This study investigates the use of entropy metrics to enhance traditional analysis of grasping movements. The authors examined whether signal unpredictability measures-Approximate Entropy (ApEn) and Sample Entropy (SampEn)-can reveal motor control aspects during reach-to-grasp actions. They aimed to determine if entropy could infer the movement's underlying intention, distinguishing social (SOC) versus individual (IND) goals. Wrist acceleration data were collected via wearable Inertial Measurement Units (IMU) from 12 older adults performing repetitive reach-to-grasp tasks in two conditions: IND (acting alone) and SOC (passing an object to an experimenter). The hypothesis predicted that social actions involve tighter motor control, resulting in lower entropy. ApEn and SampEn were computed using different parameter sets (embedding dimension m = 2, 3; similarity criterion r = 0.1, 0.2, 0.3). Mixed-model repeated measures ANOVAs and linear mixed-effects models assessed differences between conditions and parameters. Results showed significant effects of condition, m, and r, including m x r interactions. Both ApEn and SampEn yielded lower entropy in the SOC condition, confirming the hypothesis. ApEn showed greater robustness and consistency across parameter settings and preprocessing steps compared to SampEn. This study is the first systematic comparison of ApEn and SampEn for action-related time series in social versus non-social contexts. Findings highlight the impact of social context on motor control. ApEn, especially with lower m (2) and r (0.1-0.2), is recommended for future studies due to its reliability and sensitivity in distinguishing movement intention.

Background Conventional standardized assessments often fail to capture subtle but clinically meaningful language changes following neuromodulation in logopenic primary progressive aphasia (lvPPA), creating a critical need for more sensitive monitoring tools such as speech biomarkers. Aims This study evaluates speech biomarkers for monitoring neuromodulation effects in lvPPA, specifically examining whether multidimensional speech analysis can be linked to tDCS (transcranial direct current stimulation) -induced changes that complement traditional assessment methods. Methods & procedures : Using a randomized double-blind cross-over design, we administered both active and sham tDCS targeting the dorsolateral prefrontal cortex in a single lvPPA patient with Alzheimer’s disease underlying pathology. Speech analyses were conducted across naming, repetition, and narrative tasks during four longitudinal assessment phases. These speech analyses complemented standardized valid tests of tDCS targets : executive functions and naming capabilities. Outcomes & Results : Speech Analyses revealed clinically meaningful improvements in speech parameters following active stimulation, including reduced latency times, elimination of phonological paraphasias, and increased lexical diversity. Acoustic markers showed enhanced prosodic features and vocal quality. These speech changes aligned with improvements in executive functions, suggesting shared neurocognitive mechanisms. Conclusions While these promising findings warrant confirmation in larger cohorts, they provide preliminary evidence that multidimensional speech analysis offers a sensitive tool for tracking neuromodulation outcomes in neurodegenerative language disorders, potentially enhancing the assessment of therapeutic interventions beyond the limitations of traditional standardized measures.

Both passion and disordered eating attitudes (DEA) have been associated with increased injury risk among dancers, yet their interplay remains unclear. Pre-professional ballet and contemporary dancers appear particularly vulnerable. This study examined how harmonious and obsessive passion, together with DEA, relate to injury risk in contemporary and ballet pre-professional dancers aged 15+. In September 2023, participants from a French dance school completed baseline questionnaires assessing demographics (age, sex, injury history), passion (Passion Scale), and DEA (EAT-26). From October 2023 to May 2024, participants completed monthly follow-up questionnaires on injuries, using the Oslo Sports Trauma Research Center questionnaire on Health Problems (OSTRC-H). Injuries were defined as all-complaints (any physical complaint irrespective of the need for medical attention and/or time loss) and/or substantial (leading to moderate/severe/complete reduction in training volume or performance). Of 75 dancers enrolled, 62 (M = 17.2 years, SD = 2; 23 males) were included in the analyses. During follow-up, 48 dancers (77.4%) reported at least one injury, and 26 (41.9%) a substantial injury. Injury incidence rates were 2.27 per 1000 h of dance exposure for all-complaints injuries, and 0.76 for substantial injuries. Higher EAT-26 scores were associated with more all-complaints injuries (b = 0.02, p = .001), whereas harmonious passion was negatively associated with injury occurrence (b = -0.05, p = .005). Mediation analysis indicated an indirect association between obsessive passion and all-complaints injuries through DEA (indirect effect: b = 0.045, p = .031). These findings highlight the need to address obsessive passion, DEA, and to promote more autonomous, balanced engagement in dance to potentially reduce injury risk in pre-professional dancers.

Geroscience needs biomarkers that capture the progressive decline of integrated biological systems with age. Physical capacity, a direct manifestation of systemic integrity, is a core pillar of biological aging but is typically assessed through discrete clinical tests. Speech production, a complex motor act requiring coordinated respiratory, laryngeal, and articulatory control, shares fundamental physiological pathways with global physical function and may therefore serve as an accessible digital biomarker of aging. In a longitudinal cohort of 464 community-dwelling older adults (mean age 79.6 ± 8.7 years), we tested the hypothesis that changes in speech track specific changes in physical capacity. Participants underwent a 3-month adapted physical activity (APA) program. At baseline (T0) and post-intervention (T1), we performed a battery of ten objective physical tests (strength, power, endurance, gait, balance, flexibility, mobility, appendicular lean mass, fatigue) and recorded spontaneous speech during emotional autobiographical recall. Multi-layered acoustic, temporal, and linguistic features were automatically extracted. The longitudinal association was analyzed via Spearman correlations and univariate linear mixed-effects models. The APA intervention induced significant improvements in key physical domains, including mobility, gait speed, handgrip strength, and balance (all p < 0.01). These gains were specifically correlated with concurrent changes in speech features (|ρ| = 0.11–0.22). For instance, greater lower-limb strength correlated with reduced vocal shimmer and lexical diversity, while improved flexibility was associated with a lower spectral centroid and zero-crossing rate, indicating smoother phonation. Linear mixed models confirmed significant within-individual coupling between trajectories of physical function and speech dynamics. Emotional context systematically modulated these associations, revealing different speech-stress signatures under cognitive-affective load. This study provides novel longitudinal evidence that speech is a dynamic digital biomarker of domain-specific physical capacity, reflecting underlying functional integrity. The domain-specific speech-physical coupling suggests that speech analysis can serve as a novel, integrative tool for remote monitoring of aging trajectories and the functional efficacy of interventions targeting the age-related physiological decline. Speech as a longitudinal digital biomarker of physical aging.

Apathy is one of the most prevalent and disabling neuropsychiatric symptoms in dementia, characterized by reduced motivation and diminished goal-directed activity, with strong impacts for both the person living with dementia and their caregivers. Despite its prevalence, its assessment remains challenging, as it relies largely on subjective self-reports and scales. In response, objective and quantifiable biomarkers derived from digital health technologies have emerged as a promising approach. The ECOCAPTURE program at the Paris Brain Institute (ICM) has developed ecological paradigms to identify behavioral and physiological markers of apathy, highlighting a core “exploration deficit” associated with this symptom. The present paper describes ECOCAPTURE@HOME(EYE), the eye-tracking and neuroimaging component of the broader ECOCAPTURE@HOME-V2 protocol. This study integrates eye-tracking across laboratory-based tasks and an ecological at-home setting to characterize oculomotor dynamics and visual exploration patterns in behavioral variant frontotemporal dementia (bvFTD), Alzheimer’s disease (AD), and healthy controls. These measures will be combined with clinical assessments of apathy and structural and functional MRI to investigate the behavioral and neural mechanisms underlying this symptom. This protocol aims to identify objective, ecologically valid, and clinically meaningful ocular markers of apathy, and to refine its digital phenotype within a multimodal framework. More broadly, it seeks to support the development of improved assessment tools and contribute to data-driven, individualized approaches in dementia care.

Transcutaneous auricular vagus nerve stimulation (taVNS) is an emerging neuromodulation technique in rehabilitation research. While implanted vagus nerve stimulation is used clinically for epilepsy and treatment-resistant depression, its non-invasive form has mainly been explored in experimental settings, including physiotherapy and neuropsychiatry. Very few studies have investigated taVNS in speech-language pathology. However, given the vagus nerve's role in motor and sensory swallowing control, taVNS may offer a promising approach for dysphagia management - a frequent and severe complication in elderly stroke patients. Therefore, the development of an innovative protocol integrating taVNS appears pertinent in the context of swallowing rehabilitation. This protocol aims to evaluate the efficacy of taVNS combined with standard speech-language therapy for improving pharyngolaryngeal swallowing function and quality of life in elderly patients (≥70 years) with acute post-stroke dysphagia. This single-center, two-arm, randomized controlled clinical trial is conducted in a single-blind design. A total of 20 participants are expected to be enrolled. Eligible patients will be randomly allocated to receive either standard speech-language therapy combined with an inactive tVNS-E device, or standard speech-language therapy combined with non-invasive auricular vagus nerve stimulation via an active tVNS-E device. Both groups will undergo four rehabilitation sessions per week over three weeks. Clinical assessments (GUSS, SWAL-QoL, food trial) will be conducted at baseline T0 (inclusion) and at the end of the protocol at T3 (Week 3 – Day 4). The study is currently in the participant recruitment phase. Recruitment began in April 2026. Baseline and post-test data collection is expected to continue until February 2028. Data analysis is planned for March 2028, and study results are expected to be published in April 2028. This will be the first randomized controlled trial evaluating taVNS for post-stroke dysphagia rehabilitation in patients aged ≥70 years. If effective, taVNS could provide a non-invasive adjunct to conventional speech-language therapy. ClinicalTrials.gov (NCT07428590)

INTRODUCTION:Cognitive complaints are often considered early indicators of Alzheimer's disease (AD) and commonly lead to memory clinic consultations. Prior studies suggest stronger associations between cognitive complaints and mood than with objective cognition, but this interplay remains poorly understood. Using a machine learning-supported approach, we aimed to (1) identify key predictors of cognitive complaints, and (2) compare the value of gamified versus standard neuropsychological testing in detecting subtle deficits. METHODS:In this international multi-center study, 98 participants (57 females; mean age 71.9, range 55-86) from three memory clinics completed the Cognitive Failures Questionnaire (CFQ), mood and apathy questionnaires, the tablet-based gamified Adaptive Cognitive Evaluation Explorer (ACE-X), and standard neuropsychological tests. Predictors of CFQ scores were examined using elastic net regression and the Boruta algorithm, followed by linear mixed-effects modeling. RESULTS:Greater mood symptoms were associated with more cognitive complaints, whereas increasing age was linked to fewer complaints. Study center accounted for additional variance. The final model explained a substantial proportion of variance (conditional R2 = 0.48, marginal R2 = 0.33). Participants had lower z-scores on ACE-X compared to standard testing, but neither predicted the severity of cognitive complaints. DISCUSSION:Mood and age were main predictors of cognitive complaints in memory clinic patients. Although ACE-X yielded lower normative scores than standard tests, neither cognitive measure was linked to complaints. These findings highlight the importance of systematically assessing mood, adopting personalized approaches when evaluating subjective and objective cognition, and the potential value of gamified assessments for screening populations at risk of AD.

Background Early differentiation between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is a prerequisite for secondary prevention and targeted trial enrollment, yet remains challenging at disease onset. We investigated whether automated speech analysis could serve as a digital biomarker for early etiological stratification across clinically heterogeneous presentations. Methods In this prospective biomarker-confirmed prognostic study, 172 participants (108 patients with biomarker-confirmed AD or FTLD and 64 controls) completed a standardized speech protocol at initial clinical assessment. Acoustic, temporal, and phonatory features were automatically extracted. Machine learning models and a stacking ensemble were trained using stratified, repeated 5-fold cross-validation to discriminate between AD and FTLD pathology, with exploratory analysis extending to atypical and rare phenotypes crossed with physiopathology, including primary progressive aphasia (PPA) variants. Results Speech-based models achieved high sensitivity and specificity in distinguishing physiopathology independently (mean area under the curve (AUC)=0.986) and crossed phenotype and physiopathological diagnostic association (mean AUC=0.966).The ensemble identified 82% of cases with clinicopathological discordance. Interpretability analyses revealed distinct speech signatures: AD was associated with global speech slowing and phonatory instability, while FTLD was characterized by reduced verbal output and acoustic hypo-expressivity. Conclusions Automated speech analysis provides a promising non-invasive digital biomarker for the early etiological stratification of AD and FTLD, including atypical phenotypes, with high accuracy in a monocentric biomarker-confirmed cohort. These findings support the feasibility of speech-based etiological stratification and its potential to complement existing biomarker frameworks, particularly in cases of clinicopathological discordance. External validation is required before clinical deployment can be considered.

Apathy, defined as a significant and sustained reduction in goal-directed behavior associated with impaired daily functioning, is a common and clinically important syndrome in mild and major neurocognitive disorders (NCDs) and is associated with poorer outcomes for patients and carers. Despite growing research, important gaps in knowledge continue to hinder accurate recognition and effective treatment. This International Psychogeriatric Association (IPA) white paper summarizes key scientific and clinical insights from a multidisciplinary Apathy Task Force, with the aim of providing a narrative overview of current knowledge on nomenclature, biomarkers, treatments, and prevention, and of identifying gaps to inform global priorities in research and clinical care, and inform policy. The IPA convened a multidisciplinary task force of apathy researchers during the 2024 and 2025 IPA Congress meetings. Through structured discussions informed by targeted literature review, the group identified key themes and critical gaps across domains relevant to apathy in NCDs. Apathy is highly prevalent across NCDs and evidence demonstrates that it has substantial functional, emotional, and societal impact. Although diagnostic criteria have recently been refined, clinical identification remains challenging. Multiple scales exist to assess apathy, yet inconsistent use across settings and misalignment with updated diagnostic criteria limit comparability. Neuroimaging studies consistently implicate fronto-subcortical network involvement, but no reliable peripheral biomarkers have been established. While select non-pharmacological, pharmacological and neuromodulatory interventions show promising evidence, non-pharmacological are considered central to current management. Clinical guidelines and policy efforts remain limited despite the substantial burden of apathy. Emerging evidence highlights opportunities to improve diagnosis, deepen mechanistic understanding, and develop more treatments that are targeted and scalable. Key priorities identified include increasing awareness of and education around apathy, incorporating apathy into national dementia strategies, improving implementation of effective non-pharmacological interventions, and advancing the development of interventions and biomarkers aligned with contemporary diagnostic criteria. By integrating evidence across clinical, biological, and care domains, this white paper provides a structured framework to guide future research, clinical practice, and policy.

The rising global burden of pathological aging engenders an urgent need for accessible tools enabling early detection of physical decline, which significantly impacts quality of life and healthcare systems. We hypothesized that speech analysis could capture phenotype-specific signatures of physical deterioration through shared neuromuscular pathways, offering a novel approach to physical assessment. In this study, we employed machine learning to analyze multimodal speech features (acoustic, linguistic, temporal) derived from two 1-minute spontaneous emotional speech recordings obtained from 271 community-dwelling older adults (mean age: 77.3 ± 5.8 years). Our models classified physical functional deficits across ten critical domains: lower-limb strength, power, endurance, handgrip strength, flexibility, postural balance, gait speed, mobility, appendicular lean mass, and fatigue. Our ensemble approach achieved remarkable classification accuracy for each domain (mean AUC = 0.91 ± 0.04), with multimodal emotional task stacking enhancing detection for 80% of physical measures. Explainable AI (SHAP) analysis revealed distinct speech signatures for each deficit type, potentially reflecting specific pathophysiological mechanisms rather than demographic confounders. We identified three primary speech alteration clusters: lexico-syntactic simplification (decreased syntactic complexity), neuromotor-temporal slowing (diminished speech rate, increased pauses), and articulatory-spectral decline (spectral instability). This study supports the hypothesis that spontaneous speech serves as a comprehensive digital biomarker of multidimensional physical function in aging. Our approach pioneers speech analysis as a physical aging clock. This technology offers clinical-grade precision through accessible smartphone recordings, enabling domain-specific physiological mapping via interpretable biomarkers and scalable screening for precision geriatrics and underserved populations.

Defined as a complex syndrome combining attentional and behavioral difficulties, Attention Deficit Hyperactivity Disorder (ADHD) is a widespread condition affecting approximately 5.3% of children worldwide. Treatments mainly include psychotherapy and amphetamine derivative-based pharmacotherapy. Respiratory biofeedback may represent an additional, promising non-pharmacological approach for ADHD, in addition to neurofeedback, biofeedback's actual main application protocol based on cerebral activity. However, to date, there are few studies regarding other physiological rhythms, such as breathing. This study explored the potential effects of a therapeutic intervention based on respiratory, haptic and visual biofeedback, aiming to reduce the intensity of attentional dysfunction symptoms in children with ADHD aged 8 to 11 years. In this crossover protocol, we set two intervention groups: an immediate group and a delayed group. The intervention phase consisted of five weekly respiratory biofeedback sessions focused on attention processes, along with the monitoring of symptomatic manifestations using validated psychometric tools. Among the 17 participants, the results showed a significant overall decrease in ADHD evaluation scale scores both immediately after the intervention and at the 30-day follow-up, indicating short-term benefits from the sessions. However, these benefits were attenuated in the medium term, possibly due to the limited number of biofeedback sessions. In conclusion, respiratory biofeedback was well accepted, and few sessions decreased in the short term symptomatic manifestations and functional impacts related to ADHD. However, treatment effects, permanence and acceptability need to be assessed in larger and longer cohort studies. Thus, a multimodal biofeedback approach based on respiratory feedback is an innovative preliminary but promising emerging perspective for children presenting ADHD.

Dementia is often accompanied by impairments in social cognition, such as reduced empathy and difficulties in emotion recognition, which negatively impact daily functioning and increase caregiver burden. Early detection of these deficits is crucial, yet traditional assessments relying on paper-and-pencil tests or actor-based stimuli face limitations in scalability and standardization. Advances in digital technologies, particularly high-fidelity digital characters such as MetaHumans, offer a more flexible and ecologically valid alternative. This pilot study compared MetaHuman digital characters with a validated set of human actor videos in an emotion recognition task involving 20 participants. Six basic emotions ( Joy, Sadness, Anger, Fear, Surprise, and Disgust) were presented, and performance was evaluated through recognition accuracy, response times, perceived naturalness, and subjective ratings. MetaHumans achieved significantly higher accuracy and comparable response times, although actors were rated slightly more natural. These findings suggest that MetaHumans can provide standardized, reproducible, and costeffective stimuli for assessing social cognition.

Speech and language impairments are associated with cognitive decline in neurodegenerative dementias, particularly Alzheimer’s Disease (AD), where subtle speech changes may precede clinical dementia onset. As clinical trials prioritize early identification for disease-modifying treatments, digital biomarkers for timely screening become imperative. Digital speech-based biomarkers can be employed for screening populations at the earliest AD stages. An automated phone-based screening battery has been created, encompassing speech-related neurocognitive tests (Semantic Verbal Fluency and Verbal Learning Test). This allows the extraction of speech-based biomarkers alongside classical cognitive scores. This study aims to validate digital speech biomarkers in early-stage AD by comparing them to traditional evaluation methods. Within the PROSPECT-AD project, speech and gold-standard clinical data were obtained from the German DELCODE and DESCRIBE cohorts. We used data from N = 14 healthy controls (HC), N = 75 participants presenting with Subjective Cognitive Decline (SCD) and N = 18 participants presenting with amnestic Mild Cognitive Impairment (aMCI). Spearman rank correlations were computed between speech biomarkers and gold-standard clinical measures. Kruskal-Wallis test assessed group differences and regression analysis adjusted for age, sex and education, associated domain-specific speech biomarkers and cognitive assessment scores. There was a significant difference in the speech biomarker for cognition composite score (ki:e SB-C) between diagnostic groups (x2(2) = 18.06, p <0.001). Significant correlations were found between ki:e SB-C and all global anchor scores including MMSE (r = 0.48, d = 0.97, p <0.001), CDR-SoB (r = -0.49, d = -0.98, p <0.001) and PACC5 (r = 0.56, d = 1.12, p <0.001) (Figure 1). All domain-specific biomarker composite scores (memory, executive function, processing speed) significantly correlated with CDR, with strongest correlations found with the memory biomarker. All correlations remained significant when controlling for age, sex and education. Finally, based on the regression analysis results, domain-specific biomarkers were significantly associated with respective domain-specific anchors (Clock Drawing, TMT-A/B, Digits Span, Figure Drawing, ADAS-Cog, Verbal Fluency) (Table 1). Findings support prior research, emphasizing speech biomarkers as a promising tool for remote early-stage AD screening, with potential implications for scalable screening in research trials and healthcare.

Background: Body motion significantly contributes to understanding communicative and social interactions, especially when auditory information is impaired. The visual skills of people with hearing loss are often enhanced and compensate for some of the missing auditory information. In the present study, we investigated the recognition of social interactions by observing body motion in people with post-lingual sensorineural hearing loss (SNHL). Methods: In total, 38 participants with post-lingual SNHL and 38 matched normally hearing individuals (NHIs) were presented with point-light stimuli of two agents who were either engaged in a communicative interaction or acting independently. They were asked to classify the actions as communicative vs. independent and to select the correct action description. Results: No significant differences were found between the participants with SNHL and the NHIs when classifying the actions. However, the participants with SNHL showed significantly lower performance compared with the NHIs in the description task due to a higher tendency to misinterpret communicative stimuli. In addition, acquired SNHL was associated with a significantly higher number of errors, with a tendency to over-interpret independent stimuli as communicative and to misinterpret communicative actions. Conclusions: The findings of this study suggest a misinterpretation of visual understanding of social interactions in individuals with SNHL and over-interpretation of communicative intentions in SNHL acquired later in life.

BACKGROUND:Diagnostic criteria for apathy in neurocognitive disorders (DCA-NCD) have recently been updated. OBJECTIVES:We investigated whether validated scales measuring apathy severity capture the three dimensions of the DCA-NCD (diminished initiative, diminished interest, diminished emotional expression). MEASUREMENTS:Degree of mapping ("not at all", "weakly", or "strongly") between items on two commonly used apathy scales, the Neuropsychiatric Inventory-Clinician (NPI-C) apathy and Apathy Evaluation Scale (AES), with the DCA-NCD overall and its 3 dimensions was evaluated by survey. DESIGN:Survey participants, either experts (n = 12, DCA-NCD authors) or scientific community members (n = 19), rated mapping for each item and mean scores were calculated. Interrater reliability between expert and scientific community members was assessed using Cohen's kappa. RESULTS:According to experts, 9 of 11 (81.8%) NPI-C apathy items and 6 of 18 (33.3%) AES items mapped strongly onto the DCA-NCD overall. For the scientific community group, 10 of 11 (90.9%) NPI-C apathy items and 7 of 18 (38.8%) AES items mapped strongly onto the DCA-NCD overall. The overall mean mapping scores were higher for the NPI-C apathy compared to the AES for both expert (t (11) = 3.13, p = .01) and scientific community (t (17) = 3.77, p = .002) groups. There was moderate agreement between the two groups on overall mapping for the NPI-C apathy (kappa= 0.74 (0.57, 1.00)) and AES (kappa= 0.63 (0.35, 1.00)). CONCLUSIONS:More NPI-C apathy than AES items mapped strongly and uniquely onto the DCA-NCD and its dimensions. The NPI-C apathy may better capture the DCA-NCD and its dimensions compared with the AES.

BACKGROUND:Neuropsychiatric symptoms (NPS) can precede cognitive decline in Alzheimer's Disease and serve as prognostic factors of disease progression. Assessing NPS in clinical practice is challenging due to time constraints, subjectivity and limited adaptability of tools to early-stage cognitive decline. Advancements in automatic speech analysis may enable objective characterization of NPS, while structural brain morphometry associated with speech features offers insights into the underlying mechanisms of NPS. This study explored associations between extracted speech features, gold-standard NPS assessments and volumetric brain measures. METHOD:Within the PROSPECT-AD project, data were obtained from the German DELCODE and DESCRIBE cohorts. Analysis included N = 30 healthy controls and N = 44 participants with Subjective Cognitive Decline (SCD)/Mild Cognitive Impairment (MCI) with NPS, assessed by the Geriatric Depression Scale and the Neuropsychiatric Inventory. Participants answered a free-speech question ("Can you describe a positive event in your life?"). Acoustic features (spectral/temporal/frequency/energy variables) and linguistic (i.e. lexical richness, syntactic complexity) were automatically extracted. Residuals from regression models (adjusted for age, sex, MMSE) were used to compute Spearman rank correlations between speech features and clinical scores measuring depression, apathy, anxiety and agitation. In the SCD/MCI group with NPS (N = 21), adjusted correlations were computed between speech features and baseline volumetric measures of regions of interest (ROI). Hypothesis-driven mediation analysis between speech features, clinical scales and ROI volumes is ongoing. RESULTS:We found significant associations (p < 0.05) between the severity of NPS and acoustic/linguistic markers, though these did not survive multiple comparisons corrections (Figure 1). For example, anxiety positively correlated with the sum and duration of pauses, while depression positively correlated with vocal tremor. In the SCD/MCI group with NPS, speech features correlated with volumes in key ROIs, especially related to emotion regulation (i.e. amygdala, insular cortex) (Figure 2). CONCLUSION:Our exploratory findings indicate that (mainly acoustic) markers derived from free speech are associated with the severity of NPS as measured by clinical scales and with regional brain volumes in participants with NPS. These dual associations highlight the potential of speech analysis as a non-invasive, objective tool to assess NPS in early-stage cognitive decline.

BACKGROUND:Post-Acute COVID-19 Syndrome (PACS) frequently includes persistent olfactory dysfunction (OD) and may share neurocognitive features with Alzheimer's disease (AD). While fine motor impairments in handwriting are established in AD, they have not been systematically investigated in PACS. METHODS:In this prospective-retrospective study, handwriting kinematics from 30 patients with OD-related PACS were compared with those of 30 healthy participants (HP) matched for age, sex, and education. Tasks were performed on a digital tablet which automatically extracted kinematic parameters including average pressure (AVP), maximum pressure (MXP), average speed (AVS), and average jerk (AVJ) across linguistic, cognitive non-linguistic, and non-cognitive non-linguistic tasks. A separate cohort comprising 16 patients with AD or mild cognitive impairment (MCI) and 16 matched controls was also evaluated. RESULTS:Patients with OD-PACS showed significantly lower AVP, MXP, AVS, and AVJ values than HP (all p < 0.01), with deficits evident across all task categories. No significant correlations were found between olfactory test scores and kinematic parameters. In contrast, AD/MCI patients exhibited higher AVP and MXP in specific tasks. CONCLUSIONS:This first kinematic handwriting analysis in OD-PACS reveals fine motor slowing, reduced pressure, and decreased movement variability. These alterations, independent of olfactory performance, may affect the reliability of handwriting-based AD screening in this population. Longitudinal studies are warranted to clarify causality and potential reversibility.