Reading and writing habits compensate for aging effects in speech connectedness

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.

Limited connectedness of spontaneous speech may be a marker of dementia due to Alzheimer's disease

The study evaluated the connectedness of spontaneous speech production in individuals with dementia as a potential predictor of dementia severity. Data were derived from the baseline sample of 143 individuals with dementia in the English Pitt corpus. Dementia severity was assessed via the Mini Mental Status Exam, the Mattis Dementia Rating Scale, and the Blessed Dementia Scale. Language abilities were evaluated using verbal fluency and picture description tasks. Graph analysis was carried out for the picture description task using the computational tool SpeechGraphs to calculate connectedness. Results demonstrated that higher educational attainment, higher verbal fluency and strongly-connected spontaneous speech were associated with better cognitive function. Results suggest that automated language processing approaches, such as graph structure analysis, may provide a faster and ecologically valid method of detecting dementia symptoms.

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.

Neuroanatomical correlates of macrostructural receptive abilities in narrative discourse in unilateral left hemisphere stroke: A behavioural and voxel-based morphometry study

BACKGROUND

Little is known about story retelling and comprehension abilities in groups with lower levels of education and socio-economic status (SES). A growing body of evidence suggests the role of an extended network supporting narrative comprehension, but few studies have been conducted in clinical populations, even less in developing countries.

AIMS

To extend our knowledge of the impact of a stroke on macrostructural aspects of discourse processes, namely main and complementary information, in individuals with middle-low to low SES and low levels of education. Relationships were tested between the performance in story retell and comprehension and reading and writing habits (RWH). Also, the associations between retelling and comprehension measures and their structural grey matter (GM) correlates were explored.

METHODS & PROCEDURES

A total of 17 adults with unilateral left hemisphere (LH) chronic ischaemic stroke without the presence of significant aphasia and 10 matched (age, education and SES) healthy controls (HC) participated in the study. Retell and comprehension tasks were performed after listening or reading narrative stories. Voxel-based morphometry (VBM) analysis was conducted on a subgroup of nine individuals with LH stroke and the 10 matched controls using structural magnetic resonance imaging (MRI).

OUTCOMES & RESULTS

Retelling and comprehension abilities were not significantly different between LH and HC, nonetheless quantitively lower in LH. Exploratory correlations showed that retelling and comprehension abilities in both written and auditory modalities were correlated with naming abilities. At the neural level, written comprehension positively correlated with GM density of the LH, including areas in the temporal pole, superior and middle temporal gyrus as well as the orbitofrontal cortex, precentral and postcentral gyri. Auditory narrative comprehension was associated with GM density of the lingual gyrus in the right hemisphere.

CONCLUSIONS & IMPLICATIONS

The present results suggest that retelling and comprehension of auditory and written narratives are relatively well-preserved in individuals with a LH stroke without significant aphasia, but poorer than in HC. The findings replicate previous studies conducted in groups with higher levels of education and SES both at the behavioural and neural levels. Considering that naming seems to be associated with narrative retell and comprehension in individuals with lower SES and education, this research provides evidence on the importance of pursuing further studies including larger samples with and without aphasia as well as with various SES and education levels.

WHAT THIS PAPER ADDS

What is already known on this subject Story retell and comprehension of auditory and written discourse have been shown to be affected after stroke, but most studies have been conducted on individuals with middle to high SES and high educational levels. What this paper adds to existing knowledge The study reports on narrative retell and comprehension in both auditory and written modalities in groups of HC and individuals with LH brain damage, with low-to-middle SES and lower levels of education. What are the potential or actual clinical implications of this work? This study highlights the importance of taking into consideration the sociodemographic and RWH of patients when assessing discourse retell and comprehension in both auditory and written modalities. It also underlines the importance of including patients without significant aphasia following LH stroke to look at the effect of both stroke and aphasia on narrative comprehension and story retelling.

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.