The Incredible Shrinking Dewlap: Signal Size, Skin Elasticity, and Mechanical Design in the Green Anole Lizard (Anolis Carolinensis)

The expression of male secondary sexual traits can be dynamic, changing size, shape, color, or structure over the course of different seasons. However, the factors underlying such changes are poorly understood. In male Anolis carolinensis lizards, a morphological secondary sexual signal called the dewlap changes size seasonally within individuals. Here, we test the hypothesis that seasonal changes in male dewlap size are driven by increased use and extension of the dewlap in spring and summer, when males are breeding, relative to the winter and fall. We captured male green anole lizards prior to the onset of breeding and constrained the dewlap in half of them such that it could not be extended. We then measured dewlap area in the spring, summer, and winter, and dewlap skin and belly skin elasticity in summer and winter. Dewlaps in unconstrained males increase in area from spring to summer and then shrink in the winter, whereas the dewlaps of constrained males consistently shrink from spring to winter. Dewlap skin is significantly more elastic than belly skin, and skin overall is more elastic in the summer relative to winter. These results show that seasonal changes in dewlap size are a function of skin elasticity and display frequency, and suggest that the mechanical properties of signaling structures can have important implications for signal evolution and design

Kinematically complete experimental study of Compton scattering at helium atoms near the ionization threshold

Compton scattering is one of the fundamental interaction processes of light with matter. Already upon its discovery [1] it was described as a billiard-type collision of a photon kicking a quasi-free electron. With decreasing photon energy, the maximum possible momentum transfer becomes so small that the corresponding energy falls below the binding energy of the electron. Then ionization by Compton scattering becomes an intriguing quantum phenomenon. Here we report a kinematically complete experiment on Compton scattering at helium atoms below that threshold. We determine the momentum correlations of the electron, the recoiling ion, and the scattered photon in a coincidence experiment finding that electrons are not only emitted in the direction of the momentum transfer, but that there is a second peak of ejection to the backward direction. This finding links Compton scattering to processes as ionization by ultrashort optical pulses [2], electron impact ionization [3,4], ion impact ionization [5,6], and neutron scattering [7] where similar momentum patterns occur.Comment: 7 pages, 4 figure

Long-Term Neuroanatomical Consequences of Childhood Maltreatment: Reduced Amygdala Inhibition by Medial Prefrontal Cortex

Similar to patients with Major depressive disorder (MDD), healthy subjects at risk for depression show hyperactivation of the amygdala as a response to negative emotional expressions. The medial prefrontal cortex is responsible for amygdala control. Analyzing a large cohort of healthy subjects, we aimed to delineate malfunction in amygdala regulation by the medial prefrontal cortex in subjects at increased risk for depression, i.e., with a family history of affective disorders or a personal history of childhood maltreatment. We included a total of 342 healthy subjects from the FOR2107 cohort (www.for2107.de). An emotional face-matching task was used to identify the medial prefrontal cortex and right amygdala. Dynamic Causal Modeling (DCM) was conducted and neural coupling parameters were obtained for healthy controls with and without particular risk factors for depression. We assigned a genetic risk if subjects had a first-degree relative with an affective disorder and an environmental risk if subjects experienced childhood maltreatment. We then compared amygdala inhibition during emotion processing between groups. Amygdala inhibition by the medial prefrontal cortex was present in subjects without those two risk factors, as indicated by negative model parameter estimates. Having a genetic risk (i.e., a family history) did not result in changes in amygdala inhibition compared to no risk subjects. In contrast, childhood maltreatment as environmental risk has led to a significant reduction of amygdala inhibition by the medial prefrontal cortex. We propose a mechanistic explanation for the amygdala hyperactivity in subjects with particular risk for depression, in particular childhood maltreatment, caused by a malfunctioned amygdala downregulation via the medial prefrontal cortex. As childhood maltreatment is a major environmental risk factor for depression, we emphasize the importance of this potential early biomarker

Systematic Overestimation of Machine Learning Performance in Neuroimaging Studies of Depression

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls. Drawing upon a balanced sample of $N = 1,868$ MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes ($N=4$ to $N=150$) from the population and showed a strong risk of overestimation. Specifically, for small sample sizes ($N=20$), we observe accuracies of up to 95%. For medium sample sizes ($N=100$) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance overestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets

Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

Background MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. Methods We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. Results The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. Conclusions Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome

Interaction of developmental factors and ordinary stressful life events on brain structure in adults

An interplay of early environmental and genetic risk factors with recent stressful life events (SLEs) in adulthood increases the risk for adverse mental health outcomes. The interaction of early risk and current SLEs on brain structure has hardly been investigated. Whole brain voxel-based morphometry analysis was performed in N = 786 (64.6% female, mean age = 33.39) healthy subjects to identify correlations of brain clusters with commonplace recent SLEs. Genetic and early environmental risk factors, operationalized as those for severe psychopathology (i.e., polygenic scores for neuroticism, childhood maltreatment, urban upbringing and paternal age) were assessed as modulators of the impact of SLEs on the brain. SLEs were negatively correlated with grey matter volume in the left medial orbitofrontal cortex (mOFC, FWE p = 0.003). This association was present for both, positive and negative, life events. Cognitive-emotional variables, i.e., neuroticism, perceived stress, trait anxiety, intelligence, and current depressive symptoms did not account for the SLE-mOFC association. Further, genetic and environmental risk factors were not correlated with grey matter volume in the left mOFC cluster and did not affect the association between SLEs and left mOFC grey matter volume. The orbitofrontal cortex has been implicated in stress-related psychopathology, particularly major depression in previous studies. We find that SLEs are associated with this area. Important early life risk factors do not interact with current SLEs on brain morphology in healthy subjects

High-Energy Molecular-Frame Photoelectron Angular Distributions: A Molecular Bond-Length Ruler

We present an experimental and theoretical study of core-level ionization of small hetero- and homo-nuclear molecules employing circularly polarized light and address molecular-frame photoelectron angular distributions in the light's polarization plane (CP-MFPADs). We find that the main forward-scattering peaks of CP-MFPADs are slightly tilted with respect to the molecular axis. We show that this tilt angle can be directly connected to the molecular bond length by a simple, universal formula. The extraction of the bond length becomes more accurate as the photoelectron energy is increased. We apply the derived formula to several examples of CP-MFPADs of C 1s and O 1s photoelectrons of CO, which have been measured experimentally or obtained by means of ab initio modeling. The photoelectron kinetic energies range from 70 to 1000~eV and the extracted bond lengths agree well with the known bond length of the CO molecule in its ground state. In addition, we discuss the influence of the back-scattering contribution that is superimposed over the analyzed forward-scattering peak in case of homo-nuclear diatomic molecules as N$_2$

Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder

Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether these are state markers of current disease severity and recovery after a depressive episode. Human white matter network ("connectome") analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode

Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%;significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments