bluuebunny/crossref_metadata_2025_split huggingface dataset:
Everyday life episodic memory change and limbic quantitative PI‐2620 tau PET in participants with mild cognitive impairment and mild dementia
Authors: Booncharoen Kittithatch , Hiransuthikul Akarin , Thanapornsangsuth Poosanu , Thanprasertsuk Sekh , Sukprakun Chanan , Tepmongkol Supatporn , Phanthumchinda Kammant , Likitjaroen Yuttachai 
Published: January 2025

AbstractBackgroundEpisodic memory change is among the earliest symptoms of Alzheimer’s disease (AD). Accumulation of neurofibrillary tangles in limbic regions serves as a key indicator of the initial tau abnormality. This study aimed to investigate the correlations between episodic memory change and quantitative tau deposition specifically in Braak stage 3‐4 Limbic regions among participants with mild cognitive impairment (MCI) and mild dementia.MethodParticipants diagnosed with MCI and mild dementia were recruited from the memory clinic at King Chulalongkorn Memorial Hospital, Thailand. Episodic memory change was assessed using the Episodic Memory Change Score (EMCS), an informant‐based interviewing score designed to measure the severity of daily life episodic memory changes over the past 10 years, with a higher score indicating more pronounced memory impairment (Table 1). Participants underwent amyloid (Florbetaben) and tau (PI‐2620) positron emission tomography (PET) scans. amyloid PET results were defined as either amyloid‐negative (A‐) or amyloid‐positive (A+). Quantitative tau deposition was measured using the Standardized Uptake Value Ratio (SUVR). Correlations between EMCS and Braak stage 3‐4 tau SUVR were performed using Pearson’s correlation base on amyloid status (A+ and A‐).ResultAmong the 50 enrolled participants (median [interquartile range] age 72 [65‐77] years), 37 (74%) exhibited Alzheimer’s pathological changes (A+). Table 2 presents the correlation analysis between the EMCS and tau SUVR across different Braak stages. In Braak stage 3, higher EMCS significantly correlated with higher tau SUVR in A+ participants (r = 0.35, p = 0.03), but not in A‐ participants (r = 0.03, p = 0.92). In Braak stage 4, a correlation was noted, but did not reach statistical significance, between higher EMCS and increased tau SUVR in A+ participants (r = 0.20, p = 0.23) (Figure 1).ConclusionIn participants with Alzheimer’s pathologic change, we found a significant correlation between episodic memory impairment and tau abnormality in Braak stage 3 regions, which encompass critical elements of the Papez circuit. The results suggest that EMCS could serve as a practical and easily accessible tool for early detection of tau PET positivity in A+ individuals, offering a straightforward approach for identifying Alzheimer’s‐related tau changes in a timely manner.
Sensitive methods to define subtle memory decline capture intra‐individual hippocampal atrophy and increased cortical volumes in preclinical Alzheimer’s
Authors: López‐Martos David , Cacciaglia Raffaele , Suárez‐Calvet Marc , Milà‐Alomà Marta , Minguillon Carolina , Zetterberg Henrik , Blennow Kaj , Gispert Juan Domingo , Grau‐Rivera Oriol , Sánchez‐Benavides Gonzalo ,   for the ALFA study
Published: January 2025

AbstractBackgroundObjective Subtle Cognitive Decline (obj‐SCD) can be identified through standardized neuropsychological tests and may precede the development of Mild Cognitive Impairment (MCI). Nevertheless, current clinical and research criteria lack a standardized protocol for identifying obj‐SCD. This study introduces cutting‐edge sensitive methods to characterize obj‐SCD, defined through Alzheimer’s disease (AD) biomarker‐based longitudinal cognitive performance in episodic memory. Neurocognitive characterization of obj‐SCD is supported by Voxel‐Based Morphometry (VBM) analyses, examining longitudinal changes in Grey Matter volume (GMv) at the preclinical stage of the Alzheimer’s continuum.MethodThree hundred cognitively unimpaired (CU) individuals (mean age: 60, SD: 4.76) from the ALFA+ cohort study (three‐year follow‐up) were included. AT(N) profiles were defined at baseline with Cerebrospinal Fluid (CSF) biomarkers. AD biomarker‐based reliable change indices adjusted for practice effect (A‐T‐[N]‐ longitudinal performance as reference) were computed for the assessment of episodic memory (Free‐Cued Selective Reminding Test, Memory Binding Test, Wechsler Memory Scale‐IV). Considering the relationship between the number of neuropsychological measures and the base rate of impaired scores, obj‐SCD was defined as longitudinal biomarker‐based performance below ‐1.645 SD (<5th percentile) in at least 3/11 variables. Magnetic Resonance Imaging scans were performed with 3T‐scanner and a high‐resolution 3D T1‐weighted sequence (voxel‐size: 0.75mm3). Intra‐individual changes in GMv were voxel‐wise computed using longitudinal scans, smoothed at 8mm3. The associations between obj‐SCD and GMv changes were analyzed with VBM linear regression models, selecting a voxel‐wise statistical threshold of p<0.005 with a cluster‐extent correction of 100 voxels.ResultAccording to the above‐defined criteria 19 (6.33%) participants exhibited episodic memory obj‐SCD, with significant differences considering AT profiles (Table‐1). Episodic memory obj‐SCD was associated with longitudinal reductions of GMv bilaterally in the hippocampus and the left cerebellum, as well as widespread increments of GMv involving AD‐vulnerable cortical regions (Table‐2, Figure‐1).ConclusionEpisodic memory obj‐SCD captured longitudinal changes in GMv indicative of AD‐progression (i.e., hippocampus). These results suggested that episodic memory obj‐SCD is a consistent marker of AD‐related impairment. Characterizing obj‐SCD enhances preclinical stage identification, with implications for advancing early detection and intervention strategies in Alzheimer’s disease, informing about an elevated risk of AD‐dementia in an otherwise CU population.
Neuroimaging Insights into Subjective Cognitive Decline: Unveiling Differential Sensitivity of Cognitive Change Index and Everyday Cognition Scale
Authors: Anderson John , Dadar Mahsa , Morrison Cassandra 
Published: January 2025

AbstractBackgroundSubjective cognitive decline (SCD), or self‐perceived declines in memory/cognition in cognitively healthy older adults is linked to increased cognitive decline, neurodegeneration, and white matter hyperintensity (WMH) burden. However, there is no consistent definition of how to classify people with SCD. This study investigated if individual questions in the Cognitive Change Index (CCI) and Everyday Cognition Scale (ECog), commonly used to classify SCD, are associated with brain volume and WMHs to the same degree.MethodsA total of 332 cognitively healthy older adults from the Alzheimer’s Disease Neuroimaging Initiative were included in this study. Partial‐least‐squares was used to analyze the data and generate a set of orthogonal latent variables (LVs) that maximize the relationship between two sets of data. The significance of each LV was assessed by comparing the obtained result to a null distribution built with 1000 permutations. The reliability of the contributions of each of the variables to the LV was assessed with 1000 bootstrap repetitions, which were used to estimate standard errors.ResultsTwo significant LVs (p’s<0.001) explained 85.89% and 8.30% of the cross‐block covariance (Figure 1). The first LV shows higher SCD scores correlate with older age and fontal, parietal, and temporal WMH burden. This pattern also correlated with lower gray matter in the entorhinal cortex, and hippocampus and larger ventricles. These changes were associated with questions from only the CCI suggesting this questionnaire is more sensitive than the ECog. The second LV shows younger individuals with higher SCD scores on three CCI questions correlate with lower WMH burden in the temporal and parietal lobes and lower entorhinal cortex volumes. A PCA memory component positively correlated with the second brain LV, suggesting that lower WMH burden may predict better memory performance.ConclusionsOur findings identified two key LVs that demonstrate that the CCI not only more sensitively detects overall neural decline but may be more sensitive to different etiologies than the ECog.
Web Application for Deciphering Alzheimer’s Disease Prediction and Progression with Explanations
Authors: Ephrem Ben George , Varghese Abraham , Sherimon Vinu , Gouda Prasanth 
Published: January 2025

AbstractBackgroundEarly detection and personalized care for Alzheimer’s Disease (AD) mitigate the devastating consequences for millions of people around the globe. In the current scenario, there is a lack of user‐friendly AI applications for predicting and understanding the progression of AD. The application should address the critical need for a predictive analytics tool that offers timely and transparent insights by utilizing the patient data. The research aims to develop an AI‐driven application for predicting and interpreting the disease progression.MethodThe application utilizes ADNI dataset, where the available patient data has been oversampled to 13752 to balance the target class. The Random Forest Classifier, a powerful machine learning technique was used to train the set of selected features including age, cognitive scores from standardized tests, and brain imaging data. Based on these inputs, the model accurately predicts an individual’s current cognitive state, categorizing them as Normal, Mild Cognitive Impairment (MCI), or Dementia. The application also utilizes the patient’s historical and current data to predict the progression from MCI to Dementia, or Normal to MCI, or remaining in the current cognitive state. The application uses LIME (Local Interpretable Model‐agnostic Explanations) and SHAP (SHapley Additive exPlanations) for transparent explanations and providing clear insights into the rationale behind interpretability in predicting each class using different visualizations.ResultThe application (https://alzheimer‐disease‐prediction‐progression‐explanations.streamlit.app/) demonstrates accuracy in both initial diagnosis and progression prediction (Figure 1). Explainable AI techniques provide user‐friendly insights into model reasoning, promoting trust and understanding (Figure 2 and 3). The user‐friendly interface facilitates data input and interpretation for both healthcare professionals and individuals.ConclusionThe AI‐driven application emerges as a valuable tool in AD prediction and analysis. The transparent and interpretable nature of the model, facilitated by LIME and SHAP, enhances trust and understanding. Early detection and progression prediction capabilities allow healthcare professionals with timely interventions to care the patients.
Associations between performance on a non‐immersive virtual reality task and structural correlates of Alzheimer’s disease vulnerable brain regions
Authors: Holmqvist Sophia L. , Jobson Katie , Simone Stephanie M. , Tassoni Molly B. , Mckniff Moira , DeSalme Dennis , Yamaguchi Takehiko , Olson Ingrid , Martin Nadine , Giovannetti Tania 
Published: January 2025

AbstractBackgroundObjective and sensitive measures of everyday function are needed for accurate clinical diagnosis and evaluation of outcomes in clinical trials for dementia. However, most objective everyday function measures are difficult to administer and have not been validated against biomarkers of Alzheimer’s disease (AD) neuropathology. This study evaluated the neuroimaging correlates of a highly sensitive, ecologically valid, and easily implementable performance‐based test of function called the Virtual Kitchen Challenge (VKC). VKC measures have been validated against brain‐derived markers of cerebrovascular burden in past studies, but this study investigated associations between the VKC and AD biomarkers, including cortical thickness/volume of the hippocampus and medial temporal lobe associated areas.MethodsTwenty (n = 14 healthy control, n = 6 mild cognitive impairment) community‐dwelling and racially diverse older adults aged 60+ completed conventional neuropsychological tests, Functional Activities Questionnaire (FAQ), and VKC, which required completion of simulated breakfast and lunch tasks on a touch‐screen computer. VKC outcomes included completion time and validated measures of performance efficiency (i.e., screen interactions, proportion of time off screen). A global cognitive composite score was calculated as the average t score across neuropsychological tests. The hippocampus, entorhinal cortex, parahippocampal gyrus, inferior temporal cortex, and inferior parietal cortex were examined as Freesurfer‐derived a priori regions of interest. Partial correlations were conducted controlling for age and estimated intracranial volume.ResultsVKC outcomes were associated with entorhinal cortical thickness (VKC completion time, r = ‐.530, p = .024; time spent off screen, r = ‐.515, p = .029) and inferior temporal lobe thickness (proportion of time spent off screen, r = ‐.747, p<.001. Conventional measures were associated with bilateral hippocampal volume (global cognitive composite, r = .446, p = .064; memory composite, r = .408, p = .092; FAQ, r = ‐.538, p = .021) and parahippocampal gyrus thickness (global cognitive composite, r = .508, p = .031; FAQ, r = ‐.643, p = .004).ConclusionIn contrast to conventional measures, VKC outcomes were significantly associated with entorhinal and inferior temporal cortex thickness, brain regions that are typically affected early in AD‐disease progression. Overall, the VKC, particularly measures of completion time and proportion of time off screen, holds promise as a highly sensitive and valid, objective measure of everyday function in a range of contexts with racially diverse older adults, including clinical assessment and clinical trials.
Evaluating updated LATE‐NC staging criteria using National Alzheimer’s Coordinating Center data: stage 1 subtypes matter
Authors: Woodworth Davis C. , Nguyen Katelynn M. , Sordo Lorena , Scambray Kiana A. , Head Elizabeth , Kawas Claudia H. , Corrada María M. M. , Nelson Peter T , Sajjadi S. Ahmad 
Published: January 2025

AbstractBackgroundLimbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC) is a common cause of dementia in older age. LATE‐NC was first coined in 2019 with proposed staging criteria of TDP‐43 progressing from amygdala (stage 1), to hippocampus (stage 2), to middle frontal gyrus (stage 3). Criteria were updated in 2023 to further categorize stage 1 to either TDP‐43 inclusions in amygdala alone (stage 1a) or hippocampus alone (stage 1b). We applied this updated LATE‐NC staging criteria to data from participants in the National Alzheimer’s Coordinating Center (NACC) and examined associations with clinical diagnosis and other neuropathologic changes (NCs).MethodWe selected participants in NACC with regional TDP‐43 assessments, excluding those with frontotemporal dementia‐related and rare NCs, or with a non‐Alzheimer’s disease (AD) etiologic clinical diagnosis. LATE‐NC stages were assigned according to updated criteria. We performed logistic regressions with LATE‐NC stages as predictors and clinical diagnosis (dementia, cognitive impairment, memory impairment), other neuropathologic changes (ADNC, Lewy bodies, hippocampal sclerosis of aging (HS‐A), and vascular NCs), or gross atrophy at autopsy (cortical, hippocampal, frontal/temporal lobar) as outcomes. We also examined the association of LATE‐NC stages with dementia and memory impairment while accounting for other NCs.ResultOf N = 1365 participants, LATE‐NC was present in 519 (37%): 31% stage 1 (78% 1a, 22% 1b), 56% stage 2, 13% stage 3 (Table‐1). Participants with stage 1a were younger at death compared to higher stages. Stage 1a and 1b had similar associations with cognitive problems (Figure‐1). However, while other stages were associated with ADNC and Lewy bodies, stage 1b was not. Meanwhile stage 1b was associated with HS‐A more strongly than stage 1a and to a similar degree as stage 2. We observed expected associations of higher LATE‐NC stages with dementia and other NCs (Figure‐1). Lastly, LATE‐NC stage 1b was significant (and stage 1a trended) for associations with dementia and memory impairment, while higher LATE‐NC stages had associations eclipsed in strength only by highest level ADNC (Figure‐2).ConclusionThese findings highlight the utility of the updated LATE‐NC staging criteria in general, and stage 1 subtypes in particular, in capturing broad associations with cognitive impairment and specific associations with other neuropathologic changes.
Alzheimer’s disease genetic risk factors’ influence on tau onset age
Authors: Deming Yuetiva , Teague Jordan P , Heston Margo B. , Hammad Yazan , De Chavez Elena Ruiz , Morse Jacob , Chappell Richard J , Langhough Rebecca E. , Zuelsdorff Megan L. , Engelman Corinne D. , Betthauser Tobey J. ,   Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: January 2025

AbstractBackgroundNew methods developed to estimate when AD biomarkers became abnormal in individuals have shown considerable heterogeneity in amyloid and tau pathology onset age. This work used polygenic scores (PGS) generated from CSF Aβ42 and ptau181 GWAS, individual‐level genetic data, and estimated tau onset age (ETOA) to identify genetic influences on tau onset beyond APOE.MethodParticipants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with genetic data, CSF biomarkers (Aβ42 and ptau181), and longitudinal [18F]Flortaucipir (FTP) tau PET were analyzed (N = 462). Sampled iterative local approximation (SILA) was used to model longitudinal FTP meta‐temporal SUVR and generate ETOA for T+ and T‐ participants who could become T+, based on tau‐positivity (T+) defined using Gaussian mixture modeling (SUVR>1.41). Amyloid‐ and ptau‐PGS were generated from publicly available summary data, excluding the APOE region (±500kb). APOE neuropathology‐based scores (APOEscore) were used to test APOE. We analyzed individually a set of 14 SNPs that were previously associated with AD risk (P<1 × 10‐5) and both CSF biomarkers (P<0.05). Amyloid‐PGS, ptau‐PGS, APOEscore, and individual SNPs were tested as ETOA predictors using Cox regression modeling.Result73 (16%) participants were T+ by their last tau PET (Table 1). APOEscore was significantly associated with earlier ETOA (P = 1.10 × 10‐13; Figure 1) as was the amyloid‐PGS (P = 0.036), but ptau‐PGS was not (P = 0.166; Table 2). An intronic variant in complement receptor 1 (CR1), rs1408077 (A allele, MAFEUR = 0.19) which was previously associated with higher AD risk and AD‐associated changes of CSF biomarkers, was also significantly associated with earlier ETOA (P = 0.005; Figure 1). The association remained significant (P = 0.017) after adding the APOEscore (P = 3.93 × 10‐13) and both were still significantly associated with ETOA (Prs1408077 = 0.022 and PAPOEscore = 1.50 × 10‐6) after accounting for amyloid‐positivity (P = 3.91 × 10‐5; Table 2).ConclusionOur preliminary results support a key role for CR1 in the age when tau pathology becomes abnormal in AD. The tested variant, rs1408077, is in high linkage disequilibrium with a missense variant, rs2296160, and was previously associated with CR1 expression in the temporal cortex. This adds to previous studies that reported an interaction between CR1 and APOE‐ε4 associated with episodic memory decline. Future work will include genome‐wide analyses of biomarker timing and multi‐trait analyses.
The associations between CYP19A1 polymorphisms and serum 17ß‐estradiol levels in Chinese patients with Alzheimer’s disease
Authors: Chen Lu Hua , Fan Yan Hui , Song You‐Qiang , Chu Leung‐Wing 
Published: January 2025

AbstractBackgroundAccumulating evidence has shown the neuroprotective effects of estrogen on cognition function, for example delaying the cognitive deterioration in patients with Alzheimer’s disease (AD). However, the clinical usage of estrogen in AD remains controversial. The cytochrome P450 aromatase encoded by CYP19A1, is a key enzyme catalyzing the C19 androgen conversion to C18 estrogen, which induces testosterone to estradiol and androstenedione to estrone. The aromatase thus has been suggested to be an important partner of estrogen by controlling estrogen biosynthesis. Although increasing evidence has demonstrated the associations between CYP19A1 polymorphisms and AD susceptibility, previous studies are mainly carried out in Caucasian populations, little is known about the role of this gene and its impact on serum estrogen levels in Chinese population.MethodWe recruited 689 subjects including both AD patients and non‐demented controls in Hong Kong. Subjects’ serum 17ß‐estradiol were measured based on a chemiluminescent immunoassay principle. Genomic DNA was isolated from the whole blood and subsequent genotyping for 27 SNPs was analyzed on high‐throughput Sequenom® genotyping platform according to the manufacturer’s protocols. Statistical analysis was done using SPSS and PLINK.ResultAfter quality control, 3 SNPs were filtered and 24 SNPs spanning 122kb of this gene were included in data analysis. There was no significant difference for serum17ß‐estradiol levels between AD patients and non‐demented controls. The APOE ε4 allele was consistently associated with an increased AD risk (p<0.001). While the distribution of CYP19A1 polymorphisms was very similar between AD patients and non‐demented controls and no significant differences were observed for each individual polymorphism in the sample. However, using multiple linear regression analyses, we found significant associations between 4 (rs2008691, rs1902586, rs7181886, and rs17647719) CYP19A1 polymorphisms with serum17ß‐estradiol levels (p = 0.004, 0.039, 0.046 and 0.047, separately) after permutation by 10,000 times in Chinese population.ConclusionIn conclusion, our findings suggest a novel link between CYP19A1 polymorphisms and variation of serum 17ß‐estradiol levels in Chinese elderly. This novel evidence may reflect inter‐individual differences of estrogen exposure according to different CYP19A1 genotypes during aging and dementia process. Our findings may highlight a new perspective to understand the underlying mechanisms of AD.