bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of inflammatory biomarkers in Alzheimer's disease
Authors: Santos Gustavo A A 
Published: January 2025

AbstractBackgroundNeuroinflammation can be considered a risk factor for the onset or progression of Alzheimer's dementia. In a neuroinflammatory process, the death of neurons may accelerate, favoring the progression of Alzheimer's disease. The release of pro‐inflammatory proteins can, for example, cause synaptic dysfunction and impede neurogenesis.Method76 participants were invited to participate in this experiment, arranged as follows: Elderly Group without AD: 26 cognitively healthy elderly individuals without a diagnosis of AD, aged 65 years or over. Adults without AD group: 25 adult and non‐elderly individuals, cognitively healthy and without a diagnosis of AD, aged between 19 and 59 years old. Elderly group with AD: 25 patients diagnosed with probable AD, aged 65 years or over. The concentrations of cytokines (IL‐4, IL‐6, IL‐8, IL‐10 and TGF‐β) present in saliva were measured through enzymatic assays (ELISA), using the following kits, respectively: IL‐4, IL‐6, IL‐8, IL‐10 ‐ HCYTOMAG‐60K‐04 CYTOKINE PANEL WITH 04 PLEX (IL4, IL6, IL8 AND IL10); E‐EL‐H0110 ‐ Human TGF‐1(Transforming Growth Factor Beta 1), ELISA KitResultWe identified important changes in IL‐4, IL‐6, IL‐8 and TGFb1, with increased expression in participants with AD and a reduction in healthy ones, except for TGFb1, which had exactly the same levels found in participants without AD, except for women, where it was increased, a fact to be investigatedConclusionFor IL‐10 Cytokines we did not find any type of variation, however IL‐8 was elevated in patients with AD and reduced in patients without AD; IL‐6, IL‐4 and TGF‐b are also elevated in AD patients and reduced in healthy subjects. These findings support the hypothesis that AD is an inflammatory process.
An EEG study of neurophysiological markers of cognitive reserve
Authors: Katayama Osamu , Stern Yaakov , Habeck Christian G , Coors Annabell , Lee Sangyoon , Harada Kenji , Makino Keitaro , Tomida Kouki , Morikawa Masanori , Yamaguchi Ryo , Nishijima Chiharu , Misu Yuka , Fujii Kazuya , Kodama Takayuki , Shimada Hiroyuki 
Published: January 2025

AbstractBackgroundCognitive reserve (CR) is a property of the brain trait that allows for better–than–expected cognitive performance, relative to the degree of brain change over the life course. However, the neurophysiological markers of CR require further investigation. Electroencephalography (EEG) may provide an appropriate neurophysiological marker of CR. Therefore, we tested whether activity in the dorsal attention network (DAN) and the ventral attention network (VAN), as measured during resting–state EEG, moderates the relationship between hippocampal volume and episodic memory.MethodParticipants from the National Center for Geriatrics and Gerontology–Study of Geriatric Syndromes, were included; all underwent determined hippocampal volume using MRI and episodic memory was measured using word lists. After testing the effect of hippocampal volume on memory performance using multiple regression analysis, we evaluated the interactions between hippocampal volume and DAN and VAN network activity. We used the Johnson–Neyman technique to quantify the moderating effects of the DAN and VAN network activity on the relationship between hippocampal volume and word list memory, and to identify specific ranges of the DAN and VAN network activity with significant hippocampal–memory associations.ResultA total of 449 participants were included in this study. We found significant moderation of DAN with a slope of β = ‐0.0001 (95% CI: ‐0.0002; ‐0.00001, p = 0.041) and VAN was significant, with a slope of β = 0.0001 (95% CI: 0.00001; 0.0003, p = 0.032). We found that larger hippocampal volume was associated with better memory performance, and this association became stronger the lower the DAN activity. However, when the DAN activity was > 944.9, the hippocampal volume was no longer significantly related to word list memory performance. For VAN, we found higher hippocampal volume was more strongly associated with better memory performance when VAN activity was higher. However, when the VAN activity was < 914.6, the hippocampal volume was no longer significantly associated with word list memory.ConclusionOur results suggest that attention network helps maintain memory performance in the face of age related structural decline, meeting criteria for a neural implementation of cognitive reserve.
Large‐scale Proteomic Analyses of Incident Alzheimer's Disease Reveal New Pathophysiological Insights and Biomarkers
Authors: Zhang Yi , Guo Yu , YOU JIA , He Yu , Yu Jin‐Tai 
Published: January 2025

AbstractBackgroundThe pathophysiological evolutions involved in early‐stage Alzheimer’s disease (AD) are not well understood.MethodsWe used data of 2,923 plasma proteins from 51,296 non‐demented middle‐aged adults in the UK Biobank with 15 years of follow‐up. We first identified AD‐associated proteins using Cox proportional hazard models and then delineated their temporal sequences to characterize the pathophysiological evolution. The predictive value and potential as drug targets for proteins were finally estimated.ResultsWe identified 48 AD‐related proteins, with CEND1, GFAP, NEFL, and SYT1 being top hits in both near‐term (HR:1.15‐1.77; P:9.11×10‐65‐2.78×10‐6) and long‐term AD risk (HR:1.20‐1.54; P:2.43×10‐21‐3.95×10‐6). In trajectory modeling, these four proteins increased 15 years before AD diagnosis and progressively escalated, indicating early and sustained dysfunction in synapse and neurons. Proteins related to extracellular matrix organization, apoptosis, innate immunity, coagulation, and lipid homeostasis also showed early disturbances, followed by malfunctions in metabolism, adaptive immunity, and final synaptic and neuronal loss. We uncovered non‐linear alterations in human plasma proteome with AD progression, and proteins with escalating abnormalities showed stronger correlations with AD signature brain measures. Combining CEND1, GFAP, NEFL, and SYT1 with basic demographic data generated desirable predictions for 10‐year (AUC=0.901) and over‐10‐year AD (AUC=0.864), which were comparable to full model with proteomic and demographic data. In addition, CEND1 and SYT1 were potentially novel drug targets, as supported by Mendelian randomization (P<0.001) and colocalization analyses (Posterior Probability>0.8).ConclusionsProteins that first reached defined abnormality and progressively escalated were good predictive markers and potential treatment targets. Our findings highlight the importance of exploring the pathophysiological evolutions in early stages of AD, which is essential for the development of early biomarkers and precision therapeutics.
Prediction of long‐term treatment outcome in Alzheimer's disease based on EEG connectivity
Authors: Youn Tak , Park Hea Jeong , Jun Hyong Sook 
Published: January 2025

AbstractBackgroundIt is difficult to predict of long‐term treatment response of AD to medical treatment when starting medication. We explored EEG brain connectivity as a potential biomarker for long‐term medication outcomes in patients with AD.MethodsResting‐state EEG was recorded from a total of 56 AD patients (mean age = 73.23±6.39) when starting medication after diagnosis of AD. EEG was recorded in 32 channels. We divided the patients into two groups, good and poor respondent groups according to clinical evaluation after treatment and dosage changes of AD medication with MMSE score change. Starting dosage of AchE inhibitor, donepezil was 7.75±3.85mg and the mean treatment period is 104.05±57.93 weeks. The mean final medication dosages are donepezil 13.23±6.93mg and memantine 9.52±9.68mg. Initial MMSE scores of good respondent group (n=32, M=11, F=21) is 20.06±4.35 and poor respondent group (n=24, M=7, F=17) is 19.63±4.07. We analyzed brain EEG connectivity among the 246 cortical regions defined by the Brainnetome atlas.ResultsOur study found that the difference in brain EEG connectivity, especially intrahemispheric regional connectivity in gamma frequency bands, is increased at multiple regions of right hemisphere such as between Brodmann Area 44 and 39, such as DLFPC and hippocampal areas, DLFPC and caudate nucleus, middle frontal and inferior parietal areas in poor respondent group (p<0.00001). In alpha frequency band, EEG connectivity between right DLPFC(BA 9/46) and right paracentral lobule of frontal lobe (BA 4) is increased in good respondent group (p<0.0001). In theta frequency band, EEG connectivity in the areas of frontal lobe (BA 4 and 6) is decreased in good respondent group (p<0.0001).ConclusionsThis study shows increased gamma bands EEG connectivity between right anterior and posterior areas in poor respondent group. In general cortical gamma‐band activity is increased by selective attention in normal populations and related to cholinergic system. Elevated gamma band power was observed in AD when compared to MCI and control subjects was reported in a previous study. Our results suggest the potential of the gamma EEG connectivity of initial EEG when starting treatment as a biomarker to predict long‐term medical treatment for AD medication.
Relationship and impact of hand segmentation in IADLs for predicting Alzheimer
Authors: Babatope Eyitomilayo Yemisi , García‐Vázquez Mireya Saraí , Ramírez‐Acosta Alejandro Álvaro 
Published: January 2025

AbstractBackgroundA decline in Instrumental Activities of Daily Living (IADLs) indicates cognitive impairment, a marker of early detection of Alzheimer’s disease (AD). Obtaining hand information within the assessment of IADLs may be an innovative approach to predicting cognitive decline. Hands play a vital role while performing IADL and can be used in assessing human visuomotor skills. This is a non‐invasive approach to early detection and intervention. Hand segmentation techniques can help obtain hand information necessary for cognitive function assessment. This research evaluates the visuomotor skills in humans by segmenting hands in egocentric videos to obtain hand information as a feature to predict cognitive decline.MethodIn this work, visual information with motor actions was measured based on hand coordination and movement. Analysis of hand information obtained from hand interaction is measured to predict Alzheimer's disease. A convolutional neural network was used to build an improved segmentation model (RefineNet‐Pix) for hand segmentation. This model was trained with a public dataset that captures hand interaction and movements using an egocentric camera (egoHands). To validate its accuracy, we tested the model on egohands test data, GTEA (Georgia Tech Egocentric Activities) datasets, and random images from the internet. We made use of less expensive kernels and more optimization techniques in performing the segmentation task as compared to existing models.ResultThe result obtained from the semantic segmentation task yielded a better performance in terms of speed and accuracy when compared with the baseline model.ConclusionDetails of hand movements can point to precise changes in cognitive decline. An improved performance of the hand segmentation process with our RefineNet‐Pix can help in extracting hand information. This is a useful feature in identifying a decline in cognitive status. This is a promising, non‐invasive, and cost‐effective approach that can help clinicians in the early detection of Alzheimer's disease.
Design and Validation of the ADNI MR Protocol
Authors: Arani Arvin , Borowski Bret J. , Felmlee John , Reid Robert I. , Thomas David L , Gunter Jeffrey L. , Buckner Randy L. , Jung Youngkyoo , Tosun Duygu , Weiner Michael S. W. , Jack Clifford R. 
Published: January 2025

AbstractBackgroundPhase four of the Alzheimer’s Disease Neuroimaging Initiative (ADNI4) began in 2023. This time‐period corresponded to MRI vendors introducing product sequences with compressed sensing (CS), cross‐vendor adoption of arterial spin‐labelling (ASL) and multi‐band slice excitation, and hardware improvements (head‐coils, increased gradient amplitudes). These advances enabled the acquisition of new imaging measures and reduced scan times. The ADNI4 MRI protocol aims to maintain longitudinal consistency across two decades of data acquisition, while adopting new technologies. Here we describe the design and justification behind the ADNI4 protocol and list the target imaging biomarkers.MethodsThe ADNI4 MRI protocol includes 9 MRI sequences; i) T1‐weighted (T1w) MP‐RAGE, ii) a compressed sensing T1w MP‐RAGE, iii) 3D T2w FLAIR, iv) structural isotropic 3D T2w v) T2*‐weighted susceptibility imaging vi) multi‐post labelling delay (PLD) 3D ASL, vii) 2D multi‐band‐multiple‐shell diffusion (dMRI), viii) 2D multi‐band EPI BOLD ix) high resolution, limited field of view, imaging centered on the hippocampus. Table 1 gives a detailed summary of the key protocol parameters and lists some of the imaging biomarkers that can be obtained from each series.ResultsStructural imaging was kept consistent across imaging sites. Variations in vendor specific sequences, software upgrades, and hardware performance capabilities, resulted in variations in advanced imaging strategies. Table 2 highlights the advanced sequences introduced in ADNI4, which will gradually be implemented as the required upgrades and licenses are obtained at each site. The main advanced sequences that are new in ADNI4 from ADNI 3 are i) CS for T1w images, ii) pseudo‐continuous‐ASL (pCASL) being implemented on all 3 vendors (GE, Siemens, Philips), iii) multi‐PLD ASL, which allows for the generation of quantitative maps of cerebral blood‐flow (CBF) and arterial transit times (ATTs), iv) and isotropic 1mm3 resolution for 3D FLAIR. When ADNI4 began, product sequences that utilized artificial intelligence were not widely available and could not be adequately tested before the rollout of ADNI4.ConclusionsThe ADNI4 database will continue to help the neuroimaging community, not only extract valuable imaging biomarkers, but provide the data necessary to test the impact of advanced imaging strategies on diagnostic accuracy and disease sensitivity.
Identification of stage‐specific blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis
Authors: Yamakawa Akiko , Suganuma Mutsumi , Mitsumori Risa , Niida Shumpei , Ozaki Kouichi , Shigemizu Daichi 
Published: January 2025

AbstractBackgroundThe detailed mechanism of Alzheimer’s disease (AD) has not been fully elucidated, and a comprehensive gene expression analysis of the entire process leading up to the onset of AD has never been conducted on a large scale.MethodWe performed RNA‐seq analysis of 1227 blood samples consisting of 424 AD, 543 mild cognitive impairment (MCI), and 260 cognitive normal (CN) subjects, and examined differentially expressed genes (DEGs) between CN and MCI (CN‐MCI), and between MCI and AD (MCI‐AD). Pathway analysis of DEGs were performed and subsequently retrospective prediction models were constructed using the enriched genes within these pathways.ResultA total of 883 and 1,169 statistically significant DEGs were identified in CN‐MCI and MCI‐AD, respectively. Pathway analyses using these DEGs and subsequent construction of risk prediction models led to the identification of 6 and 29 representative genes, achieving area under the curves of 0.792 (95% confidence interval: 0.768‐0.853) for CN‐MCI and 0.781 (95% confidence interval: 0.712‐0.799) for MCI‐AD. Furthermore, protein–protein interaction network analysis using the representative genes revealed key roles of ribosomal genes and phagosomes in the progress of MCI, while immune‐related genes were involved in the progression of AD.ConclusionOur findings suggest that the onset of AD may be associated with gene expression changes in the immune system, protein processing and cell cycle, following observed alterations in gene expression within ribosomes and phagosomes during the MCI stage. Given the effectiveness of delaying MCI progression in preventing AD, the regulation of ribosomal expression and phagocytosis emerges as potential biomarkers.
Test‐retest reliability of FreeSurfer measures of neurodegeneration
Authors: Chen Haiyun , Bokacheva Louisa , Vedvyas Alok , Osorio Ricardo S. , Masurkar Arjun V. , Wisniewski Thomas , Rusinek Henry 
Published: January 2025

AbstractBackgroundFreeSurfer is widely used for monitoring neurodegeneration by quantifying MRI brain regions vulnerable to atrophic changes associated with Alzheimer’s disease (AD). FreeSurfer test‐retest reliability has been explored mainly in younger adults, but the reliability of AD imaging biomarkers is less well studied. We used two closely spaced T1‐weighted MRIs in 100 older adults to assess FreeSurfer 7.1 test‐retest reliability of AD imaging biomarkers of neurodegeneration.MethodConsecutive participants (n=100, age ≥60 years) with two brain MRI exams between Jan 2021 and Oct 2023 and less than 180 days between exams were selected from NYU Langone’s Alzheimer Disease Research Center database. Images were acquired on the same Siemens 3T PET‐MR system using 32‐channel head coil and sagittal T1‐weighted MPRAGE sequence (TR=2100ms; TE=2.98ms; TI=900ms; FA=8°; bandwidth, 260Hz/pixel; GRAPPA, 2; matrix, 256x240x176; voxel, 1x1x1 mm3; acquisition time, 4:35min). FreeSurfer 7.1 was used to segment the brain and determine regional volumes, cortical thickness, and intracranial volume (ICV). We compared bilateral and total hippocampal volume, entorhinal cortex thickness, AD signature (after Dickerson et al. Cereb Cortex 2009; PMID: 18632739), and ICV between two exams. Test‐retest reliability was assessed using mean absolute difference, root mean square difference, and intraclass correlation coefficient (ICC). Inter‐exam differences in AD measures were tested for association with demographic, cognitive (MOCA), and imaging features using linear regression.ResultThe 100 participants (age, 73.5±6.1 years, 64% women, 33% Black, 9‐20 years education) had MRIs within an interval of 5.5±5.2 weeks (Figure 1). FreeSurfer estimates of MRI‐based biomarkers of AD were consistent between exams (Table 1). There were no associations between the differences in any of the FreeSurfer measures and inter‐MRI interval or participants’ demographic and cognitive/biological features (Figure 2). Detecting within‐individual 1% change in AD signature, whole hippocampus, or entorhinal cortex (using paired t‐test) at p=0.05 and 80% power, would require an estimated sample of 38, 69, or 347 subjects, respectively.ConclusionOur test‐retest results reflect random errors due to image acquisition and post‐processing precision of FreeSurfer 7.1. The test‐retest reliability of most measures is excellent, except entorhinal cortex thickness. Detecting small neurodegenerative changes appears to be feasible with relatively small sample sizes.
OVERTURE Phase 2 MRI Analysis Demonstrates Reduced Corpus Callosum Atrophy in Alzheimer's Disease
Authors: Da Xiao , Hempel Evan , Hajos Mihaly , Kern Ralph , Cimenser Aylin 
Published: January 2025

AbstractBackgroundRecent research by Da et al. (2023) has demonstrated that non‐invasive gamma sensory stimulation can reduce brain white matter atrophy and myelin content loss. The impact on the Corpus Callosum (CC), the brain's largest commissural white matter tract essential for hemispheric connectivity, remains unexplored. Diffusion Tensor Imaging (DTI) studies (Delvenne et al. 2023), have identified variations and alterations in the CC's tract bundles in Alzheimer's disease (AD). We conducted an analysis of CC changes in patients along the Alzheimer’s continuum targeting both the overall and regional structural alterations.MethodIn this study, we used neuroimaging data from the OVERTURE clinical trial (NCT03556280). Participants were randomized 2:1 (Active: Sham) to receive daily, 1‐hour, audio and visual stimulation at gamma frequency or sham stimulation for 6 months. Magnetic Resonance Imaging (MRI) assessments were performed at baseline, month 3, and month 6. T1w MRI images were processed using the Yuki module within the Automated Registration Toolbox (ART) (Ardekani et al., 2014) with CC segmentation in the midsagittal plane (MSP) and its five subregions (Hampel et al., 1998). The analysis was conducted on participants with valid data for the total MSP CC and its subregions (N=50). Using Bayesian linear mixed‐effects modeling, we examined the changes in the area of the total MSP CC and its subregions.ResultThe active group demonstrated preservation of the total CC area and its subregions after six months of treatment. Differences in percentage changes between active and sham groups were: Total CC area (2.28±0.87%, p<.02), genu/rostrum (2.36±0.90%, p<.02), anterior‐body (2.64±1.26%, p<.04), mid‐body (2.79±1.18%, p<.03), posterior‐body (2.87±1.41%, p<.05) and splenium (1.58±0.73%, p<.04). Significant differences between the two groups were also observed in total CC area, genu/rostrum, and splenium at month 3.ConclusionOVERTURE Phase 2 MRI outcomes demonstrate that six months of non‐invasive gamma sensory stimulation may reduce the progression of CC atrophy in individuals with mild‐moderate AD, potentially preserving the integrity of interhemispheric communication. We are currently conducting a phase 3 clinical trial titled HOPE (NCT05637801) to evaluate the efficacy and safety of Cognito’s medical device.
Variational autoencoder latent space as a robust and pragmatic clinical classification tool for dementia
Authors: Wakefield William C , Barnard Leland R , Botha Hugo , Graff‐Radford Jonathan , Kantarci Kejal , Boeve Brad F. , Petersen Ronald C. , Jack Clifford R. , Lowe Val J. , Jones David T. 
Published: January 2025

AbstractBackgroundMany proposed clinical decision support systems (CDSS) require multiple disparate data elements as input, which makes implementation difficult, and furthermore have a black‐box nature leading to low interpretability. Fluorodeoxyglucose Positron Emission Tomography (FDG‐PET) is an established modality for the diagnosis of dementia, and a CDSS that uses only an FDG‐PET image to produce a reliable and understandable result would ease both of these challenges to clinical application.MethodA deep variational autoencoder (VAE) was used to extract a latent representation of each image through prior training from FDG‐PET brain images (n=2000). This unsupervised VAE has a novel graph convolutional architecture that makes it applicable to masked template space images. A logistic regression model was used to classify each image. A parametric study of the latent space revealed the imaging features that were used in the logistic regression model to differentiate each class. A separate dataset of participants labeled with their clinical diagnosis (n=1239) was used to assess the logistic regression model’s diagnostic accuracy of cognitively unimpaired (CU) (n=679) and between common dementia subtypes: Alzheimer’s disease (AD) (n=310), Lewy body dementia (DLB) (n=151), and behavioral variant frontotemporal dementia (bvFTD) (n=99). The logistic regression classifier was evaluated using receiver‐operator characteristic (ROC‐AUC) curves.ResultROC‐AUC curves for CU and each dementia subtype are illustrated in Figure 1a. A k nearest neighbors model was used to develop a graphical representation of the latent space where nodes are images and edges are drawn between nearest neighbors, illustrated in Figure 1b. The graph of the latent space demonstrates a comparison between dementia subtypes and CU images.ConclusionIn this study, we developed a proof‐of‐concept VAE that can achieve differential diagnosis with 84.8% balanced accuracy. The model architecture provides visual interpretability, and shows that the hippocampi, parietal lobes, and cerebellum, were useful for distinguishing AD, the frontal lobe, posterior cingulate cortex, and hippocampi were useful for distinguishing DLB, and prefrontal pole, subcortical structures, and occipital lobe were useful for distinguishing bvFTD. These regions of metabolism align with clinical pathology and metabolic patterns.