Audio and linguistic prediction of objective and subjective cognition in older adults: what is the role of different prompts?

Background: Psycho-linguistic and audio data derived from speech may be useful in screening and monitoring cognitive aging. However, there are gaps in understanding the predictive value of different prompts (e.g.

Dynamic influence of mood on subjective cognitive complaints in mild cognitive impairment: A time series network analysis approach.

Objectives: Subjective cognitive complaints (SCC) are common and clinically relevant in mild cognitive impairment (MCI) but are intertwined with mood states. Using Ecological Momentary Assessment (EMA) of SCC and network analyses we sought to uncover the links between mood and SCC and how these links may vary by the presence or absence of MCI.

Design: We used EMA to collect intensive longitudinal data.

Editorial for the special issue on "microbiomes in extremes of aging".

This special issue of the Journal of Experimental Gerontology explores the dynamic interplay between microbiomes and aging-related conditions. The four selected studies highlight the role of microbiota in Alzheimer's disease, cancer immunotherapy, myocardial infarction and tryptophan metabolism, providing insights into how microbiomes influence health and disease in aging. These studies underscore the potential for microbiome-targeted interventions to mitigate aging-related disorders and improve the quality of life for older adults.

Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses.

Trauma is very common and associated with significant co-morbidity world-wide, particularly PTSD and frequently other mental health disorders. However, it can be challenging to identify victims of abuse as self-reports can be difficult to elicit due to emotional distress. Better confirmation of a history of significant mistreatment can assist significantly in treatment planning.

Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study.

Background: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied.

Decoding loneliness: Can explainable AI help in understanding language differences in lonely older adults?

Study Objectives: Loneliness impacts the health of many older adults, yet effective and targeted interventions are lacking. Compared to surveys, speech data can capture the personalized experience of loneliness. In this proof-of-concept study, we used Natural Language Processing to extract novel linguistic features and AI approaches to identify linguistic features that distinguish lonely adults from non-lonely adults.

Comparisons of Self-Report With Objective Measurements Suggest Faster Responding but Little Change in Response Quality Over Time in Ecological Momentary Assessment Studies.

Response times (RTs) to ecological momentary assessment (EMA) items often decrease after repeated EMA administration, but whether this is accompanied by lower response quality requires investigation. We examined the relationship between EMA item RTs and EMA response quality. In one data set, declining response quality was operationalized as decreasing correspondence over time between subjective and objective measures of blood glucose taken at the same time.

A meta-cognitive Wisconsin Card Sorting Test in people with schizophrenia and bipolar disorder: Self-assessment of sorting performance.

People with serious mental illness have challenged self-awareness, including momentary monitoring of performance. A core feature of this challenge is in the domain of using external information to guide behavior, an ability that is measured very well by certain problem-solving tasks such as the Wisconsin Card Sorting Test (WCST) . We used a modified WCST to examine correct sorts and accuracy decisions regarding the correctness of sort.

Dynamics of task-based confidence in schizophrenia using seasonal decomposition approach.

Objective: Introspective Accuracy (IA) is a metacognitive construct that refers to alignment of self-generated accuracy judgments, confidence, and objective information regarding performance. IA not only refers to accuracy and confidence during tasks, but also predicts functional outcomes. The consistency and magnitude of IA deficits suggest a sustained disconnect between self-assessments and actual performance.

Childhood trauma and real-world social experiences in psychosis.

Background: Childhood trauma is associated with a variety of negative outcomes in psychosis, but it is unclear clear if childhood trauma affects day-to-day social experiences. We aimed to examine the association between childhood trauma and functional and structural characteristics of real-world social relationships in psychosis.

Methods: Participants with psychotic disorders or affective disorders with psychosis completed ecological momentary assessments (EMAs) over ten days (N = 209).

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.

Confidence, accuracy judgments and feedback in schizophrenia and bipolar disorder: a time series network analysis.

Background: Inaccurate self-assessment of performance is common among people with serious mental illness, and it is associated with poor functional outcomes independent from ability. However, the temporal interdependencies between judgments of performance, confidence in accuracy, and feedback about performance are not well understood.

Methods: We evaluated two tasks: the Wisconsin Card Sorting Test (WCST) and the Penn Emotion recognition task (ER40).

Dynamics of Loneliness Among Older Adults During the COVID-19 Pandemic: Pilot Study of Ecological Momentary Assessment With Network Analysis.

Objective: The COVID-19 pandemic has had potentially severe psychological implications for older adults, including those in retirement communities, due to restricted social interactions, but the day-to-day experience of loneliness has received limited study. We sought to investigate sequential association, if any, between loneliness, activity, and affect.

Methods: We used ecological momentary assessment (EMA) with dynamic network analysis to investigate the affective and behavioral concomitants of loneliness in 22 residents of an independent living sector of a continuing care retirement community (mean age 80.

Dynamic contextual influences on social motivation and behavior in schizophrenia: a case-control network analysis.

Contextual influences on social behavior and affective dynamics are not well understood in schizophrenia. We examined the role of social context on emotions, and the motivation to interact in the future, using dynamic network analysis of ecological momentary assessment (EMA) data. Participants included 105 outpatients with schizophrenia or schizoaffective disorder (SZ) and 76 healthy comparators (HC) who completed 7 days, 7 times a day of EMA.

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.

Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia.

People with schizophrenia (SZ) process emotions less accurately than do healthy comparators (HC), and emotion recognition have expanded beyond accuracy to performance variables like reaction time (RT) and confidence. These domains are typically evaluated independently, but complex inter-relationships can be evaluated through machine learning at an item-by-item level. Using a mix of ranking and machine learning tools, we investigated item-by-item discrimination of facial affect with two emotion recognition tests (BLERT and ER-40) between SZ and HC.

The Gut Microbiome, Aging, and Longevity: A Systematic Review.

Aging is determined by complex interactions among genetic and environmental factors. Increasing evidence suggests that the gut microbiome lies at the core of many age-associated changes, including immune system dysregulation and susceptibility to diseases. The gut microbiota undergoes extensive changes across the lifespan, and age-related processes may influence the gut microbiota and its related metabolic alterations.

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.

Text mining for modeling of protein complexes enhanced by machine learning.

Motivation: Procedures for structural modeling of protein-protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of the complex, such as knowledge of amino acids essential for the protein interaction. Previously, we showed that text mining of residues in freely available PubMed abstracts of papers on studies of protein-protein interactions may generate such constraints.

Challenges in the construction of knowledge bases for human microbiome-disease associations.

The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome.

Natural language processing in text mining for structural modeling of protein complexes.

Background: Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking.