bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging T1 MRI Images for Amyloid Status Prediction in Diverse Cognitive Conditions Using Advanced Deep Learning Models
Authors: Hosseini Seyyed Ali , Servaes Stijn , Rahmouni Nesrine , Therriault Joseph , Tissot Cécile , Macedo Arthur C. , Wang Yi‐Ting , Arias Jaime Fernandez , Socualaya Kely Monica Quispialaya , Zheng Yansheng , Chan Tevy , Trudel Lydia , Gauthier Serge , Rosa‐Neto Pedro 
Published: January 2025

AbstractBackgroundTimely and non‐invasive prediction of amyloid status are pivotal in Alzheimer’s disease (AD) diagnostics. This research leverages T1 MRI images to predict amyloid positivity or negativity, offering an economical and less invasive alternative to amyloid PET scans. Using the comprehensive TRAID dataset from McGill University, the study evaluates a spectrum of cognitive conditions including AD, atypical AD, Cognitively Normal (CN), Mild Cognitive Impairment (MCI), MCI not due to AD, Suspected Non‐Alzheimer’s Pathophysiology (SNAP), and Vascular MCI (VMCI).MethodThe study involved 588 subjects, representing a cross‐section of cognitive states. A VGG16‐based feature extraction process was meticulously applied to T1 MRI images to capture complex biomarkers of AD. These high‐dimensional features, alongside demographic data, were harnessed to train two deep learning models: a Long Short‐Term Memory (LSTM) model and a Convolutional Neural Network (CNN) ensemble model. To prevent overfitting and data leakage, the models employ 10‐fold StratifiedKFold validation and early stopping, alongside regularization techniques like dropout and batch normalization. Data preprocessing is contained within StratifiedKFolds, and an independent test set is used for unbiased evaluation. The predictive accuracy was scrutinized using a range of metrics, including accuracy, F1‐score, recall, precision, and ROC AUC on the validation sets.ResultThe LSTM model reported average validation metrics with an accuracy of 70.88%, F1‐score of 70.64%, recall of 78.07%, precision of 70.88%, and a ROC AUC of 71.86%. The CNN ensemble model showed a marked improvement, particularly in its recall of 88.00% and ROC AUC of 82.47%, alongside an accuracy of 76.27% and F1‐score of 75.86% (Figure 1 and 2). However, in another model, it was the CNN ensemble model that demonstrated remarkable predictive power, achieving an ROC AUC of 88% within the AD spectrum (CN, MCI, and AD subjects), indicating a high degree of predictive power in classifying amyloid status.ConclusionThis study underscores the CNN ensemble model’s robustness in predicting amyloid status across a broad spectrum of cognitive conditions, with particular emphasis on its application in high‐sensitivity clinical settings. These findings represent a significant advancement in the field of AD diagnostics, contributing to the development of more accessible and cost‐effective prognostic techniques.
Multimodal characterization of Alzheimer’s Disease using speech, eye movement, and handwriting
Authors: Moro‐Velazquez Laureano , Favaro Anna , Thebaud Thomas , Wang Yuzhe , Butala Ankur , Oh Esther S , Dehak Najim 
Published: January 2025

AbstractBackgroundPhenotyping Alzheimer’s Disease (AD) can be crucial to providing personalized treatment. Several studies have analyzed the use of digital biomarkers to characterize a subject’s behavior, usually obtained from a single modality, such as speech. However, combining several modalities in a single study has not been deeply studied. We aim to characterize people with AD and differentiate them from a cognitively normal control group (CN), employing metrics obtained through from three different modalities: speech, handwriting, and eye movement.MethodWe collected data from a group of 11 participants in the early stages of AD or with, mild cognitive impairment due to AD, and an age‐matched CN group of 13 participants. Each participant read out loud the Rainbow Passage, while their speech was recorded with a headset microphone and their eye movement with a high precision eye‐tracker, simultaneously. Then, the participants were asked to draw a spiral on a digital pad. We obtained 57 speech metrics, related to articulation, prosody, and hesitation; 90 handwriting metrics, characterizing drawing entropy, writing speed, and acceleration; 32 eye movement metrics related to saccade, fixation, and blink statistics. Several multilayer perceptron (MLP) classifiers were trained per modality and using all modalities together, following a cross‐validation strategy. SHAP values were calculated to rank metric impact in the differentiation between AD and CN.ResultCross‐validation accuracy in the binary classification of AD/CN ranged between 75.3% and 77.4% for single modalities, whereas the combination of the three modalities provided a 77.9% accuracy. The drawing velocity skewness metric provided the highest area under the receiver operating characteristic curve (AUC) from all the handwriting features (0.83), whereas the pause variability was the speech metric with the highest AUC (0.79). The number of fixations provided the best differentiation between groups in the eye movement modality, with AUC = 0.69.ConclusionCombining the three proposed modalities provides a higher differentiation power than using each modality separately. The combination can provide a more insightful and complementary characterization of patients. Handwriting metrics have the most impact on the prediction of the multimodal MLP model, according to the SHAP values on the multimodal classifier.
Identifying an EEG Biomarker for Memory Recall through EEG and EDA in the Presence of Music
Authors: Das Rupak Kumar , Khan Arshia A , Imtiaz Nabiha Zainab 
Published: January 2025

AbstractBackgroundThere is ample evidence that music can boost brain activity and jog deeply embedded memories. Literature indicates a significant improvement in autobiographical memory (ABM) recall for different individuals during background music sessions. Existing research is based solely on qualitative data, although music has a significant impact on physiological activity. Thus, it’s important to explore the connection between memory recall and physiological activities.MethodTo better understand memory recall, the electroencephalogram (EEG) and electrodermal activity (EDA) data were gathered from healthy participants using wearable sensors. Physiological signals such as the electroencephalogram (EEG) and electrodermal activity (EDA) were recorded as quantitative data using various wearable sensors from 40 participants of different age groups while playing different background music sessions. The study involved listening to nine music sessions (three happy, three sad, and three neutral). Immediately after each piece of music, a post‐study survey was conducted to gauge if the participants recalled any autobiographical memories. A machine learning algorithm was developed to train a model using features collected from physiological data to determine if the memory recall was successful. The purpose of the study was to identify an EEG biomarker.ResultThe results of the EEG and EDA data analysis revealed that for all four EEG channels, there was a consistent increase in the alpha power (on average 16.2%) during the memory “recall” scenario (F3: p = 0.0066, F7: p = 0.0386, F4: p = 0.0023, and F8: p = 0.0288) compared to the “no‐recall” control. There was also a significant surge in the Beta power for two channels (F3: p = 0.0100 and F4: p = 0.0210) but not for the control (F7: p = 0.6792 and F8: p = 0.0814). Additionally, the EDA data analysis revealed significant differences in the phasic standard deviation (p = 0.0260), phasic max (p = 0.0011), phasic energy (p = 0.0478), tonic min (p = 0.0092), tonic standard deviation (p = 0.0171), and phasic energy (p = 0.0478). This implies that the memory recall biomarker is alpha power (8‐12 Hz).ConclusionThe results indicate that the biomarker for memory recall is alpha power (8‐12Hz).
Humanoid Robots perform cognitive assessments: to address healthcare worker shortages
Authors: Khan Arshia A 
Published: January 2025

AbstractBackgroundThe rising demand for alternative dementia assessments, fueled by healthcare workforce shortages and the growing population of individuals affected with dementia, necessitates innovative approaches to address accessibility, logistics, and diverse populations. The utilization of robots in cognitive assessments emerges as a promising solution, promising efficiency and engagement, while navigating the complex landscape of dementia care challenges.MethodExisting cognitive assessment tools were examined to develop a humanoid robot to deliver cognitive assessment. This robot‐assisted cognitive assessment development involved integrating artificial intelligence with robotic technology to create a comprehensive tool. Initially, the design phase included defining the cognitive domains to be assessed and tailoring the assessment tasks to suit robot‐human interaction. The robot’s programming incorporated adaptive features to respond to individual variations in cognitive abilities. Sensory modules, such as cameras and microphones, in the robot were integrated for real‐time data collection, enabling the robot to observe and interact with the individual. Iterative testing and refinement were performed to ensure the accuracy and reliability of the robot‐assisted cognitive assessment tool. Throughout the development process emphasis was on creating a robust and user‐friendly system that aligns with the nuanced nature of cognitive assessments for individuals affected by dementia.ResultRobot‐assisted cognitive assessments were tested in a nursing home setting, showing promising results and providing valuable insights into residents' cognitive functioning.ConclusionThe robot assisted assessments, integrated with AI, offer a comprehensive understanding of memory, attention, and other cognitive domains. The individual variability in cognitive abilities among residents is highlighted, enabling personalized care plans tailored to specific needs. The positive engagement and usability reported by residents suggest a potential avenue for integrating this technology into routine care practices. The real‐time data collection capabilities of the robot contribute to a more dynamic and accurate assessment of cognitive health. Caregivers benefit from the objective cognitive performance data, allowing for timely interventions and adjustments in care plans. The iterative feedback loop from residents and caregivers facilitates continuous improvement in the robot‐assisted cognitive assessment tool, making it a valuable and beneficial asset in enhancing the quality of care provided in nursing homes.
Digital Cognitive Screening in Primary Care: Feasibility, Acceptability, and Usability of the MyCog Measurement System
Authors: Young Stephanie Ruth , Dworak Elizabeth , Byrne Greg Joseph , Yoshino‐Benavente Julia , Jones Callie Madison , Curtis Laura M , Wolf Michael S , Nowinski Cindy J. 
Published: January 2025

AbstractBackgroundAnnual cognitive screening in older adults is essential for early detection of cognitive impairment, yet less than half of cases are detected in primary care. We introduce two innovative app‐based screeners that help overcome barriers to routine cognitive screening. MyCog is a tablet app that is self‐administered in person during the rooming process for a primary care visit (Figure 1). MyCog Mobile is a smartphone app that is self‐administered remotely prior to a primary care visit (Figure 2), which can save clinicians even more time during an in‐person visit. We present evidence from two studies: 1) an evaluation of the adoption and usability of MyCog in primary care; and 2) a pilot study of the reliability and usability of MyCog Mobile.MethodIn Study 1, the implementation of MyCog in 10 primary care clinics was assessed as part of a pragmatic trial evaluating cognitive screening for adults aged 65+ during Annual Wellness Visits (AWVs). Using EHR data, we assessed adoption via the portion of patients who completed MyCog. In Study 2, 51 adults 65+ self‐administered MyCog Mobile remotely on their own smartphones twice about two weeks apart and rated its usability on the Simplified System Usability Scale (S‐SUS). We examined the reliability, correlations with education, and usability ratings.ResultIn Study 1, of the 3921 eligible patients, 1263 completed MyCog (32.2%), with individual clinic compliance ranging from 11% to 68% of patients. Feedback revealed several addressable clinician and patient barriers, including patient frustration on memory tests, unclear test progress, and need for a Spanish version, and minor design flaws. In Study 2, all participants successfully completed each measure, and reliability metrics were broadly acceptable (Table 1). The MyPictures and education correlation was lower than expected. Ratings on the S‐SUS indicated good usability of MyCog Mobile (M = 73.78, SD = 20.11).ConclusionMyCog demonstrated good adoption by primary care clinics in a pragmatic trial, which may be improved through design and implementation changes. Some limitations of MyCog may be addressed with MyCog Mobile, which demonstrated preliminary feasibility evidence. Practice implications and next steps are discussed.
A passive electronic medical record‐based cognitive risk calculator predicts neuropsychological performance in older adults
Authors: Saxena Saket A , Hogue Olivia , Rothberg Michael B , Misra‐Hebert Anita D , Milinovich Alexander P , Pfoh Elizabeth R , Busch Robyn M , Krishnan Kamini , Fox Robert J , Kattan Michael W , Floden Darlene 
Published: January 2025

AbstractBackgroundStratifying risk of cognitive decline for an individual patient can be difficult in primary care settings where advanced biomarkers are usually not available. Passive risk calculators that capitalize on existing information contained in the electronic medical record (EMR) hold promise, but most are developed using EMR documentation of cognitive decline which is highly unreliable. This prospective study used objective cognitive testing to build a multivariable cognitive risk model based on EMR records.Methods338 primary care patients aged 60 and older who had at least two visits over the previous 5 years completed brief neuropsychological testing to establish current cognitive function and consented to access to their medical records. Records from the preceding 5 years were mined for health status indicators (e.g., BMI, pulse, blood pressure), diagnoses, medications, demographic, and socioeconomic information and entered into a multivariable logistic regression model using Harrell’s step‐down selection. To account for changes in variable values from visit to visit, binary variables (e.g. medications, diagnoses, smoking status) were classified as absent or ever‐present during patient follow‐up, while first, last, and “worst” values were retained for continuous variables (e.g. blood pressure, pulse). Bootstrap resampling was used for internal validation. Model was adjusted for optimism and fit evaluated using calibration curves.ResultsThe sample was 59% female and 80% white, with a mean age of 72.5 (SD 7.0) and 15.5 years of education (SD 2.6)). Eighty patients (24%) were cognitively impaired. The model that best identified impairment included age, most recent pulse, race, presence of mood disorder diagnosis, family history of neurological diagnosis, NSAID use, and most recent systolic blood pressure. After adjusting for optimism, the discrimination index was 0.71, indicating good discrimination. Results from the model were transformed to yield patient‐specific probability scores used to trigger risk flag in the patient’s medical record.ConclusionsAn EMR‐based risk calculator successfully discriminated between patients with and without cognitive impairment. Strategies for implementing the cognitive risk flag into clinical practice are discussed.
Machine Learning‐Driven Speech Biomarker Analysis: A Novel Approach for Detecting Cognitive Decline in Older Adults
Authors: Li Yatian , Chen Quan , Wu Jingnan , Chen Nan , Zhou Zhixing , Xia Huanhuan , Huang Lin , Guo Qihao 
Published: January 2025

AbstractBackgroundSpeech impairment appears at early stages of Alzheimer’s disease. A mobile voice recognition‐based cognitive assessment tool, Shanghai Cognitive Screening (SCS), was developed for detecting mild cognitive impairment (MCI) and dementia in the community. The objective of this study is to investigate speech biomarkers associated with cognitive impairments based on SCS, and to evaluate the diagnostic accuracy of speech feature‐based machine learning (ML) models for detecting MCI.MethodA total of 301 older adults were recruited to perform SCS assessments, with 135 of them were diagnosed with cognitive impairment (MCI and early dementia) and the remainder were cognitively normal. Speech features were extracted from SCS voice recordings and integrated in a dataset along with SCS scores and demographic indicators. The dataset was randomly divided into training and validation sets in a 4:1 ratio and trained in various ML models with predictive speech features examined.ResultAmong the models trained, the ML models employing SVM model, Logistic Regression, Random Forest, LightGBM, and XGBoost demonstrated higher accuracy than original SCS scores (Table 1). A ML classifier using Mel spectrograms and multimodal data fusion exhibited the highest accuracy of 0.953 (AUC = 0.967, sensitivity = 0.933, specificity = 0.933). Further analysis of the LightGBM model revealed that speech markers significantly influencing the model’s output included modal particles, standard deviation of interval time, silence duration, mean interval time, total speaking time, silence time in delayed recall test, and percentage of silence time (Figure 1). These findings align with the audio waveform characteristics (i.e., smaller amplitudes, longer intervals, greater rhythm variations, and increased silence, Figure 2) in individuals with cognitive impairments. However, the top three factors were scores of delayed recall and instant memory, as well as SCS total score.ConclusionThe findings demonstrated speech biomarkers associated with cognitive impairments, facilitating more accurate detection of early cognitive impairment with ML techniques. Validation studies on larger sample sizes is necessary.
Providing In‐Home Helmet‐Based 40 Hz Light Therapy for Alzheimer’s Patients in Memory Care Facilities in Detroit, MI: A Proposal for a Feasibility Study to Expand Early Efficacy Data
Authors: Bart Jacob A , Attar Zechariah 
Published: January 2025

AbstractIntroductionAlzheimer’s disease is the most common and costly neurodegenerative disease worldwide. Early studies of helmet‐based 40 Hz light therapy have demonstrated safety and early efficacy in targeting the pathophysiologic characteristics of Alzheimer’s disease. This intervention has shown slowing and even reversal of disease progression. An extensive literature review has demonstrated this therapy to be cost‐effective and user‐friendly with minimal side effects. Implementation of this therapy has been limited by barriers to access, illustrated by small sample sizes and poor compliance in numerous studies. Further research with larger sample sizes is essential to accelerate the rate of translation from bench to bedside by addressing implementation barriers to access, thereby optimizing patient adherence.HypothesisWe hypothesize that offering helmet‐based 40 Hz light therapy in a home setting will result in increased enrollment of eligible patients and compliance by providing convenience to patients and their caregivers. This approach will develop implementation guidelines while expanding on existing efficacy data.ObjectiveTo explore our hypothesis, we plan to conduct a feasibility study by recruiting through, and providing care at, memory care facilities in Detroit, Michigan. Changing the location of care may be an easily adaptable implementation strategy that can be applied nationally. We've identified 50 memory care facilities that house Alzheimer’s patients, with capacity for over 400 residents in Detroit alone.MethodsWe plan to assemble a mobile team of research‐clinicians to administer helmet‐based 40 Hz light therapy. Scheduled weekly appointments can be facilitated at the convenience of the patients and their care providers across all facilities that allow this therapy for their residents. These participants will be recruited via rolling admission and outcomes will be evaluated using standardized neurocognitive assessments. In summary, helmet‐based 40 Hz therapy has already been shown to be safe and effective. Studying the feasibility of implementing it in the patient’s home is an important next step in making this care accessible to those that need it most.
The Evaluation of Telehealth Provider Payment Parity on Health Service Utilization among Adults with High Risk Factors for Dementia in the US
Authors: Zhang Zhang 
Published: January 2025

AbstractBackgroundCardiometabolic diseases and mental health disorders, which are high‐risk factors for dementia and cognitive decline, are associated with higher mortality and morbidity with age. Interventions before age 60 may lessen the burden of cognitive and physical function in later life. Telehealth offers early intervention and solutions for their complex demands in continuous behavior monitoring and medication refilling. Telehealth has been catalyzed by the COVID‐19 pandemic and become a new norm in healthcare. One of the important factors driving this transformation is governments' temporary modifications and policies. Our study aims to evaluate the impact of telehealth payment parity mandates, which require an equal payment rate for telehealth vs. in‐person outpatient visits, on overall health service utilization among populations with high‐risk factors for dementia.MethodUsing the IBM MarketScan dataset from 2019 to 2021, we leverage a policy shock using a quasi‐experiment by a two‐way fixed effect difference‐in‐differences (DiD) estimation. Our sample includes adults with cardiometabolic risks (diabetes, hypertension, or dyslipidemia) or mental health disorders. We compare the differences between the control and treated states in changes in the outcomes of interest after the onset of the pandemic.ResultOn average, the treated group had 0.18 more telehealth visits by patient by month than the control (p<0.05). Compared to the control group, the treated group had 7 percentage points higher probability of having at least one telehealth encounter by patient by month (p<0.1) and 5 percentage points increase in the proportion of outpatient visits via telehealth by patient by month (p<0.1). In addition, the payment parity did not lead to a significant increase in in‐person outpatient visits.ConclusionOur results suggested that the telehealth payment parity, driven by supply‐side incentives, has effectively increased telehealth adoption during and post‐pandemic. The findings addressed the public concerns that telehealth expansion policies could induce and trigger excess demand for in‐person office visits. Our study provides evidence for policymakers to establish sustainable telehealth reimbursement policies and payment models and improve equitable telehealth access for populations with high‐risk factors for dementia in the US.