INTRODUCTION:Reversion to normal cognition in mild cognitive impairment (MCI) is relatively common. This study tested whether total sleep time (TST) and sleep efficiency (SE) on the night before cognitive testing predicts MCI reversion. METHODS:Fifty-eight community-dwelling older adults with MCI (mean age = 75 years) participated. MCI was defined as cognitive performance ≥ 1.5 standard deviation below norms in at least one domain, with preserved daily functioning. Participants completed cognitive testing at baseline and 1-year follow-up, and sleep was objectively measured using actigraphy at baseline. Logistic regression assessed whether prior-night TST and SE predicts reversion, adjusting for age and sex. RESULTS:Sixteen participants (27.6%) reverted. Shorter prior-night TST was associated with higher odds of reversion, whereas SE showed no significant association. Random night or chronic sleep measures showed no associations. DISCUSSION:Acute sleep disruption may transiently impair cognition and contribute to MCI misclassification. Sleep screening before testing may improve diagnostic reliability. HIGHLIGHTS:Shorter total sleep time on the night before cognitive testing was associated with higher odds of mild cognitive impairment (MCI) reversion after 1 year. Sleep efficiency, random-night sleep, and 14-day average sleep showed no significant association with MCI reversion. Single-domain MCI cases were more likely to revert than multi-domain MCI cases, suggesting greater susceptibility to acute factors such as poor sleep. Brief screening of prior-night sleep or actigraphy-derived metrics could help identify low-reliability assessment and improve diagnostic stability in MCI research.

OBJECTIVE:This study aims to determine the minimum number of days required to obtain reliable actigraphy-measured sleep metrics in older adults with Mild Cognitive Impairment (MCI). METHODS:One hundred and fourteen older adults (61 with MCI, 53 cognitively unimpaired) wore wrist-worn actigraphy devices for at least 60 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) were calculated over cumulative windows (2 - 59) and compared to 60-day reference values using linear mixed-effect regression models. Sensitivity analyses were conducted using 30- and 45-day references. RESULTS:Across all participants, reliable estimates required a minimum of 8 days for TST and 6 days for SE and WASO. Participants with MCI required more days than controls (9 vs. 2 days for TST, 7 vs. 4 days for SE, and 6 vs. 2 days for WASO). Sensitivity analyses showed that shorter monitoring periods (30 and 45 days) could still provide reliable metrics. CONCLUSIONS:To obtain reliable sleep metrics, at least 9 days of actigraphy data are needed for older adults with MCI, compared to 4 days for controls, suggesting the need for extended monitoring in cognitively impaired individuals.

Large language models excel at processing complex clinical data and advanced reasoning, yet domain-specific adaptation is essential to realize their full potential in fields such as Alzheimer's disease and related dementias (ADRD). Here, we present a generative language model for ADRD fine-tuned via reinforcement learning with verifiable rewards using a self-certainty-aware advantage. Model development and validation leveraged data from five ADRD cohorts, totaling 54, 535 participants. Our framework integrates demographics, personal and family medical histories, medication use, neuropsychological test results, functional assessments, physical and neurological examination findings, laboratory data and multimodal neuroimaging to construct comprehensive clinical profiles. On held-out testing data involving 36, 688 participants, our model achieved robust performance on syndromic classification, primary etiological diagnosis and biomarker prediction. Model predictions were validated against postmortem-confirmed diagnoses, and clinical utility was demonstrated in a controlled within-subjects crossover study where board-certified neurologists reviewed cases with and without model assistance, showing that exposure to model responses improved diagnostic performance. These results demonstrate that targeted domain adaptation with reinforcement learning can enable language models to deliver accurate, reasoning-driven support in ADRD evaluation. Prospective validation will be essential to translate these advances into improved patient outcomes.

This study examined the pattern of change in cognitive function using the Montreal Cognitive Assessment (MoCA) in the context of physical function and identified risk factors for poor cognitive function post allogeneic HCT in adults ≥ 60 years. This is a two-center prospective cohort study, measuring cognitive and physical function pre-HCT and 3 months post-HCT. A MoCA score of 26 and above is considered normal. We defined mild impairment as a MoCA score of 23–25, and < 23 as moderate impairment. The Sankey plot was generated to assess the transition of impairment status on the MoCA screen from pre-HCT to 3 months post-HCT. The association of predetermined clinical and demographic variables and 3-month cognitive function and cognitive categories was determined using linear mixed-effects model and ordinal logistic regression, respectively. Predetermined variables including physical function, age, gender, education, depressive symptoms, KPS, HCT-CI, disease type, and treatment intensity were used in multivariate analyses. A total of 171 participants were identified. Pre-HCT, 84 (49

INTRODUCTION:Sleep disturbances are common in older adults, particularly those with cognitive impairment. This study examines how day-to-day sleep quality impacts real-world driving behaviors, offering insight into sleep as a potential functional biomarker of cognitive health. METHODS:We monitored 149 community-dwelling older adults (90 cognitively impaired, 59 unimpaired) over 12 weeks. Sleep was measured via wrist-worn actigraphy, and driving data were collected via an in-vehicle sensor system. A zero-inflated Poisson regression model examined whether sleep efficiency was associated with next-day driving likelihood and frequency, and whether these relationships varied by cognitive status. RESULTS:Better sleep efficiency increased the likelihood of driving the next day more for cognitively impaired participants than for unimpaired participants. Higher sleep efficiency was associated with increased driving frequency in both groups. DISCUSSION:These findings underscore the importance of daily sleep variability as a potential digital biomarker for functional abilities in older adults, highlighting opportunities for early intervention to preserve mobility and independence. Highlights:A 1 standard deviation increase in sleep efficiency increases next-day trip counts (incidence rate ratio = 1.014).In cognitively impaired participants, better sleep lowers the odds of not driving the next day (odds ratio = 0.877).Among those who choose to drive, trip counts do not differ by cognitive status.

Background and Objective Discrepancies between objective and subjective evaluations of sleep efficiency have been observed in individuals with pathological and healthy aging. Objective sleep evaluation using actigraphy has been proposed as a potential tool for the clinical assessment of mild cognitive impairment (MCI). Methods Habitual sleep at home was evaluated using actigraphy (objective measure) and sleep diaries (subjective measure) in 45 participants (between 28 and 72 years old). Participants were divided into four groups by age and by diagnosis (MCI and Alzheimer desease). Cognitive and sleep measures were analyzed for comparisons and correlations. Results Significant discrepancies between objective and subjective sleep efficiency were observed in healthy and pathological ages. The MCI group showed the lowest sleep efficiency compared to other groups. Correlation analysis revealed a significant relationship between cognitive impairments and sleep efficiency in MCI and AD groups. Conclusions Objective sleep evaluation, with a particular focus on sleep efficiency, should be considered as a potential marker for MCI.

BACKGROUND:Mild cognitive impairment (MCI) can revert to normal cognitive function in 15-48 % of cases1-3, suggesting that some initial MCI diagnosis reflect transient or context-dependent conditions. Sleep disruption is common in older adults, and even one night of poor sleep can adversely affect cognitive performance temporarily.4,5 We hypothesized that the greater sleep disruption on the night preceding cognitive assessments would be associated with increased likelihood of MCI reversion at one-year follow-up. METHOD:Sixty older adults (mean age = 75 years; 25 males), meeting Petersen's criteria6 for MCI based on a battery of five cognitive-domain assessments (memory, attention, visuospatial, language, and executive function) at baseline year, participated in the study. Participants wore wrist- actigraphs to collect objective sleep data and completed sleep diaries to validate time to go to bed and out of bed on the night before the assessments. Sleep duration (hours) and efficiency (%) were standardized across individuals using Z-scores. A logistic regression model, adjusted for age and gender, estimated the odds of maintaining MCI classification versus reverting to normal cognitive status at a one-year follow-up using the same battery. RESULT:At one-year follow-up, 45 (75%) maintained MCI status and 15 participants (25%) reverted to normal status (See details in Table 1). Shorter sleep duration the night before the baseline cognitive assessments was significantly associated with higher odds of reversion (b = -0.76, p =  0.049). Each standard deviation (1.27 hour) increases in sleep duration lowered the odds of reversion by approximately 53%. Sleep efficiency, age, and gender were not significant predictors (p > 0.10). CONCLUSION:In this preliminary study, participants who slept shorter than the group average on the night before cognitive examination were more likely to revert from MCI to normal cognitive classification one year later. These findings underscore the need to consider acute sleep variation in MCI diagnoses. Incorporating short-term sleep measures into clinical care and trials7 along with biological biomarkers may help refine diagnostic accuracy as prodromal Alzheimer's vs. potentially "reversible" causes, and guide targeted interventions for cognitive health.

Sleep disturbances and cognitive impairments, especially memory deficiency are one of the most common complications affecting everyday life in patient diagnosed with neurodegenerative disorder. Clinically evaluated, specific cognitive processes can be grouped into two categories: amnestic functions (AMN, clinically memory impairments) and non-amnestic functions (n-AMN, cognitive impairments non-memory related). To date, no cure treatments are available for AMN / n-AMN patients, therefore maintaining the well-being and an adequate sleep quality of people who are in prodromal state (i.e., AMN or n-AMN is a high priority for society general and for the patient / family specifical. In this study, we compared objective sleep results to self-reported sleep in AMN and n-AMN groups with respect to healthy matched controls to investigate whether the self-reported sleep results are accurate Sleep disturbances and cognitive impairments, especially memory deficiency are one of the most common complications affecting everyday life in patient diagnosed with neurodegenerative disorder. Clinically evaluated, specific cognitive processes can be grouped into two categories: amnestic functions (AMN, clinically memory impairments) and non-amnestic functions (n-AMN, cognitive impairments non-memory related). To date, no cure treatments are available for AMN / n-AMN patients, therefore maintaining the well-being and an adequate sleep quality of people who are in prodromal state (i.e., AMN or n-AMN is a high priority for society general and for the patient / family specifical. In this study, we compared objective sleep results to self-reported sleep in AMN and n-AMN groups with respect to healthy matched controls to investigate whether the self-reported sleep results are accurate Table summarizes all results found in the study. AMN group showed lower score in cognitive memory related processes (Craft story and Benson figure recall), as compared to n-AMN and Healthy groups. Both patient groups overestimated their SE in self-report data as compared to SE in objective actigraphy data. In healthy group, the SE was accurately self-reported (see Figure). Our results suggest that regardless of the severity of memory impairments objective measure of sleep should be implemented for clinical evaluation.

INTRODUCTION:Neuropsychiatric symptoms (NPS) such as increased apathy, affective symptoms, psychosis and hyperactivity are common in Alzheimer's disease (AD) and are associated with increased disease severity and caregiver burden. In contrast to well-characterized associations between AD-related cognitive deficits and focal neuropathology (e.g., memory and hippocampal atrophy), fewer studies have focused on associations between NPS-brain associations in AD. Furthermore, studies focusing on magnetic resonance imaging measures of gray matter (GM) abnormalities associated with NPS in AD have not been systematically reviewed. METHODS:To address this gap, a systematic literature review was undertaken to identify articles that assessed structural brain differences associated with NPS in AD. This review identified 29 such articles that tested associations between NPS and gray matter loss (GML: reduced GM density, reduced GM volume, decreased cortical thickness, etc.). RESULTS:Across all NPS, most symptoms were associated with GML in the prefrontal cortex and medial temporal lobe, highlighting key limbic/limbic adjacent structures including orbitofrontal cortex and parahippocampal regions. Other regions exhibiting associations included the superior and middle temporal gyri as well as anterior and posterior cingulate cortex. CONCLUSION:Understanding how GM changes in the brain relate to NPS in AD may not only improve our understanding of NPS and AD but may also provide help identify homologies/correspondence with brain changes in psychiatric diseases.

Background/Objectives: Distraction is a form of impaired selective attention that becomes more pronounced with normal aging and in pathological conditions such as mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Event-related potentials (ERPs) provide sensitive, time-resolved measures of neural mechanisms underlying distractibility. This study aimed to identify age- and disease-related ERP signatures of auditory–visual distraction as potential functional biomarkers for cognitive decline. Methods: Forty-six participants were enrolled, including young controls (Y), healthy older controls (O), individuals with MCI, and individuals with AD. Participants performed cross-modal interference tasks in which irrelevant auditory distracting sounds were paired with a relevant visual discriminating task. The distraction potential was quantified as the difference between ERP responses to novel distractors and standard stimuli, focusing on three core components: N1-enhancement, P3a, and reorienting negativity (RON). Behavioral measures (accuracy, reaction time, miss responses) were also assessed. Results: Compared to Y, O showed increased N1-enhancement and reduced P3a and RON amplitudes, consistent with age-related susceptibility to distraction. Patients with MCI and AD exhibited further abnormalities, including diminished P3a and altered RON responses, suggesting impaired orientation and reorientation of attention. Behavioral distraction effect was observed in all groups, with no significant difference between groups. ERP–cognition correlations indicated that reduced P3a amplitude and delayed RON were associated with executive dysfunction and memory deficits. Conclusions: ERP signatures of distraction, particularly altered P3a and RON components, differentiate normal aging from pathological decline and may serve as functional biomarkers for early detection of MCI and AD. These findings highlight the translational potential of distraction paradigms in clinical assessment of aging-related cognitive impairment.

Mild cognitive impairment (MCI) can revert to normal cognitive function in 15-48 % of cases 1-3 , suggesting that some initial MCI diagnosis reflect transient or context-dependent conditions. Sleep disruption is common in older adults, and even one night of poor sleep can adversely affect cognitive performance temporarily. 4,5 We hypothesized that the greater sleep disruption on the night preceding cognitive assessments would be associated with increased likelihood of MCI reversion at one-year follow-up. Sixty older adults (mean age = 75 years; 25 males), meeting Petersen's criteria 6 for MCI based on a battery of five cognitive-domain assessments (memory, attention, visuospatial, language, and executive function) at baseline year, participated in the study. Participants wore wrist- actigraphs to collect objective sleep data and completed sleep diaries to validate time to go to bed and out of bed on the night before the assessments. Sleep duration (hours) and efficiency (%) were standardized across individuals using Z-scores. A logistic regression model, adjusted for age and gender, estimated the odds of maintaining MCI classification versus reverting to normal cognitive status at a one-year follow-up using the same battery. At one-year follow-up, 45 (75%) maintained MCI status and 15 participants (25%) reverted to normal status (See details in Table 1). Shorter sleep duration the night before the baseline cognitive assessments was significantly associated with higher odds of reversion (b = -0.76, p  =  0.049). Each standard deviation (1.27 hour) increases in sleep duration lowered the odds of reversion by approximately 53%. Sleep efficiency, age, and gender were not significant predictors ( p  > 0.10). In this preliminary study, participants who slept shorter than the group average on the night before cognitive examination were more likely to revert from MCI to normal cognitive classification one year later. These findings underscore the need to consider acute sleep variation in MCI diagnoses. Incorporating short-term sleep measures into clinical care and trials 7 along with biological biomarkers may help refine diagnostic accuracy as prodromal Alzheimer's vs. potentially “reversible” causes, and guide targeted interventions for cognitive health.

To describe factors leading to delay in diagnosis of CJD.

Background: Increasing numbers of older adults are undergoing HCT. Pre-HCT frailty is predictive of survival in older adults.1 However, an association between frailty and functional outcomes and HRQOL are unknown in older adults undergoing HCT. Examining potential associations is important to identify higher-risk HCT candidates who will benefit from proactive interventions and help patients and clinicians with treatment decision making. The objective of this study is to examine the association of pre-HCT frailty status with cognitive function and HRQOL at 12-months post-HCT in adults ≥ 60 years undergoing HCT. Methods: This study is a secondary data analysis of a longitudinal cohort study at a single center conducted between 2018-2022, that included adults ≥60 years who have a diagnosis of a hematologic malignancy undergoing HCT. Participants completed the Fried Frailty assessment, the Montreal Cognitive Assessment (MoCA), and the European Quality of Life Questionnaire–Cancer 30 (QLQ-C30) prior to admission for HCT and at 12-months post-HCT. Frailty was defined as possessing three or more of the following: unintentional weight loss, low grip strength, self-reported exhaustion, slow gait speed, and low physical activity.2 Pre-frail was defined as having 1-2 of the criteria. Multinominal modeling with a random effect for subject was used to account for the correlation within patient, and to compare frailty status over time. ANOVA was used to compare 12-month post-HCT cognitive function and HRQOL between pre-HCT frailty statuses, and pair-wise comparisons were adjusted using Tukey's method. All analyses were done in SAS 9.4 and p <0.05 was considered statistically significant. Results: 104 older adults completed pre-HCT assessment. The average age at HCT was 67.7 years (range: 60.2-76.6). There were 69 (66.3%) allogeneic and 35 (33.7%) autologous HCT recipients. Pre-HCT, 10.6% were frail, 63.5% were pre-frail, and 26% were non-frail. At 12-months post-HCT (n=62), the prevalence of frail, pre-frail and non-frail were 25.8%, 67.7% and 6.5% respectively. There was a statistically significant increase in the prevalence of frailty between pre-HCT and 12 months post-HCT (odds ratio= 4.9, p= <0.001). Pre-HCT frailty status was associated with a lower 12-month MoCA score, and lower physical and emotional functioning on the QLQ-C30. The mean 12-month MoCA score for those who were frail pre-HCT was 23.4 compared to 26.2 and 25.4 for those who were pre-frail and non-frail, respectively (p=0.033). The mean score for the physical function sub-score on the QLQ-C30 at 12-month was 68.3 for patients who were frail pre-HCT compared to 83.9 and 83.2 for those who were pre-frail and non-frail, respectively (p=0.034). The mean score for the emotional function sub-score on the QLQ-C30 at 12-month was 76.1 for patients who were frail pre-HCT compared to 90.6 and 87.9 for those who were pre-frail and non-frail, respectively (p=0.034). Conclusions: Pre-HCT frailty is associated with lower cognitive performance and HRQOL at 12-months post-HCT, specifically physical and emotional functioning. At one year, the prevalence of frailty in HCT survivors approaches that of community dwelling older adults ≥ 80 years.3 The increased prevalence reflects the stress of cancer, accumulation of high-intensity therapeutic exposures, and transplant related morbidities. This study highlights the need to provide targeted interventions to mitigate and prevent frailty pre-HCT and early in the recovery process to preserve cognitive function and maximize HRQOL for older adults post-HCT. 1.Sung, A. D., Koll, T., Gier, S. H., Racioppi, A., White, G., Lew, M., ... & McCurdy, S. R. (2024). Preconditioning frailty phenotype influences survival and relapse for older allogeneic transplantation recipients. Transplantation and Cellular Therapy, 30(4), 415-e1. 2.Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-56. PMID: 11253156. 3.Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: a systematic review. J Am Geriatr Soc. 2012 Aug;60(8):1487-92. PMID: 22881367.

Identifying common clinical features of Creutzfeldt Jakob Disease.

Abstract Introduction Sleep dysfunction increases the risk of mild cognitive impairment (MCI) and Alzheimer’s Disease (AD). Emerging sensor technologies can index real world (RW) sleep and instrumental activities of daily living affected by neurodegeneration, as outlined below. Methods 85 legally licensed active drivers with cognitive decline (76 MCI, 9 mild AD) (mean age = 75.6 years; 37 females) and 54 age and education-matched controls (mean age= 74.7 years; 36 females) participated. Cognitive status was assessed using standardized neuropsychological tests according to NIA-AA guidelines. Sensor systems installed in participants’ own vehicles recorded driving incidence (driving or not each day). Sleep was recorded using wrist-worn actigraphy, and verified with self-reported sleep diaries over three continuous months. A mixed-effect logistic regression analyzed daily driving incidence with predictors including z-scores of total sleep time (TST) and sleep efficiency (SE) in previous night, and cognitive status (MCI or AD v. controls), adjusted for age, gender, employment status, and driving season. Results From 9,214 days of data on nightly sleep and next day driving, increase in TST significantly reduced driving incidence (21% per standard deviation [SD]; Odds Ratio [OR] = 0.79, 95% Confidence Interval [CI] = 0.74 – 0.84, p < 0.001). Increase in SE was associated with increased driving incidence among those with cognitive decline, compared to controls (15% per SD; OR = 1.14, 95% CI = 1.03 – 1.27, p = 0.01). Conclusion Findings underscore the value of RW digital biomarkers to track the effects of neurodegeneration on sleep and instrumental activities of daily living. Deciding to drive increased with greater sleep efficiency, particularly for those with cognitive decline. Quantitative profiles of sleep and driving behavior offer novel opportunities for early detection of the effects of AD. Support (if any) The present study is supported by the National Institute on Aging at the National Institutes of Health (5R01AG17177-18).

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care. Drawing on 51,269 participants across 9 independent, geographically diverse datasets, an AI model identifies the etiologies contributing to dementia in individuals, harnessing a broad array of data, including demographics, medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging.

People with HIV (PWH) often develop HIV-related neurological impairments known as HIV-associated neurocognitive disorder (HAND), but cognitive dysfunction in older PWH may also be due to age-related disorders such as Alzheimer's disease (AD). Discerning these two conditions is challenging since the specific neural characteristics are not well understood and limited studies have probed HAND and AD spectrum (ADS) directly. We examined the neural dynamics underlying motor processing during cognitive interference using magnetoencephalography (MEG) in 22 biomarker-confirmed patients on the ADS, 22 older participants diagnosed with HAND, and 30 healthy aging controls. MEG data were transformed into the time-frequency domain to examine movement-related oscillatory activity and the impact of cognitive interference on distinct stages of motor programming. Both cognitively impaired groups (ADS/HAND) performed significantly worse on the task (e.g., less accurate and slower reaction time) and exhibited reductions in frontal and cerebellar beta and parietal gamma activity relative to controls. Disease-specific aberrations were also detected such that those with HAND exhibited weaker gamma interference effects than those on the ADS in frontoparietal and motor areas. Additionally, temporally distinct beta interference effects were identified, with ADS participants exhibiting stronger beta interference activity in the temporal cortex during motor planning, along with weaker beta interference oscillations dispersed across frontoparietal and cerebellar cortices during movement execution relative to those with HAND. These results indicate both overlapping and distinct neurophysiological aberrations in those with ADS disorders or HAND in key motor and top-down cognitive processing regions during cognitive interference and provide new evidence for distinct neuropathology.