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A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

Genome-wide association studies have reported an association between the A-allele of rs1006737 within CACNA1C and affective disorders and schizophrenia. The aim of the present study was to investigate the relationship between rs1006737 and established and potential endophenotypes for these disorders in a population-based cohort of 3793 subjects, using an analytical method designed to assess a previously reported sex-specific effect of CACNA1C . The investigated endophenotypes included personality traits and resilience factors. At 10-year follow-up, subjects were screened for depressive symptoms. All subjects were genotyped for rs1006737. The direction of the effect and mode of inheritance of rs1006737 differed between the sexes. In men, the A-allele was associated with higher emotional lability and lower resilience, that is, lower sense of coherence ( P =0.021), lower perceived social support ( P =0.018), lower dispositional optimism ( P =0.032) and more depressive symptoms at follow-up ( P =0.007). In women, the A-allele was associated with lower emotional lability and stronger resilience, that is, higher sense of coherence ( P =0.00028), higher perceived social support ( P =0.010), lower neuroticism ( P =0.022) and fewer depressive symptoms at follow-up ( P =0.035). After conservative Bonferroni correction for 32 tests, results only remained significant for sense of coherence in women ( P =0.009). These results suggest that CACNA1C is involved in the genetic architecture of endophenotypes for affective disorders and schizophrenia, and that it shows a distinct sex-specific effect. Comprehensive phenotype characterization in case–control samples and the general population, as well as an adequate modeling of sex-specific genetic effects, may be warranted to elucidate the pathogenetic mechanisms conferred by robustly identified susceptibility genes.

Information about the energy density of gravity waves (GWs) is crucial for improving atmosphere models. So far, most space-based studies report the potential energy, Epot, of GWs, as temperature measurements from satellites are more common. We use Aeolus wind data to derive the kinetic energy density, Ekin, of GWs above the northern Atlantic and Europe. Assuming perfect instrument performance, this would be a lower limit for the kinetic energy density, as Aeolus only measures the horizontal line-of-sight wind. Aeolus, the European Space Agency's (ESA's) fourth Earth Explorer Mission, was the first Doppler wind lidar in space and measured vertical profiles of the horizontal line-of-sight wind from the ground to an altitude of ∼ 20–30 km between 2018 and 2023. With a vertical resolution of 0.25–2 km, Aeolus measurements are in principle well suited for the analysis of GWs. However, the data quality is a challenge for such analyses, as the error in the data is in the range of typical GW amplitudes in the troposphere and stratosphere. In this study, we derive daily resolved time series of Ekin before, during, and after two streamer events above the northern Atlantic and Europe. Streamers are large-scale tongue-like structures of meridionally deflected air masses, which are caused by enhanced planetary wave activity. They are linked to vertical shear of horizontal wind and a pressure system, two possible GW generation mechanisms. We find that there is a temporal coincidence between the enhanced daily averaged Ekin and occurrence of the streamer events, which we identified in total column ozone measurements. The derivation of GW signals based on Aeolus data is possible, however: we collected about 100 profiles to statistically reduce the uncertainty in the daily averaged Ekin. Compared to non-satellite measurements, those daily averaged values are at the upper border.

Measurements of hydroxyl (OH*) airglow intensity are a straightforward and cost-efficient method which allows the derivation of information about the climate and dynamics of the upper mesosphere/lower thermosphere (UMLT) on different spatiotemporal scales during darkness. Today, instrument components can be bought “off-the-shelf” and developments in detector technology allows operation without cooling, or at least without liquid nitrogen cooling, which is difficult to automate. This makes instruments compact and suitable for automated operation. Here, we briefly summarize why an OH* airglow layer exists, how atmospheric dynamics influence it and how temperature can be derived from OH* airglow measurements. Then, we provide an overview of the scientific results regarding atmospheric dynamics (mainly gravity waves (GWs) but also planetary waves (PWs) and infrasound) achieved with OH* airglow measurements. We focus on long-term ground-based OH* airglow measurements or airglow measurements using a network of ground-based instruments. The paper includes further results from global or near-global satellite-based OH* airglow measurements, which are of special importance for characterizing the OH* airglow layer. Additionally, the results from the very few available airborne case studies using OH* airglow instruments are summarized. Scientific and technical challenges for the next few years are described.

Hydroxyl (OH) radical airglow observations have been performed at the environmental research station “Schneefernerhaus” (UFS; 47.42∘ N, 10.98∘ E) since October 2008, with continuous operation since July 2009. The instrumental setup relies on the parallel operation of two identical instruments, each a GRIPS (GRound-based Infrared P-branch Spectrometer), in order to achieve maximum completeness and homogeneity. After the first decade of observations the acquired time series are evaluated with respect to the main influences on data quality and comparability to those at other sites. Data quality is essentially limited by gaps impacting the completeness. While technical failures are largely excluded by the setup, gaps caused by adverse meteorological conditions can systematically influence estimates of the annual mean. The overall sampling density is high, with nightly mean temperatures obtained for 3382 of 4018 nights of observation (84 %), but the average coverage changes throughout the year. This can bias the annual mean up to 0.8 K if not properly accounted for. Sensitivity studies performed with the two identical instruments and their retrievals show that the comparability between the observations is influenced by the annual and semiannual cycle as well as the choice of Einstein-A coefficients, which influence the estimate of the annual cycle's amplitude. A strong 11-year solar signal of 5.9±0.6 K per 100 sfu is identified in the data. The OH temperatures follow the F10.7 cm value with a time lag of 90±65 d. However, the precise value depends on details of the analysis. The highest correlation (R2=0.91) is achieved for yearly mean OH temperatures averaged around 4 February and the F10.7 cm solar flux leading ahead with 110 d. A prominent 2-year oscillation is identified between 2011 and 2015. This signal is linked to the quasi-biennial oscillation (QBO), leading to a temperature reduction of approximately 1 K during QBO westward phases in 2011, 2013, and 2015 and a respective 1 K increase in 2012 and 2014 during QBO eastward phases. The amplitude of the semiannual cycle shows a similar behavior with the decade's minimum amplitudes (∼ 2.5–3 K) retrieved for 2011, 2013, and 2015 and maximum amplitudes observed in 2012 and 2014 (∼4 K). The signal appears to disappear after 2016 when the solar flux approaches its next minimum. Although it appears as a rather strict 24-month periodicity between 2011 and 2015, spectral analyses show a more or less continuous oscillation with a period of approximately 21 months over the entire time span, which can be interpreted as the result of a nonlinear interaction of the QBO (28 months) with the annual cycle (12 months).

In this study we examine the performance of the 354.8 nm Rayleigh temperature channel of the Raman lidar at the Schneefernerhaus high-altitude research station (UFS) in the Bavarian Alps (at 2675 m a.s.l.). The temperature reference value of the retrieval is adjusted to match the temperature determined from the OH.sup.* airglow around 86 km by the GRIPS instruments at UFS. In this way the quality of the 1 h measurements of the lidar is improved above 70 km. Comparisons were made between the UFS lidar, the MLS (Microwave Limb Sounder) satellite-borne instrument and the 354.8 nm temperature channel of Hohenpeißenberg (MOHp) differential-absorption ozone lidar. Between 35 and 70 km we see a positive offset of the UFS temperatures with respect to the MLS values of up to about 9 K. This behaviour just slightly exceeds the expectations from earlier work. Despite a horizontal distance of just 40 km between UFS and MOHp acceptable agreement below 70 km was found in several cases. However, in general, the MOHp temperatures were slightly lower than those above UFS. We discuss potential technical issues and suggest solutions for upgrading the UFS lidar system. A significant enhancement of the laser repetition rate is recommended.

We demonstrate how machine learning can be easily applied to support the analysis of large quantities of excited hydroxyl (OH*) airglow imager data. We use a TCN (temporal convolutional network) classification algorithm to automatically pre-sort images into the three categories “dynamic” (images where small-scale motions like turbulence are likely to be found), “calm” (clear-sky images with weak airglow variations) and “cloudy” (cloudy images where no airglow analyses can be performed). The proposed approach is demonstrated using image data of FAIM 3 (Fast Airglow IMager), acquired at Oberpfaffenhofen, Germany, between 11 June 2019 and 25 February 2020, achieving a mean average precision of 0.82 in image classification. The attached video sequence demonstrates the classification abilities of the learned TCN. Within the dynamic category, we find a subset of 13 episodes of image series showing turbulence. As FAIM 3 exhibits a high spatial (23 m per pixel) and temporal (2.8 s per image) resolution, turbulence parameters can be derived to estimate the energy diffusion rate. Similarly to the results the authors found for another FAIM station (Sedlak et al., 2021), the values of the energy dissipation rate range from 0.03 to 3.18 W kg−1.

Cortisol has a modulatory influence on cognitive functions in humans. Both impairing and enhancing effects of cortisol administration have been shown for hippocampus-dependent declarative memory, and impairing effects have been shown for prefrontal-cortex-dependent working memory function. Given the high density of glucocorticoid (GC) receptors in the prefrontal cortex, we investigated whether common polymorphisms of the GC receptor (GR) gene (ER22/23EK, N363S, BclI, 9β A3669G) modulate the influence of cortisol administration on working memory. Working memory performance was investigated in 169 subjects on 10 mg hydrocortisone (cortisol) and placebo using an item recognition task. No impairing effect of hydrocortisone treatment became evident. However, a sex × genotype interaction on general working memory performance was revealed (p = 0.02). While female heterozygous carriers of the 9β G allele displayed faster reaction times than the other genotype groups, 9β G heterozygous men were relatively slower. Heritability estimates for memory are roughly 50%, indicating that common genetic polymorphisms have an important impact on cognitive performance. Our results suggest that variants of the GR gene might explain some of the variance attributable to genetic factors. Furthermore, it can be speculated that they modulate the individual vulnerability for memory impairments related to stress-related psychiatric disorders.

We analysed 286 nights of data from the OH* airglow imager FAIM 3 (Fast Airglow IMager) acquired at Otlica Observatory (45.93∘ N, 13.91∘ E), Slovenia, between 26 October 2017 and 6 June 2019. Measurements have been performed with a spatial resolution of 24 m per pixel and a temporal resolution of 2.8 s. A two-dimensional fast Fourier transform is applied to the image data to derive horizontal wavelengths between 48 m and 4.5 km in the upper mesosphere/lower thermosphere (UMLT) region. In contrast to the statistics of larger-scale gravity waves (horizontal wavelength up to ca. 50 km; Hannawald et al., 2019), we find a more isotropic distribution of directions of propagation, pointing to the presence of wave structures created above the stratospheric wind fields. A weak seasonal tendency of a majority of waves propagating eastward during winter may be due to instability features from breaking secondary gravity waves that were created in the stratosphere. We also observe an increased southward propagation during summer, which we interpret as an enhanced contribution of secondary gravity waves created as a consequence of primary wave filtering by the meridional mesospheric circulation. We present multiple observations of turbulence episodes captured by our high-resolution airglow imager and estimated the energy dissipation rate in the UMLT from image sequences in 25 cases. Values range around 0.08 and 9.03 W kg−1 and are on average higher than those in recent literature. The values found here would lead to an approximated localized maximum heating of 0.03–3.02 K per turbulence event. These are in the same range as the daily chemical heating rates for the entire atmosphere reported by Marsh (2011), which apparently stresses the importance of dynamical energy conversion in the UMLT.

Partial blindness after brain injury has been considered non-treatable. To evaluate whether patients with visual-field defects can profit from computer-based visual restitution training (VRT), two independent clinical trials were conducted using patients with optic nerve ( n = 19) or post-chiasmatic brain injury ( n = 19). In post-chiasma patients, VRT led to a significant improvement (29.4%) over baseline in the ability to detect visual stimuli; in optic nerve patients, the effects were even more pronounced (73.6% improvement). Visual-field enlargements were confirmed by the observation of a visual-field expansion of 4.9°–5.8° of visual angle and improved acuity in optic nerve patients. Ninety five percent of the VRT-treated patients showed improvements, 72.2% confirmed visual improvements subjectively. Patients receiving a placebo training did not show comparable improvements. In conclusion, VRT with a computer program improves vision in patients with visual-field defects and offers a new, cost-effective therapy for partial blindness.