Using narratives in differential diagnosis of neurodegenerative syndromes

Connected Speech Fluency in Poststroke and Progressive Aphasia: A Scoping Review of Quantitative Approaches and Features

PURPOSE

Speech fluency has important diagnostic implications for individuals with poststroke aphasia (PSA) as well as primary progressive aphasia (PPA), and quantitative assessment of connected speech has emerged as a widely used approach across both etiologies. The purpose of this review was to provide a clearer picture on the range, nature, and utility of individual quantitative speech/language measures and methods used to assess connected speech fluency in PSA and PPA, and to compare approaches across etiologies.

METHOD

We conducted a scoping review of literature published between 2012 and 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Forty-five studies were included in the review. Literature was charted and summarized by etiology and characteristics of included patient populations and method(s) used for derivation and analysis of speech/language features. For a subset of included articles, we also charted the individual quantitative speech/language features reported and the level of significance of reported results.

RESULTS

Results showed that similar methodological approaches have been used to quantify connected speech fluency in both PSA and PPA. Two hundred nine individual speech-language features were analyzed in total, with low levels of convergence across etiology on specific features but greater agreement on the most salient features. The most useful features for differentiating fluent from nonfluent aphasia in both PSA and PPA were features related to overall speech quantity, speech rate, or grammatical competence.

CONCLUSIONS

Data from this review demonstrate the feasibility and utility of quantitative approaches to index connected speech fluency in PSA and PPA. We identified emergent trends toward automated analysis methods and data-driven approaches, which offer promising avenues for clinical translation of quantitative approaches. There is a further need for improved consensus on which subset of individual features might be most clinically useful for assessment and monitoring of fluency.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25537237.

Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here:  https://demo.sci.tellapp.org/ .

Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech

Morphosyntactic assessments are important for characterizing individuals with nonfluent/agrammatic variant primary progressive aphasia (nfvPPA). Yet, standard tests are subject to examiner bias and often fail to differentiate between nfvPPA and logopenic variant PPA (lvPPA). Moreover, relevant neural signatures remain underexplored. Here, we leverage natural language processing tools to automatically capture morphosyntactic disturbances and their neuroanatomical correlates in 35 individuals with nfvPPA relative to 10 healthy controls (HC) and 26 individuals with lvPPA. Participants described a picture, and ensuing transcripts were analyzed via part-of-speech tagging to extract sentence-related features (e.g., subordinating and coordinating conjunctions), verbal-related features (e.g., tense markers), and nominal-related features (e.g., subjective and possessive pronouns). Gradient boosting machines were used to classify between groups using all features. We identified the most discriminant morphosyntactic marker via a feature importance algorithm and examined its neural correlates via voxel-based morphometry. Individuals with nfvPPA produced fewer morphosyntactic elements than the other two groups. Such features robustly discriminated them from both individuals with lvPPA and HCs with an AUC of .95 and .82, respectively. The most discriminatory feature corresponded to subordinating conjunctions was correlated with cortical atrophy within the left posterior inferior frontal gyrus across groups (pFWE < .05). Automated morphosyntactic analysis can efficiently differentiate nfvPPA from lvPPA. Also, the most sensitive morphosyntactic markers correlate with a core atrophy region of nfvPPA. Our approach, thus, can contribute to a key challenge in PPA diagnosis.

The contribution of discursive and cognitive factors in referential choices made by elderly people during a narrative task

The present study focuses on referential choices made by healthy aged adults during narrative discourse, and their relationship with cognitive and socio-cognitive abilities. Previously, some studies have shown that, compared to young adults, older adults produce more pronouns when referring to various entities during discourse, regardless of the accessibility level of the referent for the addressee. This referential behavior has been interpreted in relation to the decrease of cognitive abilities, such as working memory abilities. There is, as of yet, little empirical evidence highlighting which cognitive competences preferentially support referential choices during discourse production. Here, we focus on three categories of referential markers (indefinite, definite markers and pronouns) produced by 78 participants from 60 to 91 years old. We used a storytelling task enabling us to examine the referential choices made at three discourse stages (introduction, maintaining or shift of the referent in focus) and in increasing levels of referential complexity (one vs two characters, and different vs same gender). In addition to specifically assessing how increasing age influences referential choices, we also examine the contribution of various cognitive and socio-cognitive skills that are presumed to play a specific role in referential choices. We found that both age and specific cognitive abilities (planification, inhibition, and verbal episodic memory) had an effect on referential choices, but that these effects depended on when (at which discourse stage) the referential markers were produced. Overall, our study highlights the complex interplay between discursive and cognitive factors in referential choices made by healthy older speakers.

Speech and language markers of neurodegeneration: a call for global equity

In the field of neurodegeneration, speech and language assessments are useful for diagnosing aphasic syndromes and for characterizing other disorders. As a complement to classic tests, scalable and low-cost digital tools can capture relevant anomalies automatically, potentially supporting the quest for globally equitable markers of brain health. However, this promise remains unfulfilled due to limited linguistic diversity in scientific works and clinical instruments. Here we argue for cross-linguistic research as a core strategy to counter this problem. First, we survey the contributions of linguistic assessments in the study of primary progressive aphasia and the three most prevalent neurodegenerative disorders worldwide-Alzheimer's disease, Parkinson's disease, and behavioural variant frontotemporal dementia. Second, we address two forms of linguistic unfairness in the literature: the neglect of most of the world's 7000 languages and the preponderance of English-speaking cohorts. Third, we review studies showing that linguistic dysfunctions in a given disorder may vary depending on the patient's language and that English speakers offer a suboptimal benchmark for other language groups. Finally, we highlight different approaches, tools and initiatives for cross-linguistic research, identifying core challenges for their deployment. Overall, we seek to inspire timely actions to counter a looming source of inequity in behavioural neurology.

Assessment and Therapy Goal Planning Using Free Computerized Language Analysis Software

Background

We discuss a free software system (Computerized Language Analysis [CLAN]) that can enable fast, thorough, and informative language sample analysis (LSA).

Method

We describe methods for eliciting, transcribing, analyzing, and interpreting language samples. Using a hypothetical child speaker, we illustrate use KidEval to generate a diagnostic report.

Results

Given LSA results suggestive of expressive language delay, we analyze further using CLAN's Developmental Sentence Score and Index of Productive Syntax routines, and outline the child's use of Brown's morphemes.

Discussion

This tutorial provides an introduction to the use of free CLAN software. We discuss how LSA results can be used to structure therapy goals that address specific aspects of grammatical structure that the child may not yet demonstrate in their spoken language. Finally, we provide answers to common questions, including user support.

The Discourse Profile in Corticobasal Syndrome: A Comprehensive Clinical and Biomarker Approach

The aim of this study was to characterize the oral discourse of CBS patients and to verify whether measures obtained during a semi-spontaneous speech production could differentiate CBS patients from controls. A second goal was to compare the performance of patients with CBS probably due to Alzheimer's disease (CBS-AD) pathology and CBS not related to AD (CBS-non-AD) in the same measures, based on the brain metabolic status (FDG-PET) and in the presence of amyloid deposition (amyloid-PET). Results showed that CBS patients were significantly different from controls in speech rate, lexical level, informativeness, and syntactic complexity. Discursive measures did not differentiate CBS-AD from CBS-non-AD. However, CBS-AD displayed more lexical-semantic impairments than controls, a profile that is frequently reported in patients with clinical AD and the logopenic variant of primary progressive aphasia (lvPPA). CBS-non-AD presented mainly with impairments related to motor speech disorders and syntactic complexity, as seen in the non-fluent variant of PPA.

Considerations for measuring individual outcomes across contexts in Down syndrome: Implications for research and clinical trials

Individuals with Down syndrome (DS) are increasingly involved in clinical trials that target developmental outcomes, like cognition and behavior. The increased focus on treatment in DS has led to ongoing discussions regarding the selection of outcome measures using syndrome-informed criteria. This discourse is warranted as clinical trials can fail if the outcome measures selected are inappropriate for individuals with DS or do not take into account the behavioral phenotype commonly associated with DS. This review focuses on the challenges present in the measurement of outcomes in DS, with a specific focus on considerations made in evaluating cognitive, language, and behavioral/psychopathology outcomes. This review also provides a summary of recommendations for assessment of outcomes in these domains as well as recommendations for future research. The impact of physical health and assessment psychometrics on the measurement of outcomes is also reviewed.

Language changes in Alzheimer's disease: A systematic review of verb processing

Alzheimer's disease (AD) results in language impairments and higher-level communication problems. Research into the language of people with AD (pwAD) has mainly focused on nouns; however, improved understanding of verb processing by pwAD could improve diagnostic assessments and communicative interventions. This systematic review synthesizes findings of AD's effects on verbs from single-word, sentence, and discourse tasks. Review of 57 studies revealed that pwAD were less accurate than controls on single-word tasks and less accurate with verbs than nouns on these tasks. They had difficulty comprehending sentences featuring multiple verbs or verbs with reversible thematic roles. Discourse production by pwAD was marked by vagueness, including declines in total output and propositional content and a preference for generic verbs and simple syntax. Few studies examining sentence production or discourse comprehension were found. Future research should address relationships between long-term memory and language preservation as well as verb use in discourse.

Identification of the main components of spontaneous speech in primary progressive aphasia and their neural underpinnings using multimodal MRI and FDG-PET imaging

BACKGROUND

Primary progressive aphasia (PPA) is a clinical syndrome characterized by gradual loss of language skills. This study aimed to evaluate the diagnostic capacity of a connected speech task for the diagnosis of PPA and its variants, to determine the main components of spontaneous speech, and to examine their neural correlates.

METHODS

A total of 118 participants (31 patients with nfvPPA, 11 with svPPA, 45 with lvPPA, and 31 healthy controls) were evaluated with the Cookie Theft picture description task and a comprehensive language assessment protocol. Patients also underwent ¹⁸F-fluorodeoxyglucose positron emission tomography and magnetic resonance imaging studies. Principal component analysis and machine learning were used to evaluate the main components of connected speech and the accuracy of connected speech parameters for diagnosing PPA. Voxel-based analyses were conducted to evaluate the correlation between spontaneous speech components and brain metabolism, brain volumes, and white matter microstructure.

RESULTS

Discrimination between patients with PPA and controls was 91.67%, with 77.78% discrimination between PPA variants. Parameters related to speech rate and lexical variables were the most discriminative for classification. Three main components were identified: lexical features, fluency, and syntax. The lexical component was associated with ventrolateral frontal regions, while the fluency component was associated with the medial superior prefrontal cortex. Number of pauses was more related with the left parietotemporal region, while pauses duration with the bilateral frontal lobe. The lexical component was correlated with several tracts in the language network (left frontal aslant tract, left superior longitudinal fasciculus I, II, and III, left arcuate fasciculus, and left uncinate fasciculus), and fluency was linked to the frontal aslant tract.

CONCLUSION

Spontaneous speech assessment is a useful, brief approach for the diagnosis of PPA and its variants. Neuroimaging correlates suggested a subspecialization within the left frontal lobe, with ventrolateral regions being more associated with lexical production and the medial superior prefrontal cortex with speech rate.

Identification of Mild Cognitive Impairment Among Chinese Based on Multiple Spoken Tasks

BACKGROUND

Previous studies explored the use of noninvasive biomarkers of speech and language for the detection of mild cognitive impairment (MCI). Yet, most of them employed single task which might not have adequately captured all aspects of their cognitive functions.

OBJECTIVE

The present study aimed to achieve the state-of-the-art accuracy in detecting individuals with MCI using multiple spoken tasks and uncover task-specific contributions with a tentative interpretation of features.

METHODS

Fifty patients clinically diagnosed with MCI and 60 healthy controls completed three spoken tasks (picture description, semantic fluency, and sentence repetition), from which multidimensional features were extracted to train machine learning classifiers. With a late-fusion configuration, predictions from multiple tasks were combined and correlated with the participants' cognitive ability assessed using the Montreal Cognitive Assessment (MoCA). Statistical analyses on pre-defined features were carried out to explore their association with the diagnosis.

RESULTS

The late-fusion configuration could effectively boost the final classification result (SVM: F1 = 0.95; RF: F1 = 0.96; LR: F1 = 0.93), outperforming each individual task classifier. Besides, the probability estimates of MCI were strongly correlated with the MoCA scores (SVM: -0.74; RF: -0.71; LR: -0.72).

CONCLUSION

Each single task tapped more dominantly to distinct cognitive processes and have specific contributions to the prediction of MCI. Specifically, picture description task characterized communications at the discourse level, while semantic fluency task was more specific to the controlled lexical retrieval processes. With greater demands on working memory load, sentence repetition task uncovered memory deficits through modified speech patterns in the reproduced sentences.

Part of Speech Production in Patients With Primary Progressive Aphasia: An Analysis Based on Natural Language Processing

Background Primary progressive aphasia (PPA) is a neurodegenerative disorder characterized by a progressive decline of language functions. Its symptoms are grouped into three PPA variants: nonfluent PPA, logopenic PPA, and semantic PPA. Grammatical deficiencies differ depending on the PPA variant. Aims This study aims to determine the differences between PPA variants with respect to part of speech (POS) production and to identify morphological markers that classify PPA variants using machine learning. By fulfilling these aims, the overarching goal is to provide objective measures that can facilitate clinical diagnosis, evaluation, and prognosis. Method and Procedure Connected speech productions from PPA patients produced in a picture description task were transcribed, and the POS class of each word was estimated using natural language processing, namely, POS tagging. We then implemented a twofold analysis: (a) linear regression to determine how patients with nonfluent PPA, semantic PPA, and logopenic PPA variants differ in their POS productions and (b) a supervised classification analysis based on POS using machine learning models (i.e., random forests, decision trees, and support vector machines) to subtype PPA variants and generate feature importance (FI). Outcome and Results Using an automated analysis of a short picture description task, this study showed that content versus function words can distinguish patients with nonfluent PPA, semantic PPA, and logopenic PPA variants. Verbs were less important as distinguishing features of patients with different PPA variants than earlier thought. Finally, the study showed that among the most important distinguishing features of PPA variants were elaborative speech elements, such as adjectives and adverbs.