bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relationship Between Plasma Phosphorylated Tau and Tau‐PET Spatial Patterns in Thai Patients with Amnestic Mild Cognitive Impairment or Mild Dementia Due to Probable Alzheimer's Disease: A Parcel‐wise Analysis
Authors: Thanprasertsuk Sekh , Jarukasemkit Setthanan , Booncharoen Kittithatch , Luechaipanit Watayuth , Haethaisong Thanaporn , Chongsuksantikul Adipa , Supharatpariyakorn Thirawat , Likitjaroen Yuttachai , Hemachudha Thiravat , Chunharas Chaipat , Thanapornsangsuth Poosanu 
Published: January 2025

AbstractBackgroundTau‐PET and plasma phosphorylated tau (p‐tau) have emerged as pivotal biomarkers for Alzheimer’s disease. Despite the practical advantages of using plasma p‐tau, there is limited understanding regarding its relationship with the topographic distribution of tau‐PET, particularly within the Southeast Asian population. This study aims to elucidate the correlation between plasma p‐tau levels and the spatial patterns observed in tau‐PET among Thai patients diagnosed with amnestic mild cognitive impairment (MCI) or mild dementia due to probable Alzheimer’s disease (AD).MethodsThirty patients were included in this study. Correlations between plasma p‐tau and tau‐PET standardized uptake value ratio (SUVR) in each cortical region were analyzed. Patients were further stratified into low and high amyloid‐beta (Aβ) groups using Aβ centiloid (AβCL) median split for additional subgroup analyses. Plasma p‐tau217 was measured on the Mesoscale Discovery platform with the S‐PLEX Human Tau (pT217) Kit. PET tracers for tau and Aβ were PI‐2620 and 18F‐florbetaben. Brain parcellation for regional tau‐PET analyses was performed using FreeSurfer.ResultsThe mean age was 69.8 ± 8.4 years, with 20 female patients (66.7%). The median[IQR] value for plasma p‐tau217 was 7.87[11.37] pg/mL. Median[IQR] values for overall tau‐PET SUVR and AβCL were 1.20[0.35] and 43.56[53.70], respectively. Figure 1 illustrates cortical regions where plasma p‐tau217 significantly correlates with tau‐PET SUVR (p<0.05), with higher color intensity indicating stronger correlation coefficients. Cortical parcels demonstrating the highest correlation coefficients include the right anterior cingulate gyrus (r=0.74, p<0.001) and superior frontal gyri in both the left (r=0.73, p<0.001) and right (r=0.72, p<0.001) hemispheres. Figures 2 and 3 depict such cortical regions in low Aβ and high Aβ groups, respectively.ConclusionsIn our study population, plasma p‐tau and tau‐PET SUVR exhibit correlations in most cortical regions, with a characteristic sparing of primary cortices. These correlations cluster in bilateral inferomedial temporal regions in the low Aβ group. In the high Aβ group, correlations are primarily observed in neocortical areas, including frontal, anterior temporal, and temporoparietal regions. This suggests that the relationship between plasma p‐tau and tau pathology may vary based on Aβ levels, providing valuable information for understanding disease progression.
Macrophage migration inhibitory factor in prodromal Alzheimer’s disease
Authors: Behfar Qumars , Behfar Stefan Kambiz , Onur Oezguer A. 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) presents a prolonged asymptomatic phase, providing a significant timeframe for potential intervention. Leveraging this opportunity necessitates the early identification of diagnostic and prognostic biomarkers to detect Alzheimer's pathology during predementia stages. This enables the identification of individuals likely to progress to Alzheimer's‐type dementia, allowing them to benefit from targeted disease‐modifying therapies. The primary and critical risk factor for AD is age. As cells undergo aging, they enter a senescent state characterised by morphological alterations and the release of immune signaling mediators linked to systemic low‐grade inflammation. Therefore, we conducted a search for senescence associated markers in serum and CSF and examined their link with disease progression, aligning with clinical disease staging.MethodWe assessed the levels of the available senescence‐associated secretory phenotype (SASP) factors in serum and CSF samples of cognitively normal (CN), mild cognitive impairment (MCI) and AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). First, we evaluated the SASP levels in clinical stage, then we examined the association between SASP and the classical AD imaging and CSF core biomarkers, and finally we assessed the effect of SASP on the disease progression toward AD dementia.ResultCSF measures of hepatocyte growth factor (HGF), macrophage migration inhibitory factor (MIF), matrix metalloproteinase (MMP)‐3, vascular endothelial growth factor (VEGF) and serum measures of endothelial growth factor (EGF), MMP‐1, MMP‐10 and VEGF showed significantly distinguishing levels in clinical stages. We observed significant correlation between CSF measures of MIF and phospho‐tau 181 (P‐tau181) and total tau (T‐tau) and an anti‐correlation with hippocampal volume. In our longitudinal analysis, CN and MCI subjects with increased MIF showed a higher probability of progressing in the spectrum of AD dementia and a faster cognitive decline.ConclusionOur results supports the evidence on the role of SASP in development of AD and endorse the use of cerebrospinal MIF as a senescence‐associated prognostic indicator for AD dementia, advocating for its inclusion in the current AD biomarker scheme to encompass pathological aspects beyond amyloid and tau.
Combination of CSF synaptic biomarkers and qEEG leads to a better prediction of disease progression in Alzheimer’s disease
Authors: Kandiah Vidhusan , Gouw Alida A , Nilsson Johanna , Teunissen Charlotte , Twisk Jos W.R. , Blennow Kaj , Zetterberg Henrik , van der Flier Wiesje M. , Brinkmalm Ann , Duits Flora H. 
Published: January 2025

AbstractBackgroundSynaptic dysfunction plays an important role in Alzheimer’s disease (AD) and cognitive decline. We investigated the association between cerebrospinal fluid (CSF) synaptic protein levels and quantitative EEG (qEEG) measures, two modalities to measure synaptic dysfunction in AD pathology. We assessed combined and independent prognostic value of both modalities for cognitive decline along the AD continuum.MethodWe included 114 subjects with SCD or MCI (n=38 with normal Aβ42 [A‐], n=38 with abnormal CSF Aβ42 [A+], and 38 patients with AD dementia) from the Amsterdam dementia cohort, with available CSF and EEG at baseline, and more than one year clinical follow‐up. We assessed associations between CSF synaptic proteins (neurogranin, β‐synuclein, neuronal pentraxin 2 [NPTX2], synaptotagmin‐1 [SYT1], SNAP25) and qEEG measures (Global field power [GFP] ‐α,‐β,‐θ; Amplitude envelope correlation‐corrected [AEC‐c] α, AEC‐c β, and phase lag index [PLI] θ), all standardized into z‐scores, using Spearman correlations. We performed linear mixed models to assess univariate and multivariate associations with cognitive decline (i.e. repeated MMSE) along the AD continuum. We used backwards stepwise selection in multivariate models and calculated marginal R‐squared to compare prognostic accuracy.ResultThere were no correlations between CSF and qEEG markers (Table 1). In univariate models SNAP25, beta‐synuclein, neurogranin, GFP alpha, beta and theta emerged as predictors of cognitive decline (Table 2). SNAP25 showed the largest effect (stB[SE]=‐0.45 [0.13], p<0.001). For GFP‐alpha and –beta effects were reversed (0.29[0.13], p<0.05 and 0.37[0.13], p<0.01 respectively). Multivariate models combining CSF and qEEG measures improved prognostic accuracy compared to CSF or EEG only models (Table 3; R2m = 19.8%, StB[SE] = 1.26[0.28] for GFP‐α, StB[SE] = 1.32[0.32] for GFP‐β, StB[SE] = ‐0.96[0.32] for SNAP25 and StB[SE] = 0.94[0.31] for NPTX2, all p<0.01).ConclusionCSF synaptic proteins and qEEG measures were not directly correlated to each other, but both modalities were associated to cognitive decline. This suggests they represent different aspects of synaptic pathology in AD. The combination of both modalities showed increased prognostic accuracy over CSF‐ or qEEG‐only models. Hence, the combination has potential for a more personalized prognosis in AD.
Proteomic dementia risk assessment in hypertension
Authors: Ning Yuye , Wang Zhibo , Chen Meilin , Jia Jianping 
Published: January 2025

AbstractBackgroundHypertension is widely prevalent and independently increases the risk of dementia. Advances in high‐throughput plasma proteomics analyzed may offer new opportunities to improve risk stratification for these patients.MethodThis study involved a proteomic analysis of plasma samples collected during the baseline recruitment of participants in the UK Biobank. Participants included in the study were those with hypertension at baseline and no history of dementia, involving approximately 3,000 unique proteins. The primary endpoint of the study was the first occurrence of dementia. The entire cohort was divided into two subsets for feature selection: 80% of the data was used for training and 20% for testing. Feature selection was conducted using least absolute shrinkage and selection operator (LASSO) regression, followed by the development of a predictive model using Generalized Linear Models. This model was then tested within the internal cohort and compared against existing clinical risk models.ResultThis study included 14,038 hypertensive patients without dementia at baseline, with an average age of 59.94 (SD 7.06) years, of which 7,552 (53.8%) were male. During a median follow‐up of 14.4 years, 575 cases (4.1%) of dementia were identified. From the training set, using LASSO regression selected 61 proteins to constitute the primary proteomic risk model (out of a total of 2,924 measured proteins). The Area Under the Curve (AUC) for predicting dementia events using the protein model was 0.836 (95% CI 0.807–0.865). In comparison, the AUC for the demographic model (Age, sex, education, and Apolipoprotein E) was 0.766 (95% CI 0.733–0.8), and the AUC for the cognitive model was 0.589 (95% CI 0.545–0.632). The combination of models yielded an AUC of 0.845 (95% CI 0.815–0.874). Additionally, the primary 61‐protein model demonstrated good calibration (Figure 2). When the entire cohort was stratified by quartiles of predicted concentration according to the model, higher predicted values were associated with increased risk of dementia (Figure 3).ConclusionThis study has conducted a large‐scale proteomic study on the risk of dementia events in a hypertensive population. The proteomic risk prediction model offers a broad range of dynamic risk stratification and significantly outperforms common clinical feature models.
Effects of sleep quality on diurnal volumetric change of brain in older adults
Authors: Kim Jun Sung , Han Ji Won , Oh Dae Jong , Suh Seung Wan , Jo Sungman , Kwon Min Jeong , Park Jieun , Kim Jae Hyoung , Kim Ki Woong 
Published: January 2025

AbstractBackgroundBrain volume is influenced by several factors that can change throughout the day. In addition, most of these factors are influenced by sleep quality. This study investigated diurnal variation in brain volume and its relation to overnight sleep quality.MethodWe enrolled 1,003 healthy Koreans without any psychiatric disorders aged 60 years or older. We assessed sleep quality and average wake time using the Pittsburgh Sleep Quality Index, and divided sleep quality into good, moderate, and poor groups. We estimated the whole and regional brain volumes from three‐dimensional T1‐weighted brain MRI scans. We divided the interval between average wake‐up time and MRI acquisition time (INT) into tertile groups: short (INT1), medium (INT2), and long (INT3).ResultWhole and regional brain volumes showed no significance with respect to INT. However, the interaction between INT and sleep quality showed significance for whole brain, cerebral gray matter, and cerebrospinal fluid volumes (p < .05). The INT2 group showed significantly lower volumes of whole brain, whole gray matter, cerebral gray matter, cortical gray matter, subcortical gray matter, and cerebrospinal fluid than the INT1 and INT3 groups only in the individuals with good sleep quality.ConclusionHuman brain volume changes significantly within a day associated with overnight sleep in the individuals with good sleep quality.
Association Between Sleep Measures and Cognition Performance: Insights from a Digital Sleep Assessment Study
Authors: Ding Huitong , Lyu Chenglin , Searls Edward , Low Spencer , Popp Zachary T , Li Zexu , Rahman Salman , Igwe Akwaugo , Ho Kristi , Hwang Phillip H , Anda‐Duran Ileana De , Thomas Robert J , Kolachalama Vijaya B. , Au Rhoda , Lin Honghuang 
Published: January 2025

AbstractBackgroundSleep disorders as a contributing factor to cognitive impairment have spurred growing interest. The advent of digital technology facilitates the collection of comprehensive sleep measures in a home setting. The objective of this study is to examine the association between digital sleep measures and the Montreal Cognitive Assessment (MoCA).MethodThis study included participants from the Boston University Alzheimer’s Disease Research Center (BU ADRC) Clinical Core, a longitudinal study of aging which includes the Uniform Dataset (UDS) and other Alzheimer’s Disease related clinical features. Participants were asked to wear a SleepImage Ring when they went to bed at night at least three times in a two week span at quarterly intervals. A variety of sleep measures, such as duration of unstable non‐rapid eye movement (NREM) sleep and percentage of time spent with SpO2 below 80%, were collected from the device and analyzed. Linear regression models were used to assess the associations between these sleep measures and the MoCA total score as well as individual MoCA scores. All models were adjusted for sex, age, and education to account for potential confounding factors.ResultOur study included 75 participants from the BU ADRC (mean age: 74.9± 7.9 years; 64.0% women). On average, the SleepImage Ring was worn for 17 nights over the duration of the study (interquartile range: 6‐23 nights). As shown in Table 1, 11 sleep measures were associated with at least one MoCA item with nominal significance (P<0.05). Interestingly, the percentage of time spent with SpO2 below 80% was negatively associated with the MoCA total score (P=0.016) as well as three individual MoCA scores, including the Language – Naming (P<0.001), Delayed recall – No cue (P=0.027) and Abstraction (P=0.034).ConclusionOur analysis revealed multiple suggestive associations between digital sleep measures and MoCA test scores, highlighting the potential of sleep as a modifiable lifestyle factor to assess and improve cognitive health. Further studies with larger and independent samples are required to further validate these findings.
Development and Validation of Natural Language Processing (NLP)‐Based Risk Prediction Model for Cognitive Impairment in Geriatric Patients
Authors: Soleimani Laili , CHENG FU‐YUAN , Sano Mary , Kia Arash 
Published: January 2025

AbstractBackgroundDementia poses a significant global crisis, yet 60% of cases go undetected, particularly among specific sub‐populations. Timely diagnosis is crucial for implementing early intervention strategies. Challenges of current screening tools (e.g., Montreal Cognitive Assessment (MOCA) and Mini‐mental Status Exam (MMSE)) include their rule‐based nature, time‐consuming administration and potential bias against individuals with lower education or different socioeconomic backgrounds.MethodIn this study, we created a patient cohort within Mount Sinai Health System, identifying individuals at high‐risk for cognitive impairment using MMSE scores 23 and below. Extracting clinical notes from inpatient and outpatient settings, we applied specific sampling logic to select the last five notes for each patient before their MMSE. Utilizing word‐embedding techniques, we vectorized sentences and input these vectors into a deep neural network for Named Entity Recognition to extract clinical concepts (e.g diagnosis, signs and symptoms, medications). A psychiatrist reviewed and selected 337 features based on their frequency in the cohort and clinical relevance. Using these features, we created a map to generate feature vectors, and following a train‐test split, 70% of the data was used to train a classifier using the random forest algorithm. The remaining 30% of the cohort was employed for validation purposes.ResultOut of 4,366 patients, with documented MMSE scores from 2011 to 2023, 73% had one MMSE, and 29% scored below 24. The developed model demonstrated an AUC ROC of 0.93. At a threshold of 0.5, it showed a sensitivity of 0.86 and specificity of 0.85 in the validation set. On the test set, the AUC ROC was 0.68, with a sensitivity of 0.62 and specificity of 0.63 at a threshold of 0.5.ConclusionThe development and validation of this digital tool exemplify translational research that leverages Real‐World Data (RWD) from EMR. The tool aims to assist clinicians in efficient dementia detection across different stages of the disease. Our next phase involves optimizing the feature engineering, fine‐tune the model, and deploying it in the production environment. This will facilitate automated screening of geriatric patients during clinical encounters at Mount Sinai Hospital, allowing us to measure the tool’s prospective performance.
EEG biomarkers of Epileptiform Activity in Relation to Alzheimer’s Disease Biomarkers
Authors: Horvath Andras Attila 
Published: January 2025

AbstractBackgroundGrowing evidence suggests that the imbalance between excitability and inhibitory neural activity is a key aspect of cognitive decline. Subclinical epileptiform activity (SEA) has been indicated as a marker of increased cortical excitability. While SEA is considered as a benign EEG sign in the elderly population, recent studies demonstrated its role in the progression of Alzheimer’s disease. The aim of our study was to investigate the relevance and phenotype of SEA in clinically healthy elderly population.MethodWe studied 62 elderly subjects determined as healthy based on physical, neuropsychology, neuroimaging (MRI), and serological workup. All subjects underwent 24‐h Holter electroencephalography (EEG), visual analysis was conducted by two independent raters. Subjects were classified into EEG positive (EEG+) and negative (EEG‐) groups based on detecting epileptiform activity on their EEG. Neuropsychology battery and structural MRI results were compared between the groups.ResultsEEG analysis showed epileptiform activity in 26% of cases. EEG positivity was associated with significantly lower verbal and category fluency subscores on Addenbrooke Cognitive Examination (p=0,03 and p=0,01). This group also performed worse on both Trail Making Test A and B (p =0,02 and 0,003 respectively). Age and education had a significant effect on neuropsychological performance in both groups. Structural MR analysis showed significant group differences in various areas, particularly in the parietotemporal regions (p=0,001). Interestingly, EEG+ patients had larger external temporal brain volume bilaterally (p<0,001) and reduced medial parietal volume (p=0,001).ConclusionPresence of SEA among healthy elderly shows a discrete but significant association with decreased cognitive performance in certain subdomains, as well as a special imaging phenotype. Our results underscore the relevance of data suggesting that cortical hyperexcitability associate with parietal atrophy and preserved temporal structures. In Alzheimer’s disease, the detailed representation corresponds to the hippocampal sparing phenotype. Considering our findings, SEA might associate with an increased risk for cognitive decline even in healthy individuals.
Anesthetic dose‐adjusted intraoperative alpha power in association with biomarkers of preclinical Alzheimer’s disease, preoperative attention function, and postoperative delirium
Authors: Reese Melody , Cooter Mary , Roberts Kenneth C , Woldorff Marty G. , Browndyke Jeffrey N. , Shaw Leslie M. , Waligorska Teresa , Zetterberg Henrik , Blennow Kaj , Berger Miles ,   the MADCO‐PC, INTUIT, and MARBLE Investigators
Published: January 2025

AbstractBackgroundPostoperative delirium (POD) is characterized by fluctuating attention after surgery and is associated with increased risk of developing Alzheimer’s Disease (AD). While the neurophysiological changes that underlie POD and increased risk of AD are unclear, recent data has raised the possibility that an exaggerated brain response to anesthetics may be a biomarker for POD risk and preclinical AD‐like pathology. Thus, we examined whether anesthetic‐dose‐adjusted intraoperative brain activity is associated with POD or preoperative brain vulnerabilities (preclinical AD‐like pathology, preoperative inattention) that may contribute to risk of POD (and later AD).Method82 patients from 3 prospective cohort studies of non‐cardiac, non‐neurologic surgical patients age ≥60 who had sufficient 32‐channel intraoperative electroencephalographic (EEG) data and received a gas anesthetic for ≥80% of their surgery were included. Other data included preoperative cerebrospinal fluid (CSF) samples, preoperative cognitive testing, and delirium scores (before surgery and twice daily for ≤5 days after surgery). Univariable associations were evaluated between 8‐12Hz anesthetic‐dose‐adjusted frontoparietal EEG alpha power and 1) preoperative CSF AD biomarkers (pTau181p, Aβ42, pTau181p/Aβ42), 2) preoperative attention function, and 3) POD incidence and severity. To account for the brain’s response to anesthetics, alpha power was dose‐adjusted as follows: (alpha power at each minute of surgery) / (2.5 – age‐adjusted minimum alveolar concentration at each minute of surgery) and summarized by patient across minutes as the mean of median‐filtered alpha.ResultLower dose‐adjusted intraoperative frontoparietal alpha power was found among patients 1) with (vs. without) preoperative pTau/Aβ pathology (Table 1), and 2) among those who scored worse on timed preoperative attention tasks (Table 2). Low dose‐adjusted alpha power was also associated with increased odds of POD and moderate‐to‐severe delirium symptomology (Table 3). The conclusions were similar for unadjusted alpha power, though only dose‐adjusted values were associated with POD severity. Overall, the results suggest that, regardless of our anesthetic‐dose‐adjustment, low intraoperative frontoparietal alpha power is associated with CSF pTau/Aβ pathology, attention function, and POD.ConclusionFuture studies are needed to determine whether there are causal pathways between preoperative brain vulnerabilities, intraoperative frontoparietal alpha power, and POD. Understanding these pathways will improve our ability to identify at‐risk patients for targeted interventions.