Metacognitive function and fragmentation in schizophrenia: Relationship to cognition, self-experience and developing treatments

Bleuler suggested that fragmentation of thought, emotion and volition were the unifying feature of the disorders he termed schizophrenia. In this paper we review research seeking to measure some of the aspects of fragmentation related to the experience of the self and others described by Bleuler. We focus on work which uses the concept of metacognition to characterize and quantify alterations or decrements in the processes by which fragments or pieces of information are integrated into a coherent sense of self and others. We describe the rationale and support for one method for quantifying metacognition and its potential to study the fragmentation of a person\u27s sense of themselves, others and the relative place of themselves and others in the larger human community. We summarize research using that method which suggests that deficits in metacognition commonly occur in schizophrenia and are related to basic neurobiological indices of brain functioning. We also present findings indicating that the capacity for metacognition in schizophrenia is positively related to a broad range of aspects of psychological and social functioning when measured concurrently and prospectively. Finally, we discuss the evolution and study of one therapy that targets metacognitive capacity, Metacognitive Reflection and Insight Therapy (MERIT) and its potential to treat fragmentation and promote recovery

Controlling extended systems with spatially filtered, time-delayed feedback

We investigate a control technique for spatially extended systems combining spatial filtering with a previously studied form of time-delay feedback. The scheme is naturally suited to real-time control of optical systems. We apply the control scheme to a model of a transversely extended semiconductor laser in which a desirable, coherent traveling wave state exists, but is a member of a nowhere stable family. Our scheme stabilizes this state, and directs the system towards it from realistic, distant and noisy initial conditions. As confirmed by numerical simulation, a linear stability analysis about the controlled state accurately predicts when the scheme is successful, and illustrates some key features of the control including the individual merit of, and interplay between, the spatial and temporal degrees of freedom in the control.Comment: 9 pages REVTeX including 7 PostScript figures. To appear in Physical Review

Piecing together fragments: Linguistic cohesion mediates the relationship between executive function and metacognition in schizophrenia

Speech disturbances are prevalent in psychosis. These may arise in part from executive function impairment, as research suggests that inhibition and monitoring are associated with production of cohesive discourse. However, it is not yet understood how linguistic and executive function impairments in psychosis interact with disrupted metacognition, or deficits in the ability to integrate information to form a complex sense of oneself and others and use that synthesis to respond to psychosocial challenges. Whereas discourse studies have historically employed manual hand-coding techniques, automated computational tools can characterize deep semantic structures that may be closely linked with metacognition. In the present study, we examined whether higher executive functioning promotes metacognition by way of altering linguistic cohesion. Ninety-four individuals with schizophrenia-spectrum disorders provided illness narratives and completed an executive function task battery (Delis-Kaplan Executive Function System). We assessed the narratives for linguistic cohesion (Coh-Metrix 3.0) and metacognitive capacity (Metacognition Assessment Scale – Abbreviated). Selected linguistic indices measured the frequency of connections between causal and intentional content (deep cohesion), word and theme overlap (referential cohesion), and unique word usage (lexical diversity). In path analyses using bootstrapped confidence intervals, we found that deep cohesion and lexical diversity independently mediated the relationship between executive functioning and metacognitive capacity. Findings suggest that executive control abilities support integration of mental experiences by way of increasing causal, goal-driven speech and word expression in individuals with schizophrenia. Metacognitive-based therapeutic interventions for psychosis may promote insight and recovery in part by scaffolding use of language that links ideas together

pGQL: A probabilistic graphical query language for gene expression time courses

<p>Abstract</p> <p>Background</p> <p>Timeboxes are graphical user interface widgets that were proposed to specify queries on time course data. As queries can be very easily defined, an exploratory analysis of time course data is greatly facilitated. While timeboxes are effective, they have no provisions for dealing with noisy data or data with fluctuations along the time axis, which is very common in many applications. In particular, this is true for the analysis of gene expression time courses, which are mostly derived from noisy microarray measurements at few unevenly sampled time points. From a data mining point of view the robust handling of data through a sound statistical model is of great importance.</p> <p>Results</p> <p>We propose probabilistic timeboxes, which correspond to a specific class of Hidden Markov Models, that constitutes an established method in data mining. Since HMMs are a particular class of probabilistic graphical models we call our method Probabilistic Graphical Query Language. Its implementation was realized in the free software package pGQL. We evaluate its effectiveness in exploratory analysis on a yeast sporulation data set.</p> <p>Conclusions</p> <p>We introduce a new approach to define dynamic, statistical queries on time course data. It supports an interactive exploration of reasonably large amounts of data and enables users without expert knowledge to specify fairly complex statistical models with ease. The expressivity of our approach is by its statistical nature greater and more robust with respect to amplitude and frequency fluctuation than the prior, deterministic timeboxes.</p

A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease

The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to\ua0discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the\ua0top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease

Collaborative Hubs: Making the Most of Predictive Epidemic Modeling

The COVID-19 pandemic has made it clear that epidemic models play an important role in how governments and the public respond to infectious disease crises. Early in the pandemic, models were used to estimate the true number of infections. Later, they estimated key parameters, generated short-term forecasts of outbreak trends, and quantified possible effects of interventions on the unfolding epidemic. In contrast to the coordinating role played by major national or international agencies in weather-related emergencies, pandemic modeling efforts were initially scattered across many research institutions. Differences in modeling approaches led to contrasting results, contributing to confusion in public perception of the pandemic. Efforts to coordinate modeling efforts in so-called “hubs” have provided governments, healthcare agencies, and the public with assessments and forecasts that reflect the consensus in the modeling community. This has been achieved by openly synthesizing uncertainties across different modeling approaches and facilitating comparisons between them

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species.

This article has been accepted for publication inNucleic Acids Research, Volume 45, Issue D1, 4 January 2017, Pages D712–D722. https://doi.org/10.1093/nar/gkw1128 Published by Oxford University Press.The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype-phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype-phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.National Institutes of Health (NIH) [1R24OD011883]; Wellcome Trust [098051]; NIH Undiagnosed Disease Program [HHSN268201300036C, HHSN268201400093P]; Phenotype RCN [NSF-DEB-0956049]; NCI/Leidos [15x143, BD2K U54HG007990-S2 (Haussler; GA4GH), BD2K PA-15-144-U01 (Kesselman; FaceBase)]; Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy [DE- AC02-05CH11231 to J.N.Y., S.C., S.E.L. and C.J.M.]. Funding for open access charge: NIH [1R24OD011883]