Vocal expression of emotions discriminates dementia with Lewy bodies from Alzheimer's disease.

Unlabelled: Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), the two most common neurodegenerative dementias, both exhibit altered emotional processing. However, how vocal emotional expressions alter in and differ between DLB and AD remains uninvestigated. We collected voice data during story reading from 152 older adults comprising DLB, AD, and cognitively unimpaired (CU) groups and compared their emotional prosody in terms of valence and arousal dimensions.

Distinct eye movement patterns to complex scenes in Alzheimer's disease and Lewy body disease.

Background: Alzheimer's disease (AD) and Lewy body disease (LBD), the two most common causes of neurodegenerative dementia with similar clinical manifestations, both show impaired visual attention and altered eye movements. However, prior studies have used structured tasks or restricted stimuli, limiting the insights into how eye movements alter and differ between AD and LBD in daily life.

Objective: We aimed to comprehensively characterize eye movements of AD and LBD patients on naturalistic complex scenes with broad categories of objects, which would provide a context closer to real-world free viewing, and to identify disease-specific patterns of altered eye movements.

Screening of Mild Cognitive Impairment Through Conversations With Humanoid Robots: Exploratory Pilot Study.

Background: The rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations.

Structural Cerebral Network Differences in Prodromal Alzheimer's Disease and Prodromal Dementia with Lewy Bodies.

Introduction: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) have long prodromal phases without dementia. However, the patterns of cerebral network alteration in this early stage of the disease remain to be clarified.

Method: Participants were 48 patients with mild cognitive impairment (MCI) due to AD (MCI-AD), 18 patients with MCI with DLB (MCI with Lewy bodies: MCI-LB), and 23 healthy controls who underwent a 1.

Speech and language characteristics differentiate Alzheimer's disease and dementia with Lewy bodies.

Introduction: Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important, but it remains challenging. Different profiles of speech and language impairments between AD and DLB have been suggested, but direct comparisons have not been investigated.

Methods: We collected speech responses from 121 older adults comprising AD, DLB, and cognitively normal (CN) groups and investigated their acoustic, prosodic, and linguistic features.

Characteristics of Drawing Process Differentiate Alzheimer's Disease and Dementia with Lewy Bodies.

Background: Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been studied.

Objective: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB.

Latent subgroups with distinct patterns of factors associated with self-rated successful aging among 1,510 community-dwelling Americans: potential role of wisdom as an implicit promoter.

Objectives: To investigate whether latent subgroups with distinct patterns of factors associated with self-rated successful aging can be identified in community-dwelling adults, and how such patterns obtained from analysis of quantitative data are associated with lay perspectives on successful aging obtained from qualitative responses.

Methods: Cross-sectional data were collected from 1,510 community-dwelling Americans aged 21-99 years. Latent class regression was used to identify subgroups that explained the associations of self-rated successful aging with measures of physical, cognitive, and mental health as well as psychological measures related to resilience and wisdom.

Automated Early Detection of Alzheimer's Disease by Capturing Impairments in Multiple Cognitive Domains with Multiple Drawing Tasks.

Background: Automatic analysis of the drawing process using a digital tablet and pen has been applied to successfully detect Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most studies focused on analyzing individual drawing tasks separately, and the question of how a combination of drawing tasks could improve the detection performance thus remains unexplored.

Objective: We aimed to investigate whether analysis of the drawing process in multiple drawing tasks could capture different, complementary aspects of cognitive impairments, with a view toward combining multiple tasks to effectively improve the detection capability.

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets.

Background: With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations.

Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions.

Loneliness is a perceived state of social and emotional isolation that has been associated with a wide range of adverse health effects in older adults. Automatically assessing loneliness by passively monitoring daily behaviors could potentially contribute to early detection and intervention for mitigating loneliness. Speech data has been successfully used for inferring changes in emotional states and mental health conditions, but its association with loneliness in older adults remains unexplored.

Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing.

Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity.

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions.

Health-monitoring technologies for automatically detecting the early signs of Alzheimer's disease (AD) have become increasingly important. Speech responses to neuropsychological tasks have been used for quantifying changes resulting from AD and differentiating AD and mild cognitive impairment (MCI) from cognitively normal (CN). However, whether and how other types of speech tasks with less burden on older adults could be used for detecting early signs of AD remains unexplored.

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment.

Background: Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD.

Objective: We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI.

Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech.

Objective: The growing pandemic of loneliness has great relevance to aging populations, though assessments are limited by self-report approaches. This paper explores the use of artificial intelligence (AI) technology to evaluate interviews on loneliness, notably, employing natural language processing (NLP) to quantify sentiment and features that indicate loneliness in transcribed speech text of older adults.

Design: Participants completed semi-structured qualitative interviews regarding the experience of loneliness and a quantitative self-report scale (University of California Los Angeles or UCLA Loneliness scale) to assess loneliness.

Atypical Repetition in Daily Conversation on Different Days for Detecting Alzheimer Disease: Evaluation of Phone-Call Data From Regular Monitoring Service.

Background: Identifying signs of Alzheimer disease (AD) through longitudinal and passive monitoring techniques has become increasingly important. Previous studies have succeeded in quantifying language dysfunctions and identifying AD from speech data collected during neuropsychological tests. However, whether and how we can quantify language dysfunction in daily conversation remains unexplored.

Multimodal Behavior Analysis Towards Detecting Mild Cognitive Impairment: Preliminary Results on Gait and Speech.

Behavioral analysis for identifying changes in cognitive and physical functioning is expected to help detect dementia such as mild cognitive impairment (MCI) at an early stage. Speech and gait features have been especially recognized as behavioral biomarkers for dementia that possibly occur early in its course, including MCI. However, there are no studies investigating whether exploiting the combination of multimodal behavioral data could improve detection accuracy.

Using Tablet-Based Assessment to Characterize Speech for Individuals with Dementia and Mild Cognitive Impairment: Preliminary Results.

Early detection of dementia as well as improvement in diagnosis coverage has been increasingly important. Previous studies involved extracting speech features during neuropsychological assessments by humans, such as medical pro- fessionals, and succeeded in detecting patients with dementia and mild cognitive impairment (MCI). Enabling such assessment in an automated fashion by using computer devices would extend the range of application.

Word Repetition in Separate Conversations for Detecting Dementia:A Preliminary Evaluation on Data of Regular Monitoring Service.

For detecting early signs of dementia, monitoring technology has been actively investigated due to the low diagnostic coverage as well as the requirement for early intervention. Although language features have been used for detecting the language dysfunctions resulting from dementia in neuropsychological tests, features that can be extracted by regular conversations remain unexplored. Here, we propose a feature to characterize the atypical repetition of words on different days which is observed in patients with dementia.

Topic Repetition in Conversations on Different Days as a Sign of Dementia.

Detecting early signs of dementia in everyday situations becomes more and more important in a rapidly aging society. Language dysfunctions are recognized as the prominent signs of dementia. Previous computational studies characterized these language dysfunctions by using acoustic and linguistic features for detecting dementia.

Monitoring Daily Physical Conditions of Older Adults Using Acoustic Features: A Preliminary Result.

Health monitoring in everyday situations has become important due to the rapid aging of many societies. Speech changes have been suggested as a means of measuring an individual's state, such as emotion and stress, and screening for neurodegenerative diseases. However, how speech features are associated with daily physical conditions remains unknown.