bluuebunny/crossref_metadata_2025_split huggingface dataset:
Support for transmission of tau pathology in humans using individualized epicenters and structural connectomes
Authors: Brown Christopher , Das Sandhitsu R. , Nasrallah Ilya M. , Detre John A. , Yushkevich Paul A. , McMillan Corey T , Wolk David A 
Published: January 2025

AbstractBackgroundPopulation‐based functional connectomes help explain heterogeneity in tau spread in Alzheimer’s disease by demonstrating spread among connected neurons from canonical epicenters. However, if the hypothesis of cell‐to‐cell transmission of tau is correct, individual structural connectomes seeded from individual‐specific epicenters of PET‐tau pathology should improve prediction of regional tau patterns.Method86 participants from the Penn Aging Brain Cohort and 158 participants from ADNI with multi‐shell diffusion MRI and AV1451 Tau PET within 18 months of each other were included. A schematic of methods is shown in Figure 1. Tau PET standard uptake value ratios were converted to Tau Pathology Indices (TPI) based on Gaussian‐mixed models. Regions > 2.5 standard deviations above the mean TPI within an individual were used as individualized epicenters, while entorhinal cortex (ERC) was used as a canonical group epicenter. Probabilistic tractography generated individual connectomes and a group connectome in amyloid‐negative participants. Network‐analysis calculated structural connectivity distances from epicenters for each region. Data were re‐arranged by tau pathology rank to average across all participants using each combination of individualized and group epicenters and connectomes. The model of Regional TPI = β*Distance + ε was tested in the training dataset using each of the 4 approaches: 1) Group canonical epicenter and group connectome (GE‐GC), 2) Group canonical epicenter and individual connectome (GE‐IC), 3) Individualized epicenters and group connectome (IE‐GC), and 4) Individualized epicenters and connectomes (IE‐IC). The resulting models were then applied to the validation dataset to predict regional TPI and compare to actual TPI.ResultThe fully individualized model demonstrated a significantly stronger relationship between connectivity and regional tau burden (β = ‐0.95[‐1.03, ‐0.87]) compared to all other models (Z = ‐5.81, p < .001, Figure 2). When the four trained models were applied to the validation dataset, the fully individualized model explained 80% of regional TPI variance, while the other models explained ≤ 52% (Figure 3).ConclusionIndividualized structural connectomes and tau epicenters capture the heterogeneity of Alzheimer’s disease and provide strong support for the cell‐to‐cell transmission of tau pathology in vivo.
Central and Peripheral Biochemical Changes in Alzheimer's Disease: Insights from the Alzheimer Disease Metabolomics Consortium
Authors: Batra Richa , Arnold Matthias , Wang Xue , Allen Mariet , Wörheide Maria A. , Blach Colette , Levey Allan I. , Seyfried Nicholas T , Bennett David A. , Kastenmuller Gabi , Ertekin‐Taner Nilüfer , Kaddurah‐Daouk Rima , Krumsiek Jan ,   Alzheimer's Gut Microbiome Project Consortium (AGMP)
Published: January 2025

AbstractBackgroundOur Alzheimer Disease Metabolomics Consortium (ADMC), part of the Accelerating Medicines Partnership for AD (AMP‐AD) and in partnership with AD Neuroimaging Initiative (ADNI), applied state‐of‐the‐art metabolomics and lipidomics technologies combined with genomic and imaging data to map metabolic failures across the trajectory of the disease. Our studies confirmed that peripheral metabolic changes influenced by the exposome inform about cognitive changes, brain imaging changes, and ATN markers for disease confirming that peripheral and central changes are connected, in part through the metabolome.MethodsTo map the biochemical changes in AD, we used various targeted and untargeted metabolic platforms to profile ∼800 postmortem brain tissue, and ∼ 5000 blood samples.ResultsRecently, we built a comprehensive reference map of extensive AD‐related metabolic changes in brain, spanning multiple AD‐related traits, including neuropathological b‐amyloid and tau tangle burden, as well as late‐life cognitive performance. Using this resource, we extracted novel metabolic including bioenergetic pathways, cholesterol metabolism, neuroinflammation, broad impairment of osmoregulation, an imbalance between excitatory/inhibitory neurotransmitter ratios and identification of tau load as a potential driver of metabolic dysfunction in the AD brain, with minimal contributions from b‐amyloid load. As AD and progressive supranuclear palsy (PSP) share the pathological feature of tauopathy and metabolic alterations, we compared their metabolomic profiles to identify shared biological pathways that could be targeted for therapeutic interventions. Our findings indicate that both diseases display oxidative stress, mitochondrial dysfunction, and tau‐induced polyamine stress response.ConclusionOverall, through our studies, (1) We identified biochemical processes altered in AD, with findings supported across both metabolomic and proteomic data, indicating multimodal deregulation. (2) Our research pinpointed widespread AD‐related biochemical changes across various brain regions with differing levels of neuropathology. While there are many overlapping changes across the brain regions, each region also has its distinct metabolic alterations. (3) We identified biochemical processes disrupted by AD, with parallel findings in other neurodegenerative diseases, hinting at broader implications in neurodegenerative research. Currently, we are working on mapping widespread connections of the brain metabolome with various determinants of AD namely genome, gut microbiome, exposome, and linking with peripheral metabolic alterations in AD.
Fat to muscle ratio is related to neurodegeneration in midlife obesity
Authors: Naghashzadeh Mahshid , Dolatshahi Mahsa , Commean Paul K. , Kasani Sara Hosseinzadeh , Rahmani Farzaneh , Liu Jingxia , Lloyd LaKisha , Nguyen Caitlyn , Hantler Nancy , McBee‐Kemper Abby , Ly Maria , Yu Gary Z , Ippolito Joseph E. , Weeks Jake , Ceriani Lael , Sirlin Claude , Morris John C. , Benzinger Tammie L.S. , Raji Cyrus A. 
Published: January 2025

AbstractBackgroundEmerging research underscores the significance of midlife obesity, defined by a BMI of 30 kg/m2 or higher in persons age 40‐60 years, as a risk factor for Alzheimer's disease (AD) in later life. Due to the various properties of each body component, it is important to characterize the neurodegenerative effects of fat within the muscle, known as a predictor of metabolic health and cognition. We investigated the relationships between thigh total fat‐to‐muscle ratio (FMR) and brain cortical thickness in cognitively normal midlife individuals.MethodOur study focused on a sample of 35 cognitively normal midlife participants (age: 51.59±5.72 years; 42.9% male; 60% obese; average BMI: 32.04±6.98 kg/m2). The brain and thigh scans were obtained from Siemens 3T MR scanners. On thigh MRI nine mid‐thigh slices located between Ischial Ramus and the medial knee condyle were identified and preprocessed. N4ITK Bias correction was performed with 3D Slicer to correct inhomogeneities. An in‐house MATLAB program measured total fat (subcutaneous, inter‐, and intra‐muscular fat) and muscle volumes derived from the summation of compartments from both thighs to calculate total FMR. FreeSurfer 7.1.1 was utilized for automated segmentation of both cortical and subcortical brain regions using a probabilistic atlas followed by a visual review, and where necessary, adjustments were made manually. The association between FMR and cortical thickness in 22 Late‐onset Alzheimer disease (LOAD) brain regions was examined using Spearman rank test, adjusted for age and sex and multiple comparisons.ResultFMR in the thigh is highly correlated to BMI, but not insulin resistance. There was a negative association between both FMR and InterMAT/muscle ratio and cortical thickness in multiple LOAD regions. Out of 22 LOAD regions, FMR showed negative correlation with cortical thickness in right superior and inferior parietal, inferior temporal, supramarginal, entorhinal, and precuneus cortex, as well as left middle and superior temporal lobe, and temporal pole.ConclusionThigh fat/muscle ratio is highly associated with BMI and negatively correlated with cortical thickness across various AD‐related brain regions. This may suggest that interventions that improve muscle quality, by reducing fat surrounding and inside the muscle, may be preventive for Alzheimer disease risk.
Screening for preclinical and prodromal Alzheimer disease clinical trials: predicting NIA‐AA research framework ATN category by leveraging neuropsychiatric symptoms
Authors: Ismail Zahinoor , Leon Rebeca , Guan Dylan X. , Smith Eric E. 
Published: January 2025

AbstractBackgroundAs disease‐modifying therapies for Alzheimer disease (AD) emerge, the need for earlier detection becomes increasingly important. Biomarker confirmation is expensive and invasive, only implemented for persons at risk. Risk is usually assigned based on cognitive decline. Accordingly, detection at NIA‐AA Research Framework Stage 2 (preclinical disease) or early Stage 3 (prodromal) is suboptimal. Whether incorporating neuropsychiatric symptoms (NPS) helps with detection remains in equipoise. However, by leveraging risk associated with later‐life emergent and persistent behavioral changes, Mild Behavioral Impairment (MBI) has demonstrated associations with lower amyloid‐β (Aβ) and higher tau levels in blood and CSF. Whether MBI can categorically predict prevalent AD is unclear. Here, we examined cross‐sectional associations between MBI and cerebrospinal fluid (CSF) [AT(N)] biomarker categories.MethodDementia‐free participants (n=984) from the Alzheimer's Disease Neuroimaging Initiative were included. Using the validated two‐thirds visit method to operationalize symptom persistence, NPS (identified using the Neuropsychiatric Inventory or Neuropsychiatric Inventory Questionnaire) were categorized into three NPS groups: 1) NoNPS; 2) NPS not meeting MBI criteria (NPSnotMBI); and 3) MBI. Consistent with the [AT(N)] framework, three biomarker profiles were described: 1) normal AD biomarkers; 2) AD continuum (low CSF Aβ1‐42); and 3) non‐AD pathologic change (normal Aβ, elevated CSF p‐tau181 and/or t‐tau). Gaussian Mixture Modeling (GMM) was used to determine biomarker positivity thresholds with Winsorization of extreme values. Logistic regression modeled associations between MBI status and CSF biomarker positivity, adjusted for age, sex, education, and MMSE score. Multinomial logistic regression modeled associations between MBI status and [AT(N)] biomarker profile.ResultTable 1 shows demographics. Compared to the NoNPS group, MBI was significantly associated with Aβ positivity (OR=2.35, 95%CI=1.70–3.26, p<0.001), p‐tau181 positivity (OR=2.15, 95%CI=1.56–2.96, p<0.001), and t‐tau positivity (OR=1.89, 95%CI=1.37–2.59, p<0.001), while NPSnotMBI was not (Table 2). Multinomial logistic regression indicated a significant association between MBI+ status and the AD continuum category (OR=2.56, 95%CI=1.77–3.69, p<0.001), but not non‐AD pathologic change (Table 3).ConclusionIn dementia‐free older individuals, MBI predicts Aβ‐positivity and AD+ status over non‐AD pathological change; no associations were found with NPSnotMBI. Incorporating MBI into AD trial screening can improve disease detection and reduce screen failures.
Correlation of Clinical and Imaging Findings in Early‐Onset Alzheimer's Patients with Distinct Initial Symptoms
Authors: Özçelik Sinem , Yiğit Pınar , Bozdemir Tolga Can , Kaya Gamze Çapa , Yildirim Zerrin , Tee Boon Lead , Gurvit Hakan , Yener Görsev , Tempini Maria Luisa Gorno , Öz Didem 
Published: January 2025

AbstractBackgroundEarly‐onset Alzheimer’s disease (EOAD), distinct from late‐onset cases, tends to present with atypical cognitive decline, such as, progressive aphasia, apraxia, visuospatial and comportmental impairments,. This study aims to investigate correlations between clinical presentation and PET imaging findings in EOAD patients.MethodA retrospective analysis was conducted on CSF confirmed EOAD patients. Clinical assessments incorporated ACE‐R cognitive tests, MRI and FDG‐18 PET CT. MRIs were rated by an experienced neurologist using the Visual Atrophy Scale (VAS). Patients were stratified based on clinical presentation, and correlations between ACE‐R domains and PET indices were examined. FDG‐18 PET index was calculated as the count number (CN) of the same regions evaluated in VAS divided by CN of their cerebellum.ResultNineteen patients (57.9% women) with a mean age at disease onset 55.7 (±6.57 StD) years participated. The mean disease duration was 5.1 (±3.14 StD) years. In the typical amnestic group (n=8); ACE‐R total scores (52.63±15.3 StD) exhibited correlations with left parietal (r: 0.775, p: 0.025) and left occipital PET indices (r: 0.825, p: 0.012). Moreover, total memory scores (9.13±7.4 StD) correlated with left anterior temporal (r: 0.848, p: 0.008), and repetition scores correlated with left parietal PET indices (r: 0.907, p: 0.005). In the amnestic subgroup (n=5), a notable correlation was found between retrograde memory scores and left anterior temporal PET index (r: 0.905, p: 0.035). In the visuospatial group (n=8) orientation scores with left anterior temporal (r: 0.092, p: 0.02), and writing scores with right occipital (r: 0.968, p: 0.07) and left parietal (r: 0.941, p: 0.017) was found correlated. The behavioral variant group (n=3) showed a correlation with right orbitofrontal PET activity (r: 0.99, p: 0.02). A significant difference in attention scores was noted between aphasia and amnestic groups (p = 0.032), with the amnestic group scoring higher (6.20 (4.50‐10.50); 16.44 (11.50‐17.75)).ConclusionDistinct correlations emerged between cognitive domains and PET activity based on early symptoms in EOAD patients, emphasizing the intricate heterogeneity within EOAD. The findings offer valuable insights for personalized interventions and advance our understanding of the multifaceted manifestations in the EOAD spectrum.
Amyloid PET Detects the Deposition of Brain Aβ Earlier than CSF Fluid Biomarkers
Authors: Lowe Val J. , Mester Carly T. , Lundt Emily S. , Lee Jeyeon , Ghatamaneni Sujala , Algeciras‐Schimnich Alicia , Campbell Michelle R , Graff‐Radford Jonathan , Nguyen Aivi T. , Min Hoon‐Ki , Murray Melissa E. , Senjem Matthew L. , Schwarz Christopher G. , Kantarci Kejal , Boeve Brad F. , Vemuri Prashanthi , Jones David T. , Knopman David S. , Jack Clifford R. , Petersen Ronald C. , Mielke Michelle M. 
Published: January 2025

AbstractBackgroundUnderstanding the relationship between AβPET and AβCSF biomarkers will define their potential utility in Aβ treatment. Few longitudinal or neuropathological comparisons have been reported. We assessed the relationship of AβPET and AβCSF biomarkers in a large community cohort.MethodParticipants 50+ years with coincident AβPET and AβCSF biomarkers (pTau181/Aβ42, n=505, and Aβ42/40, n=54) were included. Of these participants, a subgroup was identified that had neuropathology data (n=47) of which many had longitudinal AβPET and CSF testing. The relationships of AβPET and CSF biomarkers were assessed cross‐sectionally in all participants and longitudinally in those with autopsy data. Analyses were performed using AβPET Meta‐ROI and AβPET early‐Aβ‐deposition ROIs (ROIs with early Aβ deposition: fusiform, angular, inferior temporal, middle temporal, middle occipital, and calcarine).ResultIn biomarker tests performed at the same time, more participants were AβPET+ and CSF‐ than AβPET‐ and CSF+ using Meta‐ROI AβPET. This discordance occurred more frequently when using early‐Aβ‐deposition regions (Figure 1). Of 47 participants with autopsy data, 35 had Thal stage ≥1. The sensitivity for earliest detection of cortical Aβ neuropathology by AβPET was 89% (31/35) and by CSF was 66%, (23/35). Eight of these 35 had positive AβPET findings and negative coincident CSF tests at the first time tested. Of these, 5/8 were Meta‐ROI and early AβPET ROI positive. The other 3/8 were only positive on early‐Aβ AβPET ROIs. Four of the 8 with negative CSF and positive AβPET had subsequent testing and 2/4 became positive on CSF tests after 1.5 years while 2/4 continued to be negative on subsequent testing.ConclusionAβPET is more sensitive for detection of early cortical Aβ deposition than CSF biomarkers in cross‐sectional or autopsy data. While the assessment of AβPET by standard Meta‐ROI AβPET methods showed cortical Aβ deposition occurring when not indicated by CSF biomarkers, AβPET ROIs for early Aβ deposition detected additional participants that were AβPET positive and AβCSF negative. These findings have implications for detecting early Aβ neuropathology among CU participants in Aβ prevention trials when robust detection is needed. Further work is needed to compare Aβ plasma biomarkers and AβPET using these methods.
Preparing a global workforce for implementation of evidence‐based dementia care: facilitators and barriers
Authors: Koeuth Sokha , Gitlin Laura N. , Sefcik Justine S. , Hodgson Nancy A , Bruneau Michael 
Published: January 2025

AbstractDementia is a global public health challenge, impacting > 55 million individuals worldwide. However, widespread dissemination of efficacious non‐pharmacologic interventions remains limited. A key implementation barrier is workforce preparation and support for healthcare professionals (HPs) and administrators when being trained in, implementing, and sustaining programs. Normalization Process Theory (NPT) is a useful framework for understanding how complex interventions are perceived by frontline staff and integrated into daily work routines. NPT consists of four constructs: coherence (describing the process), cognitive participation (level of engagement of staff in the program), collective action (operational work), and reflective monitoring (perceived value of the program). Using NPT, we examined training and implementation barriers and facilitators for HPs and administrators from different countries embedding two evidence‐based programs (Tailored Activity Program (TAP) or Care of Older People in the Environment (COPE) in their settings.Twenty‐nine participants (15 HP and 14 administrators) from the United States, Australia, Scotland, Italy, Chile, and Poland, were trained in either TAP or COPE, consisting of self‐paced online modules (10 hours for TAP; 16 hours for COPE), and accompanied by virtual meetings and personalized coaching. After training, participants used TAP/COPE with families in their respective countries and health systems. Seven focus groups (2‐4 participants per group) and eight individual interviews were conducted, using semi‐structured interview techniques, and qualitatively analyzed through directed content analysis to examine their experiences with these programs.Applying NPT, we found general consensus that TAP/COPE were well received and perceived as beneficial to patients/clients (NPT = reflective monitoring). Nevertheless, additional resources for training and implementation were identified as important for effective implementation and sustainability: 1) strategies/checklists for getting started (NPT = coherence and cognitive participation), 2) support (webinars with seasoned implementers/trainers; NPT = cognitive participation and collective action), 3) and for the USA, billing information for reimbursement (NPT = collective action).NPT provides a useful framework for understanding implementation and workforce preparatory challenges. We found that it is possible to effectively train and prepare a workforce using online, self‐paced approaches followed by live coaching opportunities. However, additional supports are needed for effective implementation/sustainability. A need for packaging evidence‐based programs in ways that facilitate implementation and sustainability is needed.
Adapting Implementation Strategies to Context: Moving an Evidence‐Based Dementia Care Program into PACE
Authors: Hodgson Nancy A 
Published: January 2025

AbstractBackgroundSystematic approaches are essential for adapting and tailoring implementation strategies to specific dementia care settings if we are to move evidence‐based practice into ‘real world’ care that reaches most persons living with dementia. Care of Persons with Dementia in their Environments (COPE) is an evidence‐based dementia care program that provides families with skills to maximize functional abilities and quality of life of persons with dementia and reduce difficulties managing day‐to‐day care challenges for family caregivers. The COPE in PACE study was a national non‐inferiority trial that involved the implementation of COPE in Programs of All‐Inclusive Care of the Elderly (PACE)‐ which provide support for nursing home eligible Medicaid and Medicare enrollees (NCT04165213). Most PACE enrollees have cognitive impairment or dementia yet not one of the 200+ proven caregiver supportive programs have been integrated in this setting.MethodMultiple data collection methods including observations, surveys, interviews, and focus groups were used to identify implementation barriers and the resulting program adaptations. Transcripts, templated notes, and field notes gleaned from these sources were cataloged according to the framework for reporting adaptations and modifications to evidence‐based interventions (FRAME‐IS).ResultThe leading barriers to implementation were lack of PACE staff protected time, high staff turnover, low ‘readiness’ in dementia caregivers and lack of dementia‐specific knowledge. Program adaptation strategies were made to improve the feasibility, reach/engagement and fit at the site level and are summarized in Table 1. Different implementation approaches were required at each PACE site and were driven by context dependent factors.ConclusionIt is important not to underestimate the context dependent factors that may impact uptake of evidence‐ based programs. The array of data collection methods allowed for a greater understanding of the barriers to implementation and reasons for adaptation and can provide guidance for future researchers. The FRAME‐ IS model was useful for documenting adaptations made while implementing COPE in PACE and helped illustrate how adaptations clustered into unique prototypes. Our findings demonstrate how adaptability in delivery characteristics is essential for the translation of a dementia care program into real world practice and provide an example methodology for tracking these changes.