bluuebunny/crossref_metadata_2025 huggingface dataset:
Association of CSF cortisol with cortical thickness in Alzheimer's disease continuum
Authors: Souza Laura Willers , Ros Lucas Uglione Da , Bieger Andrei , Ferrari‐Souza João Pedro , Borelli Wyllians Vendramini , de Bastiani Marco Antônio , Bauer‐Negrini Guilherme , DuBois Jonathan M. , Zimmer Eduardo R.
Published: January 2025

Background

Multiple studies have linked high cortisol levels, a frequently used biomarker of stress, with the Alzheimer's disease (AD) pathophysiology. However, the relationship between cerebrospinal fluid (CSF) cortisol levels and AD‐related brain atrophy is not fully understood. This study sought to investigate the cross‐sectional and longitudinal association between CSF cortisol levels and brain cortical thickness in patients across the biological and clinical continuum of AD.

Methods

We evaluated 310 individuals from the ADNI cohort with available baseline CSF cortisol concentrations, structural MRI and CSF Elecsys biomarkers (Aß1‐42 and p‐tau181). Cross‐sectional analysis was conducted in Freesurfer (v7.1.1) through vertex‐wise analysis using general linear models (GLMs) corrected for multiple comparisons through cluster formation (p<0.01) and permutation (Monte Carlo simulation of 10,000 iterations). Longitudinal measures of an AD signature meta‐ROI were estimated from the surface area‐weighted average of the mean cortical thickness of the following ROIs: entorhinal, inferior temporal, middle temporal, and fusiform. Linear mixed‐effects (LME) models corrected for confounders were performed to evaluate cortical thickness longitudinal trajectories. The following models were used to assess the association between cortical thickness and cortisol levels: (1) cortisol as an independent variable; (2) cortisol, clinical diagnoses, and interaction; (3) cortisol, AD biomarkers positivity, and interaction.

Results

Demographics are depicted in Table 1. Cross‐sectional analysis revealed that higher cortisol levels were associated with higher brain atrophy in several brain regions, as depicted by cortical brain thickness measures (Figure 1). These analyses also showed that cortisol does not interact with clinical diagnoses or AD biomarkers to decrease cortical thickness. In the longitudinal analyses, the association between cortisol and cortical thickness and the interaction with clinical diagnoses were not significant. However, we found that cortisol significantly interacts with AD biomarkers positivity to reduce cortical thickness values over time (ß=0.002, p=0.03; Figure 2).

Conclusions

Altogether, our results support that cortisol levels potentiate AD pathophysiology effects on AD‐related brain atrophy. These findings suggest that CSF cortisol levels may affect brain vulnerability to AD pathophysiology in the long term through neurodegeneration. Thus, our results have potential implications for understating AD‐related brain atrophy and developing innovative therapeutic strategies.
Differential Patterns of Brain and Body Aging on MR Imaging
Authors: Raji Cyrus A. , Meysami Somayeh , Hashemi Sam , Garg Saurabh , Akbari Nasrin , Gouda Ahmed , Chodakiewitz Yosef Gavriel , Nguyen Thanh Duc , Niotis Kellyann , Merrill David A. , Attariwala Rajpaul
Published: January 2025

Background

Comparative information on how whole‐body organs are linked with age and the brain is lacking.

Method

Overall, 7,149 healthy participants from four sites (Mean age 53.06 ± 12.95 years, 18‐97 years; 48% women; 52% men; 38% non‐white) were scanned on 1.5T MR machines with a whole‐body protocol. Whole body sequences utilized in the quantitative analyses were coronal T1 for organ, fat and muscle segmentation. Deep learning with FastSurfer on MPRAGE trained on 134 participants aged 27‐66 and segmented 96 brain regions. Partial correlation analysis was done controlling for sex and total intracranial volume for brain regions and total abdominal organ volume for abdominal organs. Multiple comparisons were accounted for using the Bonferroni method.

Result

Age negatively correlated with gray matter (rp = ‐0.38, p = 6.31×10‐244) and white matter (rp = ‐0.263, p = 4.03×10‐112). Age positively correlated to cerebral ventricle volume (rp = 0.493, p < .001). Increasing age was also inversely correlated with the lobar structural volumes: frontal lobe (rp = ‐0.420 p = 2.70×10‐302), temporal lobe (rp = ‐0.378 p = 4.50×10‐240), parietal lobe (rp ‐0.373 with p = 2.22×10‐233), occipital lobe (rp = ‐0.259; p = 1.58×10‐108). Age negatively correlated with AD risk regions: hippocampus (rp = ‐0.288 and p = 9.79×10‐136), posterior cingulate (rp = ‐0.338, p = 4.37×10‐189), precuneus (rp = ‐0.321, p= 1.44×10‐169). Beyond the brain, the kidney and psoas muscle showed significant negative correlations with age (kidney; rp= ‐0.114, p = 1.23×10‐211) and the psoas muscle (rp = ‐0.353, p= 1.13×10‐208). Strikingly, visceral fat (vfat) was found to have a strong positive correlation with age, (rp = 0.416, p= 8.81×10‐297). Subcutaneous fat (sfat), by contrast, showed a null correlation (rp = ‐0.01, p=1.0). Increasing age showed negative correlations with liver volumes (rp = ‐0.226, p = 2.21×10‐82), spleen (rp = ‐0.262, 5.46×10‐112) and total muscle volume (rp = ‐0.317, 3.17×10‐165).

Conclusion

Differential patterns of brain and body organ aging may lend insight into how age can increase the risk for common brain disorders in the elderly such as Alzheimer’s.
Associations of gray matter volume and white mater hyperintensity mean diffusivity and volume with PiB status in mild cognitive impairment
Authors: Santini Tales , Li Jinghang , Emanuel James E , Liou Jr‐Jiun , Sajewski Andrea , Chu Cong , de Almeida Bruno , Lopresti Brian J , Aizenstein Howard J , Wu Minjie , Gildengers Ariel , Ibrahim Tamer S
Published: January 2025

Background

White matter hyperintensities are associated with vascular diseases, while amyloid deposition serves as an early indicator of Alzheimer's disease (AD). Despite both pathologies contributing to cognitive decline, the ability to distinguish between them is crucial in selecting the appropriate course of treatment or management of these conditions.

Method

71 mild cognitive impairment (MCI) participants completed the baseline imaging (age 72.6±7.61 years, 38 females) and were included in this study. Pittsburgh Compound B (PiB) PET scans were acquired. 7T MRI images were acquired with a customized radiofrequency coil [1] and included T1w MPRAGE, T2w FLAIR and diffusion sequences. The brain was parcellated using Freesurfer and wmh‐seg [2]. We extracted the baseline (N=63) and longitudinal volumes (N=45, follow up duration = 1.22±0.26 years) of AD signature regions (hippocampus, amygdala, temporal lobes) [3] and the volumes (N=69) and mean diffusivity (N=58) in the white matter hyperintensities and compared the PiB+ (age 75.73±7.97, total SUVR > 1.346, CIRS‐G=8.8±4.1) vs PiB‐ (age 71.5±7.32, CIRS=10.62±3.9) participants. We performed t‐tests adjusting for age, sex, and intracranial volume for baseline variables and adjusting for age at baseline, follow up duration, and sex for the longitudinal variables.

Result

We found a significantly smaller volume in the Hippocampus and amygdala in the PiB+ group and significantly bigger volume of white matter hyperintensities in the PiB‐ group. PiB‐ group also presented higher mean diffusivity inside the white matter hyperintensities, potentially indicating a more advanced stage of the lesion, however partial volume effects need to be evaluated in future work. Longitudinal analysis shows a rapid volume decline in some AD signature gray matter regions in the PiB+ group.

Conclusion

Our results demonstrate the feasibility of high‐resolution and high‐quality 7T MRI in potentially differentiating between neurodegenerative processes related to AD type dementia versus vascular disease in an MCI population.
Whole genome sequence‐based association of cognitive decline and retinal thickness in the Japanese population
Authors: Taira Makiko , Fuse Nobuo , Saykin Andrew J. , Nagami Fuji , Kinoshita Kengo , Yamamoto Masayuki
Published: January 2025

Background

Dementia is age‐related with a significant genetic contribution, yet genome‐wide association studies have not fully accounted for heritability. This discrepancy may in part be due to reliance on SNPs and small indels. Whole‐genome sequencing (WGS) data in the Japanese population may reveal population‐specific susceptibility loci for dementia. Retinal imaging with optical coherence tomography (OCT) is noninvasive, reproducible, and can detect thinning associated with progressive neurodegeneration. Association of population‐specific genetic susceptibility loci with retinal thinning and cognitive decline may reveal novel aspects of dementia risk and pathophysiology.

Method

Among participants with WGS data from the Tohoku Medical Megabank Organization (ToMMo) Ophthalmology Study ("ToMMo Eye Study"), individuals with adequate quality data on retinal nerve fiber layer and ganglion cell layer thickness from spectral‐domain optical coherence tomography (SD‐OCT) scans were selected. Since retinal thinning also occurs in glaucoma, we performed a GWAS using age, sex, and 10 principal components as covariates using SAGE1.2 to obtain a set of genes responsible for glaucoma and confirm that the genotyping was successful. We then attempted to identify susceptibility loci for cognitive decline by using (1) the Mini‐Mental State Examination, Japanese version (MMSE‐J), (2) the Montreal Cognitive Assessment, Japanese version (MoCA‐J), and (3) the Mini‐COG© (a simple screening for early detection of dementia, Japanese version) scores as associated factors, respectively. Furthermore, these validation results were also compared with those obtained from GWAS using imputation data performed on custom arrays (Japonica ArrayTM, v2 or NEO) for Japanese.

Result

84 significant (p < 5.0E‐8) genome‐wide susceptibility loci (hg38) of RNFL were detected on 14K WGS‐based study (the top hit locus: Chr14, SIX6 gene, P=4.50E‐46). There were many genetic loci that have already been reported to be associated with glaucoma susceptibility, including the above locus. Among the results of GWAS for cognitive decline combining the three cognitive scores after normalization to z‐scores, several loci have shown significant susceptibility in both of RNFL and cognitive rating scale. Some loci suggested more than a high or moderate effect of altering protein efficacy.

Conclusion

We present an initial WGS‐based genetic study of retinal thickness and cognitive decline in the Japanese population.
Advancements in High‐Resolution MRI Postmortem brain imaging: Histological examination on cells exclusively exhibiting White Matter Hyperintensities made possible using a novel, 3D printed, reusable, imaging container and cutting guide
Authors: Berardinelli Jacob , Li Jinghang , Farhat Nadim , Liou Jr‐Jiun , Sajewski Andrea , Santini Tales , Wu Minjie , Mettenburg Joseph , Aizenstein Howard J , Ikonomovic Milos D , Kofler Julia , Ibrahim Tamer S
Published: January 2025

Background

Employing a custom 60‐channel single‐transmit/32‐channel receive Tic‐Tac‐Toe (TTT) coil in 7T MRI imaging has enhanced the resolution and contrast for identifying White Matter Hyperintensities (WMHs), which are critical markers in Alzheimer’s disease and related dementias (AD/ADRD).

Method

The method involves resecting the left hemisphere of the brain, excluding the cerebellum, followed by embalming in 10% formalin. 1.5% agar and 30% sucrose by weight are heated and mixed for embedding in the container. The brain is positioned in the cutting guide and inserted into the base (Figure 1). Agar is meticulously poured ensuring minimal air bubble formation in the sulci and ventricles with additional agar pipetted as needed. The lid is then attached, more agar is added until the container is filled, and the cap is secured. The container is sealed in plastic to safeguard the coil from potential liquid damage and isolate the tissue/formalin while scanning. The imaging protocol includes a B1+ map at 3.2mm, MP2RAGE at 0.4mm, GRE at .37mm, and T2‐SPACE at .36mm all isotropic resolutions. The high‐resolution T2‐weighted SPACE is used for WMH segmentation and quantification. The MP2RAGE provides a T1‐weighted image for further data analysis registration to gross anatomy as well as in vivo scans.

Result

This protocol has been applied to 120 brain specimens to date, with representative MR images in Figure 2. The techniques afford pathologists flexibility in slicing the brain into coronal or axial parallel 5 mm slabs at various angles. See Figure 3 for a visual representation of the progression of the container design and of the cutting process.

Conclusion

This innovative combination of the cutting guide, container, and the custom 7T TTT coil is reusable and reliable. It accommodates various brain sizes, offers precise cutting options, ensures fast and accurate image registration to in vivo and gross anatomy, and achieves homogeneous B1 fields, culminating in superior research data. The described method is subject to ongoing refinement for enhanced efficiency, with iterative optimization of the container’s shape, size, and function through 3D printing. This work was supported by the National Institutes of Health under award numbers R01MH111265, R01AG063525, T32MH119168, and U19AG068054.
Dynamic Light Scattering Spectroscopy as an Early Biomarker of Alzheimer’s Disease
Authors: Weiss Jeffrey Neill
Published: January 2025

Background

The early detection of neurologic damage at the microscopic level when the disease is subclinical would facilitate intervention preventing progression or potentially reversing the condition. The early determination of drug efficacy could shorten the length of drug studies, thereby reducing research costs. The eye is the only place in the body where an artery, vein, and nerve can be directly visualized The nerve fiber layer of the retina is an outgrowth of the brain.

Method

Dynamic Light Scattering (DLS) Spectroscopy measures the thermal random movement (Brownian Motion) of particles by analyzing the temporal fluctuations of scattered light. A proof‐of‐concept instrument for making retinal measurements provides optical power well below the maximal permissible exposure recommended by the ANSI Z136.1 standard. Scattered light is analyzed by a digital autocorrelator with an extended delay option for baseline determination. The testing duration is 5 seconds. DLS measurements were made from the macular area of 4 patients diagnosed with mild cognitive impairment and 4 age‐matched controls with no history of neurologic disease. Both groups of patients had normal retinal examinations, including normal Optical Coherence Tomography, (OCT).

Result

Figure 1 Screenshot ‐ 62‐year‐old female recently with mild cognitive impairment, subsequently diagnosed with Alzheimer’s disease 1 year after DLS testing. Figure 2 Screenshot ‐ Age‐matched control

Conclusion

The 4 patients with mild cognitive impairment were subsequently diagnosed with Alzheimer’s disease approximately 1 year after the abnormal DLS measurement. The differences between the patients subsequently diagnosed with Alzheimer’s disease, compared to the normal age‐matched controls, was consistent across the tested patients. Alzheimer’s measurements begin above 0.6 g 2(t)‐1 and exhibit flattening of the initial line as opposed to the curve seen in non‐Alzheimer’s patients. The development of an early, noninvasive, quantitative test to diagnose Alzheimer’s disease will lead to breakthroughs in drug development and hopefully, to the successful treatment of patients before dementia is irreversible.
Intelligent Residential Environments Catering to Dementia‐Friendly Needs
Authors: Khan Arshia A
Published: January 2025

This study was approved by the ethics review board at the University of Minnesota. In conclusion, the successful design and testing of an intelligent living space tailored for dementia care were conducted in a controlled lab environment with healthy participants. The primary aim was to assess the viability of integrating robots, wearable sensors, and spatial technology to support the well‐being of individuals affected by dementia. Utilizing pressure sensors in chairs alongside the robot’s emotion detection system effectively evaluated participants' moods, aligning with self‐reported moods in 95∖% of cases. Additionally, door sensors accurately detected exiting behavior 100∖% of the time, while wall‐mounted PIR sensors successfully identified wandering behavior. Wearable sensors, including EDA and blood pressure sensors, coupled with robot interactions, detected physiological changes during various activities, indicating the most significant impact during interactions with the robot. These findings suggest that an intelligent living space specifically designed for dementia care can effectively preserve the quality of life for affected individuals, particularly through robot interactions, showcasing promising effectiveness.
Fostering Equity in Dementia Care: The Vital Role of Culturally Sensitive Technology
Authors: Khan Arshia A , Imtiaz Rana Z
Published: January 2025

This perspective abstract highlights the critical demand for culturally sensitive technology in dementia caregiving due to the heightened risk and unique challenges faced by individuals from diverse backgrounds. The escalating prevalence of dementia necessitates a caregiving paradigm shift, especially in the context of diverse cultural backgrounds. Research consistently underscores the existence of cultural disparities in dementia risk, diagnosis, and care outcomes, emphasizing the urgent need for culturally sensitive technology in dementia care. Individuals from diverse cultural backgrounds often face unique challenges in accessing and receiving adequate dementia care. These challenges may stem from variations in communication styles, beliefs, and societal attitudes toward dementia. Culturally sensitive technology has the potential to bridge these gaps by tailoring interventions to align with specific cultural contexts. It can enhance communication, understanding, and overall caregiving effectiveness, providing personalized support that respects the cultural nuances of those affected by dementia. Moreover, incorporating cultural sensitivity into technology design not only addresses disparities but also contributes to more inclusive healthcare practices. Culturally tailored technologies can empower caregivers with tools that recognize and respect diverse perspectives, fostering a more collaborative and patient‐centered approach. In essence, the need for culturally sensitive technology in dementia care is paramount to ensuring equitable access, improving outcomes, and promoting a holistic understanding of the multifaceted challenges faced by individuals from diverse cultural backgrounds affected by dementia. This imperative aligns with the broader goal of enhancing the quality of life for all individuals affected by dementia while fostering a more inclusive and compassionate healthcare environment.
Parsing disease heterogeneity using normative modelling and Generative Adversarial Networks (GANs)
Authors: Chintapalli Sai Spandana , Govindarajan Sindhuja Tirumalai , Shou Haochang , Huang Hao , Davatzikos Christos
Published: January 2025

Background

Structural and functional heterogeneity in the brains of patients with Alzheimer’s disease (AD) leads to diagnostic and prognostic uncertainty and confounds clinical treatment planning. Normative modelling, where individual‐level deviations in brain measures from a reference sample are computed to infer personalized effects of disease, allows parsing of disease heterogeneity. In this study, GAN based normative modelling technique quantifies individual level neuroanatomical abnormality thereby facilitating measurement of personalized disease related effects in AD patients.

Method

We adapt the pix2pix GAN to translate a subject with disease to a corresponding subject without disease. We train this model using a dataset comprising of healthy controls and synthetically simulated patients. Healthy controls (n = 6000) are selected from the ISTAGING consortium. Our neuroanatomical brain measures are the 10 region of interest (ROI) volumes (covering left and right frontal, parietal, occipital, temporal lobes as well as deep brain structures) computed using a multi‐atlas segmentation technique. To simulate patients, for each healthy control we introduce 10‐30% atrophy or expansion in a random combination of ROIs while preserving clinical covariate effects. The model learns to synthesize patient‐specific controls by removing disease‐related variations from patient’s brain measures (Fig.1). Deviation of the patient from the synthesized disease‐free control acts as an image‐based biomarker that is sensitive to disease effects and severity. For performance assessment, we select 200 controls (CN) and 200 AD participants (PT) from the OASIS dataset and compute their deviations across the 10 ROI volumes using the model pretrained on the ISTAGING dataset. Logistic regression is used to assess the overall discriminative power of the derived deviations in AD classification.

Result

Larger deviations in PT compared to CN suggest disease related abnormality in brain measures (Fig.2.a). Additionally, GAN’s deviations provide a considerable gain over raw ROI volumes in AD classification as quantified using the 5‐fold AUC scores (Fig.2.b).

Conclusion

GAN‐based normative modelling technique introduced here is a useful tool to parse heterogeneity in brain measures at an individual level. We see that self‐supervised training of the model using pseudo‐synthetically simulated patient data that is agnostic to disease patterns can help detect real disease related effects.
Acceptance of wearable technology for the early detection of dementia‐causing diseases: perspectives from the CODEC II cohort
Authors: Wilson Sarah , Beswick Emily , Morrell Rachel , Bhogal Sharandeep , Tolley Clare , Whitfield Tim , Wing Kieran , Ardle Ríona Mc , Walker Zuzana , Slight Sarah
Published: January 2025

Background

With global dementia prevalence estimated to reach 139 million by 2050,(1) early detection of dementia‐causing diseases is crucial for promoting preventative interventions. Wearable technologies have the potential to detect early signs; however, they need to be acceptable amongst users. We explored user’s perspectives on the acceptability of wearable devices.

Method

A sub‐group of participants from a research cohort, Predictors of COgnitive DECline using digital devices (CODEC‐II), were recruited. Participants used four wearables (smartwatch, EEG headband, active and passive smartphone app) for two weeks every three‐months over a year. Semi‐structured interviews were conducted with participants after the first two weeks. A framework thematic analysis approach was used, assisted by N‐Vivo (QSR, version 14.23.2). The Intelligent Systems Technology Acceptance Model(2) aided in refining key themes.

Result

Twenty‐one participants were interviewed, including those with subjective cognitive decline (n = 10), mild cognitive impairment (MCI) (n = 7), dementia with Lewy bodies (n = 1), and caregivers (n = 3). Key themes included ease of use, data security, usefulness, intention of use. Many participants expressed low digital self‐efficacy, relying on the research team for support with technology setup and troubleshooting. Those with MCI needed more assistance with synching data, and experience difficulty with some cognitive testing games in the active smartphone app. Some participants expressed concerns surrounding the potential for someone to hack data generated from their device. Others questioned the accuracy of the study smartwatch, as they compared its outputs to their personal ones. Participants with professional or caregiving responsibilities described how their busy lifestyle hindered their ability to use the active smartphone app every day; this was not expressed by retired individuals.

Conclusion

Acceptance of using different wearable technologies for the early detection of dementia‐causing diseases varied amongst participants. Researchers should consider the difficulty level of cognitive testing games for those with cognitive impairment. Future work must explore ways to support individuals with low digital self‐efficacy, ensuring digital health equity is supported. References: • Prince et al., 2015, The global impact of dementia, World Alzheimer report • Vorm and Combs, 2022, Integrating transparency, trust, and acceptance: The intelligent systems technology acceptance model (ISTAM). International Journal of Human–Computer Interaction.
Early identification of participants at risk of cognitive decline: An online tool for patient prediction of cognitive decline
Authors: Cespedes Marcela Ines , Canovas Rodrigo , Gillis Cai , Maserejian Nancy N , Maruff Paul , Fowler Christopher J , Rainey‐Smith Stephanie R , Martins Ralph N , Masters Colin L , Doecke James D.
Published: January 2025

Background

The success of therapeutic options for treatment of Alzheimer’s disease (AD) and the growing emphasis for such treatment to commence in the pre‐clinical phase makes it necessary to have robust empirical models of clinical disease progression to understand findings from clinical trials, allow clinicians to evaluate effects of new drugs, and to select individuals for future trials. Such models have been developed from relatively small samples, with incomplete data/substantial loss to follow‐up. The ADOPIC consortium provides the largest complete AD natural history sample to date. We applied our sigmoid models of disease progression to cognitive data from ADOPIC, and developed an algorithm to predict cognitive change.

Method

We developed an online application (Prediction of Alzheimer’s Disease Progression Tool, [PADPT]) to estimate decline in cognition over the course of AD within 2,861 ADOPIC participants (1,434 Cognitively unimpaired [CU], 342 MCI [Mild Cognitive Impairment], 211 AD, and 847 progressed from CU→MCI or MCI→AD), with ≥1 PET Amyloid (Aβ) scan and ≥36 months of cognitive assessment with the pre‐clinical Alzheimer’s cognitive composite (PACC). Individual participant slopes were defined according to age and gender using linear mixed effects models. Aβ+ (≥50CL) participant rates of decline vs their mean PACC score were investigated using quantile polynomial regression. Within PADPT, single visit information on age, clinical classification and APOE ε4 allele status is entered to compute the estimated annual rate of decline. Candidate results are mapped to the sigmoid curve to determine their predicted rate of cognitive decline over the chosen time period.

Result

Quantile regression bands (QRB’s) aligned with participant cognitive decline. Individuals with stable cognition clustered to the top right of the plot (Figure 1). Mean slopes for APOE ε4 allele status separated QRB’s for CU and AD, but not for MCI groups. All participants allocated to cognitive decline groups (past the first sigmoid inflection point) demonstrated decline in PACC score that remained in the QRB’s, regardless of initial clinical classification.

Conclusion

Used in conjunction with baseline profile information, the PADPT application can assist with clinical decision making both pre and post‐treatment and also identify suitable clinical trial candidates who, untreated, will show subtle decline in cognition.
Relationships Between NIH Toolbox Cognitive Battery Subtests and In Vivo Amyloid and Tau in People with Amnestic Mild Cognitive Impairment and Dementia of the Alzheimer’s Type
Authors: Fox‐Fuller Joshua T , Petscavage Kenneth , Rogers Ginny , Koeppe Robert A. , Albin Roger L. , Iordan Alexandru D , Hampstead Benjamin M. , Rahman‐Filipiak Annalise
Published: January 2025

Background

The computerized NIH Toolbox Cognition Battery (NIHTB‐CB) was designed to assess cognitive functioning across the lifespan. Previous studies demonstrated that NIHTB‐CB measures discriminate between healthy controls (HCs), individuals with amnestic mild cognitive impairment (aMCI), and individuals with dementia of the Alzheimer’s type (DAT). Scores on NIHTB‐CB tasks also correspond with performance on well‐validated neuropsychological measures of the same cognitive domains. We are among the first to evaluate how NIHTB‐CB subtest performances and in vivo Alzheimer’s disease (AD) pathology are related in people with aMCI and DAT.

Method

As part of a larger clinical trial in the Research Program on Cognition and Neuromodulation Based Interventions, 139 participants with aMCI or DAT (Table 1) underwent neuropsychological assessment, including five iPad NIHTB‐CB subtests: Flanker, List Sort, Card Sort, Pattern Separation, and Picture Sequence. Participants also completed positron emission tomography (PET) scans of amyloid and tau. Amyloid PET scans were normalized to the centiloid value, and tau PET standardized uptake value ratios (SUVr) were merged into a bilateral meta‐region‐of‐interest based on Braak staging. Stepwise linear regressions were used to evaluate the relationship between age and education with NIHTB‐CB subtests (Model 1), as well as in vivo AD pathophysiology with NIHTB‐CB subtests beyond age and education (Model 2).

Result

In vivo amyloid was significantly related to List Sort (β = ‐0.31, p<.001, ΔR2 = .09) and Card Sort performance (β = ‐0.25, p = .004, ΔR2 = .06), explaining significantly more subtest variance beyond age and education. In vivo tau was significantly related to performance on all NIHTB‐CB subtests and explained significantly more subtest variance than demographic factors (Flanker: β = ‐0.25, p = .004, ΔR2 = .06; List Sort: β = ‐0.40, p<.001, ΔR2 = .15; Card Sort: β = ‐0.32, p<.001, ΔR2 = .10; Pattern Separation: β = ‐0.18, p = .04, ΔR2 = .03; Picture Sequence: β = ‐0.23, p = .01, ΔR2 = .05).

Conclusion

In vivo AD pathophysiology is related to NIHTB‐CB subtest performances in individuals with aMCI or DAT and explains significantly more NIHTB‐CB subtest variance than demographic factors alone. This work adds growing support to the relationship between NIHTB‐CB subtests and in vivo AD biomarkers.
RoboZen: Humanoid Robots Administer Mindfullness Based Stress Releif Therapy to people with dementia and help them relax and improve mood and cognition
Authors: Khan Arshia A
Published: January 2025

Background

In this study humanoid robots were programmed to deliver mindfulness‐based stress relief therapy to individuals with dementia. Precisely programmed, these robots aim to enhance well‐being, providing tailored interventions for relaxation, mood enhancement, and heightened cognition in dementia care, showcasing technology’s promise in improving overall quality of life [1, 2, 3, 4].

Methods

In this study, the humanoid robot was programmed with a carefully AI based design protocol incorporating calming activities, guided breathing exercises, and interactive engagement, tailored to the cognitive abilities and preferences of individuals with dementia, to effectively deliver mindfulness‐based stress relief therapy. This mixed‐methods study assessed the effectiveness of humanoid robots in delivering mindfulness‐based stress relief therapy to individuals with dementia. The sample included 9 participants diagnosed with dementia residing in nursing homes. Ethical approval was obtained from the Institutional Review Board. The intervention involved a humanoid robot delivering mindfulness‐based stress relief sessions. Quantitative data, obtained through pre‐ and post‐intervention assessments, included standardized scales measuring stress levels, mood, and cognitive function. Physiological indicators such as heart rate variability and electrodermal activity were recorded using wearable sensors during sessions. Qualitative data were gathered through participant observations and post‐session interviews to capture subjective experiences and perceptions of humanoid robot‐administered therapy. Thematic analysis identified recurring patterns and insights. The study aimed to offer insights into the feasibility, acceptability, and potential benefits of integrating humanoid robots into dementia care for stress relief, contributing to innovative approaches in dementia interventions.

Results

Analysis of data from participants undergoing humanoid robot‐administered mindfulness‐based stress relief therapy showed significant improvements. The Brief Introspection Mood Scale (BMIS) indicated a highly significant enhancement in mood scores post‐intervention. Participants reported consistently positive shifts in emotional states, supported by electrodermal data showing reduced stress levels during and after sessions. Cognitive assessments, using the Montreal Cognitive Assessment (MoCA), consistently demonstrated improved cognitive function following the intervention.

Conclusion

In conclusion, this study demonstrates the promising efficacy of humanoid robot‐administered mindfulness‐based stress relief therapy, revealing significant improvements in mood, physiological stress indicators, and cognitive function among individuals with dementia. This offers a valuable avenue for holistic care interventions.
Machine‐learning analysis of real‐world multimodal data collected autonomously at home detects dementia more precisely than a traditional composite scale
Authors: Rueda‐Delgado Laura M , Buick Alison R , Nolan Hugh , Rowe James B , Murphy Brian
Published: January 2025

Background

Current tools for Alzheimer’s disease screening and staging used in clinical research (e.g. ACE‐3, ADAS‐Cog) require substantial face‐to‐face time with trained professionals, and may be affected by subjectivity, “white coat syndrome” and other biases. Alzheimer’s symptomology is multi‐factorial and varies day‐to‐day. To enable stratification for precision treatments, more effective composite endpoints are required, with accurate quantification of functional impairment in individual domains. Ideally these would be measured objectively and frequently in real‐world contexts to improve validity, and to reduce clinical burden in trial and care settings.

Method

We present a machine‐learning stratification analysis using data from the Cumulus real‐world multi‐domain neuroassessment platform (Figure 1), in CNS101, a year‐long study designed with a consortium of 10 pharma companies. Participants completed a range of tablet‐based functional tasks with wake EEG, and separately recorded EEG during sleep. This cross‐sectional analysis uses 1111 sessions from repeated sampling with 101 participants (47 patients with mild Alzheimer’s type dementia, and 54 corresponding controls, recruited across 7 sites in the UK), during an early two‐week burst period of autonomous at‐home use. Bagging of decision trees was used to compare the power of different data sources in discriminating dementia from neurotypical states. This algorithm is widely used for its performance across learning tasks (linear and non‐linear), and heterogeneous datasets of differing size, levels of noise, and collinearity. For each set of input data, 100 classifiers were trained and evaluated (10‐fold cross‐validation in each of 10 random partitions), and the mean Area Under the Curve (AUC) of sensitivity/specificity trade‐off was calculated.

Result

A full set of 691 multimodal features yielded a classifier with AUC performance of 0.953 and error rate 4.7% (Figures 2, 3) – compared to AUC of 0.921 and error rate 7.9% for the set of 13 ADAS‐Cog subscores. While individual modalities/domains all performed above chance, the 50 features from Cumulus cognitive tasks alone had an AUC performance of 0.955.

Conclusion

Patients using digital technology autonomously in the home can yield data that matches or exceeds the discriminative power of a traditional composite scale. This can enable objective precision measurement of disease severity at scale.
Feasibility and Acceptability of Digital Cognitive Screening Approaches for Older Adults in Primary Care
Authors: Thompson Louisa I. , Lawrence Molly , Rosenbaum Jennifer A , Czech Stephanie , Anthony David C , Chandran Rabin F , Goldberg Arnold R , Vyshedskiy Andrey , Elwy Anashua R , Eaton Charles B
Published: January 2025

Background

New immunotherapies for early‐stage Alzheimer’s disease (AD) have ushered in fresh hope for AD research and clinical care, but also highlight barriers to AD screening and timely diagnosis in the US. Digital cognitive assessments could potentially streamline screening and referrals for AD treatment and/or clinical trials. We report preliminary data on the feasibility and acceptability of three digital cognitive approaches for older adults completing Annual Wellness or routine follow‐up visits with a primary care provider (PCP).

Methods

Data were collected for an ongoing primary care‐based study in Rhode Island. Cognitive screening approaches included: 1) remote online screening with the Boston Online Cognitive Assessment (BOCA) prior to the PCP appointment, 2) self‐administered screening with the BOCA in the waiting room immediately before or after the appointment, and 3) provider‐administered screening during the appointment using the Digital Clock and Recall (Linus Health DCRTM). Participants also completed a 30‐minute, in‐clinic cognitive health consultation including the Montreal Cognitive Assessment (as a reference standard) to receive feedback and referral options. Five PCPs aided in protocol development through focus groups and participated in data collection. Potential participants were identified via EMR. Recruitment included mailings, MyChart messages and phone calls, and direct referrals from PCPs. Exclusion criteria: dementia or other neurological disease, score of <13 on the telephone version of the MoCA.

Results

34 older adults were screened, 23 enrolled, and 2 withdrew. The sample is currently 52% female, 81% White, and mean age is 66. Most participants completed remote BOCA screening on a smartphone. Most reported that they preferred screening at home compared to in the clinic. 11 out of 11 participants completed at least one instance of the BOCA screening online prior to their PCP appointment. DCR tablet screening was successful in 9 out of 11 PCP appointments. PCP time constraints and technological issues were reasons for incomplete administration. In‐clinic self‐administered BOCA screening was discontinued due to space and time constraints, and the lack of a designated coordinator onsite.

Conclusions

Both remote online screening and tablet‐based, provider‐administered screening in‐clinic may be feasible and acceptable approaches to cognitive screening for older adults in primary care.
Using image‐text similarity scores from picture descriptions for differentiating the logopenic variant of progressive aphasia from amnestic Alzheimer’s dementia
Authors: Pugalenthi Lokesha , Dial Heather R , Li J. Jessy , Schmitz Suzanne , Henry Maya L. , Wauters Lisa , Keller Jeffrey N. , Largent Avery , Chang Patrick , Chang Joshua , Foil Heather , Lester‐Smith Rosemary A , Toprac Paul , Hilsabeck Robin C.
Published: January 2025

Background

Primary progressive aphasia (PPA) is a language‐led dementia associated with underlying Alzheimer’s disease (AD) or frontotemporal lobar degeneration pathology. As part of the Alzheimer’s spectrum, logopenic (lv) PPA may be particularly difficult to distinguish from amnestic AD, due to overlapping clinical features. Analysis of linguistic and acoustic variables derived from connected speech has shown promise as a diagnostic tool for differentiating dementia subtypes. Here we investigate the utility of a machine learning (ML)‐based classifier for differentiating lvPPA from amnestic AD using an image‐text similarity metric derived from a picture description task.

Method

Monolingual English speakers diagnosed with lvPPA (n = 18) and amnestic AD (n = 14) were asked to describe a picture (Cat Rescue). A similarity score between the image and patient’s transcription was derived with each model of the Vision‐Language encoder CLIP. This involved computing the cosine similarity between the image‐ and transcription‐based representation vectors. These image‐text similarity scores were fed into 10 ML classification algorithms to differentiate lvPPA from amnestic AD.

Result

For differentiating lvPPA from amnestic AD, our CLIP‐based feedforward neural network classifier achieved F1, AUC, and Accuracy scores of 81%, 80%, and 81% respectively. This outperformed our baselines of stratified random sampling and uniform random sampling by 30%.

Conclusion

The current study shows promising differentiation of lvPPA from amnestic AD through the single feature of image‐text similarity. This approach holds potential as an automated connected speech analysis tool for differential diagnosis of dementia subtypes. Future directions include evaluating performance in a larger dataset and comparing the classification performance between image‐text similarity and acoustic‐linguistic features.
Identification of underlying AD pathology in cognitively unimpaired individuals with subjective cognitive decline by the means of automated speech and language processing
Authors: Sánchez‐Benavides Gonzalo , Doerr Felix , Rădoi Andreea , Grau‐Rivera Oriol , Minguillon Carolina , Rodríguez Carlota Medina , Fauria Karine , Tröger Johannes , Linz Nicklas , Gispert Juan Domingo , König Alexandra
Published: January 2025

Background

Changes in speech and language functions have shown to be early symptoms of AD pathology. Recent developments in automatic speech and language processing have opened avenues for objective assessments of these changes. The primary objective of this study is to explore whether speech and language markers extracted from cognitive testing conducted during an automated phone call differ according to underlying AD pathology as measured in cerebrospinal fluid (CSF) in preclinical or early stage individuals.

Method

Within the PROSPECT‐AD project, speech and gold‐standard clinical and biomarker data were obtained from the Beta‐AARC cohort in Barcelona, Spain. The sample consisted of N = 41 cognitively unimpaired Spanish and Catalan speakers with Subjective Cognitive Decline. Speech features were extracted from phone recordings of the Rey Auditory Verbal Learning Task (RAVLT) and the Semantic Verbal Fluency Task (SVF). The Speech‐Biomarker for Cognition (SB‐C) was computed based on 43 temporal and semantic features. Based on established cutoffs, the sample was divided into amyloid beta 42 positive and negative and pTau positive and negative groups. Kruskal‐Wallis tests were used to compare the positive and negative groups with regards to their overall cognition score and the SB‐C subscores memory, executive function and processing speed for each of the CSF biomarkers respectively.

Results

The results of the group comparisons with regards to the amyloid beta 42 and pTau groups can be found in Table 1 and Table 2 respectively. Corresponding boxplots are presented in Figure 1. In summary, the amyloid beta negative group scored significantly higher on the SB‐C than the amyloid beta positive group. In particular, the difference had a strong effect size for the processing speed subscore and a medium effect size for the executive function subscore. No significant difference was found with regards to the memory subscore. For the pTau groups, the difference in overall cognition score showed a trend towards significance.

Conclusion

This approach could establish a non‐invasive and accessible means of identifying potential indicators of AD, contributing to early detection and timely intervention strategies.
The ARTS Marker of Arteriolosclerosis: Instrumental and Clinical Validation
Authors: Arfanakis Konstantions , Evia Arnold M , Leurgans Sue E. , Fornage Myriam , Lu Hanzhang , Arvind Caprihan , Decarli Charles , Gold Brian T , Maillard Pauline , Satizabal Claudia L , Stables Lara , Wang Danny JJ , Helmer Karl G , Fischl Bruce , Singh Herpreet , Schwab Kristin , Greenberg Steven M. , Schneider Julie A.
Published: January 2025

Background

The ARTS biomarker is a fully automated software container that predicts the presence of arteriolosclerosis based on in‐vivo MRI data and demographic features. The present study describes findings from the instrumental and clinical validation of ARTS conducted by the MarkVCID consortium.

Method

Instrumental validation of ARTS involved assessment of inter‐rater reliability, test‐retest repeatability, and inter‐scanner reproducibility. Inter‐rater reliability was assessed by means of the intraclass correlation (ICC) between ARTS scores generated from all MarkVCID sites using data on 20 older adults. Test‐retest repeatability was assessed by means of the ICC between test and retest ARTS scores from 41 older adults imaged twice. Inter‐scanner reproducibility was assessed by means of the ICC between ARTS scores on 20 older adults imaged on 4 different scanners. Clinical validation of ARTS included two groups of non‐demented older adults: N = 156 participating in community cohort studies at Rush Alzheimer’s Disease Center; and N = 906 participating in the Atherosclerosis Risk in Communities (ARIC) study. The primary prespecified hypothesis was that change in Trails B score (or perceptual speed score for Rush data) two years after baseline MRI is associated with baseline ARTS score, controlling for years of education. The secondary prespecified hypothesis was that baseline Trails B (or perceptual speed) is associated with baseline ARTS score, controlling for years of education.

Results

ARTS exhibited excellent inter‐rater reliability (ICC = 0.9999, p<10‐200, CI:[0.99987, 0.99997]), test‐retest repeatability (ICC = 0.996, p<10‐42, CI:[0.993, 0.998]), and inter‐scanner reproducibility (ICC = 0.955, p = 0.00018, CI:[0.622,0.989]). In Rush data, a higher baseline ARTS score was associated with a faster two‐year decline in perceptual speed (beta = ‐0.32, p = 0.04, CI:[‐0.63, ‐0.01]) (Fig.1A) and a lower baseline level of perceptual speed (beta = ‐1.04, p<10‐4, CI:[‐1.48, ‐0.60]) (Fig.1B). In ARIC data, a higher baseline ARTS score was associated with a faster annual increase in Trails B (beta = 3.7, SE = 1.7, p = 0.03) (Fig.2A), and a higher baseline Trails B (beta = 45.5, SE = 5.8, p<10‐4) (Fig.2B).

Conclusion

ARTS exhibited outstanding technical performance and baseline ARTS score was associated with subsequent decline in cognitive abilities known to be affected by small vessel disease. The above evidence supports ARTS as a marker of the risk of vascular contributions to cognitive impairment and dementia (VCID).