bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantification of PET amyloid [18] Florbetaben using the Centiloid Scale
Authors: da Silva Maria Rosa Alves , Aguzzoli Cristiano Schaffer , Garrafiel Fabricio Nery , da Silva Ana Maria Marques , Schilling Lucas Porcello 
Published: January 2025

AbstractBackgroundAlzheimer´s disease (AD) is the main neurodegenerative disorder and leading cause of dementia. AD neuropathology involves the extracellular formation of senile plaques with β‐amyloid peptide and intraneuronal tau phosphorylation, leading to neurofibrillary tangle accumulation. These can be detected by biomarkers obtained from cerebrospinal fluid or Positron Emission Tomography (PET) using specific radiotracers. Amyloid PET results can be interpreted by standard uptake values (SUVR > 1.42 positive or < 1.42 negative) or by the Centiloid scale (CL) which anchored from 0 to 100, classifying low βA uptake (<30 CL), intermediate‐high βA (>30 CL and <50 CL) and high βA (>50 CL). The aim is to analyze the distribution of CL values in [18F]Florbetaben (FBB) PET images in a sample of DA suspects.MethodA retrospective study was carried out using 28 anonymized images of FBB amyloid PET. The PET images were obtained using in the PET/CT system and the Magnetic Resonance Imaging (MRI). For this study, T1‐weighted structural images were used for co‐registration with PET images and normalization with the CL atlas. In this study, the images were processed using PMOD software with the PNEURO tool.ResultThe implementation of CL required reproducing the image processing steps with the GAAIN dataset. A total of 28 participants were included in our study and Figure 1 shows the graph of this investigation. In Table 1, when analyzing the CL, the data shows a varied distribution: 8 participants below 30CL, 9 recorded values between 30CL and 50CL, and 11 presented values above 50CL. Figure 2 shows an example of a positive and negative individual. The analysis of the distribution of individuals with different stages of aging in CL allowed us to investigate how the use of different cut‐off points for the βA classification can have an impact on the diagnosis of AD pathology.ConclusionThe use of the CL is an accurate and robust method to evaluate amyloid deposition in the brain. The communication of PET scan results with the CL scale also allows normalization for any amyloid radiopharmaceuticals, making it more suitable for analysis of disease progression and response to treatment.
Cognitive outcomes with AD tauopathy characterized by florquinitau PET
Authors: Bahr Melissa , Wintlend Madilynn , Mandel Olivia R , Studer Rachel L , Jonaitis Erin M. , Alberson Sara Frost , Meyering Emma , Ryther Brenda , Hellenbrand Teresa , Kaminski Noelle , Chin Nathaniel A. , Betthauser Tobey J. , Christian Bradley T. , Johnson Sterling C. 
Published: January 2025

AbstractBackgroundPET imaging studies examining amyloid‐β plaques and tau neurofibrillary tangles, concurrent with cognitive testing, give insight into in vivo Alzheimer’s Disease (AD) pathology burden and corresponding clinical outcomes. Braak stages IV‐VI indicate advanced tau aggregation in the association and primary cortices associated with AD neuropathological changes which are often accompanied by cognitive decline or impairment. Not all individuals with AD pathological changes present with the expected rate of cognitive decline or standard Braak staging progression, such as in cases of resilience or posterior cortical atrophy (PCA), a variant of AD identified by parieto‐occipital tau aggregation.MethodsTau PET data were obtained from two Wisconsin Alzheimer’s disease cohorts (N = 830). Participants were assigned to a Braak stage if they were positive for tau in that region and all prior regions (cumulative deposition), and those in Braak stages IV‐VI (N = 112, Table 1) were included. Chi‐square tests were completed to examine the relationship between Braak staging and concurrent research diagnoses. Participants with non‐cumulative deposition were excluded (N=40); two with non‐AD‐like patterns are described.ResultsOf the research participants with a tau PET scan rated Braak IV or higher, 71.4% were impaired (Figure 1). Diagnoses differed significantly across Braak Stages, χ2 (4, N = 112) = 30.8, p = .00050. Specifically, Stage VI differed from IV (p < .001) and V (p < .001).We present 2 individual case studies of participants who deviate from this pattern (Figure 2). Case study 1 presented without cognitive impairment, despite tau PET scans rated as Braak VI. Case study 2 presented without cognitive impairment and predominant parieto‐occipital tau burden suggestive of PCA. The tau depositions in the presumed PCA case initially localized to the medial occipital region and spread in reverse hierarchical order of tauopathy in all regions over 5 years.ConclusionThis analysis of tau PET data supports a relationship between the visual read estimate of tau burden and cognitive change. Continued research on the heterogeneity of disease progression, such as presented in these case studies, is important to further our understanding of disease development, resilience factors, and clinically relevant outcomes.
Voxel‐wise comparison of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of individuals across the Alzheimer’s disease spectrum – the HEAD study
Authors: Bellaver Bruna , Povala Guilherme , Bauer‐Negrini Guilherme , Lussier Firoza Z , Amaral Livia , Ferreira Pamela C.L. , Lowe Val J. , soleimani‐meigooni David N. , Oh Hwamee , Tudorascu Dana , Jagust William J. , Klunk William E , Pascual Belen , Gordon Brian A. , Rosa‐Neto Pedro , Baker Suzanne L. , Pascoal Tharick Ali 
Published: January 2025

AbstractBackgroundIn vivo studies using the tau PET tracers have shown high performance for the diagnosis of Alzheimer’s disease dementia and patterns of tracer uptake that resemble those observed in post‐mortem studies. However, tau tracers present distinct patterns of binding that might influence their performance in detecting AD pathology. In a head‐to‐head study, we investigated the performance of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of AD.MethodWe assessed 132 individuals from the HEAD study (58 CU Aβ‐, 15 CU Aβ+, 14 MCI Aβ‐, 32 MCI Aβ+ and 13 AD dementia) with Aβ‐PET, [18F]MK6240 and [18F]Flortaucipir. Voxel‐wise receiver operating characteristic curves (ROC) of the two tau tracers were used to contrast groups provided the area under the curve (AUC) for disease diagnosis or biomarkers positivity.ResultThe brain maps showed numerically higher and more spread AUC for [18F]Flortaucipir to discriminate CU Aβ‐ from CU Aβ+ (Figure 1A). The difference between tracers’ AUC was greater in Braaks IV and V (Figure 2A), both regions that are not expected to have tau accumulation in CU Aβ+ individuals, reflecting a potential off‐target binding for [18F]Flortaucipir. To differentiate CU Aβ‐ from MCI Aβ+ individuals, [18F]MK6240 presented a numerically higher AUC than [18F]Flortaucipir in Braak I and II and similar AUC in other Braak regions (Figure 1B, 2B). We observed a high performance of [18F]MK6240 and [18F]Flortaucipir in differentiating AD dementia from CU Aβ‐ individuals. However, [18F]MK6240 exhibits a higher AUC than [18F]Flortaucipir in all Braak regions, especially Braak V‐VI (Figure 1C, 2C). Finally, [18F]MK6240 presented higher AUC in all Braak regions to discriminate MCI Aβ‐ from MCI Aβ+ individuals (Figure 1D, 2D).ConclusionOur results indicate that [18F]MK6240 and [18F]Flortaucipir present high accuracy to discriminate AD from CU Aβ‐ individuals. However, [18F]MK6240 presents higher AUC to discriminate AD and MCI Aβ+ from CU Aβ‐ individuals than [18F]Flortaucipir. Together, our head‐to‐head study sheds light on the distinct patterns of binding for Tau‐PET tracers.
Neighborhood disadvantage and ADRD pathology assessed with plasma biomarkers
Authors: Dube Samriddhi , Powell W. Ryan , Buckingham William R. , Yang Kao Lee , Johnson Sterling C. , Ashton Nicholas J. , Blennow Kaj , Zetterberg Henrik , Kind Amy J.H. , Bendlin Barbara B. 
Published: January 2025

AbstractBackgroundNeighborhood disadvantage as measured by the Area Deprivation Index (ADI) is associated with Alzheimer’s disease (AD) pathology at autopsy, as well as neuroimaging measures of neurodegeneration. Plasma biomarker assays have emerged as an accessible tool for evaluating ADRD pathology, opening up the possibility of better understanding the effect of neighborhood disadvantage on brain disease among a broader group of research participants. In this study, we evaluated whether neighborhood‐level socioeconomic disadvantage is associated with plasma‐based biomarkers of Alzheimer's disease and related pathology.MethodThe participants were 371 individuals enrolled in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) study and data included 1095 unique plasma measurements of the participants. EDTA plasma was assayed for pTau 217, pTau181 and pTau231, neurofilament light chain protein (NfL), and glial fibrillary acidic protein (GFAP). Participants’ addresses, as on the plasma measurement date, were geolinked to their statewide ranking of neighborhood disadvantage using a time‐concordant ADI. We compared participants living in the 20% most disadvantaged areas based on state residence (ADI of 9 or 10) to all other participants (80% less disadvantaged, ADI of 1‐8) by fitting adjusted linear regression models. We used the age, sex and years of education of the participants at the point of sample collection as characteristics.ResultLiving in the 20% most disadvantaged neighborhood was associated with higher GFAP concentration, an effect that was marginally significant (b = ‐1.4898; 95% CI; ‐2.978 to ‐0.002; P = 0.050). No significant effects of ADI were observed on pTau 217, pTau181, pTau231, or NfL.ConclusionLiving in the most disadvantaged context was associated with elevated GFAP, a marker of astroglial activation. Elevated GFAP has previously been associated with amyloid pathology and neuroinflammation. The absence of significant associations with ptau biomarkers here may suggest non‐amyloid related processes, although this study may also be underpowered to detect relationships with amyloid, and further investigation is needed. There is an opportunity for future research to gain insight further into the mechanisms responsible for this association. Leveraging plasma biomarkers, future studies can provide a mechanistic understanding for the effects of contextual disadvantage on ADRD development.
Head‐to‐head comparison of four different plasma p‐tau217 assays to detect brain amyloid pathology and cognitive decline in diverse population‐based cohorts
Authors: Deek Rebecca A , Zeng Xuemei , Gonzalez‐Ortiz Fernando , Balogun Wasiu Gbolahan , Ferreira Pamela C.L. , Bellaver Bruna , Chen Yijun , Sehrawat Anuradha , Lafferty Tara K , Povala Guilherme , Klunk William E , Kamboh M. Ilyas , Villemagne Victor L , Pascoal Tharick A. , Ganguli Mary , Zetterberg Henrik , Blennow Kaj , Lopez Oscar L. , Cohen Ann D , Snitz Beth E. , Karikari Thomas K 
Published: January 2025

AbstractBackgroundPlasma p‐tau217 is a highly promising biomarker for detecting Alzheimer’s disease (AD) pathology. However, it is unclear how p‐tau217 assays from different sources perform compared to one another. Moreover, studies in diverse cohorts and population‐based settings are limited. We performed a direct comparison of four different p‐tau217 assays to detect brain amyloid‐beta (Aβ) deposition or cognitive decline.MethodWe included n=427 participants from three separate cohorts from southwestern Pennsylvania, USA. Cohort‐1 was the Monongahela‐Youghiogheny Health Aging Team (MYHAT, n=87, median age 83 [IQR: 78‐87.5], 74% female), an ongoing population‐based cohort drawn from a medically and economically underserved Rust Belt region. Cohort‐2 was the MYHAT‐NI sub‐study (n=111, median age 76 [IQR: 72‐80], 54% female), which performed two‐year longitudinal follow‐up neuroimaging assessments of Aβ, tau, and neurodegeneration by structural MRI. Cohort‐3 was the Human Connectome Project (HCP, n=229, median age 62 [IQR: 56‐70], 65% female) which targeted a 50:50 split of self‐identified Black and non‐Hispanic White individuals. Plasma p‐tau217 was measured using assays from four different sources: University of Pittsburgh (UPitt), ALZpath, University of Gothenburg (UGOT), and NULISA from Alamar Biosciences. Aβ positivity was defined as a mean SUVR > 1.346. Clinical Dementia Rating (CDR) and Mini Mental State Examination were used to assess cognitive performance.ResultIn the MYHAT‐NI cohort, three p‐tau217 assays—UPitt, ALZpath, and NULISA—were employed. The assays demonstrated equivalent AUCs for determining Aβ positivity, ranging from 0.84 to 0.90. UPitt and ALZpath assays also showed similar AUCs in the HCP cohorts, with values of 0.87 and 0.90, respectively. In MYHAT, the UGOT and ALZpath assays separated CDR=0 and CDR=0.5 participants with equivalent AUCs (0.79 vs. 0.81). Significant correlations were observed in all inter‐assay comparisons, with Pearson correlation between assays ranging between 0.40 and 0.86.ConclusionWe observed largely equivalent performances of plasma p‐tau217 assays in predicting amyloid positivity and cognitive impairment across three diverse cohorts of community‐dwelling older adults.
Robust multi‐modality segmentation of medial temporal lobe subregions using both 3‐tesla and 7‐tesla magnetic resonance imaging
Authors: Li Yue , Khandelwal Pulkit , Xie Long , Brown Christopher D. , Lyu Xueying , Denning Amanda E , Dong Mengjin , Das Sandhitsu R. , Wolk David A , Yushkevich Paul A. 
Published: January 2025

AbstractBackgroundVolumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer’s disease. However, dropout artifacts are present in some modalities, leading to poor image quality and unreliable segmentation of MTL subregions. Considering that MRI modalities with different field strength offer distinct advantages in imaging different parts of the MTL, we developed a muti‐modality segmentation model using both 7‐tesla (7T) and 3‐tesla (3T) structural MRI to obtain robust segmentation in poor‐quality images.MethodMRI modalities including 3T T1‐weighted, 3T T2‐weighted, 7T T1‐weighted and 7T T2‐weighted (7T‐T2) of 197 subjects were collected from a longitudinal aging study at the Penn Alzheimer’s Disease Research Center. Among them, 7T‐T2 was used as the primary modality, and all other modalities were rigidly registered to the 7T‐T2. A model derived from nnU‐Net took these registered modalities as input and outputted subregion segmentation in 7T‐T2 space. High‐quality 7T‐T2 images from 25 selected training subjects were manually segmented to train the multi‐modality model. Modality augmentation, which replaced certain modalities with Gaussian noise randomly, was applied during training to guide the model to extract information from all modalities. To compare our proposed model with a baseline single‐modality model in the full dataset with mixed high/poor image quality, we evaluated the ability of derived volume/thickness measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and Amyloid‐ cognitively unimpaired (A‐CU) groups, as well as the stability of these measurements in longitudinal data.ResultFigure 1 shows characteristics of both training and test sets, as well as segmentation performance of models. The multi‐modality model delivered good performance regardless of 7T‐T2 quality, while the single‐modality model under‐segmented subregions in poor‐quality example images. The multi‐modality model generally demonstrated stronger discrimination of A+MCI versus A‐CU (Table 1). Intra‐class correlation and Bland‐Altman plots demonstrate that the multi‐modality model had higher longitudinal segmentation consistency in all subregions while the single‐modality model had low consistency in poor‐quality images (Figure 2).ConclusionUsing multi‐modality MRI, we provide an automatic MTL subregion segmentation algorithm that is robust to image quality. Our findings can help develop improved imaging biomarkers for neurodegeneration.
APOE ε4 genotype confers risk for Alzheimer Disease in Cuban Americans
Authors: Sanchez Jose Javier , Akgun Bilcag , Hamilton‐Nelson Kara L. , Rivero Joe , Celis Katrina , Adams Larry D. , Valladares Glenies S , Silva‐Vergara Concepcion , Rodriguez Vanessa C , Mena Pedro R. , Whitehead Patrice L. , Prough Michael B. , Griswold Anthony J. , Dalgard Clifton L. , McInerney Katalina F , Vance Jeffery M. , Cuccaro Michael L. , Beecham Gary , Rajabli Farid , Pericak‐Vance Margaret A 
Published: January 2025

AbstractBackgroundThe risk of Alzheimer Disease (AD) conferred by APOE and other genetic factors differ across populations. The Cuban American (CA) population is 3‐way admixed (European, African, and Amerindian) and vastly underrepresented in genetic studies. Previous genetic studies in this population have solely focused on APOE in AD and shown conflicting results regarding its effects. We aimed to evaluate the effect of the APOE genotype and other variants in potential causative AD genes in the CA population.MethodsThe CA dataset includes Whole Genome Sequencing (WGS) and Array data from 141 individuals (40 AD, 50 mild cognitive impairment [MCI], 51 cognitively unimpaired) ascertained through the Cuban American Alzheimer’s Disease Initiative (CuADI). To assess the effect of APOE‐ε4 allele dosage on AD, we used a generalized estimating equations (GEE) method adjusting for sex, age, and population substructure. Subsequently, we extracted and evaluated all variants in 7 potential causative genes: APOE, APP, MAPT, PSEN1, PSEN2, SORL1, and TREM2. We calculated the admixture proportion using a model‐based clustering algorithm implemented in the ADMIXTURE software.ResultsThe APOE‐ε4 alleles were significantly associated with AD risk (p=0.013; OR=3.4[1.3‐8.7]). The causative genes analysis showed a known exonic variant (rs117260922; p.E270K) in the SORL1 gene in 1 AD and 2 MCI individuals, and a novel splicing variant (c.308‐1G>A) in the MAPT gene in an AD individual. Admixture analysis revealed average proportions of 83.4% European, 12.4% African, and 4.3% Amerindian in the CA cohort.ConclusionsWe showed that the APOE‐ε4 genotype conferred a risk for AD in the CA population. The effect size of the APOE‐ε4 in our study was found to be similar to a recent study in large non‐Hispanic white (NHW) cohort (OR=3.32, Kunkle et al. 2019). High European background in the CA population is likely cause to this similarity. Our results also support that the E270K variant in the SORL1 gene, previously linked to AD in Caribbean Hispanics and NHW studies, contributes to the risk of AD. These findings provide new insights into the genetic architecture of AD in the CA population.
Sex‐dependent Biomarker Signatures in Alzheimer's Disease
Authors: Jain Lavanya , Khrestian Maria , Tuason Elizabeth D. , Pillai Jagan A. , Rao Stephen M. , Leverenz James B , Bekris Lynn M. 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) is a pathologically heterogeneous disease making it a challenge to develop effective treatments. Emerging evidence suggests that there are pathological differences between women and men with AD. More biomarkers are needed to enhance sex‐specific precision medicine in AD. Cerebrospinal fluid (CSF) biomarkers for amyloid (A), tau (T), and neurodegeneration (N) can be utilized to more precisely define pre‐mortem pathological status (ATN research framework) to limit the heterogeneity of a study cohort. The hypothesis of this study was that non‐ATN biomarkers differ by sex and ATN in AD. The overall aim of this preliminary investigation was to understand sex differences in seven biomarkers in an ATN defined AD cohort.MethodsCerebrospinal fluid (CSF) samples from an AD/MCI cohort were obtained from the Cleveland Alzheimer’s Disease Research Center and Cleveland Clinic Lou Ruvo Center for Brain Health Aging and Neurodegenerative Disease Biobank. CSF Aβ42/40 (A), p‐Tau181 (T), and t‐Tau (N), were evaluated for ATN analysis. Only A‐T‐N‐ individuals were included in CN and only A+T‐N‐, A+T+N‐, and A+T+N+ were included in as MCI and AD (MCI/AD) individuals. Seven biomarkers namely, neurofilament light chain (NFL), soluble Triggering receptor expressed on myeloid cells 2 (sTREM2), Neurogranin, fatty acid binding protein 3 (FABP3), glial fibrillary acidic protein (GFAP), neuron specific enolase (NSE), and α‐synuclein (α‐syn) (were also evaluated. Statistical analyses such as ROC analysis and one‐way ANOVA were carried out in RStudio.ResultsThis was a cohort of over 300 individuals out of which approximately 50% were females and 50% were males. Males exhibited differences between the CN (A‐T‐N‐) and MCI/AD (A+T‐N‐, A+T+N‐, A+T+N+) group for NFL, Neurogranin, FABP3, NSE, and α‐syn, whereas females showed for NFL, sTREM2, and α‐syn. When comparing ATN status between sexes, GFAP was found to be higher in CN (A‐T‐N‐) males and MCI/AD A+T+N+ males, and sTREM2 was higher in MCI/AD A+T+N‐ males.ConclusionsThese preliminary results show significant differences between and within sex for these selected seven biomarkers. With validation from a larger dataset and more robust statistical analyses, this could have crucial impact on AD related diagnostic and therapeutic approaches.
Developing an AI algorithm to detect predictors of poor performance in a self‐administered, web‐based digital biomarker for Alzheimer’s Disease: proof of concept
Authors: Butler Joe , Owo Adewale Samuel , Watermeyer Tamlyn J , Danso Samuel O. , Parra‐Rodriguez Mario A ,   Binding in Neuropsychiatric Disorders (BIND) Group
Published: January 2025

AbstractBackgroundThe Visual Short Term Memory Binding (VSTMBT) task is a gold‐standard cognitive assessment for the identification of Alzheimer's Disease and associated risk factors, including during the preclinical stage. Previous work from our group (Butler, Watermeyer, …& Parra 2024) demonstrated in a small number (n=37) of healthy older adults that data collected using a web‐based, self‐administrated version of the task provides data comparable to that collected in laboratory conditions. Here we incorporated a machine learning (ML) approach to explore impacts of risk factors on this task in a larger digital dataset.MethodUsing data (n=359) collected from an online study incorporating the VSTMBT and lifestyle, psychological, and health data, we created a Binding Cost score which has shown to approximate AD‐related neuropathology (Parra et al., 2024). This categorised participants as either strong‐binders (SB – indicative of no pathology; 85.9% percent of the sample) or weak‐binders (WB – indicative of pathology; 14.1%).We trained three ML algorithms (Random Forest (RF), K‐Nearest Neighbour (KNN) and Decision Tree (DT) by employing SMOTE technique to overcome the imbalance in group distribution. We applied a 10‐fold cross‐validation with hyper‐parameter tuning to optimise the models based on the selected variables (including age, sex, education, BMI, loneliness, and existing‐morbidities) to predict individual’s risk of cognitive impairment based on the groupings (SB vs WB). Models’ performances were examined on 20% of unseen test set.ResultAside from existing morbidities, which were higher in weak binders (WB = 0.41 (sd+2=0.79); SB =0.22(sd+2=0.49); t=2.21; p=0.03), other measures did not differ between groups. Regarding performance of the ML models, RF achieved the best performance (accuracy: 91%; recall=91%; precision=91%; AUC=97%) compared to KNN (accuracy: 81%; recall=81%; precision=84%; AUC=91%) and DT (accuracy: 81%; recall=81%; precision=82%; AUC= 85%). Feature importance analysis of the RF model suggests mental health, BMI, and fatigue have the highest impact on the prediction model, while sex and multi‐morbidity score have the least impact.ConclusionThe study underscores the potential of web‐based cognitive assessments and ML for remote monitoring and early identification of AD risk factors, contributing to the advancement of accessible tools for early detection.