A longitudinal observational study of home-based conversations for detecting early dementia:protocol for the CUBOId TV task

INTRODUCTION: Limitations in effective dementia therapies mean that early diagnosis and monitoring are critical for disease management, but current clinical tools are impractical and/or unreliable, and disregard short-term symptom variability. Behavioural biomarkers of cognitive decline, such as speech, sleep and activity patterns, can manifest prodromal pathological changes. They can be continuously measured at home with smart sensing technologies, and permit leveraging of interpersonal interactions for optimising diagnostic and prognostic performance. Here we describe the ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) study, which explores the feasibility of multimodal data fusion for in-home monitoring of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). The report focuses on a subset of CUBOId participants who perform a novel speech task, the ‘TV task’, designed to track changes in ecologically valid conversations with disease progression. METHODS AND ANALYSIS: CUBOId is a longitudinal observational study. Participants have diagnoses of MCI or AD, and controls are their live-in partners with no such diagnosis. Multimodal activity data were passively acquired from wearables and in-home fixed sensors over timespans of 8–25 months. At two time points participants completed the TV task over 5 days by recording audio of their conversations as they watched a favourite TV programme, with further testing to be completed after removal of the sensor installations. Behavioural testing is supported by neuropsychological assessment for deriving ground truths on cognitive status. Deep learning will be used to generate fused multimodal activity-speech embeddings for optimisation of diagnostic and predictive performance from speech alone. ETHICS AND DISSEMINATION: CUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by University of Bristol. It is supported by the National Institute for Health Research Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals

Longitudinal change in language production: Effects of aging and dementia on grammatical complexity and semantic content

This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.Mixed modeling was used to examine longitudinal changes in linguistic ability in healthy older adults and older adults with dementia. Language samples, vocabulary scores, and digit span scores were collected annually from healthy older adults and semiannually from older adults with dementia. The language samples were scored for grammatical complexity and propositional content. For the healthy group, age-related declines in grammatical complexity and propositional content were observed. The declines were most rapid in the mid 70s. For the group with dementia, grammatical complexity and propositional content also declined over time, regardless of age. Rates of decline were uniform across individuals. These analyses reveal how both grammatical complexity and proposition content are related to late-life changes in cognition in healthy older adults as well as those with dementia. Alzheimer's disease accelerates this decline, regardless of age. (PsycINFO Database Record (c) 2010 APA, all rights reserved

Computational Language Assessment in patients with speech, language, and communication impairments

Speech, language, and communication symptoms enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression. Nevertheless, traditional manual neurologic assessment, the speech and language evaluation standard, is time-consuming and resource-intensive for clinicians. We argue that Computational Language Assessment (C.L.A.) is an improvement over conventional manual neurological assessment. Using machine learning, natural language processing, and signal processing, C.L.A. provides a neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. ii. facilitates the diagnosis, prognosis, and therapy efficacy in at-risk and language-impaired populations; and iii. allows easier extensibility to assess patients from a wide range of languages. Also, C.L.A. employs Artificial Intelligence models to inform theory on the relationship between language symptoms and their neural bases. It significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders, allowing them to age gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite

Marqueurs discursifs de neurodégénérescence liée à la pathologie Alzheimer

La maladie d’Alzheimer (MA) et les aphasies progressives primaires (APP) s’accompagnent de perturbations du langage expressif parfois subtiles, mais précoces dans l’évolution de ces maladies neurodégénératives. Considérés dans une approche automatisée, ces changements pourraient constituer des marqueurs de dégénérescence identifiés de façon non invasive et peu onéreuse. À ce titre, ils font l’objet d’études visant à automatiser leur utilisation clinique. Cependant, l’intégration des marqueurs langagiers à une approche diagnostique centrée sur les biomarqueurs reste à faire. À cette fin, la présente thèse a deux objectifs. D’abord, recenser systématiquement les marqueurs du discours qui distinguent le mieux les personnes avec une MA de témoins en santé. Ensuite, appliquer une approche automatisée et à un large éventail de marqueurs de discours pour identifier, dans un groupe hétérogène de patients avec une APP, lesquels ont une pathologie Alzheimer sous-jacente. Afin de mettre en contexte ces deux objectifs, nous proposons une introduction générale comprenant les éléments suivants : la pathophysiologie de la MA et des APP, le rôle croissant des biomarqueurs dans la prise de décision clinique dans les maladies neurodégénératives, les études pionnières du discours en neurodégénérescence, ainsi que de récentes études computationnelles sur les marqueurs de discours dans la MA et les APP. Nos résultats font émerger un patron multidimensionnel (acoustique, lexical, syntaxique, sémantique et pragmatique) de changements langagiers qui distinguent les personnes avec une MA de témoins en santé, avec une prépondérance des marqueurs lexicosémantiques. Dans le groupe de patients avec une APP avec une imagerie amyloïde positive ou négative, nous mesurons ensuite le pouvoir de classification d’un court échantillon de discours et montrons qu’il peut être avantageusement comparé à d’autres biomarqueurs. Nous discutons du patron spécifique de marqueurs discriminants pour ce sous-groupe de patients, notamment l’importance des marqueurs psycholinguistiques pour prédire le résultat de l’imagerie amyloïde à partir du discours.Alzheimer’s disease (AD) and primary progressive aphasias (PPA) feature changes in expressive language that appear early in the course of the disease. Within an automated analysis framework, these language changes could offer a non-invasive and inexpensive alternative to the collection of biomarkers which are not readily available in most settings. Current research is thus focused on the automated analysis of language data for clinical use. The usefulness of connected speech (CS) markers has not yet been established in a diagnostic perspective focused on biomarkers. To this aim, the present thesis contains two phases. First, we systematically review the CS markers that best differentiate persons with AD from healthy controls. Second, we automatically extract a wide array of CS markers in a heterogenous group of PPA patients by combining expert knowledge and the latest natural language processing software. A machine-learning classification approach identifies PPA patients for the presence of underlying AD pathology. The most discriminant CS features are identified. To integrate the two phases of the thesis, we provide a general introduction with the following sections: the pathophysiology of AD and PPAs, the growing importance of biomarkers in clinical decision-making for neurodegenerative diseases, the seminal studies of CS in neurodegenerative diseases, and the latest computational studies of CS markers in AD and PPA. Our results bring forth a multidimensional pattern (acoustic, lexical, syntactic, semantic, pragmatic) of language changes that distinguish people with AD from healthy controls, with an emphasis on lexical-semantic features. In the group of PPA patients with either positive or negative amyloid imaging, we then describe the classificatory power of a short sample of CS and show that it compares favorably to other biomarkers. We discuss the specific pattern of discriminant markers for this subgroup of patients, in particular the role of psycholinguistics

How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease.

Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability

Aging and the vulnerability of speech to dual task demands

This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.Tracking a digital pursuit rotor task was used to measure dual task costs of language production by young and older adults. Tracking performance by both groups was affected by dual task demands: time on target declined and tracking error increased as dual task demands increased from the baseline condition to a moderately demanding dual task condition to a more demanding dual task condition. When dual task demands were moderate, older adults' speech rate declined but their fluency, grammatical complexity, and content were unaffected. When the dual task was more demanding, older adults' speech, like young adults' speech, became highly fragmented, ungrammatical, and incoherent. Vocabulary, working memory, processing speed, and inhibition affected vulnerability to dual task costs: vocabulary provided some protection for sentence length and grammaticality, working memory conferred some protection for grammatical complexity, and processing speed provided some protection for speech rate, propositional density, coherence, and lexical diversity. Further, vocabulary and working memory capacity provided more protection for older adults than for young adults although the protective effect of processing speed was somewhat reduced for older adults as compared to the young adults. (PsycINFO Database Record (c) 2010 APA, all rights reserved

THE INFLUENCE OF TASK TYPE AND WORKING MEMORY ON THE SYNTACTIC COMPLEXITY OF NARRATIVE DISCOURSE PRODUCTION IN HEALTHY AGING ADULTS

This study investigated the lifespan influences of task type and working memory on the syntactic complexity of narrative discourse production. Participants included 180 healthy adults across three age cohorts: 20-29 years (Young Group), 60-69 years (Older Group) and 75-89 years (Elderly Group). Participants completed standardized working memory measures and four discourse tasks (single/sequential picture description, storytelling and personal recount). Syntactic complexity for each sample was measured via clausal density yielding a complexity index. For analysis, participants were placed into one of two groups based on working memory scores above (High Working Memory Group) or below (Low Working Memory Group) the mean. Significant differences in syntactic complexity between working memory groups were found for the single picture description and the storytelling; individuals in the high working memory group produced language with greater syntactic complexity. When the effects of cohort and working memory were investigated with a two-way ANOVA, working memory group was no longer significantly related to syntactic complexity. However, there was a significant relationship between cohort and syntactic complexity for the single picture description and storytelling tasks. Analyses indicate that the relationships between syntactic complexity, age, and working memory are dependent on task type

Management of Topic in the Spoken Discourse of Persons Living with Mild Cognitive Impairment and Alzheimer’s Dementia

Analyses of elicited spoken discourse can identify mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Topic management, one feature of discourse defined as acts that maintain or terminate an established topic, can be measured via global coherence measures. Little is known, however, about whether analyses of topic management can distinguish spoken discourse performances of persons living with MCI (PLwMCI) vs. persons living with AD (PLwAD). The current study investigated whether there are differences in topic management in the spoken discourse performances of PLwMCI vs. PLwAD. Analyses were conducted on 120 transcripts of spoken sequenced story picture descriptions of PLwMCI (n=83) and PLwAD (n=37). Diagnostic group performances were analyzed using average global coherence ratings. No significant group differences were found. Average global coherence ratings were not a predictor of diagnostic group membership. Findings highlight the need for further investigation of topic management in PLwMCI and PLwAD