Open-ended movements structure sensorimotor information in early human development.

Human behaviors, with whole-body coordination, involve large-scale sensorimotor interaction. Spontaneous bodily movements in the early developmental stage potentially lead toward acquisition of such coordinated behavior. These movements presumably contribute to the structuration of sensorimotor interaction, providing specific regularities in bidirectional information among muscle activities and proprioception. Whether and how spontaneous movements, despite being task-free, structure and organize sensorimotor interactions in the entire body during early development remain unknown. Herein, to address these issues, we gained insights into the structuration process of the sensorimotor interaction in neonates and 3-mo-old infants. By combining detailed motion capture and musculoskeletal simulation, sensorimotor information flows among muscle activities and proprioception throughout the body were obtained. Subsequently, we extracted spatial modules and temporal state in sensorimotor information flows. Our approach demonstrated that early spontaneous movements elicited body-dependent sensorimotor modules, revealing age-related changes in them, depending on the combination or direction. The sensorimotor interactions also displayed temporal non-random fluctuations analogous to those seen in spontaneous activities in the cerebral cortex and spinal cord. Furthermore, we found recurring state sequence patterns across multiple participants, characterized by a substantial increase in infants compared to the patterns in neonates. Therefore, early spontaneous movements induce the spatiotemporal structuration in sensorimotor interactions and subsequent developmental changes. These results implicated that early open-ended movements, emerging from a certain neural substrate, regulate the sensorimotor interactions through embodiment and contribute to subsequent coordinated behaviors. Our findings also provide a conceptual linkage between early spontaneous movements and spontaneous neuronal activity in terms of spatiotemporal characteristics.

Syntheses, Structures, and Electrochemical Properties of Metallacyclic Oxidovanadium(V) Complexes with Asymmetric Multidentate Linking Ligands.

Trinuclear metallacyclic oxidovanadium(V) complexes, [{VO(L3+2R)}3] (1-3) with asymmetric multidentate linking ligands (H3L3+2R: R = H, Me, Br), were synthesized. The molecular structure of 1 is characterized as a tripod structure, with each V(V) ion coordinated by ONO-atoms from a tridentate Schiff base site and ON-atoms from a bidentate benzoxazole site of two respective H3L3+2H ligands. The intramolecular V⋯V distances range from 8.0683 to 8.1791 Å. Complex 4 is a mononuclear dioxidovanadium(V) complex, (Et3NH)[VO2(HL3+2H)]. Cyclic voltammograms of 1-3 in DMF revealed redox couples attributed to three single-electron transfer processes.

Highly regio- and stereocontrolled preparation of α-(trifluoromethyl)-ß-(phenylthio) enamines by the hydroamination of in situ-synthesized 1-(trifluoromethyl)-2-(phenylthio)ethyne.

Various nitrogen nucleophiles were easily added to in situ-generated 1-(trifluoromethyl)-2-(phenylthio)ethyne to afford the corresponding trifluoromethyl enamines in good-to-high yields and with high regio- and stereocontrol under very mild conditions.

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. Natural language processing was used to extract important themes from qualitative responses to open-ended questions, including the participants' definitions of successful aging. RESULTS: Two latent subgroups were identified, and their main difference was that the wisdom scale was positively associated with self-rated successful aging in only one subgroup. This subgroup had significantly lower self-rated successful aging and worse scores for all health and psychological measures. In the subgroup's qualitative responses, the theme of wisdom was only mentioned by 10.6%; this proportion was not statistically different from the other subgroup, for which the wisdom scale was not statistically associated with the self-rated successful aging. CONCLUSION: Our results showed heterogeneous patterns in the factors underpinning successful aging even in community-dwelling adults. We found the existence of a latent subgroup with lower self-rated successful aging as well as worse health and psychological scores, and we suggest a potential role of wisdom in promoting successful aging for this subgroup, even though individuals may not explicitly recognize wisdom as important for successful aging.

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 1.5-Tesla magnetic resonance imaging scan. Cerebral networks were extracted from individual T1-weighted images based on the intracortical similarity, and we estimated the differences of network metrics among the three diagnostic groups. RESULTS: Whole-brain analyses for degree, betweenness centrality, and clustering coefficient images were performed using SPM8 software. The patients with MCI-LB showed significant reduction of degree in right putamen, compared with healthy subjects. The MCI-AD patients showed significant lower degree in left insula and bilateral posterior cingulate cortices compared with healthy subjects. There were no significant differences in small-world properties and in regional gray matter volume among the three groups. CONCLUSIONS: We found the change of degree in the patients with MCI-AD and with MCI-LB, compared with healthy controls. These findings were consistent with the past single-photon emission computed tomography studies focusing on AD and DLB. The disease-related difference in the cerebral neural network might provide an adjunct biomarker for the early detection of AD and DLB.

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. Lonely and non-lonely participants (based on qualitative and quantitative assessments) were compared. SETTING: Independent living sector of a senior housing community in San Diego County. PARTICIPANTS: Eighty English-speaking older adults with age range 66-94 (mean 83 years). MEASUREMENTS: Interviews were audiotaped and manually transcribed. Transcripts were examined using NLP approaches to quantify sentiment and expressed emotions. RESULTS: Lonely individuals (by qualitative assessments) had longer responses with greater expression of sadness to direct questions about loneliness. Women were more likely to endorse feeling lonely during the qualitative interview. Men used more fearful and joyful words in their responses. Using linguistic features, machine learning models could predict qualitative loneliness with 94% precision (sensitivity = 0.90, specificity = 1.00) and quantitative loneliness with 76% precision (sensitivity = 0.57, specificity = 0.89). CONCLUSIONS: AI (e.g., NLP and machine learning approaches) can provide unique insights into how linguistic features of transcribed speech data may reflect loneliness. Eventually linguistic features could be used to assess loneliness of individuals, despite limitations of commercially developed natural language understanding programs.

Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study.

BACKGROUND: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. OBJECTIVE: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. METHODS: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data-neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)-from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. RESULTS: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. CONCLUSIONS: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.

Study of Independent Living Residents of a Continuing Care Senior Housing Community: Sociodemographic and Clinical Associations of Cognitive, Physical, and Mental Health.

OBJECTIVE: To examine associations of sociodemographic and clinical factors with cognitive, physical, and mental health among independent living older adults in a continuing care senior housing community (CCSHC). METHODS: This was a cross-sectional study at the independent living sector of a CCSHC in San Diego County, California. Participants included English-speaking adults aged 65-95 years, of which two-thirds were women. Of the 112 subjects recruited, 104 completed basic study assessments. The authors computed composite measures of cognitive, physical, and mental health. The authors also assessed relevant clinical correlates including psychosocial factors such as resilience, loneliness, wisdom, and social support. RESULTS: The CCSHC residents were similar to a randomly selected community-based sample of older adults on most standardized clinical measures. In the CCSHC, physical health correlated with both cognitive function and mental health, but there was no significant correlation between cognitive and mental health. Cognitive function was significantly associated with physical mobility, satisfaction with life, and wisdom, whereas physical health was associated with age, self-rated physical functioning, mental well-being, and resilience. Mental health was significantly associated with income, optimism, self-compassion, loneliness, and sleep disturbances. CONCLUSION: Different psychosocial factors are significantly associated with cognitive, physical, and mental health. Longitudinal studies of diverse samples of older adults are necessary to determine risk factors and protective factors for specific domains of health. With rapidly growing numbers of older adults who require healthcare as well as supportive housing, CCSHCs will become increasingly important sites for studying and promoting the health of older adults.

High prevalence and adverse health effects of loneliness in community-dwelling adults across the lifespan: role of wisdom as a protective factor.

OBJECTIVES: This study of loneliness across adult lifespan examined its associations with sociodemographics, mental health (positive and negative psychological states and traits), subjective cognitive complaints, and physical functioning. DESIGN: Analysis of cross-sectional data. PARTICIPANTS: 340 community-dwelling adults in San Diego, California, mean age 62 (SD = 18) years, range 27-101 years, who participated in three community-based studies. MEASUREMENTS: Loneliness measures included UCLA Loneliness Scale Version 3 (UCLA-3), 4-item Patient-Reported Outcomes Measurement Information System (PROMIS) Social Isolation Scale, and a single-item measure from the Center for Epidemiologic Studies Depression (CESD) scale. Other measures included the San Diego Wisdom Scale (SD-WISE) and Medical Outcomes Survey- Short form 36. RESULTS: Seventy-six percent of subjects had moderate-high levels of loneliness on UCLA-3, using standardized cut-points. Loneliness was correlated with worse mental health and inversely with positive psychological states/traits. Even moderate severity of loneliness was associated with worse mental and physical functioning. Loneliness severity and age had a complex relationship, with increased loneliness in the late-20s, mid-50s, and late-80s. There were no sex differences in loneliness prevalence, severity, and age relationships. The best-fit multiple regression model accounted for 45% of the variance in UCLA-3 scores, and three factors emerged with small-medium effect sizes: wisdom, living alone and mental well-being. CONCLUSIONS: The alarmingly high prevalence of loneliness and its association with worse health-related measures underscore major challenges for society. The non-linear age-loneliness severity relationship deserves further study. The strong negative association of wisdom with loneliness highlights the potentially critical role of wisdom as a target for psychosocial/behavioral interventions to reduce loneliness. Building a wiser society may help us develop a more connected, less lonely, and happier society.

Sharp Phase Field Method.

Phase field modeling offers an extremely general framework to predict microstructural evolutions in complex systems. However, its computational implementation requires a discretization scheme with a grid spacing small enough to preserve the continuous character of the theory. We present here a new formulation, which is intrinsically discrete, in which the interfaces are resolved with essentially one grid point with no pinning on the grid and an accurate rotational invariance, improving drastically the numerical capabilities of the method. We show that interfacial kinetic properties are reproduced with a high accuracy. Finally, we apply the model to a situation where conserved and nonconserved fields are coupled.

Complex formation of nickel(ii) with dimethyl sulfoxide, methanol, and acetonitrile in a TFSA--based ionic liquid of [C2mim][TFSA].

The thermodynamics of complex formation of Ni2+ with molecular liquids (ML), dimethyl sulfoxide (DMSO), methanol (MeOH), and acetonitrile (AN) in the ionic liquid (IL) of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide ([C2mim][TFSA]) has been elucidated using ultraviolet (UV)-visible spectroscopy. X-ray structural analyses for single crystals grown from Ni2+-[C2mim][TFSA]-DMSO and -AN solutions at high ML contents have shown that six DMSO oxygen or AN nitrogen atoms coordinate with Ni2+ to form octahedral structures of [Ni(dmso)6](TFSA)2 and [Ni(an)6](TFSA)2, respectively. This is the same in the case of the Co2+ complex of [Co(dmso)6](TFSA)2. UV-visible spectroscopic experiments have revealed that the TFSA- anions that initially combine with Ni2+ in the IL are replaced with ML molecules in the IL-ML systems in three steps with increasing ML content. The electron donicities of the three MLs are larger in the order of DMSO > MeOH > AN. However, the stability of each complex does not simply depend on this order; the stability is higher in the order of [Ni(dmso)n] > [Ni(an)n] > [Ni(meoh)n]. In other words, the stability of the MeOH complexes is lower than that of the AN ones, despite the higher electron donicity of MeOH. The reasons for the order of the complex stabilities have been interpreted on the molecular scale, according to the stepwise enthalpies and entropies determined, together with the strength of the hydrogen bonding between the MLs and the imidazolium ring and the formation of MeOH clusters in [C2mim][TFSA].

Unveiling Dimensionality Dependence of Glassy Dynamics: 2D Infinite Fluctuation Eclipses Inherent Structural Relaxation.

By using large-scale molecular dynamics simulations, the dynamics of two-dimensional (2D) supercooled liquids turns out to be dependent on the system size, while the size dependence is not pronounced in three-dimensional (3D) systems. It is demonstrated that the strong system-size effect in 2D amorphous systems originates from the enhanced fluctuations at long wavelengths which are similar to those of 2D crystal phonons. This observation is further supported by the frequency dependence of the vibrational density of states, consisting of the Debye approximation in the low-wave-number limit. However, the system-size effect in the intermediate scattering function becomes negligible when the length scale is larger than the vibrational amplitude. This suggests that the finite-size effect in a 2D system is transient and also that the structural relaxation itself is not fundamentally different from that in a 3D system. In fact, the dynamic correlation lengths estimated from the bond-breakage function, which do not suffer from those enhanced fluctuations, are not size dependent in either 2D or 3D systems.

Initial experience of combined use of photodynamic diagnosis and narrow band imaging for detection of flat urothelial lesion.

BACKGROUND: This preliminary study is the first report to compare photodynamic diagnosis (PDD) with narrow band imaging (NBI) in the same patients with flat urothelial lesions suspicious of carcinoma in situ (CIS) of the bladder. METHODS: Between November 26, 2012 and April 1, 2013, 10 patients underwent transurethral resection of bladder tumor using PDD and NBI simultaneously because of suspicion of CIS. The bladder was mapped first under white light (WL), then under NBI, and subsequently under blue light in odd-numbered patients. The bladder was mapped first under WL, then under blue light, and subsequently under NBI in even-numbered patients. Biopsies were carried out from all suspicious areas, noting whether NBI, PDD or both detected lesions. Random cold cup biopsies from healthy mucosa of bladder were performed from lesions negative on PDD and NBI. RESULTS: The sensitivity and specificity of PDD for detection of CIS and dysplasia were 0.916 and 0.827, respectively. The sensitivity and specificity of NBI for detection of CIS and dysplasia were 0.625 and 0.879. The area under the curve (AUC) for detection of CIS and dysplasia was 0.872 with PDD and 0.752 with NBI. The AUC with combined use of PDD and NBI was 0.918. There was no cancer or dysplasia identified in 43 lesions that were both PDD- and NBI-negative. CONCLUSION: When both PDD and NBI are negative, the possibility of CIS or dysplasia might be very low. The usefulness of the combination of PDD with NBI was suggested in this study.

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. Methods: We collected spontaneous viewing behaviors to 200 naturalistic complex scenes from patients with AD or LBD at the prodromal or dementia stage, as well as matched control participants. We then investigated eye movement patterns using a computational visual attention model with high-level image features of object properties and semantic information. Results: Compared with matched controls, we identified two disease-specific altered patterns of eye movements: diminished visual exploration, which differentially correlates with cognitive impairment in AD and with motor impairment in LBD; and reduced gaze allocation to objects, attributed to a weaker attention bias toward high-level image features in AD and attributed to a greater image-center bias in LBD. Conclusion: Our findings may help differentiate AD and LBD patients and comprehend their real-world visual behaviors to mitigate the widespread impact of impaired visual attention on daily activities.

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

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. Compared with matched AD and CU participants, DLB patients showed reduced overall emotional expressiveness, as well as lower valence (more negative) and lower arousal (calmer), the extent of which was associated with cognitive impairment and insular atrophy. Classification models using vocal features discriminated DLB from AD and CU with an AUC of 0.83 and 0.78, respectively. Our findings may aid in discriminating DLB patients from AD and CU individuals, serving as a surrogate marker for clinical and neuropathological changes in DLB. Highlights: DLB showed distinctive reduction in vocal expression of emotions.Cognitive impairment was associated with reduced vocal emotional expression in DLB.Insular atrophy was associated with reduced vocal emotional expression in DLB.Emotional expression measures successfully differentiated DLB from AD or controls.

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. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction. OBJECTIVE: This study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination. METHODS: This was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test. RESULTS: We obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P<.001) and met the reference value r=0.1 (small) of the effect size. Additionally, filler periods (P<.01) and the proportion of fillers (P=.02) showed a significant difference; however, these did not meet the reference value of the effect size. In contrast, we obtained significant differences in 16 (94%) of 17 types of features obtained from the everyday conversations with the humanoid robot. The duration of response time, the duration of speech periods, jitter (local, relative average perturbation [rap], 5-point period perturbation quotient [ppq5], difference of difference of periods [ddp]), shimmer (local, amplitude perturbation quotient [apq]3, apq5, apq11, average absolute differences between the amplitudes of consecutive periods [dda]), and F0cov (coefficient of variation of the fundamental frequency) showed a significant difference (P<.001). In addition, the duration of response time, the duration of silent periods, the filler period, and the proportion of fillers showed significant differences (P<.05). However, only jitter (local) met the reference value r=0.1 (small) of the effect size. In the automatic classification experiment for the classification of participants into CN and MCI groups, the results showed 66.0% accuracy in the MMSE conversational speech and 68.1% accuracy in everyday conversations with the humanoid robot. CONCLUSIONS: This study shows the possibility of early and simple screening for patients with MCI using prosodic and acoustic features from everyday conversations with a humanoid robot with the same level of accuracy as the MMSE.

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. METHODS: We collected drawing data from 144 community-dwelling older adults (27 AD, 65 MCI, and 52 cognitively normal, or CN) who performed five drawing tasks. We then extracted motion- and pause-related drawing features for each task and investigated the associations of the features with the participants' diagnostic statuses and cognitive measures. RESULTS: The drawing features showed gradual changes from CN to MCI and then to AD, and the changes in the features for each task were statistically associated with cognitive impairments in different domains. For classification into the three diagnostic categories, a machine learning model using the features from all five tasks achieved a classification accuracy of 75.2%, an improvement by 7.8% over that of the best single-task model. CONCLUSION: Our results demonstrate that a common set of drawing features from multiple drawing tasks can capture different, complementary aspects of cognitive impairments, which may lead to a scalable way to improve the automated, reliable detection of AD and MCI.

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. Results: The AD group showed larger differences from the CN group than the DLB group in linguistic features, while the DLB group showed larger differences in prosodic and acoustic features. Machine-learning classifiers using these speech features achieved 87.0% accuracy for AD versus CN, 93.2% for DLB versus CN, and 87.4% for AD versus DLB. Discussion: Our findings indicate the discriminative differences in speech features in AD and DLB and the feasibility of using these features in combination as a screening tool for identifying/differentiating AD and DLB.

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. METHODS: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses. RESULTS: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD. CONCLUSION: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB.

Can AI make people happy? The effect of AI-based chatbot on smile and speech in Parkinson's disease.

INTRODUCTION: Approaches for objectively measuring facial expressions and speech may enhance clinical and research evaluation in telemedicine, which is widely employed for Parkinson's disease (PD). This study aimed to assess the feasibility and efficacy of using an artificial intelligence-based chatbot to improve smile and speech in PD. Further, we explored the potential predictive value of objective face and speech parameters for motor symptoms, cognition, and mood. METHODS: In this open-label randomized study, we collected a series of face and conversational speech samples from 20 participants with PD in weekly teleconsultation sessions for 5 months. We investigated the effect of daily chatbot conversations on smile and speech features, then we investigated whether smile and speech features could predict motor, cognitive, and mood status. RESULTS: A repeated-measures analysis of variance revealed that the chatbot conversations had a significant interaction effect on the mean and standard deviation of the smile index during smile sections (both P = .02), maximum duration of the initial rise of the smile index (P = .04), and frequency of filler words (P = .04), but no significant interaction effects were observed for clinical measurements including motor, cognition, depression, and quality of life. Explorative analysis using statistical and machine-learning models revealed that the smile indices and several speech features were associated with motor symptoms, cognition, and mood in PD. CONCLUSION: An artificial intelligence-based chatbot may positively affect smile and speech in PD. Smile and speech features may capture the motor, cognitive, and mental status of patients with PD.