Concepts of mental disorders in the United Kingdom : Similarities and differences between the lay public and psychiatrists

BACKGROUND: The lay public often conceptualise mental disorders in a different way to mental health professionals, and this can negatively impact on outcomes when in treatment. AIMS: This study explored which disorders the lay public are familiar with, which theoretical models they understand, which they endorse and how they compared to a sample of psychiatrists. METHODS: The Maudsley Attitude Questionnaire (MAQ), typically used to assess mental health professional's concepts of mental disorders, was adapted for use by a lay community sample (N = 160). The results were compared with a sample of psychiatrists (N = 76). RESULTS: The MAQ appeared to be accessible to the lay public, providing some interesting preliminary findings: in order, the lay sample reported having the best understanding of depression followed by generalised anxiety, schizophrenia and finally antisocial personality disorder. They best understood spiritualist, nihilist and social realist theoretical models of these disorders, but were most likely to endorse biological, behavioural and cognitive models. The lay public were significantly more likely to endorse some models for certain disorders suggesting a nuanced understanding of the cause and likely cure, of various disorders. Ratings often differed significantly from the sample of psychiatrists who were relatively steadfast in their endorsement of the biological model. CONCLUSION: The adapted MAQ appeared accessible to the lay sample. Results suggest that the lay public are generally aligned with evidence-driven concepts of common disorders, but may not always understand or agree with how mental health professionals conceptualise them. The possible causes of these differences, future avenues for research and the implications for more collaborative, patient-clinician conceptualisations are discussed.Peer reviewedFinal Accepted Versio

“Minimal self” locked into a model:exploring the prospect of formalizing intentionality in schizophrenia

Computational psychiatry is a quickly evolving discipline that aims to understand psychopathology in terms of computational, hence algorithmic processes. While cognitive phenomena, especially beliefs or ways of “reasoning”, can more easily be formalized, meaning re-described in mathematical terms and then entered computational models, there is speculation as to whether phenomenology might be formalizable too. In other words, there are speculations in terms of what aspects of the human experience, rather than specific cognitive processes alone, can enter computational models. Here, we explore the possibility of formalizing and modeling a phenomenological account of schizophrenia, using the concepts of “minimal self” and “intentionality”. To test the applicability of these concepts for formalization and modeling, we first aim to clarify some misunderstandings around the very nature of minimal self and intentionality, namely: whether these concepts entail a “minimal” sense of self, or might be better described in “transparent” sensory-integration and information processing terms. We then try to apply the concepts to a computational logic based on Marr’s levels of description, a fundamental account for understanding the rationale of computational psychiatry. Overall, we are asking via what conditions phenomenology can enter a computational logic.</p

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

Targeting cancer metabolism: a therapeutic window opens

Genetic events in cancer activate signalling pathways that alter cell metabolism. Clinical evidence has linked cell metabolism with cancer outcomes. Together, these observations have raised interest in targeting metabolic enzymes for cancer therapy, but they have also raised concerns that these therapies would have unacceptable effects on normal cells. However, some of the first cancer therapies that were developed target the specific metabolic needs of cancer cells and remain effective agents in the clinic today. Research into how changes in cell metabolism promote tumour growth has accelerated in recent years. This has refocused efforts to target metabolic dependencies of cancer cells as a selective anticancer strategy.Burroughs Wellcome FundSmith Family FoundationStarr Cancer ConsortiumDamon Runyon Cancer Research FoundationNational Institutes of Health (U.S.

Prefrontal GABA levels, hippocampal resting perfusion and the risk of psychosis

Preclinical models propose that the onset of psychosis is associated with hippocampal hyperactivity, thought to be driven by cortical GABAergic interneuron dysfunction and disinhibition of pyramidal neurons. Recent neuroimaging studies suggest that resting hippocampal perfusion is increased in subjects at ultra-high risk (UHR) for psychosis, but how this may be related to GABA concentrations is unknown. The present study used a multimodal neuroimaging approach to address this issue in UHR subjects. Proton magnetic resonance spectroscopy and pulsed-continuous arterial spin labeling imaging were acquired to investigate the relationship between medial prefrontal (MPFC) GABA+ levels (including some contribution from macromolecules) and hippocampal regional cerebral blood flow (rCBF) in 36 individuals at UHR of psychosis, based on preclinical evidence that MPFC dysfunction is involved in hippocampal hyperactivity. The subjects were then clinically monitored for 2 years: during this period, 7 developed a psychotic disorder and 29 did not. At baseline, MPFC GABA+ levels were positively correlated with rCBF in the left hippocampus (region of interest analysis, p = .044 family-wise error corrected, FWE). This correlation in the left hippocampus was significantly different in UHR subjects who went on to develop psychosis relative to those who did not (p = .022 FWE), suggesting the absence of a correlation in the latter subgroup. These findings provide the first human evidence that MPFC GABA+ concentrations are related to resting hippocampal perfusion in the UHR state, and offer some support for a link between GABA levels and hippocampal function in the development of psychosis

"Cognitive Penetrability" - Ch 3 of Seemings and Epistemic Justification

In this chapter I introduce the thesis that perceptual appearances are cognitively penetrable and analyse cases made against phenomenal conservatism hinging on this thesis. In particular, I focus on objections coming from the externalist reliabilist camp and the internalist inferentialist camp. I conclude that cognitive penetrability doesn’t yield lethal or substantive difficulties for phenomenal conservatism

Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis

The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as ‘integrated’ FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the ‘cartographic profile’ of time windows and k‐means clustering, and sub‐network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub‐network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub‐network comprised brain areas implicated in bottom‐up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk