Research on emotion recognition from facial expressions has found evidence of different muscle movements between genuine and posed smiles. To further confirm discrete movement intensities of each facial segment, we explored differences in facial expressions between spontaneous and posed smiles with three-dimensional facial landmarks. Advanced machine analysis was adopted to measure changes in the dynamics of 68 segmented facial regions. A total of 57 normal adults (19 men, 38 women) who displayed adequate posed and spontaneous facial expressions for happiness were included in the analyses. The results indicate that spontaneous smiles have higher intensities for upper face than lower face. On the other hand, posed smiles showed higher intensities in the lower part of the face. Furthermore, the 3D facial landmark technique revealed that the left eyebrow displayed stronger intensity during spontaneous smiles than the right eyebrow. These findings suggest a potential application of landmark based emotion recognition that spontaneous smiles can be distinguished from posed smiles via measuring relative intensities between the upper and lower face with a focus on left-sided asymmetry in the upper region.

Background: The global burden of Alzheimer's disease and related dementias is rapidly increasing, particularly in low- and middle-income countries where access to specialized healthcare is limited. Neuropsychological tests are essential diagnostic tools, but their administration requires trained professionals, creating screening barriers. Automated computational assessment presents a cost-effective solution for global dementia screening. Objective: To develop and validate an artificial intelligence-based screening tool using the Trail Making Test (TMT), demographic information, completion times, and drawing analysis for enhanced dementia detection. Methods: We developed: (1) non-image models using demographics and TMT completion times, (2) image-only models, and (3) fusion models. Models were trained and validated on data from the Framingham Heart Study (FHS) ( N = 1252), the Long Life Family Study (LLFS) ( N = 1613), and the combined cohort ( N = 2865). Results: Our models, integrating TMT drawings, demographics, and completion times, excelled in distinguishing dementia from normal cognition. In the LLFS cohort, we achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 98 . 62%, with sensitivity/specificity of 87 . 69%/98 . 26%. In the FHS cohort, we obtained an AUC of 96 . 51%, with sensitivity/specificity of 85 . 00%/96 . 75%. Conclusions: Our method demonstrated superior performance compared to traditional approaches using age and TMT completion time. Adding images captures subtler nuances from the TMT drawing that traditional methods miss. Integrating the TMT drawing into cognitive assessments enables effective dementia screening. Future studies could aim to expand data collection to include more diverse cohorts, particularly from less-resourced regions.

Objective: Cognitive impairment is associated with language changes that emerge during verbal responses to neuropsychological assessments that traditional scoring does not capture. The current study investigated the utility of a linguistic analysis of paragraph recall responses for detecting cognitive impairment. Methods: Digital voice recordings of logical memory (LM) were available from 598 Long Life Family Study participants with normal cognition and 112 with cognitive impairment. Linguistic polyfeature scores for immediate (PFS-IR) and delayed recall (PFS-DR) were created from weighted sums of features associated with cognitive impairment. Logistic regression models assessed the predictive value of each PFS for classifying cognitive impairment. Repeated measures models with generalized estimating equations assessed whether PFSs predict cognitive screener performance over time. Results: Twelve linguistic features and higher PFSs were associated with cognitive impairment (PFS-IR OR = 1.05; PFS-DR OR = 1.07). PFS-DR scores approximated the accuracy of traditional LM scoring (AUC-PR = 0.77 vs. 0.81, respectively). A higher PFS-DR was also associated with lower cognitive screener scores over an average of 7 years of follow-up ( β = –0.08, 95% CI [–0.11, –0.06]). Conclusion: Quantification of linguistic features from paragraph recall identified distinct linguistic markers that reflect differences in learning, retention, and self-monitoring. Summary feature scores detected cognitive impairment and predicted lower cognitive performance over time. This approach has potential implications for automated testing, recording, and scoring pipelines allowing for implementation of gold-standard neuropsychological assessments in broader clinical and research settings. Further studies are needed to validate these findings across clinical and community-based populations.

Objective: Cognitive impairment is associated with language changes that may be elicited from verbal responses during neuropsychological assessments that are not captured in traditional scoring. The current study investigated the utility of a linguistic analysis of paragraph recall responses for differentiating participants with and without cognitive impairment. Methods: Digital voice recordings of Logical Memory (LM) were available from 598 participants from the Long Life Family Study with normal cognition and 112 with cognitive impairment. Linguistic polyfeature scores for immediate (PFS-IR) and delayed recall (PFS-DR) were created from a weighted sum of features associated with cognitive impairment. Logistic regression models assessed the predictive value of each PFS and demographics for classifying cognitive impairment. Repeated measures models with Generalized Estimating Equations assessed whether PFSs predict decline on a cognitive screener. Results: Both immediate and delayed PFSs were significantly associated with cognitive status (PFS-IR β = 0.05, p< .001; PFS-DR β = 0.07, p< .001). A classifier with PFS-DR and demographics closely approximated the accuracy of the traditional LM score and demographics (AUC-PR = 0.81 vs 0.84, respectively). A higher PFS-DR was also associated with greater cognitive decline over an average of 5 years of follow-up (β = -0.08, p< .001). Conclusion: Quantification of linguistic features from paragraph recall using a linguistic PFS provides sufficient information for detecting cognitive impairment and predicting incident cognitive decline. The linguistic PFS has the potential to be integrated into automated testing, recording, and scoring pipelines allowing for the implementation of sensitive neuropsychological assessments in broader clinical and research settings.