bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neuroinflammation potentiates the effect of amyloid‐β on longitudinal tau changes
Authors: Rahmouni Nesrine , Wang Yi‐Ting , Therriault Joseph , Servaes Stijn , Tissot Cécile , Macedo Arthur C. , Arias Jaime Fernandez , Hosseini Seyyed Ali , Stevenson Jenna , Kunach Peter , Hall Brandon J , Trudel Lydia , Gauthier Serge , Jeromin Andreas , Zetterberg Henrik , Blennow Kaj , Zimmer Eduardo R. , Ashton Nicholas J. , Benedet Andrea L. , Pascoal Tharick Ali , Rosa‐Neto Pedro 
Published: January 2025

AbstractBackgroundIt has been proposed that microglia release of proinflammatory factors reactive to amyloid plaques constitutes an early event leading to tau pathology. Here, we assessed how the rate of progression of tau‐PET and the rate of change in plasma pTau217 are affected by baseline levels of amyloid‐ß and neuroinflammation.MethodsWe included 93 individuals from TRIAD cohort: 11 young individuals, 57 cognitively unimpaired elderlies, 15 with mild cognitive impairment and 10 individuals with Alzheimer’s Disease. Neuroinflammation, tau tangle and amyloid‐ß (Aß) deposition were assessed via [11C]PBR28‐PET, [18F]MK6240‐PET and [18F]AZD4694‐PET, respectively. Individuals also had plasma pTau217 quantified using the Alzpath assay. [11C]PBR28‐PET positivity was defined as a SUVR of 2.5SD above the mean of the young individuals in the precuneus and posterior cingulate. Voxel‐based regression models evaluated the interaction of TSPO‐ and Aß‐PET with annual change in tau‐PET and plasma pTau217. The effect of the interaction on tau‐PET was also evaluated at the region‐of‐interest level using Braak regions. All models were corrected for age, sex and RFT corrected. Comparison of models was made using the Akaike Information Criterion (AIC) and the models’ goodness‐of‐fit was evaluated using R2 analyses. Models used [18F]MK6240‐PET SUVR yearly change as the outcome and used as predictors [11C]PBR28‐PET SUVR in the precuneus and posterior cingulate and [18F]AZD4694‐PET SUVR in the neocortical region.ResultsPositive associations were found between the interaction of Aß‐ and TSPO‐PET at baseline predicting the yearly change in tau‐PET (Figure 1A). At the ROI level, the interaction significantly predicts tau load in Braak regions III‐IV in [11C]PBR28 positive individuals (Figure 1B). Positive associations were also found between the interaction of Aß‐ and TSPO‐PET at baseline predicting the yearly change in plasma pTau217 (Figure 2A), which is driven by [11C]PBR28 positive individuals (Figure 2B). In addition, the interaction between Aß‐ and TSPO‐PET predicting the yearly change in tau‐PET is the model with the highest R2 and the lowest AIC values in Braak regions III‐IV‐V‐VI (Figure 3).ConclusionThese results support the hypothesis that microglial activation potentiates the effect of Aß plaques on tau tangle progression and the pTau217 increases across the aging and AD spectrum.
Relationship between cortical diffusivity and neuropathological substrates in Alzheimer’s disease
Authors: Debatisse Justine , Leng Fangda , Edison Paul 
Published: January 2025

AbstractBackgroundPathological alterations in Alzheimer’s disease (AD) begin several years before the onset of symptoms. Grey matter mean diffusivity (MD) may be used as a measure of early grey matter damage in AD as it reflects the breakdown of microstructural barriers, which precede volumetric changes and affect cognitive function. In this study, we investigated the changes in MD early on in the disease trajectory, and relate them to the amyloid and tau deposition.MethodWe analysed multimodal PET, DTI and MRI data of 90 participants, stratified into amyloid‐ß negative and positive, cognitively normal (CN), mild cognitive impaired (MCI) and AD patients.ResultWe found that MD was significantly increased in Aß‐positive MCI and AD compared with CN in frontal, parietal, temporal cortex, hippocampus, and medial temporal lobe. We also found that cortical MD is significantly correlated with cortical thickness only in patients without amyloid deposition and is not significant in Aß‐positive patients.ConclusionOur results suggest that cortical MD is an early marker of neuronal damage as it is observed simultaneously as Aß deposition and as it is correlated with cortical thickness when there is no Aß deposition. This suggests that cortical MD provides information about early microstructural changes before macrostructural changes.
Patterns of Association of Tau Across Cortical Regions Differ Between [18F]MK6240 and [18F]Flortaucipir
Authors: Hosseini Seyyed Ali , Rahmouni Nesrine , Therriault Joseph , Servaes Stijn , Tissot Cécile , Aumont Etienne , Arias Jaime Fernandez , Macedo Arthur C. , Wang Yi‐Ting , Stevenson Jenna , Lussier Firoza Z , Chan Tevy , Zheng Yansheng , Trudel Lydia , Gauthier Serge , Baker Suzanne L. , Pascoal Tharick A. , Rosa‐Neto Pedro 
Published: January 2025

AbstractBackgroundThis study aims to investigate the differential patterns of association in tau protein imaging across cortical regions using two distinct Tau imaging agents: [18F]MK6240 and [18F]Flortaucipir. The underlying hypothesis posits that variations in the properties of these tracers, such as affinity and off‐target effects, influence the observed patterns of association in neuroimaging.MethodTo test this hypothesis, a comprehensive study was conducted involving 104 subjects part of the HEAD study at McGill University: 53 cognitively normal (CN), 19 with mild cognitive impairment (MCI), 9 with Alzheimer’s Disease (AD) and 23 non‐AD. A key methodological aspect of the study was the selection of seed cortical regions based on the highest standardized uptake value ratios (SUVR) obtained in population average images. These regions included the trans‐entorhinal area, parietal, posterior cingulate cortices, and two off‐target binding regions: the choroid plexus and substantial nigra. This approach allowed for a nuanced analysis of tracer associations in both target and off‐target regions.ResultThe results revealed notable differences between the two tracers. Specifically, [18F]MK6240 exhibited higher associations between cortical regions, with similar slopes in these associations across different areas. In contrast, the associations between the off‐target binding regions (Choroid Plexus and Substantia Nigra) and the established Braak regions were found to be non‐significant. This finding is particularly revealing, as it underscores the differential binding affinity and specificity of the two tracers.ConclusionIn conclusion, the study substantiates the initial hypothesis by demonstrating that distinct tau tracers exhibit unique patterns of cortical associations, which can be attributed to their specific properties. This insight has significant implications for the interpretation of tau imaging in neurodegenerative diseases, particularly in AD. It highlights the necessity of considering tracer‐specific characteristics when analyzing neuroimaging data and suggests that a one‐size‐fits‐all approach may not be appropriate in the nuanced field of neuroimaging. The findings of this study pave the way for more refined and accurate diagnostic practices in neurology, particularly in the context of neurodegenerative diseases where tau protein plays a pivotal role.
Developing a machine learning stack model to forecast the progression of mild cognitive impairment to Alzheimer’s dementia, using the Australian Imaging, Biomarker & Lifestyle (AIBL) Study dataset
Authors: Chu Chenyin , Wang Yihan , Maruff Paul , Masters Colin L. , Goudey Benjamin , Jin Liang , Pan Yijun 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative condition, with considerable variation in disease progression from the mild cognitive impairment (MCI) stage. Predicting disease progression will support prognostic decisions and patient management. Here we designed a machine learning (ML) stack model, where a classifier was used to differentiate MCI progressors from non‐progressors (i.e. whether an individual with MCI progress to AD), and different regression models were then applied to each category to forecast their Mini‐Mental State Examination (MMSE) score after 3 years.MethodDemographic and neuropsychological data from 2428 participants of the AIBL study was used for model construction and validation. The model was validated via a stratified 3‐fold cross‐validation approach. Support vector machine (SVM), gradian boosting (GB), random forest (RF) was experimented, and their performance were compared. MMSE score was a predicted variable indicating cognition.ResultThe performance of RF‐stack model was superior to SVM‐ and GB‐stack models. Largest area under the receiver operating characteristic curve (0.85) when RF‐stack model was employed (compared with SVM 0.79 and GB 0.70), signifying the highest accuracy in classify MCI progressors and non‐progressors. RF‐stack model achieved the best performance in predicting MMSE score after 3 years), evidenced by the smallest median mean average error (MAE) (RF 1.51, SVM 1.52, GB 2.51). The MAE indicated how much the predicted MMSE score deviated from the AIBL record. Representative prediction results from RF‐stack model for AIBL participants were presented in Table 1.ConclusionOur RF‐stack model relies on neuropsychological test scores and demographic data to predict the probability of an individual progressing from MCI to AD and accurately forecast the MMSE score after 3 years. This model achieved is comparable if not better prediction accuracy than the existing ML models using neuroimaging data. Further model optimization and validation using data collected from other longitudinal AD cohort studies are required.
Clinical Success of Biophoton Therapy in Improving Cognition of Alzheimer’s Disease
Authors: Liu James Z 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) poses a significant global health challenge, which demands innovative therapeutic approaches. The strong biophoton field generated by breakthrough medical devices has preliminarily shown a high rate of success in improving cognition and life quality of the patients with AD.MethodThis was a randomized triple blinded placebo controlled clinical study to verify if biophotons can improve cognition of AD. The clinical trial was to enroll a total of 30 participants diagnosed with moderate to severe AD. All participants continued their standard care if any.ResultDuring a 4‐week observational period, 16 participants in the treatment group used the biophoton generators daily. Cognitive function, life quality and EEG were assessed using standardized measurements. Additionally, bioenergy of brain was determined using a non‐linear bioenergy scanner. Preliminary analysis reveals a remarkable improvement in cognitive performance among participants in the biophoton therapy group compared to the control group. Seventy‐five percent (75%) participants reduced the Alzheimer’s Questionnaire severity; 88% increased life quality SF‐36 score; and 94% increased brain bioenergy. All were significantly different from the Control group (P < 0.01). EEG test also showed that the “missed response” was reduced in the Treatment group. Caregivers reduced the stress and difficulty in caring for their seniors. No adverse effects were observed.ConclusionThese promising results highlight the potential of biophoton therapy as a novel and safe intervention for Alzheimer’s disease. Further investigations are warranted to elucidate the underlying mechanisms. This clinical study represents a significant step forward in treating Alzheimer’s disease.
Intelligent dementia friendly environment: A Preliminary Study on the Feasibility of Integrating Assistive Humanoid Robots, Wearable Sensors, and Spatial Technology for maintaining quality of life
Authors: Khan Arshia A , Das Rupak Kumar , Imtiaz Rana Z 
Published: January 2025

AbstractBackgroundIntegrating humanoid robots, wearable sensors, and spatial technology into an intelligent dementia‐friendly living space is crucial for tailoring personalized and supportive environments, thereby addressing the unique needs of individuals affected by dementia and maintaining quality of life.[1‐10].MethodsWe programmed Pepper, a humanoid robot, for independent verbal communication to interact, tell jokes, and offer medications. To validate our assistive robot integrated with a sensor‐based system, we built a specifically designed living space optimized for sensor functionality and conducted a 90‐minute study involving 32 healthy participants. The participants performed various activities after consenting, filling pre and post‐experiment surveys and pre, during, and post‐sensor readings. The activities comprised of administration of a placebo sugar pill by the robot, treadmill exercises following the Bruce submaximal treadmill protocol, engagement in cognitively stimulating activities, wandering detection using spatial sensors, media presentation to induce higher arousal state, performance of some daily tasks such as preparing short meals, folding towels, cleaning the floor, and an interaction with the robot. These activities aimed to assess sensor functionality, simulate real‐world scenarios, and monitor physiological responses during various tasks, contributing to the evaluation of the integrated sensor system. Various physiological data, including Electrodermal Activity (EDA), blood pressure, and Electrocardiogram (EKG), were gathered using distinct wearable sensors throughout the experiment. To gauge the subjective experiences of participants, two surveys were employed: the Presentation survey, conducted during the International Affective Digital Sound (IADS) presentation, and the Post‐Study survey, distributed on paper after the study’s completion.ResultsBy incorporating pressure sensors in chairs and the robot’s emotion detection system, participants' moods were accurately assessed, aligning with self‐reported moods in 95% of cases. Furthermore, door sensors consistently identified exiting behavior, and wall‐mounted PIR sensors successfully recognized wandering behavior. Wearable sensors, such as EDA and blood pressure sensors, in conjunction with robot interactions, detected physiological changes during diverse activities, with the most notable impact observed during interactions with the robot. Conclusion The 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.
Applying the eHealth Literacy lens to evaluate a virtual assistant to support family carers of people with dementia
Authors: Nguyen Tuan Anh 
Published: January 2025

AbstractBackgroundWe have co‐produced with carers of people with dementia (hereafter carers) a culturally tailored iSupport Virtual Assistant (VA), namely e‐DiVA, to support English‐, Bahasa‐ and Vietnamese‐speaking carers in Australia, Indonesia, New Zealand and Vietnam. The presented research reports qualitative findings from the e‐DiVA user‐testing study.MethodFamily carers and healthcare professionals working in the field of dementia care were given the e‐DiVA to use on their smartphone or handheld device for 1‐2 weeks. They tested all functions and content of the VA and provided feedback in a ‘Think‐aloud' session, either in person or online. Data was analysed thematically, guided by the eHealth Literacy Framework (eHLF).ResultEighteen family carers and nine staff of aged care providers were recruited. Participants discussed all seven domains of eHLF, which were evidenced in the e‐DiVA. They include: 1) ability to process information, i.e. the VA contains information in a format that can easily be understood, e.g. animations; 2) engagement in own health, i.e. providing tailored lessons through need assessments and ‘chatbot'; 3) ability to engage actively with digital services, i.e. providing user manuals; 4) feeling safe and in control; i.e. secure login; 5) motivation to engage with digital services, i.e. providing quick responses/solutions to carers' queries; 6) having access to systems that work, i.e. ability to access to the VA anytime anywhere; and 7) digital services that suit individual needs, i.e. available in users' preferred language. Participants highly ranked domains 1, 2 and 5. They suggested further improvements including making content into audiobooks and messages (domain 1), as well as pointed out some potential barriers to use, especially on domains that require interactions between individuals and the digital system (domains 3, 5 and 7).ConclusionFindings show the high acceptance of the VA by both the carers and aged care providers, as well as a need for enhancement to improve accessibility.
Facilitating Conversations about Supportive Care Options in the Context of Cognitive Impairment using an Online Visual Elicitation Tool
Authors: Turner Anne M , Chen Annie T. , Child Claire E. , Miller Lyndsey M. 
Published: January 2025

AbstractBackgroundPersons with cognitive impairment may experience difficulties with language and cognition that interfere with their ability to make and communicate decisions. We developed an online visual tool to facilitate conversations about their preferences concerning supportive care.MethodsWe conducted Zoom interviews with persons with mild cognitive impairment (MCI) and mild to moderate dementia, using storytelling and a virtual tool designed to facilitate discussion. Each interview sought to discuss decision‐making in the context of three scenarios, one concerning a past decision about supportive care, and two hypothetical scenarios based on decreasing abilities and increasing care needs. Interviewers shared their screen with the visual tool, which used the visual diagramming platform draw.io (https://www.drawio.com/) to create an online “canvas.” Interviewers could drag icons (representing people, places, safety, emotions, activities, and issues) or type text onto the canvas to communicate concepts. We developed the tool with a multidisciplinary team of designers and researchers, and with input from people with cognitive impairment. Key user‐centered design goals for the tool included focusing on simplicity, minimizing cognitive load, and using concepts and images that resonated with participants. We also used pilot interviews to refine interviewers' techniques for eliciting preferences with the tool. We examined the utility of the tool for engaging participants in dialogic interactions with the interviewer, and the cognitive and communicative processes exhibited by participants.ResultsWe conducted fifteen interviews with persons with MCI, and mild or moderate dementia [mean age 77.8 years ± 7.1 (SD), 9 male, 6 female]. With respect to dialogic interactions, interviewers actively used the visualization technique to facilitate the conversation; participants engaged with the interface to varying degrees. Common communicative issues included participants being unresponsive to the question or providing unclear responses. During the sessions participants also exhibited uncertainty, introspection, and self‐awareness.ConclusionWe present a visual technique to engage persons with cognitive impairment in dialogue about complex decisions. We hope to explore the potential use of this technique in research, clinical, and community settings.
A machine learning approach to predict tau positivity using clinical features in amyloid‐positive individuals
Authors: Arnold Daniel , Borelli Wyllians Vendramini , Machado Luiza Santos , Rahmouni Nesrine , Therriault Joseph , Servaes Stijn , Stevenson Jenna , Macedo Arthur C. , Schumacher‐Schuh Artur Francisco , Mattjie Christian , Barros Rodrigo C. , de Bastiani Marco Antônio , Lussier Firoza Z , Chamoun Mira , Bezgin Gleb , Benedet Andrea L. , Pascoal Tharick , Rosa‐Neto Pedro , Zimmer Eduardo R. ,   Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: January 2025

AbstractBackgroundAnti‐amyloid therapy appears to have an increased effect on reducing cognitive decline in amyloid‐ and tau‐positive individuals. However, clinical trials inclusion criteria require solely amyloid positivity. Herein, we developed a machine‐learning prediction model to identify tau positivity in amyloid‐positive individuals using clinical variables.MethodWe selected 1191 amyloid‐positive participants and tau status from ADNI, TRIAD, PPMI databases. Commonly shared clinical features selected between datasets were age, total MOCA scores, clinical diagnosis, sex, education, BMI, heart disease, stroke, hyperlipidemia and diabetes. Amyloid positivity was defined by amyloid‐PET (PIB‐PET, FBB‐PET or AZD4694‐PET) or CSF AB42 and Tau positivity defined by Tau‐PET (MK6240‐PET or AV1451‐PET) or CSF p‐tau181. The dataset was split into training (49%), validation (21%), and testing datasets (30%). A XGBoost model was tuned, and then used to predict the tau status outcome in the testing dataset.ResultA total of 647 men and 544 women were included with mean age 66.1 ± 10.4 (mean ± SD) years, MOCA 25.1 ± 4.1 scores and education 16.1 ± 3.2 years (Table). The population consisted of 794 controls, 271 MCI and 126 dementia individuals. Tau status was negative for 854 individuals and positive for 337. The receiver‐operator characteristic (ROC) analysis showed that the area under the curve (AUC) was 0.81 for discriminating tau‐negative vs. tau‐positive, with 0.76 as sensitivity, specificity as 0.86, and accuracy as 0.83. The top 3 most impactful features were found to be age, diagnosis‐CN and MOCA score through a SHAP value analysis (Figure).ConclusionPredicting tau positivity with clinical and cognitive variables may improve the selection of individuals for AD trials. A two‐step workflow using this approach may significantly reduce the need for tau‐PET exams in trials by screening individuals using clinical variables. The following steps should include adding more individuals and other clinical variables through different cohorts to optimize the model applicability and test their performance in outcomes of clinical trials.