Impaired mentalizing in depression and the effects of borderline personality disorder on this relationship

Background: Mentalizing, the ability to understand the self and others as well as behaviour in terms of intentional mental states, is impaired in Borderline Personality Disorder (BPD). Evidence for mentalizing deficits in other mental disorders, such as depression, is less robust and these links have never been explored while accounting for the effects of BPD on mentalizing. Additionally, it is unknown whether BPD symptoms might moderate any relationship between depressive symptoms and mentalizing. / Methods: Using multivariate regression modelling on cross-sectional data obtained from a sample of 274 participants recruited from clinical settings, we investigated the association between mentalizing impairment and depression and examined whether this was moderated by the presence and number of concurrent BPD symptoms, while adjusting for socio-demographic confounders. / Results: Impaired mentalizing was associated with depressive symptoms, after adjustment for socio-demographic confounders and BPD symptoms (p = 0.002, β = − 0.18). BPD symptoms significantly moderated the association between impaired mentalizing and depressive symptoms (p = 0.003), with more severe borderline symptoms associated with a stronger effect of poor mentalization on increased depressive symptoms. / Conclusion: Mentalizing impairments occur in depression even after adjusting for the effect of BPD symptoms. Our findings help further characterise mentalizing impairments in depression, as well as the moderating effect of BPD symptoms on this association.. Further longitudinal work is required to investigate the direction of association

The chaperone protein clusterin may serve as a cerebrospinal fluid biomarker for chronic spinal cord disorders in the dog

Chronic spinal cord dysfunction occurs in dogs as a consequence of diverse aetiologies, including long-standing spinal cord compression and insidious neurodegenerative conditions. One such neurodegenerative condition is canine degenerative myelopathy (DM), which clinically is a challenge to differentiate from other chronic spinal cord conditions. Although the clinical diagnosis of DM can be strengthened by the identification of the Sod1 mutations that are observed in affected dogs, genetic analysis alone is insufficient to provide a definitive diagnosis. There is a requirement to identify biomarkers that can differentiate conditions with a similar clinical presentation, thus facilitating patient diagnostic and management strategies. A comparison of the cerebrospinal fluid (CSF) protein gel electrophoresis profile between idiopathic epilepsy (IE) and DM identified a protein band that was more prominent in DM. This band was subsequently found to contain a multifunctional protein clusterin (apolipoprotein J) that is protective against endoplasmic reticulum (ER) stress-mediated apoptosis, oxidative stress, and also serves as an extracellular chaperone influencing protein aggregation. Western blot analysis of CSF clusterin confirmed elevated levels in DM compared to IE (p < 0.05). Analysis of spinal cord tissue from DM and control material found that clusterin expression was evident in neurons and that the clusterin mRNA levels from tissue extracts were elevated in DM compared to the control. The plasma clusterin levels was comparable between these groups. However, a comparison of clusterin CSF levels in a number of neurological conditions found that clusterin was elevated in both DM and chronic intervertebral disc disease (cIVDD) but not in meningoencephalitis and IE. These findings indicate that clusterin may potentially serve as a marker for chronic spinal cord disease in the dog; however, additional markers are required to differentiate DM from a concurrent condition such as cIVDD

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

Anthropometry measures and prevalence of obesity in the urban adult population of Cameroon: an update from the Cameroon Burden of Diabetes Baseline Survey

BACKGROUND: The objective of the study was to provide baseline and reference data on the prevalence and distribution of overweight and obesity, using different anthropometric measurements in adult urban populations in Cameroon. METHODS: The Cameroon Burden of Diabetes Baseline Survey was a cross-sectional study, conducted in 4 urban districts (Yaoundé, Douala, Garoua and Bamenda) of Cameroon, using the WHO Step approach for population-based assessment of cardiovascular risk factors. Body mass index, waist circumference and waist-to-hip ratio were measured using standardized methods. Overall, 10,011 individuals, 6,004 women and 4,007 men, from 4,189 households, aged 15 years and above participated. RESULTS: Based on body mass index, more than 25% of urban men and almost half of urban women were either overweight or obese with 6.5% of men and 19.5% of women being obese. The prevalence of obesity showed considerable variation with age in both genders. Using body mass index provided the highest prevalence of obesity in men (6.5%) and waist-to-hip ratio the lowest prevalence (3.2%). Among women, using waist-to-hip ratio and waist circumference yielded the highest prevalence of obesity (28%) and body mass index the lowest (19.5%). There was a trend towards an increase in age-adjusted odd ratios of being overweight or obese with duration of education in both sexes. CONCLUSION: The study provides current data on anthropometric measurements and obesity in urban Cameroonian populations, and found high prevalences of overweight and obesity particularly over 35 years of age, and among women. Prevalence varied according to the measure used. Our findings highlight the need to carry out further studies in Cameroonian and other Sub-Saharan African populations to provide appropriate cut-off points for the identification of people at risk of obesity-related disorders, and indicate the need to implement interventions to reverse increasing levels of obesity

Health Education and Community Empowerment: Conceptualizing and Measuring Perceptions of Individual, Organizational and Community Control

Also PCMA Working Paper #38.http://deepblue.lib.umich.edu/bitstream/2027.42/51259/1/493.pd

Hierarchical Models in the Brain

This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain

Fine-Tuning and the Stability of Recurrent Neural Networks

A central criticism of standard theoretical approaches to constructing stable, recurrent model networks is that the synaptic connection weights need to be finely-tuned. This criticism is severe because proposed rules for learning these weights have been shown to have various limitations to their biological plausibility. Hence it is unlikely that such rules are used to continuously fine-tune the network in vivo. We describe a learning rule that is able to tune synaptic weights in a biologically plausible manner. We demonstrate and test this rule in the context of the oculomotor integrator, showing that only known neural signals are needed to tune the weights. We demonstrate that the rule appropriately accounts for a wide variety of experimental results, and is robust under several kinds of perturbation. Furthermore, we show that the rule is able to achieve stability as good as or better than that provided by the linearly optimal weights often used in recurrent models of the integrator. Finally, we discuss how this rule can be generalized to tune a wide variety of recurrent attractor networks, such as those found in head direction and path integration systems, suggesting that it may be used to tune a wide variety of stable neural systems