Computerized analysis of facial expression reveals objective indices of blunted facial affect.

Blunted affect is associated with severe mental illness, particularly schizophrenia. Mechanisms of blunted affect are poorly understood, potentially due to a lack of phenomenological clarity. Here, we examine clinician rated blunted affect and computerized facial metrics derived from ambulatory video assessment using machine learning. With high predictive accuracy (80-82%), we found that head orientation, eye movement, and facets of mouth movement were associated with clinical ratings of blunted affect. Features denoting larger muscle movements were associated with social cognition (R2 = 0.37) and cognition (R2 = 0.40). Findings provide potential insights on psychological and pathophysiological contributors to blunted affect.

Information shocks and celebrity exposure: The effect of "Magic" Johnson on AIDS diagnoses and mortality in the U.S.

We present evidence that Earvin "Magic" Johnson's announcement that he contracted HIV served as a public-health catalyst for rapidly correcting the public's understanding of who was at risk of infection. Using a novel identification strategy, we present evidence that there was a large but temporary increase in the number of AIDS diagnoses for heterosexual men following the announcement. This effect was concentrated in areas with greater prior exposure to Johnson. We show that these men were both more likely to have been diagnosed via a formal blood test and less likely to die within 1 decade of their initial diagnosis-suggesting that Johnson's announcement caused an intertemporal substitution in testing which prolonged patients' lifespans as a result of earlier access to medical care. We estimate that Johnson's announcement caused approximately 800 additional heterosexual males in the United States in metropolitan statistical areas with National Basketball Association franchises men to discover their underlying AIDS diagnosis and, of whom, were more likely to live at least 1 decade beyond their initial diagnosis date.

Alogia and pressured speech do not fall on a continuum of speech production using objective speech technologies.

Speech production is affected in a variety of serious mental illnesses (SMI; e.g., schizophrenia, unipolar depression, bipolar disorders) and at its extremes can be observed in the gross reduction of speech (e.g., alogia) or increase of speech (e.g., pressured speech). The present study evaluated whether clinically-rated alogia and pressured speech represent antithetical constructs when analyzed using objective metrics of speech production. We examined natural speech using acoustic and natural language processing features from two archival studies using several different speaking tasks and a combined 107 patients meeting criteria for SMI. Contrary to expectations, we did not find that alogia and pressured speech presented as opposing ends of a speech production continuum. Objective speech markers were associated with clinically rated alogia but not pressured speech, and these results were consistent across speaking tasks and studies. Implications for our understanding of speech production symptoms in SMI are discussed, as well as implications for Natural Language Processing and digital phenotyping efforts more generally.

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.

Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors.

Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.

Talking about Health: A Topic Analysis of Narratives from Individuals with Schizophrenia and Other Serious Mental Illnesses.

Individuals with schizophrenia have higher mortality and shorter lifespans. There are a multitude of factors which create these conditions, but one aspect is worse physical health, particularly cardiovascular and metabolic health. Many interventions to improve the health of individuals with schizophrenia have been created, but on the whole, there has been limited effectiveness in improving quality of life or lifespan. One potential new avenue for inquiry involves a more patient-centric perspective; understanding aspects of physical health most important, and potentially most amenable to change, for individuals based on their life narratives. This study used topic modeling, a type of Natural Language Processing (NLP) on unstructured speech samples from individuals (n = 366) with serious mental illness, primarily schizophrenia, in order to extract topics. Speech samples were drawn from three studies collected over a decade in two geographically distinct regions of the United States. Several health-related topics emerged, primarily centered around food, living situation, and lifestyle (e.g., routine, hobbies). The implications of these findings for how individuals with serious mental illness and schizophrenia think about their health, and what may be most effective for future health promotion policies and interventions, are discussed.

Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation.

BACKGROUND AND HYPOTHESIS: Despite decades of "proof of concept" findings supporting the use of Natural Language Processing (NLP) in psychosis research, clinical implementation has been slow. One obstacle reflects the lack of comprehensive psychometric evaluation of these measures. There is overwhelming evidence that criterion and content validity can be achieved for many purposes, particularly using machine learning procedures. However, there has been very little evaluation of test-retest reliability, divergent validity (sufficient to address concerns of a "generalized deficit"), and potential biases from demographics and other individual differences. STUDY DESIGN: This article highlights these concerns in development of an NLP measure for tracking clinically rated paranoia from video "selfies" recorded from smartphone devices. Patients with schizophrenia or bipolar disorder were recruited and tracked over a week-long epoch. A small NLP-based feature set from 499 language samples were modeled on clinically rated paranoia using regularized regression. STUDY RESULTS: While test-retest reliability was high, criterion, and convergent/divergent validity were only achieved when considering moderating variables, notably whether a patient was away from home, around strangers, or alone at the time of the recording. Moreover, there were systematic racial and sex biases in the model, in part, reflecting whether patients submitted videos when they were away from home, around strangers, or alone. CONCLUSIONS: Advancing NLP measures for psychosis will require deliberate consideration of test-retest reliability, divergent validity, systematic biases and the potential role of moderators. In our example, a comprehensive psychometric evaluation revealed clear strengths and weaknesses that can be systematically addressed in future research.

Evolution of the albumin protein family in reptiles.

The albumin family of proteins consists of vitamin-D binding protein/group-specific component (GC), serum albumin (ALB), alpha-fetoprotein (AFP), and afamin (AFM), which are responsible for transporting many ligands throughout the body. The albumin family proteins are physiologically and medically important, but our understanding of their functions and applications is hindered by the dearth of information regarding these proteins' evolutionary relationships and functions in non-mammalian lineages. In this study we investigate the evolution of the albumin family proteins in reptiles, using bioinformatic methods to survey available reptile genomes and transcriptomes for albumin family proteins and phylogenetically characterize their relationships. We reinforce the established evolutionary relationships among the albumin protein family in reptiles, however, they are variable in their number of domains, overall genetic sequence, and synteny. We find a novel absence of the physiologically important ALB in squamates and identify two distinct lineages of AFP, one in mammals and another in reptiles. Our study provides a comparative genomic framework for further studies identifying lineage-specific gene expansions that may compensate for the lack of serum albumin in squamates.

Multiple origins of green blood in New Guinea lizards.

Several species of lizards from the megadiverse island of New Guinea have evolved green blood. An unusually high concentration of the green bile pigment biliverdin in the circulatory system of these lizards makes the blood, muscles, bones, tongue, and mucosal tissues bright green in color, eclipsing the crimson color from their red blood cells. This is a remarkable physiological feature because bile pigments are toxic physiological waste products of red blood cell catabolism and, when chronically elevated, cause jaundice in humans and all other vertebrates. Although these lizards offer a promising system to examine the evolution of extraordinary physiological characteristics, little is known about the phylogenetic relationships of green-blooded lizards or the evolutionary origins of green blood. We present the first extensive phylogeny for green-blooded lizards and closely related Australasian lizards using thousands of genomic regions to examine the evolutionary history of this unusual trait. Maximum likelihood ancestral character state reconstruction supports four independent origins of green blood. Our results lay the phylogenetic foundation necessary to determine the role, if any, of natural selection in shaping this enigmatic physiological trait as well as understanding the genetic, proteomic, and biochemical basis for the lack of jaundice in those species that have independently evolved green blood.