Self- and Informant-Reported Loneliness and Cognitive Function: Evidence From Three HCAP Studies

Loneliness is defined as a distressing feeling that arises from the discrepancy between one’s desired and perceived social relationships. Theorists have posited that loneliness involves affective, cognitive, and behavioral components that can be noticed by close family or friends. Little is known about whether social withdrawal, a behavioral marker of loneliness observed by knowledgeable others, shares similar associations with cognition as self-reported loneliness. The present study examined whether self-reported and informant-rated loneliness are related to cognitive function in older adulthood. Data come from Harmonized Cognitive Assessment Protocol subsample included in three national aging studies from the US (HRS; N = 2,821, Mage = 75.8), England (ELSA; N = 896, Mage = 73.6), and India (LACI-DAD; N = 2,994, Mage = 69.3). Respondents reported on 1-item loneliness and informants rated the respondent’s withdrawal behavior. Regression models were used to analyze the association between both measures of loneliness and cognition, controlling for respondent (age, gender, education, race/ethnicity) and informant (age, gender, education, spouse, known years, frequency of contact) covariates. Meta-analysis showed that both respondent-reported and informant-rated loneliness were independently associated with lower cognitive functioning (memory, speed–attention–executive, visuospatial ability, and fluency) and global cognition (MMSE). The associations between observed withdrawal and cognition did not vary by informant-level characteristics nor the respondent’s cognitive impairment status. The present study indicates that withdrawal behavior observed by informants is associated with cognitive function, supporting psychosocial observations provided by knowledgeable others can be utilized in detecting cognitive function.

Developing Behavioral Markers of Social Function in Aging: A Methodology Using Pitch Mimicry

The Covid pandemic brought to the forefront the crucial role of social interactions for society at large and in gerontological practice. Social interactions play a paramount role in preserving cognitive reserve in older adults. They rely on neurobehavioral processes that are complex (engage large parts of the brain and demand integrity of multiple perceptuomotor, attentional, cognitive and memory functions). Pitch mimicry is a well-known and spontaneously arising social phenomenon that requires the integrity of numerous processes of the brain, and we hypothesize that it constitutes a potentially sensitive behavioral marker of neurodegeneration in Alzheimer’s Disease and Related Dementias (ADRD). We developed and validated a series of algorithms to parse verbal exchanges between people and quantify the level of mimicry that each exhibit with their partners. Those algorithms are based on silence thresholding, carefully parametrized CEPSTRAL algorithms for automatic pitch estimation and Synchrosqueezing Transform for validation. We introduce a theoretical model to compare our estimates of pitch mimicry with model’s expectations based on the null hypothesis that its neurobehavioral pathways retain their integrity. Our method will allow researchers to study the evolution of pitch mimicry in aging individuals and its sensitivity to diverse social contexts, including those preserving lasting social engagement. Our method will also allow us to test the hypothesis that Pitch Mimicry is a sensitive behavioral marker of dementia, a condition characterized by a breakdown in social relatedness.

Case Conceptualization Basics and Assessment

This chapter defines the behavioral markers that are included in a pattern-focused case conceptualization. Pattern-focused case conceptualization is a therapeutic strategy that can help clinicians conceptualize client issues, tailoring highly effective treatment, as well as guide them through moment-to-moment decisions made during a session. The chapter reviews the following behavioral markers that can be incorporated into both brief case conceptualizations and full-scale case conceptualizations: pattern, presentation, precipitant, predisposition, protective factors, perpetuants, personality–culture, plan, and predictive factors. The chapter defines each behavioral marker and also provides examples of each. Then, it reviews case conceptualization-informed assessment and also includes a detailed assessment that clinicians can use to inform their clinical evaluation.

Spatial proximity as a behavioral marker of relationship dynamics in older adult couples

Background: Close relationships in older adulthood are characterized by heightened interdependence, which has implications for health and well-being as partners age together. Purpose: We describe a novel method that uses partners’ spatial proximity to examine the dynamics of interpersonal relationships. Research Design: In a sample of 10 older adult couples over a 14-day study period, we linked a continuous measure of partners’ spatial proximity with partners’ heart rates—a physiological marker of arousal. Results: Cross-correlations showed that proximity was consistently associated with each partner’s heart rate, but the magnitude and sequence of the correlation varied from day-to-day, suggesting that the coupling of proximity and heart rate is a dynamic of the interaction, rather than the couple. Additionally, our predictive model showed that all three time-series were necessary for optimal prediction, demonstrating that proximity and partners’ heart rates are dynamically intertwined. Conclusion: Together, these results demonstrate meaningful and predictable variation in couple dynamics at the momentary level that consists of a complex association between physiological and spatial proximity.

Variable rather than extreme slow reaction times distinguish brain states during sustained attention

A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that ‘suboptimal’ is different from ‘slow’ at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.

A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors

Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the ability to readily distinguish VTEs from non-VTEs. Here we utilize a small set of trajectory-based features and standard machine learning classifiers to identify VTEs from non-VTEs for rats performing a spatial delayed alternation task (SDA) on an elevated plus maze. We also show that previously reported features of the hippocampal field potential oscillation can be used in the same types of classifiers to separate VTEs from non-VTEs with above chance performance. However, we caution that the modest classifier success using hippocampal population dynamics does not identify many trials where VTEs occur, and show that combining oscillation-based features with trajectory-based features does not improve classifier performance compared to trajectory-based features alone. Overall, we propose a standard set of features useful for trajectory-based VTE classification in binary decision tasks, and support previous suggestions that VTEs are supported by a network including, but likely extending beyond, the hippocampus.

A machine learning approach for detecting vicarious trial and error behaviors

Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the ability to readily distinguish VTEs from non-VTEs. Here we utilize a small set of trajectory-based features and standard machine learning classifiers to identify VTEs from non-VTEs for rats performing a spatial delayed alternation task (SDA) on an elevated plus maze. We also show that previously reported features of the hippocampal field potential oscillation can be used in the same types of classifiers to separate VTEs from non-VTEs with above chance performance. However, we caution that the modest classifier success using hippocampal population dynamics is not sufficient for identifying trials where VTEs occur, and show that combining oscillation-based features with trajectory-based features degrades classifier performance compared to trajectory-based features alone. Overall, we propose a standard set of features useful for trajectory-based VTE classification and support previous suggestions that VTEs are supported by a network including, but likely extending beyond, the hippocampus.