprecedented time resolution (100 ns) using the fluorescence detector (FD) of the Pierre Auger Cosmic‐Ray Observatory. The Auger Observatory is located in the Mendoza province of Argentina with a viewing footprint for elve observations of 3·106 km 2, reaching areas above the Pacific and Atlantic Oceans, as well as the Córdoba region, which is known for severe convective thunderstorms. Primarily designed for ultrahigh energy cosmic‐ray observations, the Auger FD turns out to be very sensitive to the ultraviolet emission in elves. The detector features modified Schmidt optics with large apertures resulting in a field of view that spans the horizon, and year‐round operation on dark nights with low moonlight background, when the local weather is favorable. The measured light profiles of 18% of the elve events have more than one peak, compatible with intracloud activity. Within the 3‐year sample, 72% of the elves correlate with the far‐field radiation measurements of the World Wide Lightning Location Network. The Auger Observatory plans to continue operations until at least 2025, including elve observations and analysis. To the best of our knowledge, this observatory is the only facility on Earth that measures elves with year‐round operation and full horizon coverage.

Reciprocal connectivity enables tightly coupled information processing across cortical areas. Here the authors develop a visual oculomotor task in mice, identify a small motor area required for it, and demonstrate selective exchange of sensory and motor information between the motor and sensory areas.

Prepared movements are more efficient than those that are not prepared for. Although changes in cortical activity have been observed prior to a forthcoming action, the circuits involved in motor preparation remain unclear. Here, we use in vivo two-photon calcium imaging to uncover changes in the motor cortex during variable waiting periods prior to a forepaw reaching task in mice. Consistent with previous reports, we observed a subset of neurons with increased activity during the waiting period; however, these neurons did not account for the degree of preparation as defined by reaction time (RT). Instead, the suppression of activity of distinct neurons in the same cortical area better accounts for RT. This suppression of neural activity resulted in a distinct and reproducible pattern when mice were well prepared. Thus, the selective suppression of network activity in the motor cortex may be a key feature of prepared movements.

Cortical plasticity is fundamental to motor recovery following cortical perturbation. However, it is still unclear how this plasticity is induced at a functional circuit level. Here, we investigated motor recovery and underlying neural plasticity upon optogenetic suppression of a cortical area for eye movement. Using a visually-guided eye movement task in mice, we suppressed a portion of the secondary motor cortex (MOs) that encodes contraversive eye movement. Optogenetic unilateral suppression severely impaired contraversive movement on the first day. However, on subsequent days the suppression became inefficient and capability for the movement was restored. Longitudinal two-photon calcium imaging revealed that the regained capability was accompanied by an increased number of neurons encoding for ipsiversive movement in the unsuppressed contralateral MOs. Additional suppression of the contralateral MOs impaired the recovered movement again, indicating a compensatory mechanism. Our findings demonstrate that repeated optogenetic suppression leads to functional recovery mediated by the contralateral hemisphere.

An overview of the multi-messenger capabilities of the Pierre Auger Observatory is presented. The techniques and performance of searching for Ultra-High Energy neutrinos, photons and neutrons are described. Some of the most relevant results are reviewed, such as stringent upper bounds that were placed to a flux of diffuse cosmogenic neutrinos and photons, bounds placed on neutrinos emitted from compact binary mergers that were detected by LIGO and Virgo during their first and second observing runs, as well as searches for high energy photons and neutrons from the Galactic center that constrain the properties of the putative Galactic PeVatron, observed by the H.E.S.S. collaboration. The observation of directional correlations between ultra-high energy cosmic rays and either high energy astrophysical neutrinos or specific source populations, weighted by their electromagnetic radiation, are also discussed. They constitute additional multi-messenger approaches aimed at identifying the sources of high energy cosmic rays.

Music played in ensembles is a naturalistic model to study joint action and leader-follower relationships. Recently, the investigation of the brain underpinnings of joint musical actions has gained attention; however, the cerebral correlates underlying the roles of leader and follower in music performance remain elusive. The present study addressed this question by simultaneously measuring the hemodynamic correlates of functional neural activity elicited during naturalistic violin duet performance using fNIRS. Findings revealed distinct patterns of functional brain activation when musicians played the Violin 2 (follower) than the Violin 1 part (leader) in duets, both compared to solo performance. More specifically, results indicated that musicians playing the Violin 2 part had greater oxy-Hb activation in temporo-parietal (p = 0.02) and somatomotor (p = 0.04) regions during the duo condition in relation to the solo. On the other hand, there were no significant differences in the activation of these areas between duo/solo conditions during the execution of the Violin 1 part (p’s > 0.05). These findings suggest that ensemble cohesion during a musical performance may impose particular demands when musicians play the follower position, especially in brain areas associated with the processing of dynamic social information and motor simulation. This study is the first to use fNIRS hyperscanning technology to simultaneously measure the brain activity of two musicians during naturalistic music ensemble performance, opening new avenues for the investigation of brain correlates underlying joint musical actions with multiple subjects in a naturalistic environment.

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses.Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods.Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models.Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.

Previously, using fMRI, we demonstrated lower connectivity between right anterior superior temporal (ATL) and anterior subgenual cingulate (SCC) regions while patients with major depressive disorder (MDD) experience guilt. This neural signature was detected despite symptomatic remission which suggested a putative role in vulnerability. This randomised controlled double-blind parallel group clinical trial investigated whether patients with MDD are able to voluntarily modulate this neural signature. To this end, we developed a fMRI neurofeedback software (FRIEND), which measures ATL-SCC coupling and displays its levels in real time. Twenty-eight patients with remitted MDD were randomised to two groups, each receiving one session of fMRI neurofeedback whilst retrieving guilt and indignation/anger-related autobiographical memories. They were instructed to feel the emotion whilst trying to increase the level of a thermometer-like display on a screen. Active intervention group: The thermometer levels increased with increasing levels of ATL-SCC correlations in the guilt condition. Control intervention group: The thermometer levels decreased when correlation levels deviated from the previous baseline level in the guilt condition, thus reinforcing stable correlations. Both groups also received feedback during the indignation condition reinforcing stable correlations. We confirmed our predictions that patients in the active intervention group were indeed able to increase levels of ATL-SCC correlations for guilt vs. indignation and their self-esteem after training compared to before training and that this differed significantly from the control intervention group. These data provide proof-of-concept for a novel treatment target for MDD patients and are in keeping with the hypothesis that ATL-SCC connectivity plays a key role in self-worth.https://clinicaltrials.gov/ct2/show/results/NCT01920490 Keywords: Real-time fMRI, fMRI neurofeedback, Clinical trial, Guilt, Major depressive disorder, Anger, Subgenual cingulate cortex, Anterior temporal lobe, Self-esteem

We report a first measurement for ultrahigh energy cosmic rays of the correlation between the depth of shower maximum and the signal in the water Cherenkov stations of air-showers registered simultaneously by the fluorescence and the surface detectors of the Pierre Auger Observatory. Such a correlation measurement is a unique feature of a hybrid air-shower observatory with sensitivity to both the electromagnetic and muonic components. It allows an accurate determination of the spread of primary masses in the cosmic-ray flux. Up till now, constraints on the spread of primary masses have been dominated by systematic uncertainties. The present correlation measurement is not affected by systematics in the measurement of the depth of shower maximum or the signal in the water Cherenkov stations. The analysis relies on general characteristics of air showers and is thus robust also with respect to uncertainties in hadronic event generators. The observed correlation in the energy range around the ‘ankle’ at lg⁡(E/eV)=18.5–19.0 differs significantly from expectations for pure primary cosmic-ray compositions. A light composition made up of proton and helium only is equally inconsistent with observations. The data are explained well by a mixed composition including nuclei with mass A>4. Scenarios such as the proton dip model, with almost pure compositions, are thus disfavored as the sole explanation of the ultrahigh-energy cosmic-ray flux at Earth. Keywords: Pierre Auger Observatory, Cosmic rays, Mass composition, Ankle

Default mode network (DMN) plays a central role in cognition and brain disorders. It has been shown that adverse environmental conditions impact neurodevelopment, but how these conditions impact in DMN maturation is still poorly understood. This article reviews representative neuroimaging functional studies addressing the interactions between DMN development and environmental factors, focusing on early life adversities, a critical period for brain changes. Studies focused on this period of life offer a special challenge: to disentangle the neurodevelopmental connectivity changes from those related to environmental conditions. We first summarized the literature on DMN maturation, providing an overview of both typical and atypical development patterns in childhood and early adolescence. Afterward, we focused on DMN changes associated with chronic exposure to environmental adversities during childhood. This summary suggests that changes in DMN development could be a potential allostatic neural feature associated with an embodiment of environmental circumstances. Finally, we discuss about some key methodological issues that should be considered in paradigms addressing environmental adversities and open questions for future investigations.

Paralleling two decades of growth in the emergent field known as educational neuroscience is an increasing concern that educational practices and programs should be evidence-based, however, the idea that neuroscience could potentially influence education is controversial. One of the criticisms, regarding applications of the findings produced in this discipline, concerns the artificiality of neuroscientific experiments and the oversimplified nature of the tests used to investigate cognitive processes in educational contexts. The simulations may not account for all of the variables present in real classroom activities. In this study, we aim to get a step closer to the formation of data-supported classroom methodologies by employing functional near-infrared spectroscopy in various experimental paradigms. First, we present two hyperscanning scenarios designed to explore realistic interdisciplinary contexts, i.e., the classroom. In a third paradigm, we present a case study of a single student evaluated with functional near-infrared spectroscopy and mobile eye-tracking glasses. These three experiments are performed to provide proofs of concept for the application of functional near-infrared spectroscopy in scenarios that more closely resemble authentic classroom routines and daily activities. The goal of our study is to explore the potential of this technique in hopes that it offers insights in experimental design to investigate teaching-learning processes during teacher-student interactions.

Hippocampal damage results in profound retrograde, but no anterograde amnesia in contextual fear conditioning (CFC). Although the content learned in the latter have been discussed, alternative regions supporting CFC learning were seldom proposed and never empirically addressed. Here, we employed network analysis of pCREB expression quantified from brain slices of rats with dorsal hippocampal lesion (dHPC) after undergoing CFC session. Using inter-regional correlations of pCREB-positive nuclei between brain regions, we modelled functional networks using different thresholds. The dHPC network showed small-world topology, equivalent to SHAM (control) network. However, diverging hubs were identified in each network. In a direct comparison, hubs in both networks showed consistently higher centrality values compared to the other network. Further, the distribution of correlation coefficients was different between the groups, with most significantly stronger correlation coefficients belonging to the SHAM network. These results suggest that dHPC network engaged in CFC learning is partially different, and engage alternative hubs. We next tested if pre-training lesions of dHPC and one of the new dHPC network hubs (perirhinal, Per; or disgranular retrosplenial, RSC, cortices) would impair CFC. Only dHPC-RSC, but not dHPC-Per, impaired CFC. Interestingly, only RSC showed a consistently higher centrality in the dHPC network, suggesting that the increased centrality reflects an increased functional dependence on RSC. Our results provide evidence that, without hippocampus, the RSC, an anatomically central region in the medial temporal lobe memory system might support CFC learning and memory.

In this article, we propose the detection method of the recorded data pattern by the envelope of the temporal magnetization dynamics of resonantly interacting spin-torque oscillator on the microwave assisted magnetic recording for three-dimensional magnetic recording. We simulate the envelope of the waveform from recorded dots with the staggered magnetization configuration, which are calculated by using a micromagnetic simulation. We study the data detection methods for the envelope and propose a soft-output Viterbi algorithm (SOVA) for partial response (PR) system as a signal processing system for three dimensional magnetic recording.

Initial studies using resting-state functional magnetic resonance imaging on the trajectories of the brain network from childhood to adulthood found evidence of functional integration and segregation over time. The comprehension of how healthy individuals' functional integration and segregation occur is crucial to enhance our understanding of possible deviations that may lead to brain disorders. Recent approaches have focused on the framework wherein the functional brain network is organized into spatially distributed modules that have been associated with specific cognitive functions. Here, we tested the hypothesis that the clustering structure of brain networks evolves during development. To address this hypothesis, we defined a measure of how well a brain region is clustered (network fitness index), and developed a method to evaluate its association with age. Then, we applied this method to a functional magnetic resonance imaging data set composed of 397 males under 31 years of age collected as part of the Autism Brain Imaging Data Exchange Consortium. As results, we identified two brain regions for which the clustering change over time, namely, the left middle temporal gyrus and the left putamen. Since the network fitness index is associated with both integration and segregation, our finding suggests that the identified brain region plays a role in the development of brain systems.

Saccades were assessed in 21 adults (age 24 years, SD = 4) and 15 children (age 11 years, SD = 1), using combined functional magnetic resonance imaging (fMRI) and eye-tracking. Subjects visually tracked a point on a horizontal line in four conditions: time and position predictable task (PRED), position predictable (pPRED), time predictable (tPRED) and visually guided saccades (SAC). Both groups in the PRED but not in pPRED, tPRED and SAC produced predictive saccades with latency below 80 ms. In task versus group comparisons, children's showed less efficient learning compared to adults for predictive saccades (adults = 48%, children = 34%, p = 0.05). In adults brain activation was found in the frontal and occipital regions in the PRED, in the intraparietal sulcus in pPRED and in the frontal eye field, posterior intraparietal sulcus and medial regions in the tPRED task. Group-task interaction was found in the supplementary eye field and visual cortex in the PRED task, and the frontal cortex including the right frontal eye field and left frontal pole, in the pPRED condition. These results indicate that, the basic visuomotor circuitry is present in both adults and children, but fine-tuning of the activation according to the task temporal and spatial demand mature late in child development.

Yoga, a mind-body activity that requires attentional engagement, has been associated with positive changes in brain structure and function, especially in areas related to awareness, attention, executive functions and memory. Normal aging, on the other hand, has also been associated with structural and functional brain changes, but these generally involve decreased cognitive functions. The aim of this cross-sectional study was to compare brain cortical thickness (CT) in elderly yoga practitioners and a group of age-matched healthy non-practitioners. We tested 21 older women who had practiced hatha yoga for at least 8 years and 21 women naive to yoga, meditation or any mind-body interventions who were matched to the first group in age, years of formal education and physical activity level. A T1-weighted MPRAGE sequence was acquired for each participant. Yoga practitioners showed significantly greater CT in a left prefrontal lobe cluster, which included portions of the lateral middle frontal gyrus, anterior superior frontal gyrus and dorsal superior frontal gyrus. We found greater CT in the left prefrontal cortex of healthy elderly women who trained yoga for a minimum of 8 years compared with women in the control group.

Assessing the neural correlates of motor and cognitive processes under naturalistic experimentation is challenging due to the movement constraints of traditional brain imaging technologies. The recent advent of portable technologies that are less sensitive to motion artifacts such as Functional Near Infrared Spectroscopy (fNIRS) have been made possible the study of brain function in freely-moving participants. In this paper, we describe a series of proof-of-concept experiments examining the potential of fNIRS in assessing the neural correlates of cognitive and motor processes in unconstrained environments. We show illustrative applications for practicing a sport (i.e., table tennis), playing a musical instrument (i.e., piano and violin) alone or in duo and performing daily activities for many hours (i.e., continuous monitoring). Our results expand upon previous research on the feasibility and robustness of fNIRS to monitor brain hemodynamic changes in different real life settings. We believe that these preliminary results showing the flexibility and robustness of fNIRS measurements may contribute by inspiring future work in the field of applied neuroscience.

Antecedentes: La disección endoscópica de la submucosa (DES) tiene una efectividad similar a la cirugía en el tratamiento de las lesiones incipientes. La técnica requiere un alto nivel de destreza técnica. El entrenamiento en modelos biológicos y el perfeccionamiento de accesorios facilita la DES. Objetivos: El objetivo fue evaluar la utilidad del Endolifter para facilitar la exposición tisular durante la DES en un modelo in vivo porcino. Fue realizado en el laboratorio de cirugía experimental de la Escuela de Medicina de la Facultad de Sao Paulo, Brasil. Material y método: Diseño experimental en modelo porcino in vivo. Se incluyeron un total de 5 cerdos Yorkshire con un peso de 20-25 kg. Las DES se realizaron usando el dispositivo Endolifter. Se empleó un Dual-Knife y el IT-Knife para la disección de la capa submucosa. Todos los procedimientos endoscópicos fueron realizados por un único endoscopista experto. Resultados: Se realizaron un total de 25 DES, con una tasa de éxito técnico del 100%. El tiempo promedio de la disección fue de 12.34 min (10.40-14.50 min) y el tamaño promedio de las lesiones fue de 2.7 cm (2.3-3.2 cm). No se presentó hemorragia ni perforaciones durante el procedimiento. Conclusiones: El Endolifter permite realizar DES de manera rápida y eficaz. Es factible, de fácil uso y puede ser empleado por un solo operador.

Background: The effectiveness of endoscopic submucosal dissection (ESD) is similar to that of surgery in the treatment of early lesions. The technique requires a high level of technical skill. Training on biologic models and the mastering of accessories facilitate ESD. Aims: The aim was to evaluate the usefulness of the Endolifter in facilitating tissue exposure during ESD in an in vivo porcine model performed at the experimental surgery laboratory of the School of Medicine at the Universidad de São Paulo in Brazil. Material and method: A study with an experimental design employing an in vivo porcine model was conducted on 5 Yorkshire pigs weighing 20-25 kg. ESDs were performed using the Endolifter. Mucosal layer dissection was carried out with a dual knife and IT knife and all the endoscopic procedures were performed by a single expert endoscopist. Results: A total of 25 ESDs were performed, with a technical success rate of 100%. The mean dissection time was 12.34 min (range: 10.40-14.50 min) and the mean lesion size was 2.7 cm (range: 2.3-3.2 cm). There were no episodes of bleeding or perforations during the procedures. Conclusions: The Endolifter enables rapid and effective ESDs to be carried out. It is an applicable and easy-to-use device that can be manipulated by a single operator.