bluuebunny/crossref_metadata_2025_split huggingface dataset:
The relationship between loneliness, depression and cognition: Evidence from smartphone‐based cross‐sectional and ecological momentary assessment
Authors: Fittipaldi Sol , Teckentrup Vanessa , Rosická Anna M , Lawlor Brian , Gillan Claire M 
Published: January 2025

AbstractBackgroundLoneliness is associated with lower cognitive function and may increase dementia risk. However, it is unclear if this effect is mediated by depression. Resolving this issue is important to design effective interventions to promote healthy aging. We adopted a complementary between‐ and within‐person approach, which allowed us to study cross‐sectional relationships as well as the dynamic interactions between loneliness, mood, and cognition in natural environments over time.MethodA total of 3,416 participants between 18 and 84 years (1,149 male; M age = 45.89±14.55) completed cross‐sectional self‐reported questionnaires of loneliness and depression alongside gamified assessments of memory, processing speed, cognitive flexibility, and planning through a smartphone app, Neureka. A subsample of 286 participants between 18 and 82 years (89 male; M age = 50.14±13.16) also underwent 8‐week ecological momentary assessment (EMA) reporting every 12 hours how lonely and down they felt. We measured cognition at these same timepoints using a recently validated passive measure of cognitive processing speed (digital questionnaire response time, DQRT). Multiple regressions and network analysis were performed to analyze cross‐sectional and EMA data, respectively.ResultLoneliness and lower cognitive function were associated cross‐sectionally (all ps<.001) except for planning (p = .08). Significant effects did not survive after controlling for depression (all ps>.06). Turning to EMA data, a contemporaneous network analysis showed that within‐person 12‐hour fluctuations in loneliness were related to fluctuations in mood (r = .35, p<.001). However, fluctuations in mood (but not loneliness) were linked to changes in DQRT (r = .13, p<.001). To understand the causal path, we conducted temporal network analysis, which revealed a bi‐directional relationship between loneliness and low mood (β = .08 and β = .06, respectively, both p<.001). In contrast, lower mood predicted slower DQRT 12‐hours later (β = .02, p = 0.03) and not the other way around (β = .005, p = 0.43). In older adults, loneliness and low mood were less related to one‐another, but the relationship between low mood and slower DQRT was stronger.ConclusionDepression symptoms mediate the effect of loneliness on cognition, both cross‐sectionally and when assessed within‐person. Older participants show less coupling between loneliness and low mood, but stronger coupling between low mood and slower cognition. Results have implications for differential interventions across the lifespan.
Comparison of Neuro‐cognitive profile of first degree relatives of Alzheimer’s Dementia with other major psychiatric illness
Authors: Singh Arushi , Dayal Pavithra , Bhattacharya Mahashweta , Joseph Mino Susan , Shaktan Alka , Kumar Pramod , Mallappagari Sreenivasulu , Narayana Shruthi , Muthukumaran Moorthy , R Alagarsami A , V Geetha C , Vinod Pratibha , Teotia Vasundhra , Syed Farooq Ali , Utagi Shridhar , Jayasankar Pavithra ,   ADBS Consortium
Published: January 2025

AbstractBackgroundFirst‐degree relatives of patients suffering from Alzheimer’s Dementia (AD) are at increased risk for developing dementia, yet the associations between family history of AD and cognitive dysfunction remain unclear. Our study aims to understand the intricate interplay between familial risk factors and neurocognitive functioning in AD FDRs versus FDRs of other major psychiatric illnesses by comparing the neuro‐ cognitive functions of unaffected first‐degree relatives (FDR) of patients with AD with unaffected FDR of other major psychiatric illness including Schizophrenia(SCHIZ), Substance use disorders(SUD), Obsessive compulsive disorders(OCD) and Bipolar disorder(BPAD). Subsequently, we also compare the Neuro cognitive performance of FDR’s of AD at baseline and after two years longitudinally.Method499 unaffected FDRs (AD‐ 100, SUD – 106, BPAD – 103, OCD ‐94, SCHIZ – 96) were recruited who underwent a comprehensive neuropsychogical battery. The battery includes Digit Span Forward (DSF), Auditory Verbal Learning Test (AVLT), N Back test (NBT) and Color trials (CT) to test for attention, verbal learning, working memory and executive function respectively. Assessments were done twice – at baseline and after two years for FDRs of AD (N = 32). Potential effect modifiers were controlled and generalised linear model was used to compare the neurocognitive functioning of different cohorts. Analysis was adjusted for years of education and gender. Wilcoxon Signed rank test was used to compare the baseline and follow up neurocognitive parameters of AD cohort.ResultFDRs of AD performed significantly worse in the domains of sustained attention, phonological memory and learning capacity as compared to the other cohorts. However, they performed significantly better in the tests covering verbal working memory, storage capacity and mental flexibility. No significant difference was found when neuro‐ cognitive parameters of FDRs of AD were compared at baseline and 2 years apart.ConclusionThe current findings provide initial evidence suggesting unsatisfactory performance of the AD FDRs in few domains as compared to others, however the stability of neurocognitive measures longitudinally over a two‐year period suggests the need for further exploration into the complex reciprocity of familial risk factors and cognitive outcomes in at‐risk populations.
Comprehensive aberrations of proteasome complex in Alzheimer’s disease
Authors: Jiang Shan 
Published: January 2025

AbstractBackgroundThe ubiquitin‐proteasome system (UPS) is the primary protein degrading mechanism in eukaryotes, and is essential for cellular homeostasis. Dysregulation of the UPS has been linked to neurodegeneration through two hallmarks, pathogenic protein aggregation and aberrant proteostasis. However, the molecular changes that alter proteasome functioning in AD are poorly understood. It is also not known whether proteasome dysfunction is a cause or consequence of neurodegeneration. We hypothesized that proteasomes in the AD brain are affected across various levels, including functional attributes, proteomic profile, and transcriptional regulation.MethodTo compare the activities of constitutive proteasome in AD patients samples with confounding factors (such as age and gender) matched controls, we used kinetics assays and native in‐gel activity assays. To compare the abundance changes of constitutive proteasome subunits in AD patients than in controls, we performed proteomics on gel‐isolated and purified proteasomes. To compare the RNA expression changes of constitutive proteasome subunits in AD patients than in controls, we interrogated RNA‐sequencing datasets from brain tissues and iPSC‐derived AD models.ResultWe observed that AD patient samples displayed a decrease in the rate of substrate degradation in kinetics assays compared to the controls. This reduction in activity was further confirmed through native in‐gel activity assays. Proteomics analysis revealed decreased amounts of the proteasome subunits comprising the 20S and 19S particles, accompanied by a considerable proteasomal burden of phosphorylated tau in AD samples. Interrogation of RNA‐seq and snRNA‐seq datasets showed replicable down‐regulation of constitutive proteasome and chaperone genes in brain tissue from early Braak stages. Further analysis attributed this decrease mainly to neurons. Interrogation of RNA‐seq datasets of iPSC‐derived neurons and cerebral organoids showed down‐regulation of constitutive proteasome genes in both Abeta and Tauopathy models than in isogenic controls. Concomitantly, glial cells showed increases in the expression of these genes across Braak stages. The down regulation of assembly chaperones were reflective of the decreased rate of substrate degradation in kinetics assays in AD patients.ConclusionTaken together, our results present not only the impairment of the constitutive proteasome in AD, especially in neurons, at RNA and protein expression level, but also the impairment of its functionality.
Cortical and ventricular brain retention of 11C‐PIB assessed by dynamic PET/CT and correlation with the Aβ1‐42/Aβ1‐40 ratio of amyloid peptides in the cerebral spinal fluid. Preliminary analysis in a healthy control volunteer population
Authors: Jiménez‐Bonilla Julio F , Gómez de la Fuente Francisco J , López‐García Sara , De Arcocha‐Torres María , Mendi‐Barcina Virginia J , Lage Carmen J , Martínez‐Dubarbie Francisco J , Quirce Remedios J 
Published: January 2025

AbstractBackgroundPreclinical Alzheimer’s disease may be linked to impaired cerebral amyloid clearance. We aim to obtain dynamic parameters of cortical and ventricular clearance of 11C‐PIB and compare them with CSF amyloid values in a population of healthy volunteers.MethodWe evaluated 8 healthy volunteers (4 men and mean age: 64.2 years) without neurological or cognitive alterations by a dynamic PET/CT for 45' starting 40 min after administration of 370 MBq of 11C‐PIB. Cortical VOIs of 1 cm3 were drawn in the cerebellar, frontal, temporal, occipital, parietal, and posterior periventricular regions and in the posterior lateral ventricle obtaining SUVmean values at 45', 60' and 75'. For each area, clearance velocity slopes were calculated in the 40‐60' and 60‐75' temporal period and SUVr values at 45, 60 and 75' considering cerebellum cortex as reference region. Its correlation with the values of the Aβ1‐42/Aβ1‐40 ratio assessed in the cerebral spinal fluid (CSF) was analyzed.ResultFour of the eight controls showed a pathological Aβ1‐42/Aβ1‐40 ratio. The Aβ1‐42/Aβ1‐40 ratio correlated with SUVr values in all of the studied regions in the total group (n = 8). The best correlations were obtained at 40' in parietal cortex (r = ‐0.821), temporal cortex (r = ‐0.816), posterior periventricular region (r = ‐0.683) and frontal cortex (r = ‐0.617). The posterior ventricular region only showed correlation at 75' (r = ‐0.523). Significant correlations were observed with the slope of the first section (45‐60') in frontal (r = 0.796), occipital (r = 0.793), parietal (r = 0.556), temporal (r = 0.426), and especially in the periventricular region. (r = 0.964). However, when these parameters are compared between the subgroup of subjects with pathological Aβ1‐42/Aβ1‐40 ratio (n = 4) and with normal ratio (n = 4), these correlations worsen in all regions in the pathological group and improves in the normal group, especially in the temporal region (‐0.149 vs ‐0.924) and in periventricular (0.158 vs 0.844) at 60' and 75' respectively.ConclusionIn our control healthy population, there is a correlation between the dynamic parameters of cerebral clearance of 11C‐PIB and the CSF amyloid value, with differences between subjects with a pathological and normal CSF amyloid values. Larger studies are necessary to determine the value of these findings in the preclinical Alzheimer’s disease.
Memory consolidation circuits and the glymphatic system in sleep
Authors: Vasciaveo Valeria , Casile Antonino , Ferro Alma , Morello Giulia , Tamagno Elena , Guglielmotto Michela 
Published: January 2025

AbstractBackgroundUnderstanding the neuronal mechanisms of learning and memory is one of the major goals in neurophysiology and neuropsychology. Disorders related to memory consolidation are often the consequences of dynamic plasticity changes, which may lead to a reduction in spine number and density, impairing neural networks. Sleep is one of the major physiological prerequisites for memory consolidation, especially during NREM sleepwhen glymphatic system clearance takes place, too.MethodWe recently validated a protocol of SF, which mimics more closely than sleep deprivation a typical impaired sleep behavior in psychiatric and neurological disorders. In this project we want to extend our previously study on the WT mouse model to unravel the mechanisms responsible for glymphatic system disruption and its correlation with memory circuitry malfunction. We will investigate which neuronal population is affected by SF by considering three regions involved in memory consolidation: hippocampus, prefrontal cortex, and the amygdala. Additionally, chronic EEG‐recordings during natural sleep/wake behavior will be performed in freely behaving mice, and all temporal and spectral parameters involved in memory consolidation will be analyzed. As for glymphatic system, we will study through MRI the possible glymphatic flux alteration with Gd‐DTPA as MRI tracer in the three interested regions.ResultIn addition to the proof that SF accelerates Alzheimer’s Disease (AD) pathology, we observed interesting modifications of cognitive abilities in wild type (WT) mice during behavioral tests. Moreover, in both the 5xFAD mouse model for AD and the corresponding WT mice after SF, we detected an aquaporin‐4 (AQP4) modulation, which is known to play a role in neurological diseases by disrupting the glymphatic system flux. Our final goal will be to understand which is the specific circuit that leads to memory impairment, and what happens to the neurons involved during SF: degeneration, excitotoxicity or astrocyte communication failure. A future study could be to test the ability to restore cognitive functions by letting the mice free to sleep one month after SF protocol.ConclusionThanks to this project, we go back to the bench to find the correlation never observed before between memory circuit deficits and the possible involvement of glymphatic system during SF.
Machine Learning Ensemble Prediction of Clinical Dementia Rating Using MultiModal Data
Authors: Gupta Danika , Ghanta Sindhu 
Published: January 2025

AbstractBackgroundEarly detection and accurate forecasting of AD progression are crucial for timely intervention and management. This study leverages multi‐modal data, including MRI scans, brain volumetrics, and clinical notes, utilizing Machine Learning (ML), Deep Learning (DL) and a range of ensemble methods to enhance the forecasting accuracy of Alzheimer’s disease.MethodWe utilize the OASIS‐3 longitudinal dataset, tracking 1,098 patients over 30 years. From OASIS‐3, we combined three modalities ‐ MRI scans, Freesurfer brain volumetrics, and Clinical Data from the Alzheimer’s Disease Research Center (ADRC). We use Convolutional Neural Networks (CNNs), specifically MobileNetV2, ResNet101, ResNet152 and ResNet200 for MRIs and Machine Learning (ML) techniques (Random Forest and K Nearest Neighbors) for Freesurfer featurized brain volumetrics and clinical data. Individual models were tuned for each modality, with the best models combined via ensembles to predict each patient’s future Clinical Dementia Rating (CDR). Ensembles evaluated included: aggressive (any modality predicting positive), conservative (all modalities predicting positive), conditional ensembles (majority voting and ADRC or both MRI/Freesurfer), and custom machine learning models built to integrate the modality predictions based on the confidence values returned from the MRI model and the predictions of other models. The figure below shows our experimental pipeline.ResultThe study achieved >95% accuracy in predicting future CDR. Ensembles notably reduced harmful False Negatives by 2x‐15x, compared to individual modalities, while incurring nominal increases in False Positives. The machine learning trained ensemble demonstrated improved accuracy over the best individual modality predictions The results highlight the potential of multi‐modal AI ensemble methods in improving the accuracy of early AD detection and prognosis. The figures below show comparative accuracy and false positives/false negatives rates for each ensemble as compared to the individual modalities.ConclusionThis work demonstrates the potential efficacy of multi‐modal data integration via ensemble learning in forecasting Alzheimer’s disease, significantly outperforming single‐modality methods. It underscores the importance of leveraging diverse data sources and advanced analytical techniques for early diagnosis and intervention in Alzheimer’s care, paving the way for future research to explore additional modalities and methods for even greater accuracy and clinical utility.
Manipulation of Microglial Sialylation in Alzheimer’s Disease Mouse Models of Amyloid and Tau Pathology with Neuraminidase 1
Authors: Fastenau Caitlyn , Smith Sabrina , Bunce Madison , Bieniek Kevin F. , Hopp Sarah C. 
Published: January 2025

AbstractBackgroundGlycosylation is the most common post‐translational modification in the brain. Aberrant glycosylation patterns are present in cerebrospinal fluid and brain tissue from Alzheimer’s disease (AD) patients. Specifically, dysregulation of a particular form of terminal glycoconjugate modification, sialylation, has been identified in AD. Terminal sialic acid modifications help facilitate important functions including cell‐to‐cell interaction, cell migration, and immune regulation. We have identified significantly increased α‐2,6 N‐linked sialylation of microglia proximal to amyloid β (Aβ) plaque pathology in post‐mortem human AD brains as well as of microglia during tauopathy. Yet we have limited understanding of the role of sialic acid residues on microglia in AD and other tauopathies.MethodsThis study aims to determine if manipulation of α‐2,6 N‐sialylation in early stages of AD pathology can impact microglia interaction with the pathological proteins Aβ and tau. The present study investigates the biological consequence of administering neuraminidase 1, the cleavage enzyme for terminal α‐2,6 sialic acid from glycoconjugates in models of amyloid and tau pathology. We have validated Sambucus Nigra (SNA) plant‐derived lectin for labeling α‐2,6 N‐linked sialic acid residues in tissue and cells.ResultsOur preliminary data demonstrate that incubation with exogenous neuraminidase 1 enzyme leads to the cleavage of terminal α‐2,6 sialic acid in BV2 murine microglia and on brain sections from post‐mortem human AD tissue. To test how neuraminidase 1 mediated α‐2,6 sialic acid cleavage affects microglial interactions with Aβ and tau, 4‐month‐old male and female 5XFAD, PS19, and wildtype mice received a single stereotaxic injection of neuraminidase 1 enzyme into the right cortex. 24 hours following injection, the mice were sacrificed, and their brains removed for histological analysis. We expect to visualize a significant decrease in α‐2,6 sialic acid levels in 5XFAD and PS19 mice following injection of neuraminidase 1 due to the increased likelihood of α‐2,6 cleavage. Additionally, we hypothesize that removal of terminal α‐2,6 sialic acids will increase microglia density near plaques and phosphorylated tau due to increased microglia phagocytic activity.ConclusionThese findings will advance the field of microglia glycobiology and provide further evidence for the functional role of sialylation for microglia response to both amyloid and tau pathology.
Effects of Mediterranean vs. Western Diets on the Cerebral Cortical Pre‐Synaptic Proteome in Nonhuman Primates
Authors: Berson Eloise , Frye Brett M. , Perna Amalia , Phongpreecha Thanaphong , Shome Sayane , Clarke Geetha , Negrey Jacob D. , Aghaeepour Nima , Montine Thomas J. , Craft Suzanne , Register Thomas C. , Shively Carol A. 
Published: January 2025

AbstractBackgroundWestern and Mediterranean diets differentially affect cerebral cortical gene expression, brain structure, and socioemotional behavior in middle‐aged female nonhuman primates (NHP) (Macaca fascicularis). In this study, we investigate the effect of diet on brain molecular composition.MethodUsing a machine learning approach, we quantified the impact of these diets on the presynaptic proteome in the lateral temporal cortex determined by synaptometry by time of flight (SynTOF) mass spectrometry and examined associations between the proteome, transcriptome, and an array of multisystem phenotypes. For this, we consider NHP fed with Mediterranean (n = 17) or Western (n = 19) diet for 31 months before brain retrieval (see study overview in Figure 1).ResultDiet has a significant effect on presynaptic proteins (AUC = 0.86, Pvalue = 0.0002) (Figure 2A). We identified six presynaptic proteins (DAT, Aβ42, calreticulin, LC3B, K48‐Ubiquitin, SLC6A8) elevated in the presynaptic proteome bythe Mediterranean compared to the Western diet (p<0.05) (Figure 2B). Interestingly, we demonstrated that transcriptomic data from adjacent cortex predict all the SynTOF markers (pvalue <0.05) (Figure 2C). We found that the SPATA22 transcript was positively correlated with five SynTOF markers (LRRK2, TMEM230, GAMT and 3NT, Aβ40) (all p<0.05). Transcription Factor AP‐2 Gamma transcript (TFAP2C) was positively correlated with SynTOF markers pTau, CD47, and GAD65. Together, the multi‐system phenotypes significantly predicted 26 SynTOF markers, the strongest relationships were between GFAP and brain volumetrics (Figure 2D). Numerous SynTOF markers correlated with hepatosteatosis, suggesting the relationships between liver health and presynapses composition. SynTOF markers were also associated with behavioral and physiological measures of social‐environmental stress.ConclusionTogether these observations demonstrate that diet composition drives temporal presynaptic protein composition, that transcription profiles strongly predict the presynaptic proteomic profile, and that presynaptic proteins are closely associated with peripheral metabolism, stress responsivity, and socioemotional behavior. These data demonstrate the impact of diet composition on brain molecular composition.