Microlinguistic aspects of the oral narrative in patients with Alzheimer's disease

Natural Language Processing Applied to Spontaneous Recall of Famous Faces Reveals Memory Dysfunction in Temporal Lobe Epilepsy Patients

Objective and Background

Epilepsy patients rank memory problems as their most significant cognitive comorbidity. Current clinical assessments are laborious to administer and score and may not always detect subtle memory decline. The Famous Faces Task (FF) has robustly demonstrated that left temporal lobe epilepsy (LTLE) patients remember fewer names and biographical details compared to right TLE (RTLE) patients and healthy controls (HCs). We adapted the FF task to capture subjects' entire spontaneous spoken recall, then scored responses using manual and natural language processing (NLP) methods. We expected to replicate previous group level differences using spontaneous speech and semi-automated analysis.

Methods

Seventy-three (N=73) adults (28 LTLE, 18 RTLE, and 27 HCs) were included in a case-control prospective study design. Twenty FF in politics, sports, and entertainment (active 2008-2017) were shown to subjects, who were asked if they could recognize and spontaneously recall as much biographical detail as possible. We created human-generated and automatically-generated keyword dictionaries for each celebrity, based on a randomly selected training set of half of the HC transcripts. To control for speech output, we measured the speech duration, total word count and content word count for the FF task and a Cookie Theft Control Task (CTT), in which subjects were merely asked to describe a visual scene. Subjects' responses to FF and CTT tasks were recorded, transcribed, and analyzed in a blinded manner with a combination of manual and automated NLP approaches.

Results

Famous face recognition accuracy was similar between groups. LTLE patients recalled fewer biographical details compared to HCs and RTLEs using both the gold-standard human-generated dictionary (24%±12% vs. 31%±12% and 30%±12%, p=0.007) and the automated dictionary (24%±12% vs. 31%±12% and 32%±13%, p=0.007). There were no group level differences in speech duration, total word count, or content word count for either the FF and CTT to explain difference in recall performance. There was a positive, statistically significant relationship between MOCA score and FF recall performance as scored by the human-generated (ρ= .327, p= .029) and automatically-generated dictionaries (ρ= .422, p= .004) for TLE subjects, but not HCs, an effect that was driven by LTLE subjects.

Discussion

LTLE patients remember fewer details of famous people than HCs or RTLE patients, as discovered by NLP analysis of spontaneous recall. Decreased biographical memory was not due to decreased speech output and correlated with lower MOCA scores. NLP analysis of spontaneous recall can detect memory dysfunction in clinical populations in a semi-automated, objective, and sensitive manner.

Language impairments in Alzheimer´s disease: What changes can be found between mild and moderate stages of the disease?

OBJECTIVE

To investigate how language deteriorates over the Alzheimer's Disease course.

METHODS

A cross-sectional, observational study was carried out. 35 patients diagnosed with dementia due to AD using the NINCDS-ARDRA criteria and undergoing treatment for AD with a therapeutic dose of acetylcholinesterase inhibitors were assessed by the Boston Diagnostic Aphasia Examination (BDAE). The sample comprised 15 patients with mild AD (MMSE > 23, CDR = 0 or 0.5‒1.0) and 20 patients with moderate AD (MMSE = 13‒23, CDR = 2). The results for the 2 groups on all language tasks were compared.

RESULTS

A statistically significant difference was found between the mild and moderate AD groups for total score on the BDAE (95% CI 47.10‒114.08, t = 5.0, DF = 21, p = 0.000*), as well as on several tasks involving oral and writing comprehension, language oral expression and writing.

CONCLUSION

The study results showed major changes in the moderate stage. Also, the decline in language performance correlated with the worsening of dementia syndrome, independently of sociodemographic variables.

Universal and Language-Specific Connected Speech Characteristics of Bilingual Speakers With Alzheimer's Disease: Insights From Case Studies of Structurally Distinct Languages

PURPOSE

Connected speech analysis has been effectively utilized for the diagnosis and disease monitoring of individuals with Alzheimer's disease (AD). Existing research has been conducted mostly in monolingual English speakers with a noticeable lack of evidence from bilinguals and non-English speakers, particularly in non-European languages. Using a case study approach, we characterized connected speech profiles of two Bengali-English bilingual speakers with AD to determine the universal features of language impairments in both languages, identify language-specific differences between the languages, and explore language impairment characteristics of the participants with AD in relation to their bilingual language experience.

METHOD

Participants included two Bengali-English bilingual speakers with AD and a group of age-, gender-, education-, and language-matched neurologically healthy controls. Connected speech samples were collected in first language (L1; Bengali) and second language (L2; English) using a novel storytelling task (i.e., Frog, Where Are You?). These samples were analyzed using an augmented quantitative production analysis and correct information unit analyses for productivity, fluency, syntactic and morphosyntactic features, and lexical and semantic characteristics.

RESULTS

Irrespective of the language, AD impacted speech productivity (speech rate and fluency) and semantic characteristics in both languages. Unique language-specific differences were noted on syntactic measures (reduced sentence length in Bengali), lexical distribution (fewer pronouns and absence of reduplication in Bengali), and inflectional properties (no difficulties with noun or verb inflections in Bengali). Among the two participants with AD, the individual who showed lower proficiency and usage in L2 (English) demonstrated reduced syntactic complexity and morphosyntactic richness in English.

CONCLUSIONS

Evidence from these case studies suggests that language impairment features in AD are not universal across languages, particularly in comparison to impairments typically associated with language breakdowns in English. This study underscores the importance of establishing connected speech profiles in AD for non-English-speaking populations, especially for structurally different languages. This would in turn lead to the development of language-specific markers that can facilitate early detection of language deterioration and aid in improving diagnosis of AD in individuals belonging to underserved linguistically diverse populations.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25412458.

Disfluency patterns in Alzheimer's disease and frontotemporal lobar degeneration

Disfluencies may reflect various mechanisms: word-finding difficulties, planning strategies, inter-individual cognitive variability, etc. In the current paper, we examined disfluency production in patients with a behavioural variant of Frontotemporal lobar degeneration (bvFTLD), compared to patients with Alzheimer's disease (AD) and healthy older adults. We showed that bvFTLD participants have lower speech rate and produce more incomplete utterances. However, those measures were not correlated with naming and fluency tasks. On the contrary, AD participants did not differ from healthy controls on disfluency production, but discourse measures were correlated with the participants' lexical-semantic impairment. This provides evidence for different causes of disfluency in AD and FTLD, and a distinct role of each disfluency phenomenon.

Spoken Language Alterations can Predict Phenoconversion in Isolated Rapid Eye Movement Sleep Behavior Disorder: A Multicenter Study

OBJECTIVE

This study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD).

METHODS

Patients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story-tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow-up period of 5 years.

RESULTS

Of 180 patients free of parkinsonism or dementia, 156 provided follow-up information. After a mean follow-up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia-first (n = 16) and parkinsonism-first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism-first with normal cognition converters (n = 17).

INTERPRETATION

Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530-543.

Assessing the Linguistic Capacity Across Alzheimer's Disease and Its Preclinical Stages: Evidence from Narrative Macrostructure in Elderly Speakers of Greek

Background

The assessment of language deficits can be valuable in the early clinical diagnosis of neurodegenerative disorders, including Alzheimer's disease (AD).

Objective

The present study aims to explore whether language markers at the macrostructural level could assist with the placement of an individual across the dementia continuum employing production data from structured narratives.

Methods

We administered a Picture Sequence Narrative Discourse Task to 170 speakers of Greek: young healthy controls (yHC), cognitively intact healthy elders (eHC), elder participants with subjective cognitive impairment (SCI), with mild cognitive impairment (MCI), and with AD dementia at the mild/moderate stages. Structural MRIs, medical history, neurological examination, and neuropsychological/cognitive screening determined the status of each speaker to appropriately groupthem.

Results

The data analysis revealed that the Macrostructure Index, Irrelevant Info, and Narration Density markers can track cognitive decline and AD (p < 0.001; Macrostructural Index: eHC versus AD Sensitivity 93.8%, Specificity 74.4%, MCI versus AD Sensitivity 93.8%, Specificity 66.7%; Narration Density: eHC versus AD Sensitivity 90.6%, Specificity 71.8%, MCI versus AD Sensitivity 93.8%, Specificity 66.7%). Moreover, Narrative Complexity was significantly affected for subjects with AD, Irrelevant Info increased in the narrations of speakers with MCI and AD, while Narration Length did not appear to indubitably differentiate between the cognitively intact groups and the clinical ones.

Conclusions

Narrative Macrostructure Indices provide valuable information on the language profile of speakers with(out) intact cognition revealing subtle early signs of cognitive decline and AD suggesting that the inclusion of language-based assessment tools would facilitate the clinical process.

Methodologies for assessing morphosyntactic ability in people with Alzheimer's disease

BACKGROUND

The detection and description of language impairments in neurodegenerative diseases like Alzheimer's Disease (AD) play an important role in research, clinical diagnosis and intervention. Various methodological protocols have been implemented for the assessment of morphosyntactic abilities in AD; narrative discourse elicitation tasks and structured experimental tasks for production, offline and online structured experimental tasks for comprehension. Very few studies implement and compare different methodological protocols; thus, little is known about the advantages and disadvantages of each methodology.

AIMS

To discuss and compare the main behavioral methodological approaches and tasks that have been used in psycholinguistic research to assess different aspects of morphosyntactic production and comprehension in individuals with AD at the word and sentence levels.

METHODS

A narrative review was conducted through searches in the scientific databases Google Scholar, Scopus, Science Direct, MITCogNet, PubMed. Only studies written in English, that reported quantitative data and were published in peer-reviewed journals were considered with respect to their methodological protocol. Moreover, we considered studies that reported research on all stages of the disease and we included only studies that also reported results of a healthy control group. Studies that implemented standardized assessment tools were not considered in this review.

OUTCOMES & RESULTS

The main narrative discourse elicitation tasks implemented for the assessment of morphosyntactic production include interviews, picture-description and story narration, whereas the main structured experimental tasks include sentence completion, constrained sentence production, sentence repetition and naming. Morphosyntactic comprehension in AD has been assessed with the use of structured experimental tasks, both offline (sentence-picture matching, grammaticality judgment) and online (cross-modal naming,speeded sentence acceptability judgment, auditory moving window, word detection, reading). For each task we considered studies that reported results from different morphosyntactic structures and phenomena in as many different languages as possible.

CONCLUSIONS & IMPLICATIONS

Our review revealed strengths and weaknesses of these methods but also directions for future research. Narrative discourse elicitation tasks as well as structured experimental tasks have been used in a variety of languages, and have uncovered preserved morphosyntactic production but also deficits in people with AD. A combination of narrative discourse elicitation and structured production tasks for the assessment of the same morphosyntactic structure has been rarely used. Regarding comprehension, offline tasks have been implemented in various languages, whereas online tasks have been mainly used in English. Offline and online experimental paradigms have often produced contradictory results even within the same study. The discrepancy between the two paradigms has been attributed to the different working memory demands they impose to the comprehender or to the different parsing processes they tap. Strengths and shortcomings of each methodology are summarized in the paper, and comparisons between different tasks are attempted when this is possible. Thus, the paper may serve as a methodological guide for the study of morphosyntax in AD and possibly in other neurodegenerative diseases.

WHAT THIS PAPER ADDS

What is already known on this subject For the assessment of morphosyntactic abilities in AD, various methodological paradigms have been implemented: narrative discourse elicitation tasks and structured experimental tasks for production, and offline and online structured experimental tasks for comprehension. Very few studies implement and compare different methodological protocols; thus, little is known about the advantages and disadvantages of each methodology. What this paper adds to existing knowledge The paper presents an overview of methodologies that have been used to assess morphosyntactic production and comprehension of people with AD at the word and sentence levels. The paper summarizes the strengths and shortcomings of each methodology, providing both the researcher and the clinician with some directions in their endeavour of investigating language in AD. Also, the paper highlights the need for further research that will implement carefully scrutinized tasks from various experimental paradigms and will explore distinct aspects of the AD patients' morphosyntactic abilities in typologically different languages. What are the potential or actual clinical implications of this work? The paper may serve as a reference point for (psycho-)linguists who wish to study morphosyntactic abilities in AD, and for speech and language therapists who might need to apply morphosyntactic protocols to their patients in order to assess them or design appropriate therapeutic interventions for production and comprehension deficits.

Translation and Adaptation of the English Story Retell Procedure to Portuguese: A Preliminary Study

INTRODUCTION

The comprehension and production of connected language are essential for effective communication. However, few assessment and intervention programs requiring connected language have been made available in Brazilian Portuguese. One connected language sampling procedure, the Story Retell Procedure (SRP), has been widely studied in English and primarily for people with aphasia. The SRP employs 12 stories, whose individual plots are quite different and still equivalent in terms of verbal productivity measures. The first objective of this study was to present the translation and adaptation of the SRP stories into Portuguese and to determine whether the translations are similar to English stories. The second objective was to analyze a small group of healthy adults' responses to this assessment, thus observing whether the retellings of the SRP story forms in Portuguese would be similar to each other in the number of words, information units (IUs), and propositions - as demonstrated in English.

METHODS

This preliminary study translated and adapted into Portuguese, the 12 original English SRP stories. Only small cultural adaptations were made, preserving the essential content of the stories. The stories were then presented to 14 healthy adults, and the participants' retellings from each story were compared to the other stories regarding the number of words, IUs, and propositions.

RESULTS

Few differences were found in the retellings considering the variables analyzed. Particularly, the retells of Gasolina (Gas), Biblioteca (Library), Empréstimo (Loan), Sanduíche (Sandwich), Futebol (Baseball), and Multas (Ticket) were not significantly different in the three aspects investigated in this study (percentages of words, IUs, and propositions).

CONCLUSION

The SRP stories adapted to Portuguese, despite having a quite close number of words, IUs, and propositions to those in the original stories, did not result in retellings with similar number of words, IUs, or propositions across stories. Nonetheless, the parameters analyzed were not significantly different among the majority of the stories, and some were nearly identical. This study identified the SRP stories that can be equivalently used in assessment, reassessment, and possibly in the rehabilitation of patients with communication disorders. Likewise, differences must be considered when the stories are used with pathological populations.

Lexical and syntactic deficits analyzed via automated natural language processing: the new monitoring tool in multiple sclerosis

Background

Impairment of higher language functions associated with natural spontaneous speech in multiple sclerosis (MS) remains underexplored.

Objectives

We presented a fully automated method for discriminating MS patients from healthy controls based on lexical and syntactic linguistic features.

Methods

We enrolled 120 MS individuals with Expanded Disability Status Scale ranging from 1 to 6.5 and 120 age-, sex-, and education-matched healthy controls. Linguistic analysis was performed with fully automated methods based on automatic speech recognition and natural language processing techniques using eight lexical and syntactic features acquired from the spontaneous discourse. Fully automated annotations were compared with human annotations.

Results

Compared with healthy controls, lexical impairment in MS consisted of an increase in content words (p = 0.037), a decrease in function words (p = 0.007), and overuse of verbs at the expense of noun (p = 0.047), while syntactic impairment manifested as shorter utterance length (p = 0.002), and low number of coordinate clause (p < 0.001). A fully automated language analysis approach enabled discrimination between MS and controls with an area under the curve of 0.70. A significant relationship was detected between shorter utterance length and lower symbol digit modalities test score (r = 0.25, p = 0.008). Strong associations between a majority of automatically and manually computed features were observed (r > 0.88, p < 0.001).

Conclusion

Automated discourse analysis has the potential to provide an easy-to-implement and low-cost language-based biomarker of cognitive decline in MS for future clinical trials.

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach

AIM

The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages.

METHODS

The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged.

RESULTS

The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnation-related features were higher.

CONCLUSION

The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

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.

Investigating sentence processing and working memory in patients with mild Alzheimer and elderly people

INTRODUCTION

Linguistic disorders are one of the common problems in Alzheimer's disease, which in recent years has been considered as one of the key parameters in the diagnosis of Alzheimer (AD). Given that changes in sentence processing and working memory and the relationship between these two activities may be a diagnostic parameter in the early and preclinical stages of AD, the present study examines the comprehension and production of sentences and working memory in AD patients and healthy aged people.

METHODS

Twenty-five people with mild Alzheimer's and 25 healthy elderly people participated in the study. In this study, we used the digit span to evaluate working memory. Syntactic priming and sentence completion tasks in canonical and non-canonical conditions were used for evaluating sentence production. We administered sentence picture matching and cross-modal naming tasks to assess sentence comprehension.

RESULTS

The results of the present study revealed that healthy elderly people and patients with mild Alzheimer's disease have a significant difference in comprehension of relative clause sentences (P <0.05). There was no significant difference between the two groups in comprehension of simple active, simple active with noun phrase and passive sentences (P> 0.05). They had a significant difference in auditory and visual reaction time (P <0.05). Also there was a significant difference between the two groups in syntactic priming and sentence completion tasks. However, in non-canonical condition of sentence completion, the difference between the two groups was not significant (P> 0.05).

CONCLUSION

The results of the present study showed that the mean scores related to comprehension, production and working memory in people with mild Alzheimer's were lower than healthy aged people, which indicate sentence processing problems at this level of the disease. People with Alzheimer have difficulty comprehending and producing complex syntactic structures and have poorer performance in tasks that required more memory demands. It seems that the processing problems of these people are due to both working memory and language problems, which are not separate from each other and both are involved in.

The functional connectivity of language network across the life span: Disentangling the effects of typical aging from Alzheimer's disease

Language is usually characterized as the most preserved cognitive function during typical aging. Several neuroimaging studies have shown that healthy aging is characterized by inter-network compensation which correlates with better language performance. On the contrary, language deficits occur early in the course of Alzheimer's disease (AD). Therefore, this study compares young participants, healthy older participants, and prodromal AD participants, to characterize functional connectivity changes in language due to healthy aging or prodromal AD. We first compared measures of integrated local correlations (ILCs) and fractional amplitude of low-frequency oscillations (fALFFs) in language areas. We showed that both groups of older adults had lower connectivity values within frontal language-related areas. In the healthy older group, higher integrated local correlation (ILC) and fALFF values in frontal areas were positively correlated with fluency and naming tasks. We then performed seed-based analyses for more precise discrimination between healthy aging and prodromal AD. Healthy older adults showed no functional alterations at a seed-based level when the seed area was not or only slightly impaired compared to the young adults [i.e., inferior frontal gyrus (IFG)], while prodromal AD participants also showed decreased connectivity at a seed-based level. On the contrary, when the seed area was similarly impaired in healthy older adults and prodromal AD participants on ILC and fALFF measures, their connectivity maps were also similar during seed-to-voxel analyses [i.e., superior frontal gyrus (SFG)]. Current results show that functional connectivity measures at a voxel level (ILC and fALFF) are already impacted in healthy aging. These findings imply that the functional compensations observed in healthy aging depend on the functional integrity of brain areas at a voxel level.

Mental Time Travel and Time Reference Difficulties in Alzheimer's Disease: Are They Related? A Systematic Review

Mental time travel and language enable us to go back and forth in time and to organize and express our personal experiences through time reference. People with Alzheimer's disease have both mental time travel and time reference impairments, which can greatly impact their daily communication. Currently, little is known about the potential relationship between time conceptualization (i.e., mental time travel) and time reference difficulties in this disease. A systematic review of the literature was performed to determine if this link had already been investigated. Only three articles integrated both time conceptualization and time reference measures. However, the link between the two was not systematically analyzed and interpreted. This review highlights the lack of research addressing the question of the influence of time conceptualization impairments in Alzheimer's disease on other cognitive domains, and especially language.

Breaking the flow of thought: Increase of empty pauses in the connected speech of people with mild and moderate Alzheimer's disease

INTRODUCTION

The profile of spontaneous speech in Alzheimer's disease (AD) includes increased pausing as a window into cognitive decline. We here aimed to further characterize the pausing profile of AD by linking pauses to the syntactic positions in which they appear and disease progression.

METHODS

Speech was obtained through a picture description task, thus minimizing demands on episodic memory (EM), from a group of mild (N = 21) and moderate AD (N = 19), and healthy elderly controls (N = 40). Pauses were sub-indexed according to whether they occurred within-clauses, clause-initially, or utterance-initially, and whether they preceded nouns, verbs, or adjectives/adverbs, when occurring within-clauses. Additionally, relations to verbal fluency (VF) measures at the single-word level were explored.

RESULTS

Pause rate but not duration distinguished controls from both AD groups, while fillers did not distinguish any groups. The analysis by syntactic position revealed a highly differentiated picture, with largest effect sizes of significant group differences seen in the utterance-initial pause rate. The two AD groups patterned differently when compared to controls, while none of the measures differentiated the AD groups. Specifically, moderate but not mild AD differed from controls in clause-initial pauses, while mild but not moderate AD differed from controls in within-clause positions. At the within-clause level, the effect dividing controls from mild-AD was specifically driven by pauses ahead of nouns. A significant negative correlation emerged between pausing rate in spontaneous speech and VF measures in the mild-AD group only.

CONCLUSIONS

Increased empty (non-filled) pauses in AD are not confined to pauses in within-clause positions, which are most directly related to problems in the retrieval of words. Even in early disease stages, where these within-clause pause effects are seen, they are confined to nouns, revealing a grammatically specific problem possibly related to the referencing of objects. At all disease stages, pauses increase in utterance-sized units of structure, indicating progressive problems in the creative configuration of complete thoughts.

Neural bases of elements of syntax during speech production in patients with aphasia

The ability to string together words into a structured arrangement capable of conveying nuanced information is key to speech production. The assessment of the neural bases for structuring sentences has been challenged by the need of experts to delineate the aberrant morphosyntactic structures in aphasic speech. Most studies have relied on focused tasks with limited ecological validity. We characterized syntactic complexity during connected speech produced by patients with chronic post-stroke aphasia. We automated this process by employing Natural Language Processing (NLP). We conducted voxel-based and connectome-based lesion-symptom mapping to identify brain regions crucially associated with sentence production and syntactic complexity. Posterior-inferior aspects of left frontal and parietal lobes, as well as white matter tracts connecting these areas, were essential for syntactic complexity, particularly the posterior inferior frontal gyrus. These findings suggest that sentence structuring during word production depends on the integrity of Broca's area and the dorsal stream of language processing.

Connected Speech Characteristics of Bengali Speakers With Alzheimer's Disease: Evidence for Language-Specific Diagnostic Markers

Background and aim: Speech and language characteristics of connected speech provide a valuable tool for identifying, diagnosing and monitoring progression in Alzheimer's Disease (AD). Our knowledge of linguistic features of connected speech in AD is primarily derived from English speakers; very little is known regarding patterns of linguistic deficits in speakers of other languages, such as Bengali. Bengali is a highly inflected pro-drop language from the Indo-Aryan language family. It is the seventh most spoken language in the world, yet to date, no studies have investigated the profile of linguistic impairments in Bengali speakers with AD. The aim of this study was to characterize connected speech production and identify the linguistic features affected in Bengali speakers with AD. Methods: Participants were six Bengali speaking AD patients and eight matched controls from the urban metropolis, Kolkata, India. Narrative samples were elicited in Bengali using the Frog Story. Samples were analyzed using the Quantitative Production Analysis and the Correct Information Unit analyses to quantify six different aspects of speech production: speech rate, structural and syntactic measures, lexical measures, morphological and inflectional measures, semantic measures and measure of spontaneity and fluency disruptions. Results and conclusions: In line with the extant literature from English speakers, the Bengali AD participants demonstrated decreased speech rate, simplicity of sentence forms and structures, and reduced semantic content. Critically, differences with English speakers' literature emerged in the domains of Bengali specific linguistic features, such as the pro-drop nature of Bengali and its inflectional properties of nominal and verbal systems. Bengali AD participants produced fewer pronouns, which is in direct contrast with the overuse of pronouns by English AD participants. No obvious difficulty in producing nominal and verbal inflections was evident. However, differences in the type of noun inflections were evident; these were characterized by simpler inflectional features used by AD speakers. This study represents the first of its kind to characterize connected speech production in Bengali AD participants and is a significant step forward toward the development of language-specific clinical markers in AD. It also provides a framework for cross-linguistic comparisons across structurally distinct and under-explored languages.

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.

Language impairment in the moderate stage of dementia due to Alzheimer's disease

BACKGROUND

During the moderate stage of dementia due to Alzheimer's disease (AD), language disorder is more evident and it impacts on communication. An overview of language impairment could be helpful to find compensatory communication strategies for these patients.

OBJECTIVE

To identify all language impairments among patients with moderate-stage of AD.

METHODS

20 patients diagnosed with probable AD based on the criteria of the NINCDS-ARDRA, with a MMSE score of 13-23 points and CDR=2, who were undergoing treatment for AD with therapeutic doses of acetyl cholinesterase inhibitors, were assessed using the Boston Diagnostic Aphasia Examination (BDAE), a test that provides a broad assessment of language. The results were compared with the performance of a normal population.

RESULTS

The patients assessed in this study presented normal scores for oral and written word recognition, repetition, mechanics of writing, primer-level dictation and spelling to dictation but also had impairment at most levels of linguistic processing, in oral and written comprehension and production. In general, as expected, the tasks relying on access to the mental lexicon were most significantly affected. However, they performed well in the naming task, in which semantic cues were presented. Moreover, the patients assessed in this study had better performance in written comprehension tasks than in oral ones.

CONCLUSION

The severity of the language impairments was not homogenous, with some linguistic abilities more impaired than others. The abilities that were found to be preserved can help to guide strategies for aiding in communication at this stage of AD.

Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features

Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder mainly characterized by memory loss with deficits in other cognitive domains, including language, visuospatial abilities, and changes in behavior. Detecting diagnostic biomarkers that are noninvasive and cost-effective is of great value not only for clinical assessments and diagnostics but also for research purposes. Several previous studies have investigated AD diagnosis via the acoustic, lexical, syntactic, and semantic aspects of speech and language. Other studies include approaches from conversation analysis that look at more interactional aspects, showing that disfluencies such as fillers and repairs, and purely nonverbal features such as inter-speaker silence, can be key features of AD conversations. These kinds of features, if useful for diagnosis, may have many advantages: They are simple to extract and relatively language-, topic-, and task-independent. This study aims to quantify the role and contribution of these features of interaction structure in predicting whether a dialogue participant has AD. We used a subset of the Carolinas Conversation Collection dataset of patients with AD at moderate stage within the age range 60–89 and similar-aged non-AD patients with other health conditions. Our feature analysis comprised two sets: disfluency features, including indicators such as self-repairs and fillers, and interactional features, including overlaps, turn-taking behavior, and distributions of different types of silence both within patient speech and between patient and interviewer speech. Statistical analysis showed significant differences between AD and non-AD groups for several disfluency features (edit terms, verbatim repeats, and substitutions) and interactional features (lapses, gaps, attributable silences, turn switches per minute, standardized phonation time, and turn length). For the classification of AD patient conversations vs. non-AD patient conversations, we achieved 83% accuracy with disfluency features, 83% accuracy with interactional features, and an overall accuracy of 90% when combining both feature sets using support vector machine classifiers. The discriminative power of these features, perhaps combined with more conventional linguistic features, therefore shows potential for integration into noninvasive clinical assessments for AD at advanced stages.

Language Network Connectivity Increases in Early Alzheimer's Disease

Background:

Language production deficits occur early in the course of Alzheimer’s disease (AD); however, only a few studies have focused on language network’s functional connectivity in mild cognitive impairment (MCI) due to AD.

Objective:

The current study aims to uncover the extent of language alteration at the MCI stage, at a behavioral and neural level, using univariate and multivariate analyses of structural MRI and resting-state fMRI.

Methods:

Twenty-four MCI due to AD participants and 24 matched healthy controls underwent a comprehensive language evaluation, a structural T1-3D MRI, and resting-state fMRI. We performed seed-based analyses, using the left inferior frontal gyrus and left posterior temporal gyrus as seeds. Then, we analyzed connectivity between executive control networks and language network in each group. Finally, we used multivariate pattern analyses to test whether the two groups could be distinguished based on the pattern of atrophy within the language network; within the executive control networks, as well as the pattern of functional connectivity within the language network and within the executive control networks.

Results:

MCI due to AD participants had language impairment during standardized language tasks and connected-speech production. Regarding functional connectivity, univariate analyses were not able to discriminate participants, while multivariate pattern analyses could significantly predict participants’ group. Language network’s functional connectivity could discriminate MCI due to AD participants better than executive control networks. Most notably, they revealed an increased connectivity at the MCI stage, positively correlated with language performance.

Conclusion:

Multivariate analyses represent a useful tool for investigating the functional and structural (re-)organization of the neural bases of language.

The effectiveness of speech-language therapy at the discursive level: a case study of cognitive-linguistic deficit in dementia

People with dementia present cognitive, language and behavioral impairments. The language deficits can be identified in the speech of these patients in the early stages. However, there are still few studies about the effectiveness of speech-language therapy in these cases. This study aimed to describe the language manifestations observed in the speech of a patient with a linguistic-cognitive disorder and to analyze the effectiveness of speech-language therapy. A longitudinal single-case study with a patient with mild to moderate mixed dementia was carried out. The patient underwent pre- and post-speech-language therapy evaluation with the Oral comprehension and the Narrative discourse subtests of the MTL-Brazil Battery and The Dog Story test. We observed severe impairments of the oral comprehension and deficits involving the micro and macrolinguistic dimensions of the discourse, with a score of 2/10 in the oral discourse evaluation index. After the speech-language therapy, the patient achieved adequate performance in the Oral Comprehension subtests (words, sentences and text) and the Oral Narrative discourse (number of words and information units) of the MTL-Brazil Battery. In The Dog Story test, the participant obtained better performance in the discourse production regarding the items lexical change and syntactic complexity (microlinguistic dimension) and macroproposition (macrolinguistic dimension), achieving a score of 5/10 on the index. It is possible to state that the speech-language therapy was effective in the short term for this patient with linguistic-cognitive disorder and impairments in the discursive level of the language.

Intensive naming training for low-educated demented and non-demented elderly

ABSTRACT. Complaints about naming difficulties may be common in the elderly. In dementia, anomia is the most frequent symptom of language disorders. Naming training can improve lexical access and promote better quality of communication for elderly with or without dementia. Objective: To analyze naming scores, response time and the generalization of responses for naming of neurotypical and demented low-educated older adults before and after receiving a naming training program, with and without oral comprehension stimulation. Method: Twenty elderly participants, 10 with dementia and 10 neurotypical, were included after interview, screening for cognition and functionality. The naming training was based on retrieval practice and carried out in 5 sessions. Half of the group underwent exclusive naming training, while the other half received naming training associated with oral comprehension stimulation. Results: Elderly people with dementia performed better after training for scores on oral naming and comprehension of oral words, except for object manipulation. The response time for naming trained and untrained stimuli was also better for elderly people with dementia. After the intervention, neurotypical individuals performed statistically better in comprehension time and in the score in oral naming, comprehension of oral words and object manipulation, for trained and untrained words. Conclusion: Naming training, exclusive or associated with oral comprehension, using the recovery technique benefits the language performance of neurotypical and demented elderly, and provides improvements even for untrained stimuli.

Linguistic markers predict onset of Alzheimer's disease

BACKGROUND

The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis.

METHODS

To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-description task. We compared the predictive performance of linguistic variables with clinical and neuropsychological variables. The study included 703 samples from 270 participants out of which a dataset consisting of a single sample from 80 participants was held out for testing. Half of the participants in the test set developed AD symptoms before 85 years old, while the other half did not. All samples in the test set were collected during the cognitively normal period (before MCI). The mean time to diagnosis of mild AD was 7.59 years.

FINDINGS

Significant predictive power was obtained, with AUC of 0.74 and accuracy of 0.70 when using linguistic variables. The linguistic variables most relevant for predicting onset of AD have been identified in the literature as associated with cognitive decline in dementia.

INTERPRETATION

The results suggest that language performance in naturalistic probes expose subtle early signs of progression to AD in advance of clinical diagnosis of impairment.

FUNDING

Pfizer, Inc. provided funding to obtain data from the Framingham Heart Study Consortium, and to support the involvement of IBM Research in the initial phase of the study. The data used in this study was supported by Framingham Heart Study's National Heart, Lung, and Blood Institute contract (N01-HC-25195), and by grants from the National Institute on Aging grants (R01-AG016495, R01-AG008122) and the National Institute of Neurological Disorders and Stroke (R01-NS017950).

Detecting Alzheimer's Disease from Continuous Speech Using Language Models

BACKGROUND

Recently, many studies have been carried out to detect Alzheimer's disease (AD) from continuous speech by linguistic analysis and modeling. However, few of them utilize language models (LMs) to extract linguistic features and to investigate the lexical-level differences between AD and healthy speech.

OBJECTIVE

Our goals include obtaining state-of-art performance of automatic AD detection, emphasizing N-gram LMs as powerful tools for distinguishing AD patients' narratives from those of healthy controls, and discovering the differences of lexical usages between AD patients and healthy people.

METHOD

We utilize a subset of the DementiaBank corpus, including 242 control samples from 99 control participants and 256 AD samples from 169 "PossibleAD" or "ProbableAD" participants. Baseline models are built through area under curve-based feature selection and using five machine learning algorithms for comparison. Perplexity features are extracted using LMs to build enhanced detection models. Finally, the differences of lexical usages between AD patients and healthy people are investigated by a proportion test based on unigram probabilities.

RESULTS

Our baseline model obtains a detection accuracy of 80.7%. This accuracy increases to 85.4% after integrating the perplexity features derived from LMs. Further investigations show that AD patients tend to use more general, less informative, and less accurate words to describe characters and actions than healthy controls.

CONCLUSION

The perplexity features extracted by LMs can benefit the automatic AD detection from continuous speech. There exist lexical-level differences between AD and healthy speech that can be captured by statistical N-gram LMs.

Connected Speech Deficit as an Early Hallmark of CSF-defined Alzheimer's Disease and Correlation with Cerebral Hypoperfusion Pattern

BACKGROUND

Diagnosis of prodromal Alzheimer's disease (AD) still represents a hot topic and there is a growing interest for the detection of early and non-invasive biomarkers. Although progressive episodic memory impairment is the typical predominant feature of AD, communicative difficulties can be already present at the early stages of the disease.

OBJECTIVE

This study investigated the narrative discourse production deficit as a hallmark of CSFdefined prodromal AD and its correlation with cerebral hypoperfusion pattern.

METHODS

Narrative assessment with a multilevel procedure for discourse analysis was conducted on 28 subjects with Mild Cognitive Impairment (15 MCI due to AD; 13 MCI non-AD) and 28 healthy controls. The diagnostic workup included CSF AD biomarkers. Cerebral hypoperfusion pattern was identified by SPECT image processing.

RESULTS

The results showed that the discourse analysis of global coherence and lexical informativeness indexes allowed to identify MCI due to AD from MCI non-AD and healthy subjects. These findings allow to hypothesize that the loss of narrative efficacy could be a possible early clinical hallmark of Alzheimer's disease. Furthermore, a significant correlation of global coherence and lexical informativeness reduction with the SPECT hypoperfusion was found in the dorsal aspect of the anterior part of the left inferior frontal gyrus, supporting the hypothesis that this area has a significant role in communicative efficacy, and in particular, in semantic selection executive control.

CONCLUSION

This study contributes to the understanding of the neural networks for language processing and their involvement in prodromal Alzheimer's disease. It also suggests an easy and sensitive tool for clinical practice that can help identifying individuals with prodromal Alzheimer's disease.

Quantifying the Uncertainty of Parameters Measured in Spontaneous Speech of Speakers With Dementia

Purpose Corpus analyses of spontaneous language fragments of varying length provide useful insights in the language change caused by brain damage, such as caused by some forms of dementia. Sample size is an important experimental parameter to consider when designing spontaneous language analyses studies. Sample length influences the confidence levels of analyses. Machine learning approaches often favor to use as much language as available, whereas language evaluation in a clinical setting is often based on truncated samples to minimize annotation labor and to limit any discomfort for participants. This article investigates, using Bayesian estimation of machine learned models, what the ideal text length should be to minimize model uncertainty. Method We use the Stanford parser to extract linguistic variables and train a statistic model to distinguish samples by speakers with no brain damage from samples by speakers with probable Alzheimer's disease. We compare the results to previously published models that used CLAN for linguistic analysis. Results The uncertainty around six individual variables and its relation to sample length are reported. The same model with linguistic variables that is used in all three experiments can predict group membership better than a model without them. One variable (concept density) is more informative when measured using the Stanford tools than when measured using CLAN. Conclusion For our corpus of German speech, the optimal sample length is found to be around 700 words long. Longer samples do not provide more information.

A Conversational Robot for Older Adults with Alzheimer’s Disease

Amid the rising cost of Alzheimer’s disease (AD), assistive health technologies can reduce care-giving burden by aiding in assessment, monitoring, and therapy. This article presents a pilot study testing the feasibility and effect of a conversational robot in a cognitive assessment task with older adults with AD. We examine the robot interactions through dialogue and miscommunication analysis, linguistic feature analysis, and the use of a qualitative analysis, in which we report key themes that were prevalent throughout the study. While conversations were typically better with human conversation partners (being longer, with greater engagement and less misunderstanding), we found that the robot was generally well liked by participants and that it was able to capture their interest in dialogue. Miscommunication due to issues of understanding and intelligibility did not seem to deter participants from their experience. Furthermore, in automatically extracting linguistic features, we examine how non-acoustic aspects of language change across participants with varying degrees of cognitive impairment, highlighting the robot’s potential as a monitoring tool. This pilot study is an exploration of how conversational robots can be used to support individuals with AD.

Influence of age and schooling in written discourse of healthy adults

BACKGROUND

Discourse production is a very complex cognitive task that requires the integration of several linguistic cognitive skills. Socio-demographic factors such as schooling can impact on cognitive tasks. This study investigated the impact of age and schooling in some macrolinguistic and microlinguistic aspects in the written discourse of healthy adults.

METHODS

Individuals with no previous history of language, hearing, neurological, or psychiatric disorders were asked to write a story based on a figure that showed a "bank robbery." A total of 463 graphic narrative were analyzed. The schooling was stratified into the following three bands: 5 to 8 years, 9 to 11 years and 12 or more, and the age ranged from 19 to 75 years.

RESULTS

Individuals with high schooling (12 years or more) produced discourses with more information units, more coherent, and cohesive. The oldest group (60 to 75 years) needed more time to finish the written production.

CONCLUSION

The schooling influences some micro and macrolinguistic aspects in the written discourse production. A higher educational level provided a greater number of words as well as a higher number of information units, and the discourses produced are more coherent and cohesive. The age influenced only the time of discourse production.

Recognizing hotspots in Brief Eclectic Psychotherapy for PTSD by text and audio mining

Background: Identifying and addressing hotspots is a key element of imaginal exposure in Brief Eclectic Psychotherapy for PTSD (BEPP). Research shows that treatment effectiveness is associated with focusing on these hotspots and that hotspot frequency and characteristics may serve as indicators for treatment success. Objective: This study aims to develop a model to automatically recognize hotspots based on text and speech features, which might be an efficient way to track patient progress and predict treatment efficacy. Method: A multimodal supervised classification model was developed based on analog tape recordings and transcripts of imaginal exposure sessions of 10 successful and 10 non-successful treatment completers. Data mining and machine learning techniques were used to extract and select text (e.g. words and word combinations) and speech (e.g. speech rate, pauses between words) features that distinguish between 'hotspot' (N = 37) and 'non-hotspot' (N = 45) phases during exposure sessions. Results: The developed model resulted in a high training performance (mean F ₁-score of 0.76) but a low testing performance (mean F ₁-score = 0.52). This shows that the selected text and speech features could clearly distinguish between hotspots and non-hotspots in the current data set, but will probably not recognize hotspots from new input data very well. Conclusions: In order to improve the recognition of new hotspots, the described methodology should be applied to a larger, higher quality (digitally recorded) data set. As such this study should be seen mainly as a proof of concept, demonstrating the possible application and contribution of automatic text and audio analysis to therapy process research in PTSD and mental health research in general.

Narrative impairment, white matter damage and CSF biomarkers in the Alzheimer's disease spectrum

BACKGROUND

Narrative discourse (ND) refers to one's ability to verbally reproduce a sequence of temporally and logically-linked events. Impairments in ND may occur in subjects with Amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD), but correlates across this function, neuroimaging and cerebrospinal fluid (CSF) AD biomarkers remain understudied.

OBJECTIVES

We sought to measure correlates among ND, Diffusion Tensor Imaging (DTI) indexes and AD CSF biomarkers in patients within the AD spectrum.

RESULTS

Groups differed in narrative production (NProd) and comprehension. aMCI and AD presented poorer inference abilities than controls. AD subjects were more impaired than controls and aMCI regarding WB (p<0.01). ROIs DTI assessment distinguished the three groups. Mean Diffusivity (MD) in the uncinate, bilateral parahippocampal cingulate and left inferior occipitofrontal fasciculi negatively correlated with NProd. Changes in specific tracts correlated with T-tau/Aβ1-42 ratio in CSF.

CONCLUSIONS

AD and aMCI patients presented more ND impairments than controls. Those findings were associated with changes in ventral language-associated and in the inferior parahippocampal pathways. The latest were correlated with biomarkers' levels in the CSF.

METHODS

AD (N=14), aMCI (N=31) and Control (N=39) groups were compared for whole brain (WB) and regions of interest (ROI) DTI parameters, ND and AD CSF biomarkers.

Evaluation of macrolinguistic aspects of the oral discourse in patients with Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is a degenerative syndrome that impairs cognitive functioning, including speech and language. Discourse can be used to analyze language processing, which is organized into microlinguistic and macrolinguistic dimensions.

OBJECTIVES

To identify the occurrence of changes in the macrolinguistic dimension of oral discourse in AD patients. Design: This was developed as a cross-sectional study. Setting: Outpatient clinic of the Behavioural Neurology Division of São Paulo Federal University.

PARTICIPANTS

121 elderly patients, with ≥ 4 years of education, divided into AD and comparison groups.

MEASUREMENTS

The subjects were asked to create a narrative based on seven figures that made up a story. The macrolinguistic aspects of the narratives were analyzed.

RESULTS

The performance of the AD group was inferior to that of the comparison group on content-related, no-content-related complete and incomplete propositions as well as macropropositions, main information units, appropriated local and global coherence, cohesive devices and all subtypes, cohesive errors and some of their subtypes. Global coherence, macropropositions and ellipsis subtype of cohesive devices were the variables that best differentiated the groups.

CONCLUSIONS

Changes were observed in most aspects of the macrolinguistic dimension of oral discourse in patients with AD.

Study protocol: Language profile in mild cognitive impairment: A prospective study

Background: The present study will be a longitudinal investigation of language abilities in individuals with mild cognitive impairment (MCI). The research question will include whether there will be an evidence for language impairment in individuals with MCI, and if so, what aspects of language will be the most affected and whether language abilities will be significantly changed over a 12-month period.

Methods: We will diagnose 30 individuals with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and controlled participants using Montreal Cognitive Assessment-Basic (MoCA-B), as a cognitive test, and by asking expert opinions and conducting interviews. Participants will be selected from memory clinics and nursing homes in Tehran during 2018-2019. A comprehensive language test (Barnes Language Assessment (BLA)) will be performed to obtain baseline performance in the elderly. These tests will be repeated after 3, 6, and 12 months. Repeated measures analysis of variance (ANOVA) will be used to determine whether there will be a significant change in participants' language abilities over a 12-month period. In the case of deficient language performance, a discriminant function analysis will be used to identify the language task type that will be highly sensitive to change.

Results and Conclusion: The present study will provide evidence for the nature of language change and will be done in a-year course on individuals with MCI and AD and on healthy elders. Also, in this study, the relative sensitivity of various language components to MCI will be determined, and the relationship between language performance and performance on (MoCA-B) neuropsychological test will be examined.

Connected Speech Features from Picture Description in Alzheimer's Disease: A Systematic Review

The language changes that occur over the course of Alzheimer's disease (AD) can impact communication abilities and have profound functional consequences. Picture description tasks can be used to approximate everyday communication abilities of AD patients. As various methods and variables have been studied over the years, current knowledge about the most affected features of AD discourse in the context of picture descriptions is difficult to summarize. This systematic review aims to provide researchers with an overview of the most common areas of impairment in AD discourse as they appear in picture description tasks. Based on the 44 articles fulfilling inclusion criteria, our findings reflect a multidimensional pattern of changes in the production (speech rate), syntactic (length of utterance), lexical (word-frequency and use of pronouns), fluency (repetitions and word-finding difficulties), semantic (information units), and discourse (efficiency) domains. We discuss our findings in the light of current research and point to potential scientific and clinical uses of picture description tasks in the context of AD.

Discourse macrolinguistic impairment as a marker of linguistic and extralinguistic functions decline in early Alzheimer's disease

BACKGROUND

Alzheimer's disease is characterized by macrolinguistic changes. This decline is often analyzed with quantitative scales.

AIMS

To analyze discourse production in early Alzheimer's disease (AD) and to identify qualitative markers of macrolinguistic decline.

METHODS & PROCEDURES

We analyzed macrolinguistic features of a clinical narrative task along with patients' cognitive changes. To do so, 17 early AD participants and 17 healthy controls were recruited and given a full neuropsychological and language assessment. Narrative discourses produced during the language assessment were transcribed and macrolinguistic features were qualitatively analyzed (i.e., local and global coherence marks and discourse informativeness). Inter-group comparison was complemented by intra-group correlation. As some inter-group comparisons revealed the existence of subgroups of patients, permutation tests were used to investigate how these subgroups differed vis-à-vis cognitive measures.

OUTCOMES & RESULTS

Overall, the results indicate that AD participants presented declines in informativeness and global coherence, correlated with declines in memory and executive functions. Permutation tests showed that participants with AD producing referential errors or misinterpretations had a deeper lexical-executive decline and a lower Mini-Mental State Evaluation (MMSE).

CONCLUSIONS & IMPLICATIONS

This study shows that two clinically relevant, qualitative signs differ in discourse production between typical ageing and early AD, namely information units and modalizing discourse. It also shows that macrolinguistic assessment is a useful tool for revealing impaired communication and cognition in early AD. Although lexical processing decline probably contributes to patients' macrolinguistic impairment, implications of extralinguistic functioning should be further investigated.

Narrative discourse in Persian-speaking patients with mild Alzheimer's disease

Despite the significance of discourse impairments, they have not been thoroughly investigated in Persian-speaking patients with Alzheimer's disease (AD).

OBJECTIVE

the aim of this study was to perform a multi-level analysis of narrative discourse in Persian-speaking patients with mild AD and to compare them with healthy elderly.

METHODS

the study included 14 older adults with mild AD and a matched group of 14 healthy elderly. Using a storytelling task based on serial pictures, both macro- and micro-linguistic aspects of narrative discourse were assessed. Cohesion ratio and coherence were investigated as macrolinguistic dimensions of discourse. The studied microlinguistic features included syntactic complexity and verbal errors (mostly involving phonological and semantic paraphasias and mazes). Severity of AD was determined using the Cognitive Dementia Rating (CDR).

RESULTS

there were significant differences between the groups regarding cohesion ratio (0.9 ± 0.34 vs. 1.29 ± 0.45, p = 0.02) and coherence scores (2.43 ± 0.41 vs. 3.02 ± 0.81, p = 0.03). Verbal errors and syntactic complexity did not differ significantly between the groups.

CONCLUSION

Persian-speaking patients with mild AD show macrolinguistic impairments in producing discourses based on picture description. Therefore, intervention protocols should focus on the ability to organize information on a specific subject and also to connect sentences produced using appropriate cohesive ties.

Talk2Me: Automated linguistic data collection for personal assessment

Language is one the earliest capacities affected by cognitive change. To monitor that change longitudinally, we have developed a web portal for remote linguistic data acquisition, called Talk2Me, consisting of a variety of tasks. In order to facilitate research in different aspects of language, we provide baselines including the relations between different scoring functions within and across tasks. These data can be used to augment studies that require a normative model; for example, we provide baseline classification results in identifying dementia. These data are released publicly along with a comprehensive open-source package for extracting approximately two thousand lexico-syntactic, acoustic, and semantic features. This package can be applied arbitrarily to studies that include linguistic data. To our knowledge, this is the most comprehensive publicly available software for extracting linguistic features. The software includes scoring functions for different tasks.

What happens when nothing happens? An investigation of pauses as a compensatory mechanism in early Alzheimer's disease

Lexical-semantic impairment is one of the earliest symptoms of Alzheimer's disease (AD) and is usually examined by single word processing tasks. During speech production, pauses are often investigated as a hallmark of a patient's lexical-semantic decline. In the current study, we put forward the hypothesis that pauses reflect different processes according to the type of discourse. We believe that lexical and semantic impairment would predict a patient's pause frequency in a picture-based narrative (PBN) while anterograde memory would predict a patient's pause frequency in a memory-based narrative (MBN). To demonstrate this, we recruited 17 early AD patients and 17 matched controls. They underwent a full neuropsychological and language assessment and two narrative production assessments. We compared pause duration and frequency in the AD participants' and healthy controls' PBN and MBN. A multiple regression model was used in each narrative and in each group individually to assess the relationship between cognitive processes and pause frequency. Our results show that participants with AD produced more pauses in the PBN only. The frequency was predicted by semantic fluency performance with which it was positively correlated, contrary to what was expected. In the MBN, pause frequency in the AD participants was positively correlated with and predicted by their memory performance. We then examined the neuroanatomical correlates of pause frequency in the AD participants. Considering the PBN, pause frequency was also positively correlated with the grey matter density of the anterior temporal lobe. These findings suggest that patients use pauses as compensatory mechanisms in the earliest stages of AD. Pauses therefore may reflect the time required for the compensation and the realisation of a weak process depending on the narrative task and should be considered as a positive sign.

The Latent Structure and Test-Retest Stability of Connected Language Measures in the Wisconsin Registry for Alzheimer's Prevention (WRAP)

INTRODUCTION

While it is well known that discourse-related language functions are impaired in the dementia phase of Alzheimer's Disease (AD), the presymptomatic temporal course of discourse dysfunction are not known earlier in the course of AD. To conduct discourse-related studies in this phase of AD, validated psychometric instruments are needed. This study investigates the latent structure, validity, and test-retest stability of discourse measures in a late-middle-aged normative group who are relatively free from sporadic AD risk factors.

METHODS

Using a normative sample of 399 participants (mean age = 61), exploratory factor analyses (EFA) and confirmatory factor analyses (CFA) were conducted on 18 measures of connected language derived from picture descriptions. Factor invariance across sex and family history and longitudinal test-retest stability measures were calculated.

RESULTS

The EFA revealed a four-factor solution, consisting of semantic, syntax, fluency, and lexical constructs. The CFA model substantiated the structure, and factors were invariant across sex and parental history of AD status. Test-retest stability measures were within acceptable ranges.

CONCLUSIONS

Results confirm a factor structure that is invariant across sex and parental AD history. The factor structure could be useful in similar cohorts designed to detect early language decline in investigations of preclinical or clinical AD or as outcome measures in clinical prevention trials.

Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks

While people with mild cognitive impairment (MCI) portray noticeably incipient memory difficulty in remembering events and situations along with problems in decision making, planning, and finding their way in familiar environments, detailed neuropsychological assessments also indicate deficits in language performance. To this day, there is no cure for dementia but early-stage treatment can delay the progression of MCI; thus, the development of valid tools for identifying early cognitive changes is of great importance. In this study, we provide an automated machine learning method, using Deep Neural Network Architectures, that aims to identify MCI. Speech materials were obtained using a reading task during evaluation sessions, as part of the Gothenburg MCI research study. Measures of vowel duration, vowel formants (F1 to F5), and fundamental frequency were calculated from speech signals. To learn the acoustic characteristics associated with MCI vs. healthy controls, we have trained and evaluated ten Deep Neural Network Architectures and measured how accurately they can diagnose participants that are unknown to the model. We evaluated the models using two evaluation tasks: a 5-fold crossvalidation and by splitting the data into 90% training and 10% evaluation set. The findings suggest first, that the acoustic features provide significant information for the identification of MCI; second, the best Deep Neural Network Architectures can classify MCI and healthy controls with high classification accuracy (M = 83%); and third, the model has the potential to offer higher accuracy than 84% if trained with more data (cf., SD≈15%). The Deep Neural Network Architecture proposed here constitutes a method that contributes to the early diagnosis of cognitive decline, quantify the progression of the condition, and enable suitable therapeutics.

A computerized test for the assessment of mild cognitive impairment subtypes in sentence processing

This study examines thesentence processing ability of mild cognitive impairment (MCI) subtypes. In addition to standard MCI neuropsychological tests, an experimental approach was applied to assess language. 133 people (93 MCI/40 controls) participated in novel computerized sentence processing tasks. Results presented statistically significant differences between MCI/controls andMCI subtypes (ANOVA):(a) duration F(2,92) = 19.259,p < .001) in sentence construction; (b) correct answers (F(2, 89) = 8.560,p < .001) and duration (F2,89) = 15.525,p < .001)in text comprehension; (c) correct answers (F(2, 92) = 8.975,p < .001) andduration (F(2, 92) = 4.360,p = .016) in metaphoric sentences comprehension; (d) correct answers (F(2, 92) = 12.836,p < .001) andduration (F(2, 92) = 10.974,p < .001) in verb form generation. Subtle changes in MCIsubtypes could affect sentence processing and provide useful information for cognitive decline risk estimation and screening purposes.

Language and communication non-pharmacological interventions in patients with Alzheimer's disease: a systematic review. Communication intervention in Alzheimer

Alzheimer's disease considerably compromises communication skills. Language changes become more prominent as the disease progresses. Deterioration of language and cognition reduces the ability of holding conversations, which has a negative impact on social interaction.

Connected Speech in Neurodegenerative Language Disorders: A Review

Language assessment has a crucial role in the clinical diagnosis of several neurodegenerative diseases. The analysis of extended speech production is a precious source of information encompassing the phonetic, phonological, lexico-semantic, morpho-syntactic, and pragmatic levels of language organization. The knowledge about the distinctive linguistic variables identifying language deficits associated to different neurodegenerative diseases has progressively improved in the last years. However, the heterogeneity of such variables and of the way they are measured and classified limits any generalization and makes the comparison among studies difficult. Here we present an exhaustive review of the studies focusing on the linguistic variables derived from the analysis of connected speech samples, with the aim of characterizing the language disorders of the most prevalent neurodegenerative diseases, including primary progressive aphasia, Alzheimer's disease, movement disorders, and amyotrophic lateral sclerosis. A total of 61 studies have been included, considering only those reporting group analysis and comparisons with a group of healthy persons. This review first analyzes the differences in the tasks used to elicit connected speech, namely picture description, story narration, and interview, considering the possible different contributions to the assessment of different linguistic domains. This is followed by an analysis of the terminologies and of the methods of measurements of the variables, indicating the need for harmonization and standardization. The final section reviews the linguistic domains affected by each different neurodegenerative disease, indicating the variables most consistently impaired at each level and suggesting the key variables helping in the differential diagnosis among diseases. While a large amount of valuable information is already available, the review highlights the need of further work, including the development of automated methods, to take advantage of the richness of connected speech analysis for both research and clinical purposes.

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BACKGROUND

The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.

RESULTS

Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).

CONCLUSIONS

Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.

Linguistic Features Identify Alzheimer's Disease in Narrative Speech

BACKGROUND

Although memory impairment is the main symptom of Alzheimer's disease (AD), language impairment can be an important marker. Relatively few studies of language in AD quantify the impairments in connected speech using computational techniques.

OBJECTIVE

We aim to demonstrate state-of-the-art accuracy in automatically identifying Alzheimer's disease from short narrative samples elicited with a picture description task, and to uncover the salient linguistic factors with a statistical factor analysis.

METHODS

Data are derived from the DementiaBank corpus, from which 167 patients diagnosed with "possible" or "probable" AD provide 240 narrative samples, and 97 controls provide an additional 233. We compute a number of linguistic variables from the transcripts, and acoustic variables from the associated audio files, and use these variables to train a machine learning classifier to distinguish between participants with AD and healthy controls. To examine the degree of heterogeneity of linguistic impairments in AD, we follow an exploratory factor analysis on these measures of speech and language with an oblique promax rotation, and provide interpretation for the resulting factors.

RESULTS

We obtain state-of-the-art classification accuracies of over 81% in distinguishing individuals with AD from those without based on short samples of their language on a picture description task. Four clear factors emerge: semantic impairment, acoustic abnormality, syntactic impairment, and information impairment.

CONCLUSION

Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD.

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

INTRODUCTION

The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient.

OBJECTIVE

An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection. Some useful tools and specific issues towards this goal will also have to be reviewed.

METHODS

An extensive literature review was performed following a specific search strategy, inclusion criteria, data extraction and quality assessment in the Inspec, Compendex and PubMed databases.

RESULTS

This work reviews the extensive list of psychological, physiological, behavioural and cognitive measurements that could be used for AD detection. The most promising measurements seem to be magnetic resonance imaging (MRI) for AD vs control (CTL) discrimination with an 98.95% accuracy, while electroencephalogram (EEG) shows the best results for mild cognitive impairment (MCI) vs CTL (97.88%) and MCI vs AD distinction (94.05%). Available physiological and behavioural AD datasets are listed, as well as medical imaging analysis steps and neuroimaging processing toolboxes. Some issues such as "label noise" and multi-site data are discussed.

CONCLUSIONS

The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible. Such a multimodal system might probably be based on physiological monitoring of MRI or EEG, as well as behavioural measurements like the ones proposed along the article. The mentioned AD datasets and image processing toolboxes are available for their use towards this goal. Issues like "label noise" and multi-site neuroimaging incompatibilities may also have to be overcome, but methods for this purpose are already available.

Microlinguistic processes that contribute to the ability to relay main events: influence of age

The purpose of the current study was to determine the microlinguistic processes that contribute to picture description in healthy adults across the life span. Two-hundred forty healthy adults were separated into three groups, young (n = 80; 20-39), middle (n = 80; 40-69), and older (n = 80; 70-89). Participants provided language samples in response to two single and two sequential pictures analyzed for total number of words, informativeness, lexical diversity, syntactic complexity, and main events. The older group produced a significantly lower proportion of main events for the single and sequential pictures compared to the other groups. Group differences on the microlinguistic measures varied depending on the measure and the stimulus type. Further, regardless of task, total number of words significantly related to main event production for the young and middle aged groups, but not the older group. Results of the current study extend previous findings by researchers who have investigated discourse production in cognitively healthy, older adults. Using a multi-level approach, we found that linguistic processes across different levels interact; however, the relationship is age-dependent. By including a middle-aged group we identify the potential course of documented change and our results indicate that the changes in language processes with age may not be linear.