bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diffuse juxtacortical white matter texture changes in pre‐clinical Alzheimer’s disease
Authors: ILESANMI ADEMOLA E , Lyu Xueying , McGrew Emily , Yushkevich Paul A. , Das Sandhitsu R. , Wolk David A 
Published: January 2025

AbstractBackgroundDetection of neurodegeneration during preclinical stages of Alzheimer’s disease (AD) is critical for disease prediction and monitoring. Macrostructural brain changes (volume/thickness) during preclinical AD are often not detectable in cross‐sectional studies. Microstructural changes manifested as alterations in texture features may capture subtle neurodegeneration at this stage. Here we investigated the discriminative ability of texture‐based radiomics features in both gray and juxtacortical white matter in preclinical AD.MethodThe dataset included amyloid negative (CN) and amyloid positive (preclinical AD, pAD) cognitively normal individuals from two independent cohorts (ADRC: 122 CN, 35 pAD; ADNI: 183 CN, 95 pAD). T1‐weighted MRI was segmented. Subsequently, we extracted thirty texture features each from 100 gray matter (GM) regions of interest (ROI) and corresponding adjacent white matter (WM) ROIs extending 2mm from the GM/WM interface. Principal Component Analysis was employed to reduce these features to eight: total energy (TE), sum variance, skewness, sum average, entropy, contrast, kurtosis, and inverse difference moment (IDM).ResultInitial analysis in the ADRC cohort demonstrated group differences in TE and IDM in juxtacortical white matter regions broadly throughout the brain (Figure 1C, D), but not in the grey matter (Figure 1A, B). Given the exploratory nature of the analysis in the ADRC, we then examined TE and IDM in ADNI in groups defined in the same manner. These findings were largely replicated in the ADNI dataset (Figure 2). Conversely, gray matter thickness measures in both cohorts displayed minimal group discrimination (Figures 1E and 2E).ConclusionTexture features in juxtacortical white matter were sensitive to pAD in two independent cohorts, whereas GM thickness, a macrostructural measure, was not. These data suggest that texture features, a possible surrogate of microstructural changes, may be particularly sensitive to early downstream effects of AD. However, the diffuse nature of these findings across the brain suggests the possibility that they are related to the presence of cerebral amyloid rather than the more focal effects that would be expected with tau‐related neurodegeneration. Future work will test the longitudinal nature of these texture findings and their relationship to prediction of future cognitive decline.
BOLD fMRI signal changes in preclinical Alzheimer’s disease
Authors: Hrybouski Stanislau , Das Sandhitsu R. , Xie Long , Lyu Xueying , Wisse Laura E.M. , Kelley Melissa , Lane Jacqueline , Sherin Monica , DiCalogero Michael , Nasrallah Ilya M. , Detre John A. , Yushkevich Paul A. , Wolk David A 
Published: January 2025

AbstractBackgroundPrevious work suggests functional abnormalities in the human brain in preclinical Alzheimer’s disease. However, little has been explored about the relationship between BOLD fMRI signal amplitude/energy over time and AD pathology. In this work we analyzed the effects of AD progression on amplitude of low‐frequency fluctuations (ALFF) during resting‐state fMRI scans both at the whole‐brain level and at a more granular level, focused on regions of the medial temporal lobe (MTL) that are most vulnerable to AD pathology.MethodIn this cross‐sectional study, we analyzed data from 224 individuals from the Penn ADRC cohort (Table 1). All participants underwent structural and functional MRI on a Siemens 3T Prisma system, and 18F‐Florbetaben or 18F‐Florbetapir amyloid‐PET imaging. 125 participants also underwent 18F‐Flortaucipir tau‐PET scans. Functional images were preprocessed using a custom implementation of fMRIprep and ALFF was extracted using Conn software. In the whole‐brain analyses we performed voxelwise GLMs with age and sex as covariates.ResultsWe observed reduced ALFF in both preclinical AD (Amyloid‐positive (Aß+) cognitively unimpaired, CU) and Aß+ cognitively impaired (CI) individuals. Relative to Aß‐ controls, individuals with preclinical AD displayed lower ALFF in frontal, parietal and temporal association cortices (Fig 1, top left). CI individuals displayed lower ALFF in most of the brain, except in inferior temporal cortex, temporal pole, and MTL. The effect of AD progression on ALFF was characterized by a progressive reduction primarily in frontal and parietal regions that roughly align with the anatomy of the default mode network. In contrast, transentorhinal tau pathology was negatively associated with ALFF in frontal and anterior temporal lobes, as well as insula and MTL (Figure 1 bottom right). Negative association between tau burden and MTL ALFF was observed in all main MTL subregions, and strongest in the transetorhinal cortex (Figure 2).ConclusionWe conclude that: (1) ALFF might be a promising biomarker for studying functional abnormalities in preclinical AD, and (2) based on the spatial topography of amyloid and tau effects on ALFF (Figure 1 bottom), there is likely a differential effect of of the two on ALFF that needs to be further explored.
A Neuroimaging Perspective on Familial Alzheimer’s Functional Data through Bayesian Eyes
Authors: Grazia Alice , Dyrba Martin , Nunzio Pomara , Temp Anna G.M. , Grothe Michel J. , Teipel Stefan 
Published: January 2025

AbstractBackgroundFamilial Alzheimer’s disease research necessitates innovative methodologies to disentangle the intricate relationships between genetic factors and neuroimaging measures. Traditional frequentist approaches, often hampered by small sample sizes in this population and challenges in incorporating prior knowledge transparently, may limit the robustness of findings.MethodsWe analyzed neuroimaging data of preclinical PSNE1 single mutation carriers, utilizing the software JASP to test effects of carrier status on measures of basal forebrain functional connectivity using both frequentist and Bayesian approach. Bayesian Analysis of Covariance was first implemented to test the hypothesis of a basal forebrain functional connectivity reduction in mutation carriers compared to non‐mutation carriers. To get more precise information, Informative priors, derived from the existing literature on preclinical sporadic Alzheimer’s disease, were integrated into post‐hoc Independent samples t‐tests. Student t‐tests were utilized for parameter estimation, using an Informed Cauchy specification of prior’s location (‐0.7) and scale (0.707) allowing the derivation of posterior distributions.ResultsOur findings demonstrated that preclinical mutation carriers exhibited no significant alterations in basal forebrain functional connectivity compared to non‐carriers. However, the Bayesian analysis revealed distinct advantages over the traditional frequentist approach (anterior basal forebrain: t = ‐1.126 (213), p = 0.131, Cohen’s d = ‐0.165, 95% CI for Cohen’s d: [‐Inf., 0.077]; posterior basal forebrain: t = ‐1.337 (213), p = 0.091, Cohen’s d = ‐0.196, 95% CI for Cohen’s d: [‐Inf., 0.046]. Incorporating informative priors significantly improved the precision of parameter estimates, allowing us to reject our hypothesis with a relatively high degree of confidence, from anecdotal (anterior basal forebrain: BF10 = 1.167, d = ‐ 0.319, 95% CI: [‐0.599, ‐0.061]) to strong evidence (posterior basal forebrain: BF10 = 0.033, d = ‐0.067, 95% CI: [‐0.260, ‐0.003]).DiscussionOur results underscore the importance of adopting Bayesian frameworks in familial Alzheimer’s disease research. The explicit integration of prior knowledge, as facilitated by Bayesian analysis, enhances the reliability of conclusions drawn from neuroimaging data. These methodological advantages are crucial in advancing the field and encouraging replication efforts.
Subject Harmonization of Multi‐Modal Digital Markers: Improved Detection of Mild Cognitive Impairment Using Language and Facial Expression
Authors: Hoang Bao , Pang Yijiang , Dodge Hiroko H , Zhou Jiayu 
Published: January 2025

AbstractBackgroundMild Cognitive Impairment (MCI) is the prodromal stage of dementia, including Alzheimer’s Disease (AD). Early identification and accurate assessment of MCI are critical for clinical trial enrichment as well as the early intervention of AD. Digital makers offered a unique opportunity for ecologically valid and affordable early detection approaches. Language markers extracted from verbal communications have shown diagnostic efficacy in detecting early MCI. Recent studies have shown that in addition to semantic and syntactic information in dialogues, emotions in communication can also be helpful in early MCI detection. A joint analysis of language markers and emotion indicative of facial expression is thus of great interest. Features from emotion could have additional predictive benefits to language markers. One general challenge of digital biomarkers is that feature distributions are very distinct. We hereby conduct a multi‐modal analysis of language and facial expression, combined with different harmonization.MethodWe used 3,501 conversations from 69 participants from the Internet‐Based Conversational Engagement Clinical Trial (I‐CONECT) (NCT02871921) and extracted language and emotion variables. For language variables, we used 99‐dimensional features from four types: Linguistic Inquiry and Word Count (LIWC) (64), Syntactic Complexity (23), Lexical Diversity (10), and Response Length (2). For emotion features, we extracted a 7‐dimensional emotion feature (angry, disgust, fear, happy, sad, surprise, neutral) from a facial frame using the DeepFace library. For analysis, we used the subject harmonization approach to remove information that is predictive of subjects. The harmonized features are then used to build a neural network for MCI detection.ResultWe evaluated Area‐under‐Curve (AUC) on subject classification. Our results show that applying harmonization will increase detection performance when only using language markers. If we harmonize both language and emotion features, the performance decreases. However, if we do harmonization on language and without harmonization on emotion, then the performance of subject classification increases to 0.701 AUC.ConclusionOur study has shown the additional benefits of emotion variables to augment language variables in the early detection of MCI. Multimodality analyses need careful selection of harmonization: harmonization strategy should be chosen for individual modality rather than the concatenated feature variables.
BreatheBot Buddy: Fostering Cognitive Well‐being with a Cuddly Companion for Regulated Breathing
Authors: Khan Arshia A , Sawchuk Matthew , Saheel Sabir , Quigley Jack , Heck Detlef 
Published: January 2025

AbstractBackgroundRegulated breathing is increasingly recognized as a vital component in enhancing cognition. Scientific studies suggest that intentional and controlled breathing techniques, such as deep and rhythmic breathing, can promote relaxation, reduce stress, and improve oxygen flow to the brain [1, 2]. Consequently, this may contribute to heightened cognitive function, better concentration, and increased mental clarity, accentuating the importance of incorporating mindful breathing practices as a potential avenue for cognitive improvement [3]. The increasing recognition of the cognitive benefits of regulated breathing is leading to the development of innovative technologies, such as robots, for better breathing practices. These technologies can be used to potentially enhance the cognitive well‐being and mindfulness of people in their daily routines.MethodsIn order to accomplish a breathing robot, the entire design consists of two linear actuators controlled by an L298N Motor Driver Interface and a Raspberry Pi 3B. The Raspberry Pi coordinates both linear actuators to extend, hold, and contract in opposite directions for 4 seconds at each stage. Additionally, the mechanical design utilizes two “push caps” that push at the sides of a contained space, such as a stuffed animal, simulating breathing (see Fig. 1). This design simulates a breathing pattern in any stuffed animal (see Fig. 2).ResultsThe developed robot underwent rigorous testing in both a lab and poster presentation setting. Various case studies were examined during the poster presentation, revealing the effectiveness of a tool designed to aid in regulated breathing. The present robot demonstrated increased participant engagement in mindful breathing practices, which suggests a promising avenue for leveraging robot‐assisted therapies to enhance cognitive well‐being in people.ConclusionThe present project aimed to build a robot that fosters cognitive well‐being through regulated breathing. The case study findings of this project highlight the effectiveness of a cuddly bear‐shaped robot as a valuable tool for promoting regulated breathing, offering positive implications for integrating such robotic assistance in enhancing cognitive well‐being.
Reciprocating the Future: Exploring Natural Human Behaviors in Humanoid Social Robot Interaction
Authors: Moberg Reilly , Khan Arshia A 
Published: January 2025

AbstractBackgroundWhen designing cutting‐edge technology, particularly humanoid social robots, an essential consideration is understanding how individuals naturally engage in social interactions, shaping their relationships with technology and media.MethodIn pursuit of insights into the application of natural human behavior, specifically reciprocation, in human‐robot interaction, an experiment involving 72 participants, involving facial electromyography, focusing on zygomatic and corrugator muscles, served as a tool to gauge users' emotional valence during interactions. The study assessed users' willingness to reciprocate a favor and measured compliance by tracking the number of raffle tickets purchased by users at the robot’s request. Events were recorded during the participant’s interaction with Pepper the humanoid robot, analyzing facial EMG data in five‐second intervals to capture changes in muscle activity before and after key events. The study comprised three scripted events, including the participant’s first sighting of Pepper, Pepper offering water (favor condition only), and a simulated glitch on Pepper’s tablet. Post‐interaction, participants filled out a final survey, contributing to the comprehensive examination of their experiences. The research involved 72 undergraduate students from diverse majors. The study aimed to assess initial impressions of humanoid robots, utilizing a phasic study design with a five‐second response time, aligning with previous research practices. The participants, largely lacking prior experience with social, service, or humanoid robots, provided valuable insights into human‐robot interaction dynamics.ResultsIn testing two hypotheses on participants' interaction with a Humanoid Social Robot (HSR), we found no significant differences in ticket purchases between favor and non‐favor conditions using statistical tests, including chi‐square and Mann‐Whitney U. However, an independent samples t‐test, after outlier removal, revealed a significant difference, supporting both hypotheses. Pearson’s correlation coefficients showed positive correlations between emotional responses, but no significant relation with self‐reported affect or ticket purchases. These findings enhance understanding of human‐robot interaction dynamics and inform future research directions.ConclusionThis study emphasizes the importance of incorporating natural human behaviors, like reciprocation, in designing humanoid social robots, indicating that initiating positive interactions significantly impacts users' compliance in subsequent interactions, advancing the field of human‐robot interaction with theoretical implications and proposing future research avenues despite acknowledged study limitations.
Deployment of Tablet‐based Cognitive Assessment Tool (TabCAT) for detecting cognitive impairment in elderly Primary Health Care Population in Southeast Nigeria
Authors: Ogbuagu Chukwuanugo , Uwakwe Richard , Kahn James G , OGBUAGU Ekenechukwu , Emelumadu Obiageli , Okereke Uzoma , Okeke Irene , Chisom Chigbo , Javandel Shireen , Possin Katherine L , Miller Bruce L. , Valcour Victor , Allen Isabel Elaine , Goode Collette A 
Published: January 2025

AbstractBackgroundCognitive assessment should be actively incorporated into the clinical evaluation of patients in Primary Health Care (PHC) settings. This is because of the imminent demand with the changing demographics especially in low‐ and middle‐income countries (LMICs) concerning dementia and Alzheimer’s disease.MethodA cross‐sectional mixed‐method descriptive study was conducted to evaluate the useability and performance of a tablet‐based cognitive assessment tool (TabCAT) for use in geriatric primary healthcare settings in southeast Nigeria.ResultIn our study involving 207 participants, with an average age of 64.69 years, educational levels varied, with 51.8% having primary education, 41.2% secondary, and 6.6% tertiary. The research found that education had a significant positive impact on associative memory, processing speed, executive function, language skills, and visuospatial skills (p<0.001). Rural residents exhibited higher scores in associative memory, processing speed, executive function, language, and visuospatial skills (p<0.000). However, gender and location did not show significant contributions to cognitive performance across the examined domains.ConclusionThere is an urgent need to deploy and routinely conduct cognitive assessments in primary healthcare clinics for elderly patients for early detection of cognitive changes to improve healthy living.
Digitally Assessed Sleep Disturbances for Early Diagnosis of Alzheimer’s Disease
Authors: Krix Sophia , Odeh Joud , Lentzen Manuel , Val‐Guardiola Andrea , Martínez Neus Falgàs , Atreya Alankar , Conde Pauline , Doherty Aiden , Vairavan Srinivasan , Muurling Marijn , de Boer Casper , Curcic Jelena , Grammatikopoulou Margarita , Nikolopoulos Spiros , Brem Anna‐Katharine , Coello Neva , Narayan Vaibhav , Wittenberg Gayle , Van Duijn Cornelia M , Hinds Chris , Aarsland Dag , Sanchez‐Valle Raquel , Fröhlich Holger 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) is associated with sleep disturbances. Moreover, individuals with sleep disturbances have been reported to have a higher risk for developing AD. The measurement of sleep behavior therefore opens the opportunity for a potential digital biomarker of AD.MethodWe modeled sleep patterns coming from the RADAR‐AD cohort from two sleep monitoring devices (Tab. 1). We applied a stochastic modeling approach, multi‐state models, and analyzed the times spent in each sleep state before transitioning and transition probabilities in and between sleep states. We further applied statistical analysis of sleep monitoring data and sleep questionnaires (ESS, PSQI) from the RADAR‐AD study (Fitbit Charge 3, DREEM) and preliminary data from the ADIS study (MotionWatch8) (Tab. 1), using a likelihood ratio test with the aim to assess the diagnostic potential of sleep monitoring devices compared to traditional sleep questionnaires.ResultModeling of digital device data showed that preclinial (preAD), prodromal (proAD) and mild‐to‐moderate (mildAD) AD patients spent more time in the light sleep and awake state, and less time in the REM sleep states compared to healthy controls (HC) before transitioning to the next state, showing non‐linear associations between diagnostic stage and sojourn times (Fig. 1A). ProAD and mildAD patients had a higher probability to transit to a light sleep phase compared to HC and to subsequently wake up (Fig. 1B). Based on our current and partially still preliminary data, only digital sleep monitoring via Fitbit allowed for a separation between HC and preclinical AD at nominal significance (preAD) but findings were not significant when adjusting for multiple testing (Tab. 2A). A significant distinction between HC and proAD (p < 0.001) as well as between HC and mildAD (p < 0.01) was possible using Fitbit sleep monitoring data, whereas traditional sleep questionnaires were only able to distinguish HC from mildAD (p < 0.05) (Tab. 2).ConclusionSleep patterns assessed via tested digital devices were able to separate proAD from HC – in case of Fitbit ‐ which was not possible by traditional sleep questionnaires. Digital sleep monitoring has thus the potential to support the early diagnosis of dementia.
Acceptance of using digital technologies to aid the timely detection of dementia: perspectives from Boston University Alzheimer’s Disease Research Centre
Authors: Beswick Emily , Wilson Sarah , Popp Zachary T , Rahman Salman , Khan Raiyan R , Tolley Clare , Low Spencer , Slight Sarah 
Published: January 2025

AbstractBackgroundUsing digital technologies to detect the early signs of dementia‐causing diseases could improve the timely detection of these diseases. To support this approach, we explored users' perspectives on the acceptability of using a variety of digital technologies.MethodA sub‐group of participants from Boston University’s Alzheimer’s Disease Research Centre were recruited. Participants used up to nine study technologies (active smartphone apps (n = 3), passive apps (n = 2), wrist worn activity monitor (n = 2), EEG headband (n = 1), Oximeter ring (n = 1)) for two weeks, every three‐months, over a year. Semi‐structured interviews were conducted after the first two weeks. A framework thematic analysis approach was applied to the interview transcripts, assisted by N‐Vivo (QSR, version 14.23.2). The Technology Acceptance Model(1) aided in refining key themes.ResultTwenty‐four individuals were interviewed: 13 with mild cognitive impairment (MCI) and 11 without cognitive impairments. Three key themes emerged; perceived usefulness, intention to use and enjoyment of use. Participants were excited about the possibility of using digital technologies to remotely monitor their brain health, but some raised concerns that this could reduce personal interaction with their healthcare professional. Participants with pre‐existing health conditions, questioned the accuracy of the technology. For example, a participant wondered what effect an oxygen mask used for sleep apnoea would have on the data collected from the oximeter ring, reducing their perceived usefulness of the devices. Many participants using seven or more devices reflected on the burden of using multiple devices, finding it was challenging to familiarise themselves with them and then remember to use them all. Participants with MCI enjoyed the cognitive stimulation they felt after using the gamified cognitive tasks in the active smartphone apps, whereas participants without cognitive impairment wanted more variety to improve engagement.ConclusionResults highlight the acceptability of a variety of digital technologies for the timely detection of dementia‐causing diseases and discussed the burden of using multiple devices. Future research should explore how pre‐existing health conditions could affect the accuracy of digital technologies to improve real world applicability. References • Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly. 1989 Sep 1:319‐40.