Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

ImportancePsychological traits reflecting neuroticism, depressive symptoms, loneliness, and purpose in life are risk factors of AD dementia; however, the underlying biologic mechanisms of these associations remain largely unknown. ObjectiveTo examine whether one or more multi-omic brain molecular subtypes of AD is associated with neuroticism, depressive symptoms, loneliness, and/or purpose in life. DesignTwo cohort-based studies; Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP), both ongoing longitudinal clinical pathological studies that began enrollment in 1994 and 1997. SettingOlder priests, nuns, and brothers from across the U.S. (ROS) and older adults from across the greater Chicago metropolitan area (MAP). Participants822 decedents with multi-omic data from the dorsolateral prefrontal cortex. Exposure(s)Pseudotime, representing molecular distance from no cognitive impairment (NCI) to AD dementia, and three multi-omic brain molecular subtypes of AD dementia representing 3 omic pathways from no cognitive impairment (NCI) to AD dementia that differ by their omic constituents. Main outcome(s) and measure(s)We first ran four separate linear regressions with neuroticism, depressive symptoms, loneliness, purpose in life as the outcomes, and pseudotime as the predictor, adjusting for age, sex and education. We then ran four separate analyses of covariance (ANCOVAs) with Bonferroni-corrected post-hoc tests to test whether the three multi-omic AD subtypes are differentially associated with the four traits, adjusting for the same covariates. ResultPseudotime was positively associated (p<0.05) with neuroticism and loneliness. AD subtypes were differentially associated with the traits: AD subtypes 1 and 3 were associated with neuroticism; AD subtype 2 with depressive symptoms; AD subtype 3 with loneliness, and AD subtype 2 with purpose in life. Conclusions and RelevanceThree multi-omic brain molecular subtypes of AD dementia differentially share omic features with four psychological risk factors of AD dementia. Our data provide novel insights into the biology underlying well-established associations between psychological traits and AD dementia. Key pointsO_ST_ABSQuestionC_ST_ABSAre three distinct multi-omic brain molecular subtypes of Alzheimers disease (AD) dementia associated with four well-established psychological AD risk factors (neuroticism, depressive symptoms, loneliness and purpose in life)? FindingsWe found differential associations: AD subtypes 1 and 3 were associated with neuroticism, AD subtype 2 was associated with depressive symptoms, AD subtype 3 was associated with loneliness; and AD subtype 2 was associated with purpose in life. MeaningPsychological risk factors might be associated with AD dementia via shared multi-omic molecular pathways.

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSWe describe the reliability of remote self-administered digital cognitive measures completed via the Mayo Test Drive (MTD) web-based platform. METHODS1,846 participants (mean age=70, SD=12, range 31-101; 48% male; 96% White; 99% non-Hispanic; 97% cognitively unimpaired) with 2-4 complete MTD sessions at ~7.5-month intervals were included. Test-retest reliability was assessed using single-rating, absolute-agreement, and two-way mixed intraclass correlation coefficients (ICCs) with 95% confidence intervals. ICCs for in-person-administered traditional neuropsychological measures were compared to MTD for a subset of 244 participants. RESULTSReliability was good for the MTD Composite [total ICC = 0.79 (0.77, 0.80)], and moderate-to-good for the primary outcome variables for each MTD subtest [total ICCs 0.70-0.83 for Stricker Learning Span and Symbols]. The reliability of the remote self-administered MTD was similar to in-person-administered cognitive measures. DISCUSSIONMTD showed moderate-to-good reliability, supporting its use in longitudinal monitoring.

ObjectivesWe explored whether adapting traditional neuropsychological tests for online administration against the backdrop of COVID-19 was feasible for people with diverse forms of dementia and healthy older controls. We compared face-to-face and remote settings to ascertain whether remote administration affected performance. DesignWe used a longitudinal design for healthy older controls who completed face-to-face neuropsychological assessments between three and four years before taking part remotely. For patients, we used a cross-sectional design, contrasting a prospective remote cohort with a retrospective face-to-face cohort matched in age, education, and disease duration. SettingRemote assessments were performed using video-conferencing and online testing platforms, with participants using a personal computer or tablet and situated in a quiet room in their own home. Face-to-face assessments were carried out in dedicated testing rooms in our research centre. ParticipantsThe remote cohort comprised ten healthy older controls (also seen face-to-face 3-4 years previously) and 25 patients (n=8 Alzheimers disease (AD); n=3 behavioural variant frontotemporal dementia (bvFTD); n=4 semantic dementia (SD); n=5 progressive nonfluent aphasia (PNFA); n=5 logopenic aphasia (LPA)). The face-to-face patient cohort comprised 64 patients (n=25 AD; n=12 bvFTD; n=9 SD; n=12 PNFA; n=6 LPA). Primary and secondary outcome measuresThe outcome measures comprised the strength of evidence under a Bayesian analytic framework for differences in performances between face-to-face and remote testing environments on a general neuropsychological (primary outcomes) and neurolingustic battery (secondary outcomes). ResultsThere was evidence to suggest comparable performance across testing environments for all participant groups, for a range of neuropsychological tasks across both batteries. ConclusionsOur findings suggest that remote delivery of neuropsychological tests for dementia research is feasible. Strengths and limitations of this studyO_ST_ABSMethodological strengths of this study includeC_ST_ABSO_LIDiverse patient cohorts representing rare dementias with specific communication difficulties C_LIO_LISampling of diverse and relevant neuropsychological domains C_LIO_LIUse of Bayesian statistics to quantify the strength of evidence for the putative null hypothesis (no effect between remote and face-to-face testing) C_LI Limitations includeO_LIRelatively small cohort sizes C_LIO_LILack of direct head-to-head comparisons of test environment in the same patients C_LI

Digital health technologies (DHT) offer a promising solution to the timely identification of early Alzheimer's disease (eAD) to enable early treatment. This study evaluated the feasibility, acceptability, adherence, reliability, and preliminary clinical and content validity of the novel AD Digital Assessment Suite (AD-DAS). 123 individuals (32 healthy controls (HC), 31 amyloid-PET negative (SCDn), 30 amyloid-PET positive (SCDp) with subjective cognitive decline, and 30 early AD (eAD)) participated. AD-DAS was remotely deployed for 28 days. Remote testing was feasible (97.6% completers), acceptable (>85% ''good''), and associated with high adherence (96%). Metrics showed moderate to excellent test-retest reliability (ICC 0.53-0.91), associations with clinical comparators (adjusted R2 0.01-0.24), differentiated eAD from other known groups (absolute log odds differences 0.6-3.28), and correlated with brain atrophy in expected regions. Episodic and working memory AD-DAS metrics differentiated SCDp from SCDn participants. These preliminary findings suggest that AD-DAS may be a promising tool for detecting cognitive impairments in early AD stages.

Objectives Alzheimer's disease (AD) progresses from a cognitively unimpaired stage through mild cognitive impairment (MCI) to dementia. Because language impairment often emerges before dementia, it is a candidate marker of disease stage. Yet the standard batteries of large AD research cohorts rely on a narrow set of tests that mainly evaluate the lexical-semantic domain (i.e., picture naming and verbal fluency), leaving other language domains, such as semantic memory, phonology, morphosyntax, and single-word reading, rarely assessed. We evaluated the diagnostic utility of standard and specialized language tests for distinguishing amyloid-positive MCI and dementia of the Alzheimer's type (DAT) from healthy controls (HC) and from each other. Methods We included 286 participants from the National Alzheimer's Coordinating Center (125 HC, 53 MCI, 108 DAT) assessed with three standard tests from UDSv3 (semantic fluency, letter fluency, and Multilingual Naming Test [MINT]) and nine specialized tests from the FTLD Module (regular and irregular word reading, semantic word-picture matching, semantic associates, Northwestern Anagram Test - Short Form [NAT-S], Northwestern Naming Battery noun and verb naming, sentence repetition, and sentence reading). For each test, the area under the receiver operating characteristic curve (AUC) was computed for DAT vs. HC, MCI vs. HC, and DAT vs. MCI, and AUCs were compared between tests with DeLong's test. Results Eight of 12 measures reached acceptable discrimination (AUC [&ge;] .700) for DAT vs. HC, five for MCI vs. HC, but only semantic fluency and NAT-S for DAT vs. MCI. Semantic fluency showed the highest discrimination in each contrast, though it did not significantly outperform the NAT-S for MCI vs. HC or DAT vs. MCI. MINT, letter fluency, and irregular word reading reached clinically meaningful accuracy in distinguishing MCI and DAT from HC, but not DAT from MCI. The remaining specialized tests reached it only for DAT vs. HC, or not at all. In a post hoc regression, adjusting for cognitive flexibility and working memory attenuated group differences; the DAT difference was no longer significant, whereas the MCI difference remained significant. Discussion Semantic fluency was the strongest single language marker across the AD continuum. Among specialized tests, NAT-S was the most informative, challenging the view that syntax is spared in early AD. Its performance was partly explained by executive function, mainly in DAT, whereas a residual morphosyntactic component remained in MCI, and several specialized tests were limited by ceiling effects. Clinically, the NAT-S may complement semantic fluency for staging, and specialized tests may help characterize heterogeneous language profiles in MCI and DAT.

BackgroundCompared to other components of social cognition, knowledge of social norms has received less attention, even more so in Alzheimers disease (AD). While semantic memory deficits have been identified early in the course of AD, no study has delved into the knowledge of social norms at these preliminary stages, although evidence suggests it shares common ground with semantic memory. In addition, it is unclear whether the knowledge of social norms in AD is associated socioemotional deficits, as seen in the behavioral variant of frontotemporal dementia (bvFTD). Finally, how social norms knowledge impairments predict behaviours in real-world settings remains unknown in the context of AD. MethodsThis study included 286 participants with mild cognitive impairment (MCI), 157 with AD, 285 with bvFTD along with 384 older healthy controls. All participants were selected from the National Alzheimers Coordinating Center. They completed the Social Norms Questionnaire, which assesses the tendency to break or overadhere to social norms. They also completed tests assessing executive, semantic and socioemotional functions, along with tests measuring spontaneous interpersonal behaviours. ResultsBetween-group comparisons show that individuals with AD and MCI break and overadhere to social norms significantly more than HC, while they perform better than individuals with bvFTD. Knowledge of social norms was mainly associated with semantic knowledge across groups, and predicted insensitivity and disinhibition severity in patients. ConclusionsThis study suggests that declines in semantic memory likely play a key role in social norms knowledge decreases and that these decreases predict behavioural tendencies.

AO_SCPLOWBSTRACTC_SCPLOWWe evaluated a digital cognitive assessment platform, Philips IntelliSpace Cognition, in a cross-sectional cohort of patients diagnosed with mild cognitive impairment (MCI). Performance on individual neuropsychological tests, cognitive domain scores, and Alzheimers disease (AD) specific composite scores in MCI were compared with a cohort of cognitively normal adults (CN). The cohorts were matched for age, sex, and education. The performance on all but two neuropsychological tests was worse in the MCI group. After ranking the cognitive domains by effect size, we found that the memory domain was most impaired, followed by executive functioning. The Early AD/MCI Alzheimers Cognitive Composite (EMACC) and Preclinical Alzheimers Cognitive Composite (PACC) scores were constructed from the digital tests on Philips IntelliSpace Cognition. Both AD-specific composite scores showed greater sensitivity and specificity than the Mini-Mental State Examination, as well as individual neuropsychological tests and individual cognitive domain scores. Together, these results demonstrate the diagnostic value of Philips IntelliSpace Cognition in patients with MCI.

BackgroundThe global rise of cognitive impairment and dementia poses significant public health challenges. Existing clinical practice and many social services focused on diagnosis and management after onset. The Hong Kong-Vigilance and Memory Test (HK-VMT) platform combines dementia risk assessment and cognitive test in one accessible tool to enable early detection of dementia in community setting. ObjectiveThis study aimed to evaluate the effectiveness of the HK-VMT platform in assessing dementia risk and a broad spectrum of cognitive impairment in community-dwelling adults. It also assesses the impact of the platform on improving public awareness and encouraging lifestyle changes. MethodsThis cross-sectional study assessed 517 adults aged 50 and above recruited through outreach activities between July 2024 and March 2025. Participants underwent a two-stage screening process consisting of dementia risk assessment and cognitive test. The platform collected data on socio-demographic, psychological, medical, and physiological factors for assessing dementia risk using Cognitive Ageing Risk Score (CARS). Cognitive performance was measured by the HK-VMT. User feedback on platform accessibility, adoption, user engagement, public awareness, and attitudes toward healthy lifestyles was obtained through interview. Results19.7% of participants were at high risk of dementia. Cognitive impairments were detected in 34.3% of participants through the HK-VMT platform. For user experience, 78% of participants with cognitive impairments were unaware of their condition before screening. Over 95% of participants reported improved understanding of their cognitive health status and over 80% expressed intentions to adopt healthy lifestyle. ConclusionsThe HK-VMT platform shows to enhance early detection of cognitive impairments, improve accessibility, increase public awareness and engage the public in brain health management. It represents a scalable solution to support healthy ageing and reduces disparities in early dementia preventive care by bridging community cognitive health services.

The slow progression of Alzheimers disease (AD) poses a challenge for the rapid and individual quantification of early disease-driven cognitive decline. Here, we show that frequently administered remote and unsupervised digital cognitive assessments can detect cognitive decline within 30 weeks in early AD. The sample comprised 202 individuals (52-85 years old), who were cognitively unimpaired (CU) or had mild cognitive impairment (MCI). Participants self-administered remote tasks testing object and scene memory precision, associative memory, and familiarity-dependent memory. A short-term decline in the familiarity-dependent task was observed in all patients with an MCI diagnosis, while both the familiarity-dependent task and memory precision for objects were sensitive to decline in amyloid-positive MCI patients specifically. Change in the remote familiarity-dependent task was correlated with multi-year change on annual in-person neuropsychological assessments. In conclusion, frequent remote cognitive testing is a promising tool to feasibly capture and monitor subtle and short-term cognitive decline.

Background: Cognitive rehabilitation (CR) is an established behavioral intervention that improves daily functioning for individuals with mild cognitive impairment (MCI) and early-stage dementia. Traditional models of in-person delivery limit access, particularly for individuals living in rural areas. This study evaluated the efficacy of a novel telephone-based virtual CR model combining speech-language pathologist (SLP)-led sessions with cognitive exercises delivered by an automated voice agent between visits. Methods: We conducted a retrospective observational analysis of 141 older adults who completed treatment to discharge (58% female; mean age 71.2, standard deviation 10.8 years; MCI diagnosis rate 61.7%, dementia diagnosis rate 29.1%; Montreal Cognitive Assessment mean score 20.8, standard deviation 4.3). Changes in four outcome measures from initiation of treatment to discharge were evaluated for statistical significance. The four outcomes studied were patient-reported quality of life and three therapist-rated Functional Communication Measures (FCMs): overall cognition, spoken language, and language comprehension. Changes were compared to FCM averages from the American Speech-Language-Hearing Association National Outcomes Measurement System (ASHA NOMS). Models were developed to predict changes in outcome measures based on patient demographics, clinical status, program engagement and treating therapist. Results: All four outcomes improved significantly over the course of treatment (p<0.05), with medium to very large effect sizes. Mean changes in the three FCM outcomes exceeded ASHA NOMS benchmarks for in-person outpatient care. A majority of patients saw an improvement in each clinical outcome measure. Models with meaningful predictive power were identified for changes in all outcome measures except the FCM for language comprehension. Baseline cognitive function was the most influential and negatively correlated predictor of an improvement in overall cognitive abilities and language expression. Baseline quality of life was the dominant and negatively correlated predictor of improvement in quality of life. Conclusions: Telephone-based virtual CR led by SLPs with automated exercises delivered by a voice agent produced clinically meaningful functional and quality of life gains relative to external benchmarks for in-person clinical practice. These results support the use of virtual CR within post-diagnostic care for older adults experiencing cognitive impairment, particularly for rural and underserved communities.

Mild cognitive impairment (MCI) is clinical diagnosis that refers to individuals whose performance is below average on standardized cognitive tests, but who otherwise function independently in instrumental activities of daily living. Few prior studies have addressed the problem of selecting the optimal combination of behavioral instruments and cutoff scores for detecting MCI in an outpatient setting. The aim of this work is to provide insight into two related questions: (1) What is the relative sensitivity and specificity of a battery of standardized tests frequently used to assess for MCI, as defined using receiver operating characteristic (ROC)-based analysis? (2) What are the optimal "cut point" scores for distinguishing patients mildly impaired performance based on these instruments? Two hundred forty outpatient behavioral neurology evaluations were retrospectively analyzed. All work was conducted with the formal approval of the Johns Hopkins University School of Medicine Institutional Review Board. All instruments that were evaluated performed very well in the detection of dementia (mean AUC = 0.8). However, fewer tasks performed acceptably in the detection of MCI (mean AUC = 0.7). Instruments that performed best in the detection of MCI tended to have higher total possible scores or not to reflect a score out of a total number possible. Cognitive screening tools, like the MMSE, did not perform well in the detection of MCI, raising important considerations for their interpretation. No one task in isolation is sufficient to detect MCI, and behavioral performance is not the only relevant consideration in differential diagnosis. However, these findings highlight the relative weakness of many assessments when used to build a comprehensive profile of a very large portion of outpatients presenting at clinic, those whose deficits are more subtle.

ImportanceCognitive impairment (CI) is often under detected in primary care due to time and resource constraints. Passive analysis of clinical dialogue may offer an accessible approach for screening. ObjectiveTo assess whether audio recordings of patient-physician dialogue during routine primary care visits can be used to identify CI using acoustic speech features and machine learning (ML). DesignThis observational study conducted among older primary care patients involved audio recording primary care visits using a microphone and portable device. An external validation cohort was recruited in a separate city to assess reproducibility of findings. SettingThe study was conducted in primary care practices in New York City, with additional participants recruited from primary care practices in Chicago, Illinois, for validation. ParticipantsThe study included 787 English-speaking patients aged 55 years and older, without documented history of dementia or mild CI. Eligible patients were recruited from primary care practices during routine visits. For validation, 179 patients meeting the same eligibility criteria were recruited from primary care practices in Chicago. ExposuresMultiple thirty-second speech segments were extracted from recordings. Acoustic features were derived using foundation models (Whisper, HuBERT, Wav2Vec 2.0) and expert-defined methods (eGeMAPS, prosody). Main Outcomes and MeasuresCI was defined as Montreal Cognitive Assessment score [&ge;]1.0 standard deviations below age and education-adjusted norms. ML classifiers were trained to predict CI status from audio recordings. We calculated area under the receiver operating characteristic curve (AUC-ROC) and maximum F1 score (Fmax) for identifying CI participants. ResultsThe mean age was 66.8 years and 21% had CI. Models using Whisper-derived acoustic features performed best (AUC-ROC=0.733, 95% confidence interval [95%CI]=0.714-0.752; Fmax(CI)=0.504, 95%CI=0.474-0.534). Results generalized to the external site with similar performance (AUC-ROC=0.727, 95%CI=0.714-0.740; Fmax(CI)=0.459, 95%CI=0.442-0.476). Model interpretation identified pitch, timing, and variability features as key predictors. When used for screening, the algorithm achieved positive predictive value of 30.4% (95%CI=28.7%-32.1%), sensitivity of 68.2% (95%CI=61.8%-74.6%), and specificity of 63.6% (95%CI=59.8%-67.4%) on the holdout cohort. Conclusions and RelevanceML models trained on acoustic features from brief clinical conversations identified CI with high accuracy. These findings support the feasibility of passive, speech-based screening during routine primary care. Key Points QuestionCan acoustic features extracted from audio recordings of patient-physician conversations during routine primary care visits be used to screen for cognitive impairment? FindingsIn this study including 787 older adults without diagnosis of cognitive problems, machine learning models trained on acoustic features from audio segments of recordings of primary care visits achieved area under the receiver operating characteristic curve values of 0.72 for predicting cognitive impairment. The algorithm achieved a sensitivity of 83%, specificity of 44%, and positive predictive value of 28%, identifying a subset of primary care patients at higher risk for cognitive impairment. Models performed similarly on an external validation dataset of 179 participants. Interpretability analyses highlighted patient pause duration and energy-related features as salient indicators of cognition status. MeaningThese findings suggest that short segments of naturalistic clinical dialogue may contain useful acoustic signals for passively screening patients for cognitive impairment.

BackgroundDigital language markers show promise in detecting early cognitive impairment related to Alzheimers disease (AD), yet their relationship with cerebrospinal fluid (CSF) biomarkers of AD pathology remains unclear mainly due to the lack of data with both CSF and language markers. ObjectiveThis study aims to build links between digital language markers and fluid biomarkers through surrogate CSF biomarkers. MethodsUsing NACC clinical data as anchor variables, language makers in the I-CONECT study were linked to NACC CSF data. Surrogate CSF biomarkers were created for I-CONECT subjects using machine learning models from common NACC clinical variables. Correlations assessed associations between CSF and language markers. ResultsLower predicted amyloid-{beta} correlated significantly with reduced syntactic complexity and shorter speech responses. Higher predicted total tau and phosphorylated tau correlated with reduced syntactic complexity. ConclusionsThis study demonstrates novel links between language markers and fluid biomarkers, highlighting conversational language as a potential accessible, non-invasive approach for early detection and monitoring of Alzheimers pathology.

IntroductionAlzheimers disease (AD) affects speech, language, and executive functions. Combining blood biomarkers with non-invasive speech analysis may aid early detection. MethodsA speech battery was administered to three groups aged 65-70: cognitively normal low-risk (LRC), high-risk (HRC), and mild cognitive impairment (MCI). ResultsVariance in Mel-Frequency Cepstral Coefficients (MFCCs) predicted p-tau217 (F = 3.71, p = 0.005), with group-specific effects in LRC and HRC. In MCI, articulation rate, speech percentage, and pause percentage predicted p-tau217 (all p < 0.01). Language features including idea density, content density, and open-class word rate also predicted p-tau217 (all p < 0.05). MFCCs and syllable timing correlated with NfL (all p < 0.01). Lexical diversity differed between groups, notably in HRC and MCI. DiscussionSpeech timing, fluency, and voice quality, along with lexical richness and idea density, predicted p-tau217 and NfL, supporting acoustic and linguistic features as non-invasive digital biomarkers for early AD-related neurodegeneration.

Objective: Regression-based normative approaches are widely used in neuropsychology but often rely on score transformations to satisfy model assumptions. We compared previously published linear regression (LR)-based norms with norms derived using Generalized Additive Models for Location, Scale and Shape (GAMLSS) for the brief cognitive battery used in the Norwegian Dementia Disease Initiation (DDI) cohort. Method: GAMLSS norms were developed using the same normative samples as the original LR norms for the Consortium to Establish a Registry for Alzheimers Disease (CERAD) word list test, Trail Making Test (TMT) A and B, FAS phonemic fluency, and Visual Object and Space Perception Battery (VOSP) Silhouettes. Expected low-score frequencies and empirical base rates were assessed in a normative subsample (n = 131). Clinical implications were evaluated in the DDI clinical cohort (n = 643) using Mild Cognitive Impairment (MCI) classification, two-year diagnostic stability and change, and cerebrospinal fluid (CSF) biomarkers. Results: Compared with LR norms, GAMLSS yielded lower frequencies of low scores, primarily driven by CERAD delayed recall. Nevertheless, concordance between approaches was high (kappa = 0.91), with only 4.2% discordant classifications. Two-year diagnostic stability and change were broadly similar across approaches, and CSF biomarker profiles did not clearly favor either normative method. Conclusions: GAMLSS provided a more faithful representation of neuropsychological score distributions, particularly for bounded and non-normal outcomes. However, downstream clinical differences were modest in this setting, suggesting that well-calibrated LR norms may remain robust for clinical classification.

INTRODUCTION: Voice data combined with large language models may detect cognitive impairment, yet an interpretable framework has not been formally established. We developed and validated a scalable and interpretable framework, the Neurocognitive Speech Taxonomy (NST). METHODS: NST maps 314 voice features to 7 neurocognitive domains derived from neuropsychology, speech pathology, neurology and related fields. The domains capture features including articulatory precision, cognitive-linguistic, executive fluency & planning, phonation & laryngeal control, prosodic modulation, lexical-semantic, and morphosyntactic complexity. We evaluated NST structural stability, clinical validity and racial-educational equity in three cohorts (total N = 1,479). Cohort data included demographic and neuropsychological evaluations and AD biomarker status. We tested whether NST scores distinguish cognitive status (unimpaired vs impaired) and AD-biomarker status, track cognitive change, and correlate with AD neuroimaging measures. RESULTS: NST showed stable cross-cohort structure (Mantel r = 0.71-0.77) and differentiated MCI (d = -0.39) and AD biomarker status (d = -0.23). NST tracked longitudinal cognitive change, correlated with hippocampal volume (r up to 0.28, P < .001), and showed smaller racial and educational disparities relative to standard cognitive testing (NST d = 0.03-0.33, MoCA d = 0.37-0.72). DISCUSSION: NST provides a validated neurocognitive construct that facilitates interpretation of AI-driven voice biomarkers of AD. This study was not registered in a public trials registry.

Introduction: Narrative recall is widely used to detect cognitive impairment, but dominant instruments carry proprietary restrictions. The Craft Story 21 (CS), the non-proprietary NACC UDS4 standard, is not available standalone. Here, we validate the freely available Puppy Escape (PE). Methods: 346 participants (153 cognitively normal, 106 subjective cognitive impairment, 87 mild cognitive impairment) completed PE and CS. Analyses evaluated convergent and criterion validity, MCI-vs-control discrimination, and incremental validity. Results: PE and CS converged (r=.43-.47) and were equivalent on 10/12 neuropsychological measures. PE Delayed discriminated MCI from controls (d=1.03; ROC-AUC equal to CS, DeLong p=.510) and added variance beyond CS (R2=+.054, p<.001). Automated subscores revealed MCI deficits in location, action, and name content. PE-18 short form retained discrimination (d=1.02) with 18 items. Discussion: PE matched CS across all validation domains and captured complementary diagnostic information. PE and PE-18 are available via online registration explicitly permitting industry-sponsored research and fee-for-service clinical use.

With promising disease-modifying therapies (DMTs) emerging and good evidence to support risk reduction in the delay of dementia onset and progression, it is important to understand the profile of patients attending memory assessment services to estimate what proportion of patients might benefit from different types of interventions. The Oxford Brain Health Clinic (OBHC) is a psychiatry-led, clinical-research service that offers memory clinic patients detailed clinical assessments and equal access to research opportunities as part of their secondary care pathway. In this work, we describe the characteristics of OBHC patients in terms of demographics, diagnoses and prevalence of potentially modifiable risk factors compared with a cohort of healthy volunteers and the average memory clinic population. Our results suggest that high research consent rates (91.5%) in the OBHC resulted in a highly representative cohort of the clinical population. Based on Lecanemab trial inclusion criteria, 24.6% of the OBHC population may be suitable for further investigation into DMTs. Furthermore, 67.4% of OBHC patients have at least one potentially modifiable risk factor that may benefit from lifestyle interventions, particularly those focused on depression, sleep and physical activity.

INTRODUCTIONRepeated cognitive testing can boost scores due to practice effects (PEs). It remains unclear whether PEs persist across multiple follow-ups and long durations. We examined PEs across multiple assessments from midlife to old age in a nonclinical sample. METHODMen (N=1,608) in the Vietnam Era Twin Study of Aging (VETSA) underwent neuropsychological assessment across 4 waves from mean age 56 to 74. We leveraged age-matched attrition-replacement (AR) participants to estimate PEs at each wave. We compared cognitive trajectories and prevalence of mild cognitive impairment (MCI) using unadjusted versus PE-adjusted scores. RESULTSAcross follow-ups, a range of 7-12 out of 30 measures demonstrated significant PEs, especially in episodic memory and visuospatial domains. Adjusting for PEs resulted in steeper cognitive decline with up to 29% higher MCI prevalence. DISCUSSIONPEs persist across multiple assessments and decades. The AR-participant method provides accurate sample-specific PE estimates that enable significantly earlier detection of MCI.

INTRODUCTIONList-learning tasks are important for characterizing memory in ADRD research, but the Uniform Data Set neuropsychological battery (UDS-NB) lacks a list-learning paradigm; thus, sites administer a range of tests. We developed a harmonized memory composite that incorporates UDS memory tests and multiple list-learning tasks. METHODSItem-banking confirmatory factor analysis was applied to develop a memory composite in a diagnostically heterogenous sample (n=5943) who completed the UDS-NB and one of five list-learning tasks. Construct validity was evaluated through associations with demographics, disease severity, cognitive tasks, brain volume, and plasma phosphorylated tau (p-tau181 and p-tau217). Test-retest reliability was assessed. Analyses were replicated in a racially/ethnically diverse cohort (n=1058). RESULTSFit indices, loadings, distributions, and test-retest reliability were adequate. Expected associations with demographics and clinical measures within development and validation cohorts supported validity. DISCUSSIONThis composite enables researchers to incorporate multiple list-learning tasks with other UDS measures to create a single metric.