Speech connectedness predicts reading performance three months in advance: a longitudinal experiment

Aiming to verify the predictive value of oral narrative structure on reading acquisition, we followed 253 children (first and second graders) during an entire school year, assessing oral narratives and reading performances in five sessions. Transcriptions of oral narratives were represented as word-recurrence graphs to measure connectedness attributes. Connectedness predicted performance in phonological awareness, reading comprehension, and word reading accuracy 3-4 months in advance.

Speech as a Graph: Developmental Perspectives on the Organization of Spoken Language

Language has been used as a privileged window to investigate mental processes. More recently, descriptions of psychopathological symptoms have been analyzed with the help of natural language processing tools. An example is the study of speech organization using graph theoretical approaches that began approximately 10 years ago. After its application in different areas, there is a need to better characterize what aspects can be associated with typical and atypical behavior throughout the lifespan, given the variables related to aging as well as biological and social contexts. The precise quantification of mental processes assessed through language may allow us to disentangle biological/social markers by looking at naturalistic protocols in different contexts. In this review, we discuss 10 years of studies in which word recurrence graphs were adopted to characterize the chain of thoughts expressed by individuals while producing discourse. Initially developed to understand formal thought disorder in the context of psychotic syndromes, this line of research has been expanded to understand the atypical development in different stages of psychosis and differential diagnosis (such as dementia) as well as the typical development of thought organization in school-age children/teenagers in naturalistic and school-based protocols. We comment on the effects of environmental factors, such as education and reading habits (in monolingual and bilingual contexts), in clinical and nonclinical populations at different developmental stages (from childhood to older adulthood, considering aging effects on cognition). Looking toward the future, there is an opportunity to use word recurrence graphs to address complex questions that consider biological/social factors within a developmental perspective in typical and atypical contexts.

Happy thoughts: What computational assessment of connectedness and emotional words can inform about early stages of psychosis

In recent years, different natural language processing tools measured aspects related to narratives' structural, semantic, and emotional content. However, there is a need to better understand the limitations and effectiveness of speech elicitation protocols. The graph-theoretical analysis applied to short narratives reveals lower connectedness associated with negative symptoms even in the early stages of psychosis, but emotional topics seem more informative than others. We investigate the interaction between connectedness and emotional words with negative symptoms and educational level in participants with and without psychosis. For that purpose, we used a speech elicitation protocol based on three positive affective pictures and calculated the proportion of emotional words and connectedness measures in the first-episode psychosis (FEP) group (N: 24) and a control group (N: 33). First, we replicated the association between connectedness and negative symptoms (R2: 0.53, p: 0.0049). Second, the more positive terms, the more connected the narrative was, exclusively under psychosis and in association with education, pointing to an interaction between symptoms and formal education. Negative symptoms were independently associated with connectedness, but not with emotional words, although the associations with education were mutually dependent. Together, education and symptoms explained almost 70 % of connectedness variance (R2: 0.67, p < 0.0001), but not emotional expression. At this initial stage of psychosis, education seems to play an important role, diminishing the impact of negative symptoms on the narrative connectedness. Negative symptoms in FEP impact narrative connectedness in association with emotional expression, revealing aspects of social cognition through a short and innocuous protocol.

LSD and language: Decreased structural connectivity, increased semantic similarity, changed vocabulary in healthy individuals

Language has been explored as a window into the mind. Psychedelics, known to affect perception and cognition, seem to change language, but a systematic, time-dependent exploration is lacking. Therefore, we aimed at mapping the psychedelic effects on language over the time course of the acute and sub-acute effects in an explorative manner. For this, 24 healthy volunteers (age [mean±SD, range]: 35±11, 25-61 years; 33% women) received 50 μg lysergic acid diethylamide (LSD) or inactive placebo in a randomized, double-blind, placebo-controlled, crossover study. We assessed different language productions (experience reporting, storytelling), components (structure, semantics, vocabulary) and time points (+0 h to +24 h). Language productions included 5-min experience reporting (+1.5 h, +6.5 h) and 1-min storytelling (+0 h, +2 h, +4 h, +6 h, +24 h). Language structure was assessed by computing speech topology (SpeechGraphs), semantics by semantic distances (FastText), vocabulary by word categories (LIWC). LSD, compared to placebo, changed language structure, including decreased verbosity, lexicon, global and local connectivity (+1.5 h to +4 h); decreased semantic distances between neighbouring words and overall words (+2 h to +24 h); and changed vocabulary related to grammar, persons, time, space and biological processes (+1.5 h to +24 h). In conclusion, low to moderate LSD doses changed language over diverse production types, components and time points. While simpler and disconnected structure and semantic similarity might reflect cognitive impairments, changed vocabulary might reflect subjective perceptions. Therefore, language under LSD might provide a window into the psychedelic mind and automated language quantifications should be better explored as valuable tools to yield more unconstrained insights into psychedelic perception and cognition.

How can computational tools help to understand language patterns in mental suffering considering social diversity

The complex interaction between biological and social factors challenges measuring human behavior. Language has been a crucial source of information that mirrors inner processes like thoughts. The development of a novel computational strategy that helps to understand language needs to consider social factors that could also impact human behavior. Ten years ago, I developed a computational approach based on graph theory to measure structural aspects of the narrative's mental organization expressed in spontaneous oral reports. It was possible to measure the decrease in narrative graph connectedness associated with the schizophrenia diagnosis and negative symptoms severity. However, I was worried that the psychiatric field neglected factors from diverse social realities (such as poor access to education). Formal education impacts language by mastering grammar and syntax. Changes in language structure could be related to symptoms and lack of exposure to formal education. Indeed, the same connectedness markers increase according to typical cognitive and academic development. In this paper, I describe the reasons and methods for investigating both factors (psychiatric symptoms and formal education) on language patterns. Further, I evaluate concerns and future challenges of using computational strategies that include social diversity in mental health conditions.

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.

Low-dose LSD and the stream of thought: Increased Discontinuity of Mind, Deep Thoughts and abstract flow

RATIONALE

Stream of thought describes the nature of the mind when it is freely roaming, a mental state that is continuous and highly dynamic as in mind-wandering or free association. Classic serotonergic psychedelics are known to profoundly impact perception, cognition and language, yet their influence on the stream of thought remains largely unexplored.

OBJECTIVE

To elucidate the effects of LSD on the stream of thought.

METHODS

In a randomized, double-blind, placebo-controlled, crossover study, 24 healthy participants received 50 μg lysergic acid diethylamide (LSD) or inactive placebo. Mind-wandering was measured by the Amsterdam Resting State Questionnaire (ARSQ), free association by the Forward Flow Task (FFT) for three seed word types (animals, objects, abstract words). ARSQ and FFT were assessed at +0 h, +2 h, +4 h, +6 h, +8 h and +24 h after drug administration, respectively.

RESULTS

LSD, compared to placebo, induced different facets of mind-wandering we conceptualized as "chaos" (Discontinuity of Mind, decreased Sleepiness, Planning, Thoughts under Control, Thoughts about Work and Thoughts about Past), "meaning" (Deep Thoughts, Not Sharing Thoughts) and "sensation" (Thoughts about Odours, Thoughts about Sounds). LSD increased the FFT for abstract words reflecting an "abstract flow" under free association. Overall, chaos was strongest pronounced (+2 h to +6 h), followed by meaning (+2 h to +4 h), sensation (+2 h) and abstract flow (+4 h).

CONCLUSIONS

LSD affects the stream of thought within several levels (active, passive), facets (chaos, meaning, sensation, abstractness) and time points (from +2 h to +6 h). Increased chaos, meaning and abstract flow at +4 h indicate the utility of a late therapeutic window in psycholytic therapy.

A Brazilian bottom-up strategy to address mental health in a diverse population over a large territorial area - an inspiration for the use of digital mental health

Brazil is a continental country with a history of massive immigration waves from around the world. Consequently, the Brazilian population is rich in ethnic, cultural, and religious diversity, but suffers from tremendous socioeconomic inequality. Brazil has a documented history of categorizing individuals with culturally specific behaviors as mentally ill, which has led to psychiatric institutionalization for reasons that were more social than clinical. To address this, a "network for psychosocial care" was created in Brazil, that included mental health clinics and community services distributed throughout the country. This generates local support for mental health rehabilitation, integrating psychiatric care, family support and education/work opportunities. These clinics and community services are tailored to provide care for each specific area, and are more attuned to regional culture, values and neighborhood infrastructure. Here we review existing reports about the Brazilian experience, including advances in public policy on mental health, and challenges posed by the large diversity to the psychosocial rehabilitation.  In addition, we show how new digital technologies in general, and computational speech analysis in particular, can contribute to unbiased assessments, resulting in decreased stigma and more effective diagnosis of the mental diseases, with methods that are free of gender, ethnic, or socioeconomic biases.

Nonsemantic word graphs of texts spanning ∼ 4500 years, including pre-literate Amerindian oral narratives

Non-semantic word graphs obtained from oral reports are useful to describe cognitive decline in psychiatric conditions such as Schizophrenia, as well as education-related gains in discourse structure during typical development. Here we provide non-semantic word graph attributes of texts spanning approximately 4500 years of history, and pre-literate Amerindian oral narratives. The dataset assessed comprises 707 literary texts representative of 9 different Afro-Eurasian traditions (Syro-Mesopotamian, Egyptian, Hinduist, Persian, Judeo-Christian, Greek-Roman, Medieval, Modern and Contemporary), and Amerindian narratives (N = 39) obtained from a single ethnic group from South America (Kalapalo, N = 18), or from a mixed ethnic group from South, Central and North America (non-Kalapalo, N = 21). The present article provides detailed information about each text or narrative, including measurements of four graph attributes of interest: number of nodes (lexical diversity), repeated edges (short-range recurrence), largest strongly connected component (long-range recurrence), and average shortest path (graph length).

Low Speech Connectedness in Alzheimer's Disease is Associated with Poorer Semantic Memory Performance

Connected speech is an everyday activity. We aimed to investigate whether connected speech can differentiate oral narrative production between adults with Alzheimer's disease (AD; n = 24) and cognitively healthy older adults (n = 48). We used graph attributes analysis to represent connected speech. Participants produced oral narratives and performed semantic, episodic, and working memory tasks. AD patients produced less connected narratives than cognitively healthy older adults. Connectedness was associated with semantic memory in AD and with episodic memory in controls. Word-graphs connectedness represents a practical tool to assess cognitive impairment in AD patients.

Network analysis of narrative discourse and attention-deficit hyperactivity symptoms in adults

Previous research investigating language in attention-deficit hyperactivity disorder (ADHD) has demonstrated several deficits in many aspects. However, no previous study employed quantitative methodology providing objective measures that could be compared among different studies with diverse samples. To fill this gap, we used network analysis to investigate how ADHD symptomatology impacts narrative discourse, a complex linguistic task considered to be an ecological measure of language. Fifty-eight adults (34 females and 24 males) with a mean age of 26 years old and a mean of 17 years of educational level were administered the Adult Self-Rating Scale for ADHD symptomatology. They also completed the State-Trait Anxiety Inventory, the Beck Depression Inventory and the Urgency, Premeditation, Perseverance, Sensation Seeking Behavior Scale. Intelligence quotient was calculated. Individuals were asked to tell a story based on a wordless picture book. Speech was recorded and transcribed as an input to SpeechGraphs software. Parameters were total number of words (TNW), number of loops of one node (L1), repeated edges (RE), largest strongly connected component (LSC) and average shortest path (ASP). Verbosity was controlled. Statistical analysis was corrected for multiples comparisons and partial correlations were performed for confounding variables. After controlling for anxiety, depression, IQ, and impulsiveness ADHD symptomatology was positively correlated with L1 and negatively correlated with LSC. TNW was positively correlated with ADHD symptoms. In a subdomain analysis, both inattention and hyperactivity-impulsivity were negatively correlated with LSC. Only hyperactivity-impulsivity positively correlated with TNW and L1. Results indicated a correlation between ADHD symptoms and lower connectedness in narrative discourse (as indicated by higher L1 and lower LSC), as well as higher total number of words (TNW). Our results suggest that the higher the number of ADHD symptoms, the less connectivity among words, and a higher number of words in narrative discourse.

The History of Writing Reflects the Effects of Education on Discourse Structure: Implications for Literacy, Orality, Psychosis and the Axial Age

BACKGROUND

Graph analysis detects psychosis and literacy acquisition. Bronze Age literature has been proposed to contain childish or psychotic features, which would only have matured during the Axial Age (∼800-200 BC), a putative boundary for contemporary mentality.

METHOD

Graph analysis of literary texts spanning ∼4,500 years shows remarkable asymptotic changes over time.

RESULTS

While lexical diversity, long-range recurrence and graph length increase away from randomness, short-range recurrence declines towards random levels. Bronze Age texts are structurally similar to oral reports from literate typical children and literate psychotic adults, but distinct from poetry, and from narratives by preliterate preschoolers or Amerindians. Text structure reconstitutes the "arrow-of-time", converging to educated adult levels at the Axial Age onset.

CONCLUSION

The educational pathways of oral and literate traditions are structurally divergent, with a decreasing range of recurrence in the former, and an increasing range of recurrence in the latter. Education is seemingly the driving force underlying discourse maturation.

The maturation of speech structure in psychosis is resistant to formal education

Discourse varies widely with age, level of education, and psychiatric state. Word graphs have been recently shown to provide behavioral markers of formal thought disorders in psychosis (e.g., disorganized flow of ideas) and to track literacy acquisition in children with typical development. Here we report that a graph-theoretical computational analysis of verbal reports from subjects spanning 6 decades of age and 2 decades of education reveals asymptotic changes over time that depend more on education than age. In typical subjects, short-range recurrence and lexical diversity stabilize after elementary school, whereas graph size and long-range recurrence only steady after high school. Short-range recurrence decreases towards random levels, while lexical diversity, long-range recurrence, and graph size increase away from near-randomness towards a plateau in educated adults. Subjects with psychosis do not show similar dynamics, presenting at adulthood a children-like discourse structure. Typical subjects increase the range of word recurrence over school years, but the same feature in subjects with psychosis resists education.

Computational Tracking of Mental Health in Youth: Latin American Contributions to a Low-Cost and Effective Solution for Early Psychiatric Diagnosis

The early onset of mental disorders can lead to serious cognitive damage, and timely interventions are needed in order to prevent them. In patients of low socioeconomic status, as is common in Latin America, it can be hard to identify children at risk. Here, we briefly introduce the problem by reviewing the scarce epidemiological data from Latin America regarding the onset of mental disorders, and discussing the difficulties associated with early diagnosis. Then we present computational psychiatry, a new field to which we and other Latin American researchers have contributed methods particularly relevant for the quantitative investigation of psychopathologies manifested during childhood. We focus on new technologies that help to identify mental disease and provide prodromal evaluation, so as to promote early differential diagnosis and intervention. To conclude, we discuss the application of these methods to clinical and educational practice. A comprehensive and quantitative characterization of verbal behavior in children, from hospitals and laboratories to homes and schools, may lead to more effective pedagogical and medical intervention.