Can RDoC Help Find Order in Thought Disorder?

Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function

Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders.

Dispositional and ideological factor correlate of conspiracy thinking and beliefs

This study explored how the Big Five personality traits, as well as measures of personality disorders, are related to two different measures of conspiracy theories (CTs)The two measures correlated r = .58 and were applied to examine generalisability of findings. We also measured participants (N = 397) general knowledge levels and ideology in the form of religious and political beliefs. Results show that the Big Five and ideology are related to CTs but these relationships are generally wiped out by the stronger effects of the personality disorder scales. Two personality disorder clusters (A and B) were significant correlates of both CT measures, in both cases accounting for similar amounts of variance (20%). The personality disorders most predictive of conspiracy theories were related to the A cluster, characterized by schizotypal symptoms such as oddities of thinking and loose associations. These findings were corroborated by an additional analysis using Latent Semantic Analysis (LSA). LSA demonstrated that the items measuring schizotypal and related symptoms are cognitively related to both our measures of CTs. The implications for the studying of CTs is discussed, and limitations are acknowledged.

Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review)

Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients. Such biomarkers are based on the results of instrumental and laboratory studies (neuroimaging, electro-physiological, biochemical, immunological, genetic, and others) and are successfully being used in neurosciences for understanding the mechanisms of the emergence and development of nervous system pathologies. Presently, with the advent of new effective neuroimaging, laboratory, and other methods of investigation and also with the development of modern methods of data analysis, machine learning, and artificial intelligence, a great number of scientific and clinical studies is being conducted devoted to the search for the markers which have diagnostic and prognostic value and may be used in clinical practice to objectivize the processes of establishing and clarifying the diagnosis, choosing and optimizing treatment and rehabilitation tactics, predicting the course and outcome of the disease. This review presents the analysis of the works which describe the correlates between the diagnosis of schizophrenia, established by health professionals, various manifestations of the psychiatric disorder (its subtype, variant of the course, severity degree, observed symptoms, etc.), and objectively measured characteristics/quantitative indicators (anatomical, functional, immunological, genetic, and others) obtained during instrumental and laboratory examinations of patients. A considerable part of these works has been devoted to correlates/biomarkers of schizophrenia based on the data of structural and functional (at rest and under cognitive load) MRI, EEG, tractography, and immunological data. The found correlates/biomarkers reflect anatomic disorders in the specific brain regions, impairment of functional activity of brain regions and their interconnections, specific microstructure of the brain white matter and the levels of connectivity between the tracts of various structures, alterations of electrical activity in various parts of the brain in different EEG spectral ranges, as well as changes in the innate and adaptive links of immunity. Current methods of data analysis and machine learning to search for schizophrenia biomarkers using the data of diverse modalities and their application during building and interpretation of predictive diagnostic models of schizophrenia have been considered in the present review.

Detecting formal thought disorder by deep contextualized word representations

Computational linguistics has enabled the introduction of objective tools that measure some of the symptoms of schizophrenia, including the coherence of speech associated with formal thought disorder (FTD). Our goal was to investigate whether neural network based utterance embeddings are more accurate in detecting FTD than models based on individual indicators. The present research used a comprehensive Embeddings from Language Models (ELMo) approach to represent interviews with patients suffering from schizophrenia (N=35) and with healthy people (N=35). We compared its results to the approach described by Bedi et al. (2015), referred to here as the coherence model. Evaluations were also performed by a clinician using the Scale for the Assessment of Thought, Language and Communication (TLC). Using all six TLC questions the ELMo obtained an accuracy of 80% in distinguishing patients from healthy people. Previously used coherence models were less accurate at 70%. The classifying clinician was accurate 74% of the time. Our analysis shows that both ELMo and TLC are sensitive to the symptoms of disorganization in patients. In this study methods using text representations from language models were more accurate than those based solely on the assessment of FTD, and can be used as measures of disordered language that complement human clinical ratings.

The role of the gut microbiome in the development of schizophrenia

Schizophrenia is a heterogeneous neurodevelopmental disorder involving the convergence of a complex and dynamic bidirectional interaction of genetic expression and the accumulation of prenatal and postnatal environmental risk factors. The development of the neural circuitry underlying social, cognitive and emotional domains requires precise regulation from molecular signalling pathways, especially during critical periods or "windows", when the brain is particularly sensitive to the influence of environmental input signalling. Many of the brain regions involved, and the molecular substrates sub-serving these domains are responsive to life-long microbiota-gut-brain (MGB) axis signalling. This intricate microbial signalling system communicates with the brain via the vagus nerve, immune system, enteric nervous system, enteroendocrine signalling and production of microbial metabolites, such as short-chain fatty acids. Preclinical data has demonstrated that MGB axis signalling influences neurotransmission, neurogenesis, myelination, dendrite formation and blood brain barrier development, and modulates cognitive function and behaviour patterns, such as, social interaction, stress management and locomotor activity. Furthermore, preliminary clinical studies suggest altered gut microbiota profiles in schizophrenia. Unravelling MGB axis signalling in the context of an evolving dimensional framework in schizophrenia may provide a more complete understanding of the neurobiological architecture of this complex condition and offers the possibility of translational interventions.

End-of-life care in schizophrenia: a systematic review

OBJECTIVE

Schizophrenia is a severe and persistent mental illness with profound effects on patients, families, and communities. It causes immense suffering on personal, emotional, and socioeconomic levels. Individuals with schizophrenia have poorer health outcomes and die 10-20 years younger than the general population. Economic costs associated with schizophrenia are substantial and comprise 2.5% of healthcare expenditures worldwide. Despite psychosocioeconomic impacts, individuals with schizophrenia are subject to inequitable care, particularly at end of life. A systematic review was conducted to examine disparities in end-of-life care in schizophrenia and identify factors that can be targeted to enhance end-of-life care in this vulnerable population.

DESIGN

A comprehensive search was conducted using the databases Ovid MEDLINE(R), Ovid EMBASE, Ovid PsycINFO, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus from 2008-2018. Keywords included schizophrenia, palliative, end-of-life, and hospice. Two authors independently reviewed titles and abstracts; disagreements were resolved by consensus.

RESULTS

The search identified 123 articles; 33 met criteria: 13 case reports, 12 retrospective studies, 5 literature reviews, and 3 prospective studies. Articles were divided into major themes including healthcare disparities, ethics, and palliative care. Palliative care was the most frequent theme comprising >50% of the articles, and there was considerable thematic overlap with ethics and palliative care. Almost half the articles (45%) were related to schizophrenia and comorbid cancer.

CONCLUSIONS

Increased awareness of potential healthcare disparities in this population, creative approaches in multidisciplinary care, and provision of adequate palliative services and resources can enhance end-of-life care in schizophrenia.

Cortical thickness and formal thought disorder in schizophrenia: An ultra high-field network-based morphometry study

BACKGROUND

Persistent formal thought disorder (FTD) is a core feature of schizophrenia. Recent cognitive and neuroimaging studies indicate a distinct mechanistic pathway underlying the persistent positive FTD (pFTD or disorganized thinking), though its structural determinants are still elusive. Using network-based cortical thickness estimates from ultra-high field 7-Tesla Magnetic Resonance Imaging (7T MRI), we investigated the structural correlates of pFTD.

METHODS

We obtained speech samples and 7T MRI anatomical scans from medicated clinically stable patients with schizophrenia (n = 19) and healthy controls (n = 20). Network-based morphometry was used to estimate the mean cortical thickness of 17 functional networks covering the entire cortical surface from each subject. We also quantified the vertexwise variability of thickness within each network to quantify the spatial coherence of the 17 networks, estimated patients vs. controls differences, and related the thickness of the affected networks to the severity of pFTD.

RESULTS

Patients had reduced thickness of the frontoparietal and default mode networks, and reduced spatial coherence affecting the salience and the frontoparietal control network. A higher burden of positive FTD related to reduced frontoparietal thickness and reduced spatial coherence of the salience network. The presence of positive FTD, but not its severity, related to the reduced thickness of the language network comprising of the superior temporal cortex.

CONCLUSIONS

These results suggest that cortical thickness of both cognitive control and language networks underlie the positive FTD in schizophrenia. The structural integrity of cognitive control networks is a critical determinant of the expressed severity of persistent FTD in schizophrenia.

Examining Self-Reported Thought Disorder: Continuous Variation, Convergence with Schizotypy, and Cognitive Correlates

When measured from the perspective of a clinician, the severity of 'objective' thought disorder (TD) has been found to vary continuously between people with and without psychosis-related diagnoses, and has been linked with semantic and executive dysfunctions in people with psychosis. Measures of 'subjective' TD that are derived from a first-person perspective have also been produced, but their relationships with objective TD and cognition are unclear. The aims of the current study were: to determine whether responses on a self-report TD questionnaire correspond with responses to a self-report measure of schizotypal disorganization; and to explore the association between these self-reported subjective TD severities and cognitive performance. Data was collected from a sample of 33 people without psychiatric diagnoses and 38 people diagnosed with schizophrenia or schizoaffective disorder evincing mild symptomatology, and this data was pooled for analysis in accordance with the continuum model. Self-reported TD frequencies were associated with the endorsement of disorganized schizotypal experiences. Moreover, self-reported TD frequencies showed relationships with measures of semantic and executive functioning. Thus, at mild severities, self-reported TD shows continuous variation and is associated with altered cognitive function.

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.

Schizophrenia spectrum and other psychotic disorders

Since its earliest conceptualization, schizophrenia has been considered a disorder of "young men." Contemporary research suggests that there are sex differences in schizophrenia that are both transdiagnostic and representative of general sex/gender differences across the psychopathology spectrum. This chapter selectively summarizes representative sex/gender differences in clinical expression, epidemiology, risk factors, treatment, as well as course and outcome in schizophrenia. The consistent sex differences found, such as onset age, generic brain anomalies, and hormonal involvement, are not specific to schizophrenia or necessarily to psychopathology. It is suggested that in working with those diagnosed as meeting the current criteria for schizophrenia, clinicians adopt a transdiagnostic framework informed by sex and gender role processes.

Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D

With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals.

Measuring disorganized speech in schizophrenia: automated analysis explains variance in cognitive deficits beyond clinician-rated scales

BACKGROUND

Conveying information cohesively is an essential element of communication that is disrupted in schizophrenia. These disruptions are typically expressed through disorganized symptoms, which have been linked to neurocognitive, social cognitive, and metacognitive deficits. Automated analysis can objectively assess disorganization within sentences, between sentences, and across paragraphs by comparing explicit communication to a large text corpus.

METHOD

Little work in schizophrenia has tested: (1) links between disorganized symptoms measured via automated analysis and neurocognition, social cognition, or metacognition; and (2) if automated analysis explains incremental variance in cognitive processes beyond clinician-rated scales. Disorganization was measured in schizophrenia (n = 81) with Coh-Metrix 3.0, an automated program that calculates basic and complex language indices. Trained staff also assessed neurocognition, social cognition, metacognition, and clinician-rated disorganization.

RESULTS

Findings showed that all three cognitive processes were significantly associated with at least one automated index of disorganization. When automated analysis was compared with a clinician-rated scale, it accounted for significant variance in neurocognition and metacognition beyond the clinician-rated measure. When combined, these two methods explained 28-31% of the variance in neurocognition, social cognition, and metacognition.

CONCLUSIONS

This study illustrated how automated analysis can highlight the specific role of disorganization in neurocognition, social cognition, and metacognition. Generally, those with poor cognition also displayed more disorganization in their speech-making it difficult for listeners to process essential information needed to tie the speaker's ideas together. Our findings showcase how implementing a mixed-methods approach in schizophrenia can explain substantial variance in cognitive processes.

Is formal thought disorder in schizophrenia related to structural and functional aberrations in the language network? A systematic review of neuroimaging findings

Formal thought disorder (FTD) is a core feature of schizophrenia, a marker of illness severity and a predictor of outcome. The underlying neural mechanisms are still a matter of debate. This study aimed at 1) reviewing the literature on the neural correlates of FTD in schizophrenia, and 2) testing the hypothesis that FTD correlates with structural and functional aberrations in the language network. Medline, PsychInfo, and Embase were searched for neuroimaging studies, which applied a clinical measure to assess FTD in adults with schizophrenia and were published in English or German in peer-reviewed journals until December 2016. Of 412 articles identified, 61 studies were included in the review. Volumetric studies reported bilateral grey matter deficits (L > R) to be associated with FTD in the inferior frontal gyrus, the superior temporal gyrus and the inferior parietal lobe. The same regions showed hyperactivity in resting state functional magnetic resonance imaging (fMRI) studies and both hyper- and hypoactivity in fMRI studies that employed semantic processing or free speech production tasks. Diffusion tensor imaging studies demonstrated white matter aberrations in fibre tracts that connect the frontal and temporo-parietal regions. FTD in schizophrenia was found to be associated with structural and functional aberrations in the language network. However, there are studies that did not find an association between FTD and neural aberrations of the language network and regions not included in the language network have been associated with FTD. Thus, future research is needed to clarify the specificity of the language network for FTD in schizophrenia.

Cross Talk: The Microbiota and Neurodevelopmental Disorders

Humans evolved within a microbial ecosystem resulting in an interlinked physiology. The gut microbiota can signal to the brain via the immune system, the vagus nerve or other host-microbe interactions facilitated by gut hormones, regulation of tryptophan metabolism and microbial metabolites such as short chain fatty acids (SCFA), to influence brain development, function and behavior. Emerging evidence suggests that the gut microbiota may play a role in shaping cognitive networks encompassing emotional and social domains in neurodevelopmental disorders. Drawing upon pre-clinical and clinical evidence, we review the potential role of the gut microbiota in the origins and development of social and emotional domains related to Autism spectrum disorders (ASD) and schizophrenia. Small preliminary clinical studies have demonstrated gut microbiota alterations in both ASD and schizophrenia compared to healthy controls. However, we await the further development of mechanistic insights, together with large scale longitudinal clinical trials, that encompass a systems level dimensional approach, to investigate whether promising pre-clinical and initial clinical findings lead to clinical relevance.