bluuebunny/crossref_metadata_2025_split huggingface dataset:
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, δ = ‐ 0.319, 95 % CI: [‐0.599, ‐0.061]) to strong evidence (posterior basal forebrain: BF10 = 0.033, δ = ‐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.
Hierarchical Bayesian continuous‐time dynamic modeling approach to investigate the dynamic interplay between aerobic exercise and time‐locked cognitive performance in older adults with AD and metabolic risk
Authors: Schwarck Svenja , Voelkle Manuel C , Bernal Jose , Berron David , Baumeister Hannah , Gellersen Helena M. , Daamen Marcel , Boecker Henning , Ziegler Gabriel , Düzel Emrah 
Published: January 2025

AbstractBackgroundTraining studies report beneficial effects of physical (PP) on cognitive performance (COG) in older adults, but are often accompanied by potentially biased parameters, conclusions, and lack of directionality. To address these issues, we used a dynamic Bayesian approach to analyse the dynamic session‐to‐session change and coupling of PP and COG over time.MethodsWe used two studies (N = 17 each): Study 1 contained 24‐weeks (72 sessions) of training of older adults with suspected Alzheimer’s disease (AD). Study 2 included four months (40 sessions) of training of older adults at metabolic risk. The hierarchical Bayesian continuous‐time dynamic modeling approach (R package ctsem) comprised: (i) A subject‐level latent dynamic model, (ii) subject‐level measurement model, and (iii) population model. The dynamic model was specified with two fully connected state variables enabling bidirectional coupling between PP and COG using default priors and starting values (4 chains and 8.000 iterations). Intercept and drift parameters were set to vary freely. Population and individual level parameters are estimated simultaneously using all data from all subjects. Second‐level models included MMSE, SF12 (physical health questionnaire) in study 1 and whole hippocampal volumes (wHCV: T2‐ASHS, longitudinal) and white matter hyperintensities (SAMSEG, longitudinal) in study 2 as time independent covariates.ResultsStudy 1: Higher PP was dynamically linked to COG (‐1.335, 95%‐BCI [‐1.725, ‐0.954]). The effect was short‐term, lasting up to five days (‐0.368, 95%‐BCI [‐0.479, ‐0.266]). Study 2: PP improved COG in subsequent sessions (‐1.11, 95%‐BCI[‐1.22, ‐0.99]), which lasted for up to 15 days. Higher wHCV was associated with a stronger coupling‐effect of PP on COG (‐0.15, 95%‐BCI[‐0.18, ‐0.11] ) and lower persistence of COG (‐0.145, 95%‐BCI[‐0.21, ‐0.08]).DiscussionOur results show immediate exercise‐induced improvements in COG, with a longer‐lasting effect for participants at metabolic risk compared to older adults with suspected AD. Despite impaired cognitive performance, the cognitive system was still able to fluctuate and change favorably. Physical exercise seems to mobilize capacities that would otherwise remain unused. Utilizing a Bayesian approach to examine time‐locked effects can provide insights for implementing individualized training approaches and guiding clinical decision‐making.
Using Bayesian statistics for longitudinal modelling and individual prediction in neuroimaging
Authors: Sala‐Llonch Roser , Pérez‐Millan Agnès 
Published: January 2025

AbstractLarge neuroimaging datasets play a crucial role in longitudinal modelling and prediction of neurodegenerative diseases, as they provide the opportunity to study biomarker trajectories over time. Noteworthy, the availability of these large datasets coexists with a paradigm shift in the theoretical understanding of these diseases: while classical studies aimed at defining disease signatures as group patterns obtained with static cross‐sectional analyses, novel approaches focus on providing individual predictions in the context of phenotypical and temporal heterogeneity. This scenario is often aggravated by the fact that datasets are not homogeneous and suffer from missing points and noisy data.Given this complexity, Bayesian approaches appear as an elegant solution to model longitudinal data as they rely on the probabilistic distribution of the data rather than referring to a hypothesis derived from the sampling distribution. Here, we provide empirical examples together with a comprehensive state‐of‐the‐art review and future perspectives on the field.In the first example, we compare Bayesian and frequentist approaches in a Linear Mixed Effects analysis using MRI‐derived hippocampal volumes, from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We simulate different datasets resembling real‐life clinical scenarios. Our results show that in large homogeneous datasets, both approaches perform similarly in differentiating groups, being the frequentist approach simpler and computationally faster. However, the Bayesian approach was the preferred option with limited datasets with less homogeneous groups and large number of missing data [1].In another example, we discuss the use of Gaussian Processes (GPs), a Bayesian non‐parametric model estimation technique that generates predictions based on the posterior distribution of functions using predefined kernels [2]. We show novel results on the ability of GPs to identify trajectories within the ADNI database.Finally, we review the use of machine learning approaches to generate individual predictions. We discuss our own results [3], together with other promising tools that use novel approaches derived from Bayesian statistics that go beyond the classical group classifications [4].References:[1] Pérez‐Millan A, et al., Sci Rep. 2022.[2] O'Muircheartaigh J, et al., Brain. 2020.[3] Pérez‐Millan A, et al., Research Square. 2023.[4] Azevedo T, et al., Commun Med. 2023
White matter hyperintensities in AD and bvFTD patients from Underrepresented countries
Authors: Altschuler Florencia , Canziani Veronica , Fraile‐Vazquez Matias , Ibáñez Agustín , Campo Cecilia Gonzalez 
Published: January 2025

AbstractBackgroundWhite matter hyperintensities (WMH) are very common brain MRI signal abnormalities linked to age, small vessel cerebrovascular disease, cognitive impairment, and dementia. Despite extensive research on WMH in Alzheimer's disease (AD), their prevalence in behavioral variant Frontotemporal dementia (bvFTD) remains less explored. Additionally, Latin American countries (LA) exhibit a higher prevalence of cerebrovascular disease due to distinct demographic, socioeconomic, cultural, and ethno‐racial factors. However, limited research on WMH exists in this context. Our aim was to characterize the WMH burden and its relationship with neurodegeneration and cognition in healthy aging and dementia subjects from LA in comparison to the United States (US).MethodsThis cross‐sectional multicenter study involved 638 LA and 355 US subjects from the ReDLat consortium, including healthy controls (HC), bvFTD, and AD patients. Participants underwent brain MRI and neuropsychological assessments. Voxel‐based morphometry analysis was performed to calculate WMH load and distribution from T2‐FLAIR images, and gray matter (GM) atrophy from T1 images. To assess the differences in the spatial distribution of WMH, two sample t‐tests were run between HC and neurodegenerative groups (AD and bvFTD) for each region (LA and US). The association between total load of WMH with regional GM volume and the association between cognitive performance (MMSE) with tract‐specific WMH load was tested via voxel‐wise regression analyses. In all analysis significance was set at p<0.05 family‐wise error‐corrected for multiple comparisons with a cluster extent threshold of 50 voxels. Age, years of education, sex, TIV and scanner were included as covariates of no interest.ResultsbvFTD exhibited higher WMH load than AD in both regions (LA and US, Figure 1). Notably, the association between WMH burden and GM atrophy was substantially stronger for all LA groups (bvFTD, AD, and HC) compared to the US (Figure 2). Additionally, an association between WMH burden and cognitive impairment (MMSE) was found in all pathological groups (Figure 3).ConclusionThis study highlights regional and disease‐specific effects of WMH burden in brain atrophy and cognition, emphasizing the importance of region‐adapted dementia research for a comprehensive understanding of these disparities.
What’s in a name? Terms persons supporting people living with MCI or dementia use to describe their role
Authors: Erickson Claire M , Clapp Justin , Gupta Anjali , Kleid Melanie , Harkins Kristin , Stites Shana D. , Peterson Andrew H , Karlawish Jason , Largent Emily A. 
Published: January 2025

AbstractBackgroundBest practice recommendations suggest a person close to a patient with mild cognitive impairment (MCI) or dementia be involved in their care. This person is often referred to as a “caregiver,” though the term “care partner” has increasingly been used in research and care instead of “caregiver.” Unlike “caregiver,” “care partner” suggests a collaborative relationship between the patient and their support person, in which the patient actively participates rather than passively receives help. It is not known, however, what nomenclature people in this care role themselves use and why. Establishing terminology that accurately reflects the experiences of these individuals is important for research and care.MethodSemi‐structured interviews were conducted with 14 people assisting patients with diagnoses of MCI or mild dementia, identified through an NIA‐funded Alzheimer’s Disease Research Center. Interviewees were asked if they identified as a caregiver, care partner, or something else. Data analysis was guided by a constructivist grounded theory approach and consisted of iterative rounds of coding with checks for intercoder reliability.ResultInterviewees who provided relatively less assistance to patients, such as with activities of daily living (ADLs) and decision making, described themselves as a “care partner,” while those providing relatively more saw themselves as “caregivers” or “caretakers.” Preferred nomenclature may shift as the disease progresses and care needs change. Some interviewees currently preferring “care partner” suggested that “caregiver” was presently inappropriate because the patient was not yet that impaired. Support persons using “care partner” often highlighted the patient’s dignity and agency and emphasized their shared responsibility for ensuring the patient’s wellbeing. Finally, discussions of preferred nomenclature elicited reflections from the support person on how the patient’s cognitive and functional impairments have changed the dyad’s preexisting relationship (e.g., spousal, parental).ConclusionThe terminology used to describe a person assisting in the care of a person with MCI or mild stage dementia has descriptive and normative dimensions. Asking support persons what label they use for themselves can provide insights into a patient’s current care needs, social and functional qualities of the dyadic relationship, and demonstrate respect for the support person.
Implementation and Validation of Face De‐Identification (de‐facing) in ADNI‐4
Authors: Schwarz Christopher G. , Tosun Duygu , Yushkevich Paul A. , Choe Mark , Rossi Stephanie , Das Sandhitsu R. , Ittyerah Ranjit , Fletcher Evan , Maillard Pauline , Singh Baljeet , Harvey Danielle J. , Malone Ian B. , Prosser Lloyd , Senjem Matthew L. , Matoush Leonard C. , Ward Chadwick P. , Prakaashana Carl M. , Decarli Charles , Weiner Michael S. W. , Jack Clifford R. ,   The Alzheimer's Disease Neuroimaging Initiative
Published: January 2025

AbstractBackgroundRecent advances in automatic face recognition have increased the risk that de‐identified research imaging data could be re‐identified from face imagery in brain scans.MethodAn ADNI committee of independent imaging experts evaluated 11 published techniques for face‐deidentification (“de‐facing”) and selected four algorithms (FSL‐UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de‐faced images (confirming negligible side effects on analyses).ResultEffects of the de‐facing methods on FreeSurfer at UCSF (Figure 1‐top), ASHS at University of Pennsylvania (Figure 1 bottom‐left), and longitudinal atrophy at Mayo Clinic (Figure 1 bottom‐right) were comparable with each other and sufficiently small vs. unmodified scans to recommend de‐facing in ADNI‐4. The FSL and mri_reface methods were more thorough in removing facial features, partly because these also removed ears (Figure 2‐top). mri_reface was recommended for ADNI‐4 primarily because fewer (zero) images had modified brain (Figure 2‐bottom), and ADNI leadership approved de‐facing with mri_reface. ADNI performed a second validation of its latest version on another diverse sample of 100 ADNI‐3 participants. These analyses included: boundary shift interval at University College London, where average differences in measurements were all <0.05%; cortical white matter hyperintensities at UC Davis, where they found “minimum impact” (R>0.97); and others.ConclusionADNI‐4 de‐faces all indicated image types (Table 1) before subsequent pre‐processing, analyses, and public release. Sites upload images to the central repository (LONI), where they are queued to the MRI Core at Mayo Clinic for de‐facing. Image types requiring de‐facing are de‐faced using mri_reface. Trained analysts inspect de‐faced images to confirm complete removal of the face and complete non‐modification of the brain, and any flagged images are re‐processed manually until issues are resolved. De‐faced images are converted back to DICOM and re‐uploaded for further distribution. Image types not requiring de‐facing are indicated to proceed directly to subsequent steps. In summary, ADNI‐4 has adopted mri_reface because it successfully de‐faces without substantially affecting brain measurements.
The Association of Nursing Home Residents’ Pain with Preference‐Based Recreational Activity Attendance over time
Authors: VanHaitsma Kimberly S , Heid Allison R. , Rovine Michael J , Eshraghi Karen , Akter Nahida , Abbott Katherine M 
Published: January 2025

AbstractBackgroundKnowledge of nursing home (NH) residents’ everyday care preferences is foundational in that it allows for the delivery of person‐centered care and individualized care planning. However, little is known about how integrating preferences into care delivery impact outcomes of care. The Preference Match Tracker is an objective metric that tracks the number of recreation activities NH residents attend that match or is “congruent” with resident important preferences. Here, we explored how Preference Match Tracker data were linked to NH residents’ with and without dementia self‐reported pain over the course of one year.MethodThe Preferences for Everyday Living Inventory (PELI) was used to assess important activity preferences for <I>N = </I>586 residents with varying levels of cognitive capacity from January 1, 2016, to March 13, 2020 (pre‐COVID‐19). Resident attendance at activities, as well as activities declined, per week was tracked as a metric for quality improvement. Pain data were collected from the MDS 3.0 and dichotomously coded as pain or no pain. We used PROC GLIMMIX in SAS 9.4, to model repeated measures logistic regression with time as the repeated measure, over 1 year. Covariates included age, gender, cognitive ability, length of stay, sensory impairment, and activities of daily living (ADL) functional ability.ResultsThere were no associations of age, gender, or hearing impairment with pain. Pain reduced over time. Greater cognitive capacity and shorter length of stay in the NH (< 90 days) were associated with more pain over time. More resident vision impairment and less ADL disability were also associated with less pain. After accounting for covariates, attending recreational activities was associated with less pain over time. Specifically, greater attendance at preferred activities was associated with less pain.ConclusionActivity attendance may be beneficial for pain experience for NH residents with and without dementia, possibly by distracting residents from pain through meaningful engagement. Higher attendance, and specifically attendance in preferred activities, can support individuals experiencing pain, especially those new to a NH and in the early stages of dementia with greater cognitive capacity. Controlling pain may also promote residents’ capacity for engagement in activities, specifically preferred activities.
Analytical performance assessment and suitability of the plasma β‐Amyloid 1‐40 and 1‐42 assays on the Lumipulse G instrument from Fujirebio to support Alzheimer's Disease clinical trials
Authors: Dupont Magali , Frantz Philippe , Thabuis Séverine , Nalayanda Divya , Troksa Erica 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) is a major neurodegenerative disorder characterized by amyloid deposits in brain tissues and representing a continuously increasing global burden in need of disease‐modifying therapeutic options. Amyloid beta 1‐42 and 1‐40 peptides and the amyloid beta 1‐42/1‐40 ratio are hallmarks of AD and are commonly monitored in Cerebro‐Spinal Fluid (CSF) along other AD biomarkers, to support diagnosis and management of AD patients. Over the past few years, blood‐based AD biomarkers have emerged as highly relevant and more practical alternatives to CSF biomarkers, and further technical performance characterization of the associated assays would be beneficial to the AD research and medical community.MethodThe Lumipulse G β‐Amyloid 1‐42 and 1‐40 Plasma assays are two‐step chemiluminescent enzyme immunoassays (CLEIA) performed on the Lumipulse G system and designed for the quantitative measurement of amyloid beta 1‐42 and 1‐40 in plasma specimens. We evaluated these two assays in a CAP/CLIA environment, utilizing CLSI guidance to set validation parameters and acceptance criteria. Linearity of results, assay precision within and between analytical runs, functional sensitivity as well as other performance parameters were evaluated in endogenous samples, neat or spiked to obtain appropriate concentrations.ResultUtilizing a set of samples from apparently healthy donors above 75 years old, we were able to show an expected reference range of 154.05 – 455.50 pg/mL and 12.57 – 42.43 pg/mL for amyloid beta 1‐40 and 1‐42, respectively. Our results support a linear measurement range of 0.57 – 3,404.60 pg/mL and 0.83 – 682.00 pg/mL for amyloid beta 1‐40 and 1‐42, respectively, hence covering appropriately the expected reference range of the healthy population and allowing to capture abnormally elevated or depressed concentrations. For amyloid beta 1‐40, experiments revealed CV% ranging from 3.3 to 3.7% for intra‐ and 5.5 to 7.0% for inter‐assay precision. For amyloid beta 1‐42, CV% ranged from 3.4 to 5.4% for intra‐ and 5.8 to 8.8% for inter‐assay precision.ConclusionOverall, these assays demonstrate technical robustness and reliability to support clinical trials for AD in the plasma matrix, which can offer a less invasive, yet valuable test to enrich for amyloid positivity in neurodegenerative clinical trials.