bluuebunny/crossref_metadata_2025 huggingface dataset:
A‐T+ PET participants in preclinical AD: Clinical progression and concordance with fluid markers
Authors: Yakoub Yara , St‐Onge Frédéric , Fajardo Alfonso , Mohammediyan Bery , Dery Christine , Sylvain Elisabeth , Tremblay‐Mercier Jennifer , Remz Jordana , Gonneaud Julie , Vogel Jacob W. , Soucy Jean‐Paul , Binette Alexa Pichet , Villeneuve Sylvia ,   PREVENT‐AD Research Group
Published: January 2025

Background

Amyloid‐negative tau‐positive PET (A‐T+) participants have been reported in several studies. We assessed the prevalence and characteristics of A‐T+ participants in a cohort of cognitively unimpaired individuals with a first‐degree family history of Alzheimer’s disease (AD) dementia.

Method

We studied 252 participants from the longitudinal PREVENT‐AD cohort (mean cognitive follow‐up = 3.42 years, SD = 2.86) who received an amyloid (18F‐NAV4694) and a tau (18F‐Flortaucipir) PET‐scan. The amyloid‐positivity threshold was 18 centiloids (SUVR=1.27) and tau positive threshold was set as 2SD above the mean of A‐ participants in a temporal meta‐ROI (SUVR=1.29). Follow‐up PET scans were available for 109 participants. We characterized A‐T+ participants on clinical (age, sex, APOE4 status, education, depression, apathy, anxiety, sleep), cognitive (RBANS, MMSE, ECoG) and fluid (CSF, plasma) measurements.

Result

In the PREVENT‐AD cohort, only 3 (1.2 %) of all studied participants were classified as A‐T+ (see Table 1 for participants characteristics). Participants 1 and 2 were positive based on plasma Aβ42/40 and they had relatively low levels of tau binding that could represent early pathology. Participant 3 was also positive based on CSF and plasma, her Aβ‐PET scan was classified as positive on visual read and became quantitatively positive at follow‐up. Participant 3 had extensive unilateral tau binding at baseline that became bilateral at follow‐up (Figure 1). Figure 2 details the longitudinal cognitive trajectory of Participant 3. During the cognitive follow‐up, participants 1 developed mild cognitive impairment and participant 3 developed dementia.

Conclusion

A‐T+ individuals are rare in the PREVENT‐AD cohort and the 3 A‐T+ were classified as Aβ positive based on fluid biomarkers. One of the 3 A‐T+ individuals showed a very fast clinical progression and a tau uptake pattern atypical of AD.
Cortical tau aggregation drives axonal degeneration in white matter fiber tracts in Alzheimer’s disease
Authors: Pescoller Julia , Dewenter Anna , Steward Anna , Dehsarvi Amir , Wagner Fabian , Gross Mattes , Zhu Zeyu , Ewers Michael , Roemer Sebastian Niclas , Brendel Matthias , Franzmeier Nicolai
Published: January 2025

Background

In Alzheimer’s disease (AD), cortical tau aggregation is a strong predictor of cortical brain atrophy as shown by MRI and PET studies, particularly driving the degeneration of neuronal somata in the grey matter. However, tau’s physiological role is to stabilize microtubules within axons in the brain’s white matter (WM) pathways. Therefore, tau’s white‐to‐grey‐matter translocation and aggregation in neurofibrillary tangles close to neuronal somata may induce WM degeneration through destabilization of axonal microtubule integrity. To address this, we determined whether cortical tau predicted faster atrophy of connected WM tracts in AD.

Method

We included from ADNI cohort 37 amyloid‐PET negative (Aβ‐) cognitively normal (CN) participants and 88 amyloid‐PET positive (Aβ+) participants across the AD‐spectrum (i.e. CN/MCI/Dementia = 50/28/10), with baseline amyloid‐PET, longitudinal tau‐PET and longitudinal structural MRI data. For replication, we included baseline amyloid‐PET, tau‐PET and MRI data of 321 CN‐Aβ+ subjects from the A4 cohort. T1‐weighed MRIs were segmented into grey and white matter and non‐linearly normalized to MNI space using CAT12. The cortical Brainnetome Atlas and a diffusion imaging‐based tractography atlas were applied tau‐PET and MRI data to i) determine cortical tau‐PET accumulation rates within Brainnetome ROIs, and ii) assess WM volume changes within fiber tracts connected to each cortical ROI. Statistical regression‐models were adjusted for age, sex, WM hyperintensity volume, global amyloid, intracranial volume, and APOE4‐status.

Result

In ADNI, higher baseline temporo‐parietal tau‐PET predicted faster volume reductions in connected WM tracts (Figure 1A), especially pronounced in Aβ+ subjects (Figure 1B). Similarly, faster tau accumulation was strongly linked to widespread WM volume reductions in connected fiber tracts (Figure 2A; Figure 2B), particularly exacerbated in Aβ+ APOE4 carriers (Figure 3A) compared to non‐carriers (Figure 3B). These results suggest that tau accumulation and WM degeneration are parallel processes in AD, modulated by APOE4. In the A4 validation sample of preclinical AD patients, we detected congruent associations between inferio‐temporal tau‐PET increase and reduced WM volume in connected fiber tracts (Figure 4).

Conclusion

Cortical tau aggregation is associated with progressive WM atrophy in connected fiber tracts throughout the brain, suggesting that tau accumulation triggers axonal degeneration, which may induce neuronal disconnection and dysfunction and thereby contributing to AD progression.
Health Outcomes of Transitional Space Design for Older Adults with Dementia
Authors: Chadalavada Myna , DeCaro Renee , Karaca Meltem , Hagan Brenna , Morreale Kristina , Waskow Emily , Turk Katherine W. , Budson Andrew E. , Anderson Diana
Published: January 2025

Background

The built environment is increasingly recognized as a valid medical intervention, known to affect mental and social health, which can carry consequences for cognitive function. “Transitional spaces” are indoor/outdoor areas designed to foster connections to the surrounding world and boost well‐being including windows, porches, and public parks. Little is known about the space design characteristics that might help improve social and mental health. The goal of this study is to better understand how transitional space engagement impacts health outcomes in those with and without Alzheimer’s disease and related dementias (ADRD).

Method

Surveys were remotely administered to community‐dwelling healthy older adults and those with early‐stage ADRD. A novel built environment survey assessing community and home design features as well as questionnaires measuring loneliness, social isolation, mood, anxiety, and cognitive performance were included. We hypothesized that greater use of residential transitional design features (such as windows) and greater access to outdoor spaces (such as parks) will be associated with positive health outcomes (e.g., lower depression, loneliness).

Result

51 older adults with and without ADRD completed the assessment, consisting of 27 healthy adults and 24 cognitively impaired individuals, ages 55 to 90 (M = 73.9, SD = 6.8). Of the sample, 65% of these individuals reported single family homes, and 78% reported living with someone else. The majority of individuals reported access to community parks (Table 1). Indoor space and window use was also characterized, and individuals who frequently look out the window in their home experienced significantly less social isolation, t(41) = 2.92, p = .006 (Figure 1). No such relationships were apparent for depression, anxiety, and loneliness. Additional transitional space features and their use will be discussed in further analyses (Table 2).

Conclusion

For older adults who increasingly stay in the home, transitional spaces may allow ways of engaging with the social landscape, promoting benefit, and mitigating effects of isolation. Recognizing the built environment’s role in social connections, mental health and cognition is imperative in designing opportunities for improving the lives of older adults, with and without ADRD.
Therapeutic evaluation of low‐dose IL‐2‐based immunomodulatory approach in patients with early AD
Authors: Sarazin Marie , Lagarde Julien , Olivieri Pauline , Puchois Virginie , Federici Laetitia , Chaigneau Thomas , Potier Marie‐Claude , Auvity Sylvain , Bottlaender Michel , Dorothée Guillaume
Published: January 2025

Background

Chronic innate neuroinflammation mediated by microglia and astrocytes in response to Aβ and pathological Tau species is a cardinal feature of AD that contributes to disease pathogenesis. Accumulating evidence now also highlight an instrumental role of T cells and peripheral‐central immune crosstalk in the pathophysiology of AD. Both preclinical and clinical reports suggest the potential therapeutic interest of peripheral immunomodulatory approaches aimed at amplifying regulatory T cells (Tregs), e.g. through low‐dose IL‐2 treatment, as a strategy for rebalancing T cell subsets and proper central innate neuroinflammation towards beneficial neuroinflammatory patterns.

Method

Our study aims at evaluating the therapeutic efficacy and safety of low‐dose IL‐2 immunomodulatory treatment in 35 patients with early AD, in a randomized (2:1), double blind, placebo‐controlled phase II clinical trial. Treatment consists in 21 cures of subcutaneous injections of either placebo or low‐dose of IL‐2 (PROLEUKIN®) at 1 MIU/day for 5 consecutive days for the first week (induction phase), then one injection per week for 16 weeks from day 15 (maintenance phase; total duration of treatment: 18 weeks). Patients are followed‐up for cognitive and functional assessment at 6, 12 and 18 months after the first injection. All subjects undergo [18F]‐DPA‐714‐PET/MRI at inclusion and 18 months after treatment induction, for monitoring the evolution of brain neuroinflammation.

Result

An intermediate analysis of the variation of Tregs percentage was performed after 9 patients were included and treated for 4 doses into the maintenance phase. Percentages of Tregs, among both CD4+ or total CD3+ T cells, showed a 1.5‐fold increase in the treated group (n = 6), while remaining stable in the placebo group (n = 3), as expected. One patient decided to discontinue the treatment because of depression, which appears unlikely to be treatment‐related. No other adverse effect was observed.

Conclusion

Our intermediate biological analysis evidence a 1.5‐fold increase in Tregs percentages after 4 doses of the maintenance phase, without any significant adverse effect. These data validate our predefined objective of Treg amplification and thus target engagement, while supporting optimal safety of the treatment.
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

Background

Population‐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.

Method

86 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.

Result

The 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).

Conclusion

Individualized 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

Background

Our 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.

Methods

To map the biochemical changes in AD, we used various targeted and untargeted metabolic platforms to profile ∼800 postmortem brain tissue, and ∼ 5000 blood samples.

Results

Recently, 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.

Conclusion

Overall, 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

Background

Emerging 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.

Method

Our 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.

Result

FMR 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.

Conclusion

Thigh 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

Background

As 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.

Method

Dementia‐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.

Result

Table 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).

Conclusion

In 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

Background

Early‐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.

Method

A 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.

Result

Nineteen 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)).

Conclusion

Distinct 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

Background

Understanding 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.

Method

Participants 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).

Result

In 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.

Conclusion

AβPET 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

Dementia 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

Background

Systematic 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.

Method

Multiple 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).

Result

The 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.

Conclusion

It 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.
Evaluation of multiple ATN biomarker definitions for prediction of cognitive impairment
Authors: Earnest Tom , Yang Braden , Kothapalli Deydeep , Sotiras Aristeidis
Published: January 2025

Background

Application of the amyloid/tau/neurodegeneration (ATN) framework is varied, with some research relying on binary assessment of biomarkers and some using continuous or multidimensional measures. There are few investigations which directly evaluate how differing operationalizations of ATN affect prediction of cognitive impairment.

Method

We selected 473 individuals from ADNI who received PET imaging for amyloid‐beta (AV‐45) and tau (flortaucipir), as well as volumetric MRI. We evaluated a wide range of methods for deriving ATN biomarker assessments, spanning continuous, binary, and non‐binary categorical (i.e., staging models or quantiles of continuous values) measures (Table 1). We trained linear regression models to predict Preclinical Alzheimer Cognitive Composite (PACC) scores, combining different variations of ATN measures as predictors. We additionally used support vector machines (SVMs) to derive multivariate ATN measures; these models used regional PET uptakes or gray matter volumes (68 FreeSurfer regions) to predict PACC. A first set of experiments tested how addition of ATN improved prediction over a baseline model with just covariates (age, sex, APOE status), while a second set of experiments tested how models including non‐binary or SVM‐based ATN measures compared to ones using only binary measures. We used a repeated, nested, and cross‐validated design to evaluate the out‐of‐sample model performance as quantified by the root mean square error. Error estimates were compared using bias‐corrected t‐tests.

Result

Compared to models with standard covariates, addition of any ATN measure resulted in an increase in accuracy for predicting cognitive ability (Figure 1A). Benefits were largest for models which included tau or all three ATN assessments. SVM‐based models also outperformed baseline models. However, when compared to models using all‐binary ATN measures, addition of categorical, continuous, or SVM‐based ATN measures did not significantly improve prediction accuracy in any combination (Figure 1B). Feature importance analysis indicated that some ATN definitions consistently outperformed others (Figure 2).

Conclusion

Binary measures of ATN may be sufficient for prediction of cross‐sectional cognitive impairment. Further work is required to evaluate if these results hold in specific subpopulations (e.g., preclinical AD) or for predicting other measures of interest (e.g., longitudinal cognitive decline).
Plasma ADRD biomarkers predict longitudinal declines in intra‐network functional brain connectivity, and baseline functional connectivity predicts longitudinal cognition
Authors: Dark Heather E , Shafer Andrea T , Cordon Jenifer , An Yang , Lewis Alexandria , Moghekar Abhay , Landman Bennett A. , Resnick Susan M. , Walker Keenan A.
Published: January 2025

Background

AD is defined by cortical amyloid‐β (Aβ), tau neurofibrillary tangles, and neurodegeneration, pathological processes which may contribute to cognitive decline by altering large scale functional brain networks. To test this hypothesis, we examined whether plasma biomarkers of AD pathology (Aβ42/40, phosphorylated tau [pTau‐181]), astrogliosis (glial fibrillary acidic protein [GFAP]), and neuronal injury (neurofilament light chain [NfL]) related to longitudinal changes in resting‐state functional connectivity (rsFC) in cognitively unimpaired participants from the Baltimore Longitudinal Study of Aging.

Method

Baseline plasma biomarkers were measured with Quanterix SIMOA assays. Functional connectivity (3T resting‐state fMRI) was derived using a predefined cortical parcellation mask from which intra‐network connectivity from seven functional networks was extracted for each participant. Amyloid status (positive/negative) was defined using plasma Aβ42/40 (Figure 1). Linear mixed effects models adjusted for age, sex, race, education, gray matter volume, and time‐covariate interactions were used to determine whether 1) baseline plasma biomarkers predicted longitudinal changes in rsFC, 2) the magnitude of the biomarker‐related rsFC changes differed by amyloid status, and 3) rsFC predicted longitudinal changes in cognition.

Result

Longitudinal connectivity analyses (mean age±SD=65.49±16.17) included 490 participants (1190 visits; mean follow‐up time=4.31±1.68 years). Higher Aβ42/40, GFAP, and NfL were associated with faster declines in rsFC within several networks (P‐range=0.01‐0.04; Figure 2). Overall, plasma biomarker‐rsFC associations differed by amyloid status (P‐range=0.01‐0.045). Among amyloid‐positive participants, lower levels of Aβ42/40, and higher levels of GFAP, and NfL (Figure 2) were associated with faster declines in rsFC in the visual, dorsal and ventral attention, limbic, and frontoparietal networks (P range=<0.002‐0.04). There were no statistically significant associations between plasma biomarkers and rsFC change among amyloid‐negative participants. Among 760 participants with at least one rsFC scan (mean age±SD=67.21±14.93; 1550 visits, follow‐up time=3.94±1.60 years), we found that baseline rsFC in several networks predicted changes in cognition, e.g., working memory, verbal fluency, and visuospatial abilities (P range=0.02‐0.049; Figure 3).

Conclusion

Among cognitively normal individuals, plasma biomarkers of Aβ42/40, astrogliosis, and neuronal injury are associated with future intra‐network functional brain changes, particularly in the context of elevated amyloid. Hypo‐ and hyper‐ intra‐network connectivity may drive changes in cognitive performance.
Serving the Underserved in an Underserved Setting
Authors: Tefera Ayda K , Grinberg Lea T.
Published: January 2025

Background

The COVID‐19 pandemic in Ethiopia posed additional challenges to an already strained mental health service. Eka Kotebe Hospital, the second‐largest mental health facility with a capacity of 175 beds, was transformed into a dedicated COVID‐19 treatment center, leaving mental health service users, especially vulnerable elderly patients with cognitive impairments, without adequate support.

I had the challenge to implement alternatives to provide mental health services coverage to underserved elderly population. Here, I describe the process and results of a novel community outreach project done in partnership with a local nursing home (Mekedonia) which shelters homeless elderly and people with mental illness.

Method

Following the formalization of MOU, our master’s level professionals and psychiatric nurses, started to conduct four visits per week to Mekedonia. The primary goal was to identify complex cases and tailor management plan.

Results

At the program inception, the shelter housed approximately 5000 individuals, necessitating an initial task of conducting comprehensive health assessments and classifying patients based on their clinical conditions. Most of the residents in the shelter are persons with intellectual disabilities, elderly individuals with mental illness and dementia.

The ongoing pandemic further compounded the difficulties in conducting thorough assessments, with majority of assessments relied heavily on clinical history, which was inaccurate due to lack of knowledgeable informant, and Mini‐Mental State Examination (MMSE).

Remarkably, a significant portion of the clients had their first encounter with a medical personnel through our program, revealing a high prevalence of multiple medical comorbidity among this population. Management includes controlling aggression, treatment of insomnia, addressing comorbidity and facilitating referrals. The data indicated that most clients were 40 and above, In 2021 we were able to identify and manage 50 dementia patients and the number has doubled to 100 in 2022 and 153 cases people were identified in 2023. Currently the program has sustained on bigger scale, and we are deploying GPs, Neurologists and Psychiatrists to address the diverse health needs of this vulnerable population.

Conclusion

Despite all the challenges, the project underscored the importance of tailored outreach initiatives to address the unique needs of homeless elderly individuals during unprecedented times.
Microglial activation impacts amyloid‐β effects on reactive astrogliosis
Authors: Ferrari‐Souza João Pedro , Povala Guilherme , Bellaver Bruna , Ferreira Pamela C.L. , Lussier Firoza Z , Leffa Douglas Teixeira , Aguzzoli Cristiano Schaffer , Soares Carolina , Carello‐Collar Giovanna , Borelli Wyllians Vendramini , Therriault Joseph , Rahmouni Nesrine , Macedo Arthur C. , Servaes Stijn , Stevenson Jenna , Gauthier Serge , Souza Diogo O. , Schilling Lucas Porcello , Lourenco Mychael V , Tudorascu Dana , Rosa‐Neto Pedro , Pascoal Tharick A. , Zimmer Eduardo R.
Published: January 2025

Background

Glial reactivity is a key phenomenon in Alzheimer’s disease (AD) and is closely associated with amyloid‐β (Aβ) pathology. Although compelling experimental data suggest that microglial activation modulates reactive astrogliosis, it remains to be elucidated whether microglial activation influences the association of Aβ pathology with reactive astrogliosis in the living AD human brain. Here, we tested the association of microglial activation and Aβ pathology with reactive astrogliosis in individuals across the aging and AD clinical spectrum.

Method

We studied 101 participants (62 cognitively unimpaired [CU], 26 with mild cognitive impairment [MCI], and 13 with AD dementia) from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort. Individuals had available positron emission tomography (PET) for Aβ ([18F]AZD4694) and microglial activation ([11C]PBR28), as well as magnetic resonance imaging. We further assessed reactive astrogliosis with plasma glial fibrillary acidic protein (GFAP). Linear regression analyses were used to investigate the associations between Aβ, microglial activation and GFAP.

Result

Demographic characteristics of the study population are reported in Table 1. Regression analyses revealed a significant positive association between Aβ PET burden and plasma GFAP levels in microglial activation‐positive but not in microglial activation‐negative individuals (Figure 1A). A significant interaction between continuous values of Aβ PET burden and [11C]PBR28 PET uptake on plasma GFAP levels (Figure 1B) supported that microglial activation affects the association of Aβ pathology with reactive astrogliosis. Analysis of variance further confirmed that the model with the interaction term was the most adequate to describe the association of Aβ PET and microglial activation PET with plasma GFAP (P = 0.007). In additional analyses investigating the topography of the observed findings, we found that higher Aβ PET burden was associated with higher plasma GFAP levels only in the presence of microglial activation positivity across Aβ‐vulnerable cortical brain regions (Figure 1C).

Conclusion

Our results suggest that microglial activation impacts Aβ‐dependent reactive astrogliosis in the living AD brain. This can help to better understand the complementary roles of glial cells in neurodegenerative diseases, as well as provide insights for the development of novel therapeutic strategies for AD targeting the interplay between Aβ and glial reactivity.
Feasibility of using plasma p‐tau217 as a surrogate for brain Aβ PET in the community
Authors: Balogun Wasiu Gbolahan , Deek Rebecca A , Zeng Xuemei , Sehrawat Anuradha , Chen Yijun , Ferreira Pamela C.L. , Bellaver Bruna , Lafferty Tara K , Povala Guilherme , Kamboh M. Ilyas , Villemagne Victor L , Klunk William E , Pascoal Tharick A. , Ganguli Mary , Lopez Oscar L. , Cohen Ann D , Snitz Beth E. , Karikari Thomas K
Published: January 2025

Background

Plasma p‐tau217 is probably the best‐performing blood biomarker to detect brain amyloid‐beta (Aβ) accumulation. However, previous studies were mostly performed in highly selected cohorts. Thus, it is unclear if it can be used independently of confirmatory neuroimaging tests to identify individuals with abnormal brain Ab load in the wider population where positron emission tomography (PET) may be unavailable. We evaluated the feasibility of plasma p‐tau217 replacing Ab PET in two population‐based cohorts.

Methods

This study used data from two population‐based cohorts; the Monongahela‐Youghiogheny Health Aging Team (MYHAT) Neuroimaging study (n=111, median age 76 IQR: 72‐80, 54% female), in a medically and economically‐depressed Rust Belt area of southwestern Pennsylvania, and the Human Connectome Project (HCP) (n=229, median age was 62 IQR: 56‐70, 65% female) of self‐identified Black and non‐Hispanic Whites in Pittsburgh city. Plasma p‐tau217 was measured using assays from the University of Pittsburgh, ALZpath Inc., and NULISA from Alamar Biosciences. Aβ imaging was performed with 11C‐PiB, where global Aβ+ was defined using a threshold of 1.346 SUVR.

Results

The primary analysis used ALZpath p‐tau217 for both studies. Specifically, MYHAT‐NI reported a Positive Predictive Value (PPV) of 0.6 and Negative Predictive Value (NPV) of 0.92, as well as sensitivity and specificity of 0.82 and 0.80, respectively. PPV measures the probability a predicted positive patient has Ab accumulation, NPV is defined analogously for negative patients. Secondary subset analyses using the UPitt p‐tau217 and NULISA p‐tau217 assays showed similar performances (PPV: 0.56‐0.60, NPV: 0.89‐0.93). The primary analysis in HCP showed a PPV of 0.40 and NPV of 0.97, with corresponding sensitivity of 0.85 and specificity of 0.78. The secondary analysis using UPitt p‐tau217 performed comparably (PPV: 0.38 and NPV: 0.97).

Conclusion

The consistently high NPV but poor PPV suggest that all the plasma p‐tau217 assays will be most useful as a first‐screening tool to identify individuals without pathological evidence of brain Ab for exclusion from clinical trials, therapeutic trials, and prescription of approved therapeutic agents. This would enable streamlined enrichment of individuals who would require confirmatory testing. Future research will investigate impacts of population health outcomes including comorbidities.