bluuebunny/crossref_metadata_2025_split huggingface dataset:
The cerebrospinal fluid Neutrophil gelatinase‐associated lipocalin (NGAL) concentration in Alzheimer’s Disease (AD)
Authors: Doroszkiewicz Julia , Kulczynska‐Przybik Agnieszka , Borawska Renata , Mroczko Jan , Slowik Agnieszka , Mroczko Barbara 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD), a prevalent neurological illness, is the most common form of dementia worldwide. Given that AD symptoms manifest gradually, it is imperative to discover new biomarkers that support early diagnosis of this diseases. Some research indicates that Neutrophil gelatinase‐associated lipocalin (NGAL) may influence a number of neurobiological processes, including inflammation occurring in the central nervous system. Moreover, this protein was discovered in brains of AD patients. Literature data suggests that NGAL could be a potential biomarker of severity of Alzheimer’s disease. Therefore, the purpose of our research was to assess the usefulness of the measuring of the concentration of soluble NGAL in cerebrospinal fluid in the diagnosis of AD patients as well as compare it with classical AD biomarkers.MethodThe concentrations of NGAL were measured in cerebrospinal fluid (CSF) of 20 AD patients and 20 non‐demented controls using ELISA method. Classical biomarkers, such as Aβ‐42, Aβ‐42/Aβ‐40, tau and pTau181 were assessed by immunoenzyme assays.ResultNGAL concentrations were significantly higher in AD patients in comparison to non‐demented controls. Additionally, increased CSF levels of NGAL correlated positively with Tau and pTau181 proteins as well as negatively with Aβ‐42/Aβ‐40 ratio in the whole study group.ConclusionFindings of our research suggest a potential role of NGAL in pathology of AD. However, follow‐up studies on larger study group are needed.
Resting‐state activity across cortical hierarchies provides readiness for memorizing in aging humans
Authors: Han Feng , Chen Xi , Varghese Leah , Cassady Kaitlin , Chadwick Trevor , Mason Suzanne , Jagust William J. 
Published: January 2025

AbstractBackgroundThe neural basis of memory aging remains elusive. The default mode network (DMN) supports memory encoding and retrieval, and its connectivity decreases in aging. Young adults with larger differences in resting‐state functional connectivity (rsFC) between higher‐order DMN and lower‐order sensory/motor network (SMN) have better cognition and memory. We combined resting‐state and task‐based fMRI during an associative memory task to explore how this cross‐hierarchical contrast of rsFC is linked to memory aging.MethodWe included resting‐state prior to (an object‐scene‐pair encoding) task fMRI from 16 young adults (YA; 24.6 ± 3.6 years; 7 females) and 42 cognitively normal older adults (OA; 76.8 ± 6.9 years; 24 females) in the Berkeley Aging Cohort Study. We subtracted the averaged rsFC within lower‐order regions from higher‐order ones, and correlated the cross‐hierarchy contrast of rsFC with age and memory task performance. Brain function contrast between the higher and lower order regions may also manifest as the temporal difference/delay of cortical activation, seen in propagating waves between hierarchical cortices. The propagating number (i.e., frequency) in each direction (“High‐to‐low” or “Low‐to‐high”) was therefore also correlated with memory performance and activation in anterior‐temporal (AT), posterior‐medial (PM), and hippocampal (HPC) memory regions during task‐fMRI.ResultAmong OAs, cross‐hierarchy contrast of rsFC decreased with aging (Spearman’s ρ = ‐ 0.32, p = 0.040) and in females (p = 2.3 ×10‐3). OAs with lower rsFC contrast had worse memory during retrieval (ρ = 0.36, p = 0.019; Figure 1). Memory scores were not correlated with mean rsFC within higher‐order regions or lower‐order ones. Propagating number along the “Low‐to‐high” direction decreased in OAs. More propagation was associated with better memory and lower task‐evoked activation in AT, PM, and HPC regions (all ρ strength > 0.35, p < 0.030; Figure 2&3).ConclusionPatterns of resting‐state functional connectivity related to hierarchical cortical organization are associated with aging and memory decline. In particular, slow propagating waves of cortical activation transiting across these hierarchies may serve a preparatory function, readying the brain for memory formation, a process that is affected by aging.
A comparison of tau aggregation and seeding competency of anti‐tau antibodies under clinical trials and their target epitopes itself
Authors: Kim Min‐Seok , Song Ha‐Lim , Yoon Seung‐Yong 
Published: January 2025

AbstractBackgroundAbnormal aggregation and accumulation of tau is a hallmark of tauopathy including Alzheimer’s disease. Effective targeting of tau for therapeutic purposes requires a clear understanding of its epitope landscape with identification of a key pathogenic tau species. Despite numerous proposed and tested tau epitopes, ranging from the N‐terminus to the microtubule‐binding region and C‐terminus, the most effective target remains elusive.MethodWe compared the impact of tau aggregation and seeding using various peptides representing epitopes of anti‐tau antibodies in clinical trials or fragments of the MTBR found in the cerebrospinal fluid of tauopathies. We also evaluated the effects of several tau antibodies on the inhibition of tau aggregation and seeding using recombinant tau protein or AD brain extracts.ResultPeptide containing acetylated lysine‐280 was most potent on tau seeding measured by tau‐FRET and on inducing tau aggregation evaluated by thioflavin T compared to other peptides. Further, inhibition of tau aggregation and seeding was most prominent in anti‐acetylated lysine‐280 antibody‐treated cells.ConclusionOur results demonstrate that acetylated lysine‐280 is the most potent inducer of tau aggregation and seeding. These findings offer valuable insights into the design of tau‐targeted therapeutics and highlight acetylated lysine‐280 as a promising target for the treatment of tauopathies.
Towards Automatic Early Detection: Assessing LANGaware's Language and Speech Biomarkers in Neurocognitive and Affective Disorders
Authors: Rentoumi Vassiliki , Vassiliou Evangelos , Sali Dimitra , Demiraj Admir , Paliouras George 
Published: January 2025

AbstractBackgroundRecent advancements in automatic language and speech analysis, coupled with machine learning (ML) methods, showcase the effectiveness of digital biomarkers in non‐invasively detecting subtle changes in cognitive status. While successfully distinguishing between Alzheimer's Disease (AD) and Normal Control (NC) individuals, classifying Mild Cognitive Impairment (MCI) proves to be a more challenging task. MCI can progress to AD or result from various factors, including affective disorders, necessitating multiple expert examinations for accurate detection. Building upon previous research, we create an experimental setup to assess LANGaware’s biomarkers pool on three objectives: a) binary separation into Dementia and NC cohorts, b) broad three‐class separation into Dementia, NC, and MCI groups, c) binary differentiation into Depression coupled with Anxiety disorder and NC cohorts.MethodPatient audio recordings and ASR‐generated transcripts were fed into LANGaware’s multimodal ML pipeline, extracting hundreds of linguistic and audio features, distilled into interpretable categories with a neural network assigning weights. These categorical values served as inputs to a final neural network layer generating probabilities for target labels (Dementia, NC). Similar methodologies were applied to our second (Dementia vs MCI vs NC) and third discrimination task (Depression/Anxiety vs NC), where the neural network allocated varying weights to input features for each of the aforementioned cases.ResultIn all scenarios, data were split into a 70% training set and a 30% testing set, validated against medical expert diagnosis. For binary separation, with 2927 Dementia and 815 NC instances, the model demonstrated 89% accuracy and an 85% macro‐averaged F1 score. For three‐class separation (3752 Dementia, 1117 NC, 5993 MCI instances), the model achieved 70% accuracy and a 71% F1 score. Discriminating affective disorders (1016 Depression/Anxiety, 1630 NC instances) resulted in 71% accuracy and a 71% F1 score.ConclusionThe assessment suggests that our modelling approach aptly discerns language and speech patterns, distinguishing individuals with MCI from those with Dementia or in optimal health (NC). These outcomes contribute significantly to automatic evaluation, offering early diagnosis and timely treatment access. Our third experiment showcases the methodology's applicability in detecting affective disorders, specifically Depression and Anxiety, which may co‐occur with or precede MCI.
Markers of inflammation in cerebrospinal fluid can predict post‐assessment trajectory progression and classification on the ADCOMS scale
Authors: Evenden Dave , Michopoulou Sofia , Prosser Angus , Teeling Jessica , Kipps Christopher 
Published: January 2025

AbstractBackgroundNeuroinflammation is an integral part of Alzheimer’s Disease (AD) pathology, whereby inflammatory processes contribute to the production of amyloid‐β, the propagation of tau pathology, and neuronal loss. We recently investigated data‐driven methods for determining distinct progression trajectory groups on the ADCOMS scale. This study evaluates whether biomarkers of inflammation in cerebrospinal fluid (CSF) can predict progression rate and membership of those progression rate groups.MethodADNI’s Biomarkers Consortium dataset provided 83 quality‐controlled CSF analyte biomarker levels taken at baseline for 307 subjects. Individual ADCOMS composite score trajectories were calculated for the same ADNI subjects, using ADAS‐Cog, MMSE, and CDR‐SB assessment raw data over time.Progression rate for each subject was determined using linear regression. As a further step, each trajectory was partitioned into one of four progression groups using longitudinal clustering methods. These groups were labelled a) Fast, b) Intermediate, c) Slow, and d) None, the last indicating no overall progression.Spearman rank correlation significance with individual trajectory slope was used to down‐select the 83 biomarkers. The resulting candidate list of 21 biomarkers were assessed for Pearson correlation with progression group membership and adjusted for false discovery rate. This resulted in a priority list of 7 biomarkers used in GLM Logistic regression models to predict progression group membership, and to calculate ROC and AUC summary values to distinguish Fast and Intermediate from Slow and no progression, and other group configurations.ResultDecreased levels of ApoE, CD40, Chromogranin A (CgA), and VEGF, and increased levels of FABP, FGF4, and PPP were significantly correlated with progression rate and group (adjusted p‐value < 0.05). The Area Under the Curve (AUC) values ranged from 0.718 to 0.774 for between 3 and 7 biomarkers, with considerable variability in repeated 5‐fold cross‐validation. These results indicate a significant but moderate ability of CSF immune biomarkers to differentiate between progression trajectories.ConclusionWithin ADNI’s clinical trial cohort, CSF biomarker levels of inflammation and immune metabolism demonstrated useful predictive power for progression trajectory group membership, prompting further work to refine the modelling to better understand the vascular and metabolic interaction complexities.
Multimodal neuroimaging and plasma biomarker evidence of white matter macrostructure loss in Parkinson’s dementia
Authors: Zarkali Angeliki , Hannaway Naomi , McColgan Peter , Heslegrave Amanda J , Veleva Elena , Laban Rhiannon , Zetterberg Henrik , Lees Andrew , Fox Nick C , Weil Rimona S 
Published: January 2025

AbstractBackgroundParkinson’s (PD) is common and debilitating with over half of patients progressing to postural instability, dementia or death within 10 years. However, onset and rate of progression is highly variable, reflecting heterogeneity in underlying pathology, and biomarker studies to‐date have been limited to a single modality or assessed patients with established cognitive impairment.MethodWe assessed multimodal neuroimaging and plasma biomarkers in 98 PD patients (mean disease duration at baseline 4.33 years) and 28 age‐matched controls followed‐up over 3 years. Of PD patients, 31 (31.6%) had a poor outcome (PD poor outcomes), defined as mild cognitive impairment, dementia, frailty, or death during follow‐up. We compared biomarkers of gray matter (cortical thickness), white matter macrostructure (fibre cross‐section) and microstructure (fibre density) at whole‐brain and tract level, structural and functional connectivity, and plasma levels of neurofilament light chain (NFL) and phosphorylated tau 181 (p‐tau) between PD who progress to poor outcomes versus those with good outcomes.ResultWe found extensive white matter macrostructural changes in PD who progress to poor outcomes already at baseline: with 19% reduction in fibre cross‐section and a subnetwork of reduced structural connectivity (105 nodes, 215 edges, p=0.017), particularly involving interhemispheric connections. Gray matter and functional connectivity were preserved. NFL but not p‐tau was increased in PD poor outcomes and correlated with white matter loss.ConclusionPD patients who progress to poor outcomes, show white matter macrostructural loss already at baseline, evident both from imaging and fluid biomarkers. Our findings highlight white matter imaging and plasma NFL as potential biomarkers of poor outcomes in Parkinson’s, informing the design of future clinical trials.
FDG‐PET patterns of LATE & co: disentangling the pathological heterogeneity of Tau‐negative amnestic syndromes
Authors: Silva‐Rodríguez Jesús , Moscoso Alexis , Labrador‐Espinosa Miguel , Sanchez‐Juan Pascual , Schöll Michael , Grothe Michel J. 
Published: January 2025

AbstractBackgroundAbout 20‐30% of clinically diagnosed AD dementia patients do not meet pathologic criteria for AD and this proportion is even higher in amnestic MCI. Among tau‐negative amnestic patients, limbic‐predominant age‐related TDP‐43 encephalopathy (LATE) has been described as a principal diagnostic alternative, especially at advanced age. LATE is characterized by a specific temporo‐limbic hypometabolic signature on FDG‐PET that may aid in differential diagnosis. However, little is known about the prevalence of this and other hypometabolism patterns among tau‐negative amnestic patients.Method360 cognitively impaired subjects (261 aMCI, 99 ADD) from the ADNI study who underwent 18F‐Flortaucipir Tau‐PET and 18F‐FDG‐PET were divided into Tau+ and Tau‐ using a visual read method. After screening Tau‐ patients for regional hypometabolism, LATE‐like hypometabolism patterns were identified using an automated pattern‐matching approach based on pathology‐specific FDG‐PET signatures derived from autopsy‐confirmed cases. FDG‐PET patterns among Tau‐ patients not fitting the LATE‐specific pattern were further examined using a hierarchical clustering approach. Group differences in age, hippocampal volume, and cognitive variables were studied.Result185 patients (162 aMCI, 23 ADD) were visually rated as Tau‐. Among those presenting any regional hypometabolism (N=95), 37 patients (39%) had a LATE‐like hypometabolism pattern (Figure 1). Hierarchical clustering of the remaining individuals revealed 3 different clusters suggestive of other neurodegenerative conditions, including a left‐lateralized temporal pole pattern reminiscent of semantic dementia (C1, 18%), a posterior‐occipital pattern characteristic for DLB (C2, 24%), and a more diffuse frontal pattern (C3, 19%). Compared to the other Tau‐ groups, the LATE‐like group was significantly older (p=0.02) and had more pronounced memory impairment (p=0.02) and hippocampal atrophy (p=0.03) (Figure 2). When initially screening Tau‐ patients for older age (>75y) and hippocampal atrophy, the majority of patients showed a LATE‐like FDG‐PET pattern (59%), but other patterns remained to be present in this population at smaller proportions (19%, 9%, and 13%, for C1, C2, and C3, respectively).ConclusionFDG‐PET provides valuable diagnostic information after a negative Tau‐PET scan. Among older tau‐negative patients with hippocampal atrophy, temporo‐limbic hypometabolism suggestive of LATE is the most prevalent pattern, but distinct hypometabolic patterns suggestive of other neurodegenerative conditions are also present in non‐negligible proportions.
Improved Disease Subtyping and Staging using Dual Biomarker Amyloid PET
Authors: Scott Catherine J , Coath William , Dickson John C , McQuaid Sarah J , Cash David M , Schott Jonathan M ,   the Insight 46 team
Published: January 2025

AbstractBackgroundData‐driven disease progression models of Alzheimer’s disease (AD) have identified subtypes in regional patterns of Aβ deposition using amyloid PET. In addition to Aβ accumulation, early frame measures of tracer delivery from amyloid PET are strongly correlated with blood flow. This work explores whether combining tracer delivery with amyloid binding measures can improve the subtype and stage characterisation over amyloid binding alone.MethodsData was included from participants in three studies: Insight46, a neuroimaging sub‐study of the Medical Research Council National Survey of Health and Development (n=405), AVID2, a study of patients with a differential diagnosis of Alzheimer’s Disease (n=15), and YOAD, a study of patients with clinically diagnosed young onset Alzheimer’s disease (n=15). All participants underwent a full dynamic 18F‐Florbetapir PET/MR scan. PET images were reconstructed from 0‐2 and 40‐50 minutes post injection to generate the tracer delivery and amyloid binding images, respectively. T1 parcellations were used to calculate regional standardised uptake value ratios (SUVRs) using a cerebellar grey matter reference region. Tracer delivery and amyloid binding SUVRs for 6 cortical regions were converted into z‐scores, assuming unimodal and bimodal distributions respectively, and modelled using subtype and stage inference (SuStaIn). Participants fitted as stage zero were excluded.ResultsFigure 1 shows the three subtypes found using regional amyloid binding SUVR. Subtype 2 accumulates amyloid first in the occipital lobe, while for subtypes 1 and 3, which accumulate Aβ first in the precuneus and insula respectively, this is the last area to show Aβ deposition. Two distinct subtypes were found with blood flow SUVR; frontal or precuneus first blood flow reduction, Figure 2. When both measures are combined, Figure 3, the data show that subtypes 1 and 2 exhibit amyloid accumulation before blood flow changes, whereas subtype 3 shows blood flow changes in all regions first. The two blood flow subtypes are spread across all three combined subtypes, suggesting that each amyloid subtype may have several blood flow subtypes.ConclusionsCombining blood flow and amyloid SUVR within SuStaIn reveals new patterns of disease progression compared to amyloid alone. This may help us better understand heterogeneity in aging and disease.
Small extracellular vesicles derived from plasma and the progression of Alzheimer's disease: insights into synaptic dysfunction and neuroinflammation
Authors: Singh Rishabh , RAI Sanskriti , Bharti Prahalad Singh , Chatterjee Prasun , Kumar Saroj 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) is a neurodegenerative disease characterized by Aβ plaques and neurofibrillary tangles, with chronic inflammation and synaptic dysfunction playing a significant contributor to disease progression and cognitive decline. Small extracellular vesicles (sEVs) are implicated in AD progression by facilitating the spread of pathological proteins and inflammatory cytokines. This study investigates the role of plasma‐derived sEVs (PsEVs) in synaptic dysfunction and neuroinflammation and their association with amyloid‐β and tau pathologies in AD progression.MethodA total of 45 [15 each in AD, mild cognitive impairment (MCI), and age‐matched healthy control (AMC)] subjects were recruited, and written informed consent was obtained (Ethics Ref No: IECPG‐670/25.08.2022). PsEVs were isolated using a chemical precipitation method, and their morphology was characterized by transmission electron microscopy. The size and concentration of PsEVs were determined using nanoparticle tracking analysis (NTA). Antibody‐based validation of PsEVs was done using CD63, CD81, TSG101, and L1CAM antibodies. Synaptic dysfunction and neuroinflammation were evaluated with Synaptophysin, Glial Fibrillary Acidic Protein (GFAP), IL‐1β, and TNF‐α antibodies. AD‐specific markers, Aβ‐42, and pTau were examined using Western blot and ELISA.ResultOur findings reveal higher concentrations of PsEVs in AD and MCI compared to AMC (p<0.0001). Aβ42 and pTau expression are significantly elevated in MCI (AUC = 0.9711, sn=100%, sp=80%, p<0.0001; AUC = 0.9689, sn=100%, sp=93.3%, p<0.0001, respectively); AD (AUC = 1, sn=100%, sp=100%, p= p<0.0001; AUC = 1, sn=100%, sp=100%, p<0.0001, respectively). Synaptophysin exhibits decreased expression from AMC to MCI to AD (p=0.047), whereas, IL‐1β, TNF‐α, and GFAP showed increased expression in MCI and AD compared to AMC (p=0.002, 0.0006, 0.0184, respectively). The increased levels of PsEVs correlate with synaptic dysfunction and neuroinflammation.ConclusionElevated PsEVs and upregulated Aβ42 and pTau expression show high diagnostic accuracy in AD. The decreasing synaptophysin expression and increased neuroinflammatory markers in AD and MCI patients suggest synaptic degeneration and neuroinflammation as potential indicators. These findings support the potential of sEV‐associated biomarkers for AD diagnosis and highlight synaptic dysfunction and neuroinflammation in disease progression.