Social brain, social dysfunction and social withdrawal

The human social brain is complex. Current knowledge fails to define the neurobiological processes underlying social behaviour involving the (patho-) physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Unfortunately, such a high complexity may also be associated with a high susceptibility to several pathogenic interventions. Consistently, social deficits sometimes represent the first signs of a number of neuropsychiatric disorders including schizophrenia (SCZ), Alzheimer's disease (AD) and major depressive disorder (MDD) which leads to a progressive social dysfunction. In the present review we summarize present knowledge linking neurobiological substrates sustaining social functioning, social dysfunction and social withdrawal in major psychiatric disorders. Interestingly, AD, SCZ, and MDD affect the social brain in similar ways. Thus, social dysfunction and its most evident clinical expression (i.e., social withdrawal) may represent an innovative transdiagnostic domain, with the potential of being an independent entity in terms of biological roots, with the perspective of targeted interventions

Thixotropy in macroscopic suspensions of spheres

An experimental study of the viscosity of a macroscopic suspension, i.e. a suspension for which Brownian motion can be neglected, under steady shear is presented. The suspension is prepared with a high packing fraction and is density-matched in a Newtonian carrier fluid. The viscosity of the suspension depends on the shear rate and the time of shearing. It is shown for the first time that a macroscopic suspension shows thixotropic viscosity, i.e. shear-thinning with a long relaxation time as a unique function of shear. The relaxation times show a systematic decrease with increasing shear rate. These relaxation times are larger when decreasing the shear rates, compared to those observed after increasing the shear. The time scales involved are about 10000 times larger than the viscous time scale and about 1000 times smaller than the thermodynamic time scale. The structure of the suspension at the outer cylinder of a viscometer is monitored with a camera, showing the formation of a hexagonal structure. The temporal decrease of the viscosity under shear coincides with the formation of this hexagonal pattern

Social brain, social dysfunction and social withdrawal

The human social brain is complex. Current knowledge fails to define the neurobiological processes underlying social behaviour involving the (patho-) physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Unfortunately, such a high complexity may also be associated with a high susceptibility to several pathogenic interventions. Consistently, social deficits sometimes represent the first signs of a number of neuropsychiatric disorders including schizophrenia (SCZ), Alzheimer's disease (AD) and major depressive disorder (MDD) which leads to a progressive social dysfunction. In the present review we summarize present knowledge linking neurobiological substrates sustaining social functioning, social dysfunction and social withdrawal in major psychiatric disorders. Interestingly, AD, SCZ, and MDD affect the social brain in similar ways. Thus, social dysfunction and its most evident clinical expression (i.e., social withdrawal) may represent an innovative transdiagnostic domain, with the potential of being an independent entity in terms of biological roots, with the perspective of targeted interventions

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Linear viscoelastic behavior of dense hard-sphere dispersions

The complex shear viscosity of sterically stabilized colloidal dispersions of different-sized silica particles (radius a=28–76 nm) was measured with torsion resonators and a nickel-tube resonator between 80 Hz and 200 kHz. The volume fraction of the samples was varied from 0.10 to 0.60. In the intermediate-frequency region, the real and the imaginary parts of the complex shear viscosity decay as ω-1/2 to their limiting values. The viscoelastic behavior can be described in terms of one relaxation strength G1 and a series of relaxation times with τp=τ1 p-2. The complex shear viscosity scales with the dimensionless relaxation strength a2G1/D0ηs, the dimensionless relaxation time D0τ1/a2, and the dimensionless angular frequency a2ω/D0. The dimensionless groups a2G1/D0ηs and D0τ1/a2 are a function of the volume fraction only. At higher volume fractions the high-frequency limiting values of the real part of the complex shear viscosity, η∞’, corroborate values calculated by Beenakker [Physica 128A, 48 (1984)]

Interpretation of the complex viscosity of dense hard-sphere dispersions

The complex viscosity of dense hard-sphere dispersions has been determined recently over a large frequency range. If conceived as a homogeneous system with continuously distributed elasticity and viscosity, the complex viscosity can be described theoretically with a constant relaxation strength and relaxation times τp=τ1/p2, with p the relaxation number. This is consistent with the empirical analysis of the data. The distributed elasticity can be interpreted microscopically as due to statistical springs acting between the spheres. The springs are modeled as Fraenkel springs to take into account the excluded-volume effect. The relaxation strength has been calculated quantitatively. The resulting deduced relaxation strengths are in fair agreement with the experimentally observed ones. The given interpretation is compared with literature theory

Predictive factors for difficult intravenous cannulation in pediatric patients at a tertiary pediatric hospital

Background: It is generally believed that certain patient characteristics (e.g., Body Mass Index and age) predict difficulty of intravenous cannulation in children, but there is not much literature evaluating these risk factors. In this study, we investigated predictive factors for success rate at first attempt and time needed for intravenous cannulation. Methods/Materials: In a prospective cohort study, we observed characteristics of intravenous cannulations in pediatric patients at the operating room (n = 1083) and the outpatient care unit (n = 178) of a tertiary referral pediatric hospital. Time to successful intravenous cannulation, success at first attempt, and potential predictors for difficult cannulation (age, gender, skin color, BMI or weight-to-age z-score, the child being awake or anesthetized, operator profession and surgical specialty) were recorded. Regression models were constructed to find significant predictors. Results: Success at first attempt was 73% and 81%, respectively. In the operating room age, operator and surgical specialty were predictive for a successful first attempt and time to successful cannulation. No significant predictive factors were found for the outpatient care unit. BMI or weight-to-age was not related to difficult intravenous cannulation. Conclusions: This study shows that in one-fifth to one-third of the patients, intravenous cannulation required more than one attempt. It is difficult to predict with accuracy the difficulty of intravenous cannulation solely with easily obtainable patient characteristics. © 2011 Blackwell Publishing Ltd