bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial neural network application to characterize patients with Alzheimer ’s disease and Mild Cognitive Impairment from Cognitive Normal.
Authors: Cheung Eva YW , Mak Henry , Chiu Patrick Ka‐Chun , Shea Yat Fung , Chau Anson CM , Kwan Joseph SK 
Published: December 2023

AbstractBackgroundThis study aimed to build an artificial neural network model to differentiate patients with various diagnosis, including Alzheimer’s disease (AD), mild cognitive impairment (MCI) from cognitive normal (CN) using structural MRI images.MethodsMRI T1 image of 580 participants (97 AD, 291 MCI and 192 CN) were downloaded from ADNI, which is a publicly available database. The MPRAGE T1W MRI were pre‐processed by SPM software, including re‐orientation, segmentation and normalized to the whole brain mask template. 3D slicer software with pyradiomics extension were employed to extract the 107 radiomics features within the brain. Also, 45 brain regional volumes were segmented and calculated using Freesurfer. The AD, MCI and CN groups were divided into a training group and a validation group based on 70:30 ratio. Ten‐fold cross validation were performed to minimize the chance of overfitting. Three feed forward neural network model were built using the neural network toolbox of MATLAB, with one model using 107 radiomics alone (R model), one with 45 brain regional volumes (V Model) and the last one with both radiomics and volumes features (R+V model).ResultsThe receiver operating characteristics (ROC) curves showed that the V model and the R+V model achieved excellent accuracy, with 96.6% and 94.8% respectively. The area under the curve (AUC) was 99.6% and 97.9% respectively.ConclusionThe neural network model can accurately differentiate AD, MCI from HC. The application is practical and can be fully automatic, which can be used for dementia screening.
Sleep problems predict decline in attention over time in older adults with essential tremor
Authors: Tsapanou Angeliki , Ghanem Ali , Nguyen Neil , Chapman Silvia , Dargie Samantha A. , Huey Edward D. , Louis Elan D. , Cosentino Stephanie 
Published: December 2023

AbstractBackgroundThere is growing evidence that patients with Essential Tremor (ET) are at higher risk for Mild Cognitive Impairment (MCI) and dementia than controls. ET is a quite heterogeneous disorder, including not only motor symptoms, but sleep deficits as some of its non‐motor characteristics. The aim of the current research was to investigate the extent to which sleep problems predict cognitive change over time in patients with ET.MethodTwo hundred and one patients enrolled in a longitudinal, observational study of cognitive function in ET (COGNET) were included in the current analyses. Sleep was examined used the Pittsburg Sleep Quality Index (PSQI). Five cognitive domains were extracted from an extensive neuropsychological assessment: memory, executive function, attention, language, and visuo‐spatial ability. Generalized estimated equations were used to examine the association between sleep problems at baseline and cognitive changes over four‐intervals follow‐up spanning of 4 years.ResultOur sample was mostly women (60%), with 78 mean years of age, and 16 mean education years. Habitual sleep efficiency (dividing the amount of time spent asleep divided by the total amount of time in bed) was significantly associated with attention performance longitudinally, so that poorer efficiency was associated with more rapid decline in attentional abilities over the years. No other sleep problem was associated with change in any cognitive domain.ConclusionPeople with ET have increased chance for decline in attentional abilities when having sleep problems and more specifically, less habitual sleep efficiency. Early detection of, and intervention for, difficulty with sleep efficiency may protect against aspects of cognitive decline in people with ET.
The effect of basal forebrain atrophy on the rate of cognitive decline in older adults without dementia
Authors: Xia Ying , Maruff Paul , Dore Vincent , Bourgeat Pierrick , Villemagne Victor L , Rowe Christopher C , Coulson Elizabeth J , Fripp Jurgen 
Published: December 2023

AbstractBackgroundIn early Alzheimer’s disease, degeneration of the cholinergic basal forebrain (BF) system correlates with amyloid‐β (Aβ) burden and contributes to cognitive decline, particularly in the domains of memory and attention processing. However, the nature of these interrelationships remains unclear. This study investigated the effects of Aβ and BF atrophy on cognitive decline of memory and attention in older individuals without dementia.MethodThe BF volumes were quantified from MRIs of 577 participants (73.3 ± 6.3 years old, 55.1% female) from the Australian Imaging, Biomarker and Lifestyle (AIBL) study, including 495 cognitive unimpaired (CU) individuals and 82 with mild cognitive impairment. Aβ‐PET assessment was completed at baseline, and Aβ+ was determined using Centiloid > 20. The cognitive decline in memory and attention was assessed using the relevant AIBL composite scores, over 6.0 ± 3.1 years (up to 13 years). BF z‐scores were calculated using the mean and standard deviation of BF volumes in the CU Aβ‐ group, and z‐scores < ‐1 was classified as BF atrophy (BF+). Participants were then grouped according to the Aβ status and presence of BF atrophy (Table 1). Linear mixed‐effects models assessed differences in rates of cognitive decline in memory and attention across different classification groups. Magnitudes of differences in slopes of decline were expressed using Cohen’s d.
ResultIn Aβ‐ individuals, BF atrophy was associated with faster rates of cognitive decline in both memory and attention, with medium effect sizes (Table 2 and Figure 1). In re‐analyses restricted to Aβ+ subjects, BF atrophy remained related to memory decline (p = 0.035, d = 0.33) but not to decline in attention. Compared to Aβ‐ subjects without BF atrophy, both Aβ+ groups had faster decline in both domains, with large effect sizes (all d > 0.8).ConclusionThese findings indicate that the effects of Aβ and BF atrophy are additive in terms of their influence on the rate of decline in memory. However, BF atrophy influences the rate of decline in attention only in the absence of abnormal Aβ burden.
Rapid, scalable assay of amylin‐β amyloid co‐aggregation in brain tissue and blood
Authors: Kotiya Deepak , Leibold Noah S , Verma Nirmal , Jicha Gregory , Goldstein Larry B , Despa Florin 
Published: December 2023

AbstractBackgroundIslet amyloid polypeptide (amylin) secreted from the pancreas crosses the blood brain barrier into the brain parenchyma and interacts with Alzheimer’s disease (AD) associated cerebral β‐amyloid (Aβ) plaques, which are found in both sporadic and early‐onset familial AD. Immunoprecipitated amylin from human AD brain tissue analyzed by Western blot using an anti‐Aβ antibody detects Aβ immunoreactivity, which indicates that the amylin and Aβ peptides aggregate in the brains of humans with AD, forming amylin‐Aβ hetero‐oligomers that are sodium dodecyl sulfate (SDS)‐soluble. We report the development of an ELISA to detect amylin‐Aβ hetero‐oligomers in brain tissue and blood.MethodThe amylin‐Aβ ELISA relies on a monoclonal anti‐Aβ middomain antibody (detection) and a polyclonal anti‐amylin antibody (capture) designed to recognize an epitope that is distinct from the high affinity amylin‐Aβ binding sites. We screened temporal cortex homogenates obtained from 13 cognitively unaffected (CU) humans and 47 persons with sporadic AD (sAD) for amylin‐Aβ hetero‐oligomers. Individuals were of similar age (sAD, 85.65 ± 7.04 years vs. CU, 87.22 ± 7.30 years; p = 0.482). Brain tissue amylin and Aβ concentrations were measured using conventional ELISAs. Amylin‐Aβ oligomer levels were also measured in whole blood lysates from APP/PS1 rats and APP/PS1 rats expressing amyloid forming amylin in the pancreas (APP/PS1/HIP rats; all rats age‐matched, 16‐months).ResultThe brains of most AD patients had detectable amylin‐Aβ hetero‐oligomer concentrations, whereas about half of those CU had brain tissue amylin‐Aβ hetero‐oligomers below the detection limit (0.02 ng/mg total protein; one‐way ANOVA, P < 0.0001). Amylin‐Aβ hetero‐oligomers were more common in brains with frequent neuritic plaques (CERAD C). Higher brain tissue amylin‐Aβ hetero‐oligomer levels were associated with greater brain tissue amylin concentrations (r = 0.38; P < 0.05). There was no correlation between cerebral amylin‐Aβ hetero‐oligomer accumulation and age in the analysis of samples from sAD and CU groups (r = ‐0.22; P > 0.05). Levels of amylin‐Aβ hetero‐oligomers in whole blood lysates obtained from APP/PS1/HIP rats were higher than those from APP/PS1 littermates (P < 0.05).ConclusionStudies are needed to determine the extent to which amylin‐Aβ hetero‐oligomerization can be detected in human blood.
Analysis of blood and cerebrospinal fluid flows in preclinical AD. Preliminary results from ALFA+ cohort
Authors: Falcon Carles , Mansilla‐Usero Celia , Morante‐Alberti Carla , Blennow Kaj , Zetterberg Henrik , Kollmorgen Gwendlyn , Carboni Margherita , Grau‐Rivera Oriol , Minguillon Carolina , Suarez‐Calvet Marc , Gispert Juan Domingo ,   ALFA study
Published: December 2023

AbstractBackgroundIntracranial cerebrospinal fluid (CSF) motion is driven by the wave pressure associated with arterial blood flow into the skull (Figure 1). Since one of the roles of CSF is the clearance of neural waste, it was investigated the relationship between blood and CSF flow parameters (as measured by magnetic resonance imaging [MRI]) and CSF Ab42/40 and ptau‐181 levels in the preclinical stages of Alzheimer’s disease (AD).MethodThree hundred sixty‐five subjects from the ALFA+ cohort underwent two MRI phase‐encoded velocity sequences (30 frames per cardiac cycle) to measure (1) blood flow in carotids below carotid bifurcation and (2) CSF flow through foramen magnum. The scanner’s Q‐flow tool was used to quantify stroke volume (volume per cardiac cycle), percent of vessel’s area increase and flow velocity increase, as a proxy for vessel elasticity and flow pulsatility, respectively. Associations between flow parameters and age, CSF Ab42/40 and ptau‐181 concentrations were sought, correcting for sex, age and time between lumbar puncture and MRI (P<0.05).ResultOne hundred thirty‐two subjects were excluded because the scanning session was not completed, showed missing or outlier values or did not pass quality control (artifacts or unbalanced outwards‐inwards CSF flow measures), resulting in a final sample size of 233 participants (mean age [SD]: 60.8±4.5 years; male: n = 73 [31.3%]). Figure 2 shows the plots with the most significant relationships between flow parameters, CSF biomarkers, CSF volume and age. It was observed that a higher stroke volume in CSF flow was associated with lower Ab42/40 and higher ptau‐181 concentrations in CSF.ConclusionThese results indicate that, despite the role of CSF in waste clearance, the accumulation of amyloid and tau in the brain might be neither caused nor enhanced by a global deficit in CSF circulation. We hypothesize that a higher volume of CSF associated with age and high ptau‐181 levels and/or the loss of parenchyma elasticity might reduce the global intracranial compressibility, resulting in an enhanced response to pressure increase due to blood flow into the skull, which could explain the observed increase in CSF flow associated with the early stages of AD.
Dysregulated bioenergetics pathways in the Alzheimer’s mouse brain
Authors: Raju Raghavan Pillai , CAi Lun P , Tyagi Alpna , Pugazhenthi Subbiah 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is the most common cause of dementia in the aging population. The pathological characteristics include extracellular senile plaques and intracellular neurofibrillary tangles. In addition, mitochondrial dysfunction, oxidative stress, and neuroinflammation contribute to AD pathogenesis.MethodRNA‐seq analysis was done to compare the transcriptome in the brain of 5XFAD mice to the WT brain.ResultThere was significant changes in the expression of genes related to pathways such as, oxidation‐reduction, oxidative phosphorylation, innate immune response, ribosomal protein synthesis, and ubiquitin proteosome system. The most striking feature was the alteration of the oxidation‐reduction process with changes in the expression of a large number of mitochondrial genes. We also observed an upregulation of several immune response genes. Gene interaction network of oxidation‐reduction related genes further confirmed a tight “Gordian knot” of mitochondrial genes. Furthermore, gene interaction analysis of all the 1288 genes showed at least three distinct interaction clusters, with the predominant one relating to cellular energeticsConclusionWe identified 1288 genes distinctly different in the 5XFAD brain compared to the WT brain and found cellular energetics to be the most distinct gene cluster in the AD mouse brain compared to the WT.
Predictive biomarkers for cognitive decline in patients with idiopathic REM sleep behavior disorder
Authors: Wang Ruihan , Cai Hanlin , Gao Hui , Qin Linyuan , Yang Feng , Chen Qin 
Published: December 2023

AbstractBackgroundIdiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is regarded as an early stage of the synucleinopathy, which may precede the onset of cognitive impairment or parkinsonism. Biomarkers for cognitive decline in patients with iRBD would be helpful to early intervention. Our aim was to identify biomarkers for predicting the cognitive deterioration risk in patients with iRBD.MethodWe involved patients with polysomnographically confirmed iRBD who has at least 2 visits from the Parkinson’s Progression Markers Initiative (PPMI) database. Progression of cognitive state to MCI or dementia during follow‐up was defined as the endpoint. Baseline clinical features, biofluid and genetic biomarkers were compared between iRBD with or without progression of cognitive decline. Predictive biomarkers were identified with Kaplan‐Meier and Cox proportional hazards analysis, adjusting for age, sex, and years of education.ResultA total of 38 patients with iRBD were enrolled (average age: 69.53 ± 5.54, male: 84.21%) . During follow‐up (median 5.45, IQR 4.08‐7.1 years), Progression of cognitive decline occurred in 57.89% (22/38) patients with iRBD. Baseline clinical features including hallucinations and psychosis (HR = 13.783, p = 0.022) and higher confidence abnormalities consistent with a neurodegenerative parkinsonian syndrome (HR = 2.258, p = 0.017) increased the risk. Patients with a lower loge‐transformed the ratio of Aβ 1‐42 in cerebrospinal fluid (CSF) to ΝfL in serum (Aβ/NfL, HR = 0.375, p = 0.044) and t‐tau in CSF to ΝfL in serum (t‐tau/NfL, HR = 0.277, p = 0.044) had a higher risk of cognitive decline. Genetic biomarkers consisting of GBA rs75548401(HR = 13.783, p = 0.022), STK39 rs1955337 (HR = 11.882, p<0.001), and COMT rs4680 (HR = 2.376, p = 0.021) variants increased the risk, while MCCC1 rs12637471 (HR = 0.408, p = 0.024), COMT rs165656 (HR = 0.451, p = 0.033), COMT rs4633 (HR = 0.421, p = 0.021), and COMT rs165599 (HR = 0.427, p = 0.023) variants decreased the risk. A model combining age and variables with p<0.05 exhibited above presented the best predictive performance, with an AUC of 0.963 (95% CI 0.896–1.000, p<0.001).ConclusionOur findings provide potential biomarkers to evaluate the risk of progression to MCI or dementia in patients with iRBD. Combination of clinical features, biofluid and genetic biomarkers performs optimal predicting accuracy.
Isoform‐ and cellular metabolism‐specific apolipoprotein E (ApoE) lipidation in astrocytes
Authors: Lindner Karina 
Published: December 2023

AbstractBackgroundApolipoprotein E (ApoE) is the major lipoprotein in the brain and connects the metabolic interactions between astrocytes and neurons. The E4 variant (APOE4) affects this function and represents a genetic predisposition for Alzheimer’s disease (AD), but the molecular mechanisms remain unclear.MethodWe have integrated cell‐free in vitro assays, a new cell system based on isogenic iPSC‐derived astrocytes and lipidomics to study ApoE lipidation, and how this is affected by genetic polymorphism.ResultWe show that ApoE produces different types of lipoprotein particles via distinct lipidation pathways. ApoE forms high‐density lipoprotein (HDL)‐like, cholesterol‐rich particles via the ATP‐binding cassette transporter 1 (ABCA1), a mechanism largely unaffected by ApoE polymorphism. Alternatively, ectopic accumulation of fat in astrocytes, a stress‐associated condition, redirects ApoE toward the assembly and secretion of triacylglycerol‐rich lipoprotein particles, a process boosted by the APOE4 variant. We demonstrate in vitro that ApoE can detect triacylglycerol in membranes and spontaneously assemble lipoprotein particles (10‐20 nm) rich in unsaturated triacylglycerol, and that APOE4 has remarkable properties behaving as a strong triacylglycerol binder. Fatty APOE4 astrocytes have reduced ability to clear toxic fatty acids from the extracellular milieu, because APOE4 reroutes them back to secretion.ConclusionThe molecular mechanisms discussed here provide a new model explaining the interaction between genetic polymorphism and environmental factors, such as dyslipidaemia or aging, in the aetiology of late‐onset AD, as well as inspire possible new strategies for therapeutic intervention.Graphical abstract from https://doi.org/10.1016/j.celrep.2022.110435.
Targeted proteomic profiling in cerebrospinal fluid and plasma identifies biomarkers for Alzheimer’s disease
Authors: Ye Yuanqing , Saxena Satya , Michael Nagle , Stomrud Erik , Janelidze Shorena , Palmqvist Sebastian , Mattsson‐Carlgren Niklas , Devanarayan Viswanath , Sachdev Pallavi , Hansson Oskar 
Published: December 2023

AbstractBackgroundIn the ATN (Amyloid b, Tau, Neurodegeneration) biomarker research framework proposed for Alzheimer’s Disease (AD), a critical need is the identification of robust markers of neurodegeneration (“N”) for more accurate disease state diagnosis and prognosis. Targeted proteomic profiling can be highly valuable for identifying such markers.MethodWe reanalyzed Olink™ data from 270 CSF and plasma samples from the Swedish BioFINDER study (Whelan et al. 2019) using additional normalization and adjustments to reduce technical variation and inter‐subject heterogeneity. These include 1) plate‐level quantile normalization, 2) adjustment for age, gender, BMI, and anti‐inflammatory drugs, and 3) first principal component as a covariate to account for heterogeneity in control subjects from different cohorts. Our analysis cohort included 140 Aβ+ cognitively normal (ApCN), 65 Aβ+ mild cognitive impairment (ApMCI), and 138 Aβ + AD (ApAD) subjects. Proteins with false discovery rate < 5% and Cohen’s D ≥ 0.5 were considered statistically significant. Multiscale Embedded Gene Co‐expression Network Analysis (MEGENA) was used to identify co‐expressing network modules and hub proteins.ResultOur analysis identified significant alterations of 65 CSF and 94 plasma proteins comparing different pairs of ApAD, ApMCI and ApCN groups. AUC ranged from 61% to 86% for differentiating ApAD vs ApCN. MMP‐10 was found to be significantly altered in all CSF comparisons, but not in plasma. We identified many significantly altered proteins not reported by Whelan et al (2019), including TFF3, EFNA4, and CTSC that are known to be associated with neurodegeneration or neuroinflammation. MEGENA analysis identified 10 network modules for CSF and 17 network modules for plasma. The most significant CSF modules were represented by IGFB‐2 and PLC hub proteins and enriched for positive regulation of cellular function. The most significant plasma modules were represented by SIRT2 and LAT and enriched for cellular response to stimulus.ConclusionTargeted proteomic profiling of matching CSF and plasma samples along with co‐expression network analysis can yield additional markers in the ATN framework for early detection and screening for AD.
Odor Identification Moderates Relationship Between Cognitive Training and Cognitive Decline
Authors: Deehan Emily G. , Phillips Julia , Qian Min , Doraiswamy P. Murali , Goldberg Terry E. , Devanand D. P. 
Published: December 2023

AbstractBackgroundOlfactory deficits have been shown to predict cognitive decline. Recent findings suggest that computerized cognitive training may reduce the risk of cognitive decline and dementia. However, the impact of olfaction deficits on cognitive training outcomes is unclear. In this study, we investigate how lower odor identification may moderate the relationship between treatment assignment (cognitive games versus crossword puzzles) and cognitive outcomes in older adults diagnosed with mild cognitive impairment (MCI).MethodData were drawn from the Cognitive Training and Neuroplasticity in Mild Cognitive Impairment (COG‐IT) Trial (NCT03205709). In this two‐site, single‐blinded, 78‐week trial, participants with MCI — stratified by age, severity (early/late MCI), and site — were randomly assigned to 12 weeks of intensive, home‐based, Computerized Cognitive Training (CCT) or computerized Crossword Puzzle Training (CPT), followed by six booster sessions. 106 patients received neuropsychological and olfactory assessments through the 11‐item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS‐Cog) and the 40‐item University of Pennsylvania Smell Identification Test (UPSIT). In linear mixed‐model analyses, the effect of baseline UPSIT score on change in the ADAS‐Cog score from baseline over 78 weeks by treatment group, adjusted for age and baseline ADAS‐Cog, was assessed.ResultAmong 106 older adults (n = 50 [games]; n = 56 [crosswords]), higher baseline UPSIT scores predicted improved ADAS‐Cog performance at week 78 for all patients randomized to CPT (beta coefficient, 0.15, 95% confidence interval [CI], 0.02 to 0.27; P = 0.02). Greater baseline UPSIT score predicted improved ADAS‐Cog performance at weeks 12, 52, and 78 for early MCI patients randomized to CPT (Week 12: beta coef. 0.26, 95% CI 0.09 to 0.43; Week 52: beta coef. 0.23, 95% CI 0.05 to 0.40; Week 78: beta coef. 0.22, 95% CI 0.05 to 0.39). This was not shown in early MCI patients randomized to CCT. Higher baseline UPSIT predicted significant improvement in ADAS‐Cog for late MCI patients randomized to CCT at week 78 (beta coefficient, 0.24, 95% CI 0.05 to 0.42; P = 0.01).ConclusionOur results are consistent with literature, suggesting that impaired olfaction moderates memory decline. Our preliminary results show that individuals with less impaired olfaction may experience greater cognitive improvement following cognitive training.