bluuebunny/crossref_metadata_2025_split huggingface dataset:
Correlation between MRI Visual Atrophy Scales and cognitive performance in a Brazilian Outpatient Memory Clinic
Authors: Real Ana Paula Bernardes , Engelmann Gabriela Tomé Oliveira , Moro Giovanna Correia Pereira , Vieira Carolina Portugal , Dias Bruna Fugêncio , Queiroz Thaise Vallesca , Kimoto Leonardo Ryuiti , Melgaço Marcilene Anacleto , de Souza Aline Siqueira , Burgos Ivonne Carolina Bolaños , de Paula Jonas Jardim , Romano‐Silva Marco Aurelio , Bicalho Maria Aparecida Camargos , Viana Bernardo M 
Published: January 2025

AbstractBackgroundBrain imaging plays an important role in Alzheimer’s disease (AD) assessment. Combined with cognitive assessment, MRI helps to exclude other brain disorders and to support AD diagnosis based on the atrophy of specific regions. Visual atrophy scales are useful in clinical practice, especially in low‐income areas where access to biomarkers and automated volumetric analysis are limited.ObjectiveTo assess the correlation between different MRI Visual Atrophy Scales and cognitive performance in a cohort of a Memory Clinic of Brazilian older adults with heterogeneous educational background.MethodsData were collected from the Cog‐Aging Cohort, including participants with normal cognition, mild cognitive impairment, and Alzheimer’s Disease Dementia. Thirty‐five MRI scans were analyzed using the following visual scales: Global Cortical Atrophy (GCA), the Entorhinal Cortical Atrophy Score (ERICA), the Medial Temporal Lobe Atrophy (MTA) scale, and the KOEDAM scale, Fazekas scale for white matter lesions. They were rated by consensus of two memory specialists. These scores were correlated to sociodemographics, to Dementia Rating Scale (DRS), Mini‐Examination of State Examination (MMSE), 5‐minute recall of the CERAD word list, and immediate episodic memory recall (A6) from the of the Rey Auditory Verbal Learning Test (RAVLT).ResultsThe mean age of participants was 77.15 years (SD: 74‐80), and the median education was 6.53 years (IQ: 4.48‐8.59). No difference in sex was found. The ERICA scale showed a statistically significant moderate correlation with RAVLT (Rho = ‐.454, p = .017) and a weak correlation with the 5‐minute recall from CERAD’s Word List (Rho = ‐.381, p = .026). No other visual scale demonstrated statistically significant correlations with any cognitive tests. We found strong correlations between the ERICA and MTA scores (Rho = ‐.616, p<.001), and weak but significant correlations between ERICA and KOEDAM (Rho = ‐.390, p = .023).ConclusionA higher ERICA score correlated to a worst memory performance. These data corroborate other studies addressing the importance of the use of this scale in research and clinical practice.
Blood Gene Expression Network Expression Strongly Relates to Brain Amyloid Burden
Authors: Janve Vaibhav A , Seto Mabel , Sperling Reisa A , Aisen Paul S. , Rissman Robert A. , Koran Mary Ellen I. , Dumitrescu Logan C. , Buckley Rachel F , Hohman Timothy J. ,   A4 and LEARN Study Group
Published: January 2025

AbstractBackgroundAmyloid deposition occurs during the preclinical stages of Alzheimer’s disease (AD) a decade or more before clinical symptoms emerge. We leveraged blood transcriptomics and positron emission tomography (PET) measures of amyloidosis to identify cell types and gene networks in the blood that relate to amyloid burden in the brain.MethodWhole blood RNA sequencing and amyloid PET data were leveraged from 1771 participants (62% females, mean age 71, 32% amyloid+) in the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) Study. RNAseq data were aligned with STAR, sorted, counted using Subread, and batch normalized with voom. 19,945 genes were available for analysis following quality control. Cell fractions were quantified using CIBERSORTx (LM22 used as reference) and WGCNA was used to calculate gene co‐expression modules (soft threshold = 12). Amyloid PET data were acquired using 18F‐florbetapir 50‐70 minutes post injection and a global cortical standardized uptake value ratio (SUVR) was quantified (whole cerebellum as reference). Linear regression related gene module expression or cell fraction to mean SUVR, covarying for age, sex, education, APOE ε2 and ε4 status. Correction for multiple comparisons used false discovery rate. Gene enrichment was performed using Gene Ontology.ResultTwo gene modules were associated with amyloid deposition. One included 48 genes whereby higher expression was associated with lower amyloid burden (β = ‐12.37, p = 0.0001). This module was enriched for DNA damage pathway genes and the top gene associations in the module included histone genes such as H3C3 and H2AC14. The second module included 241 genes whereby higher expression related to higher amyloid burden (β = 7.15, p = 0.001). This module was enriched for leukocyte migration and regulation of RNA pathways. The top gene association in the module was the calcium homeostasis gene CHERP. Finally, a higher proportion of CD4 positive activated memory T cells was associated with higher amyloid burden (β = ‐1.940, p = 0.04), but did not survive FDR correction.ConclusionOur results implicate DNA damage and activated memory T cells as contributing to amyloid deposition in preclinical AD. Future work will seek to identify modifiers of the APOE effect on amyloid and evaluate blood transcription networks as complementary biomarkers of amyloid deposition.
Association between blood‐based protein biomarkers and brain MRI in the Alzheimer’s disease continuum: A systematic review
Authors: Mitolo Micaela , Lombardi Gemma , Manca Riccardo , Nacmias Benedetta , Venneri Annalena 
Published: January 2025

AbstractBackgroundBlood‐based biomarkers are becoming emerging tools, easily detectable and minimally invasive, to reveal neurodegeneration and neuroinflammation in Alzheimer’s disease (AD). However, a comprehensive and up‐to‐date overview of the association between blood‐based biomarkers and brain parameters as measured by MRI is not available. The aim of this review is to fill this gap and clarify the relationship between the main peripheral blood‐based protein biomarkers (i.e., Aβ, p‐tau, t‐tau, NfL and GFAP) and brain parameters derived from MRI in the AD continuum.MethodA literature search was carried out searching the PubMed and Web of Science databases for articles published up to May 2023. Articles were excluded according to the following criteria: no data on protein biomarkers; no data on the association between biomarkers and MRI data; articles not presenting novel data; articles that included only cognitively unimpaired older adults or patients with other neurological conditions; animal studies; molecular imaging studies; non‐peer reviewed articles; non‐English language articles; case reports.ResultA total of 33 articles were included in this systematic review. The findings revealed the following: hippocampal volume was positively correlated with Aβ42 and Aβ42/Aβ40 and negatively with Aβ40 plasma levels; p‐tau181 and p‐tau217 concentrations were negatively correlated with temporal grey matter volume and cortical thickness; NfL levels were negatively correlated with white matter microstructural integrity, whereas GFAP levels were positively correlated with myo‐inositol values in the posterior cingulate cortex/precuneus. Moreover, higher plasma GFAP levels were also negatively associated with hippocampal atrophy, lower cortical thickness, and smaller white matter volumes in temporal and parietal regions.ConclusionOur systematic review showed strong associations between the main blood‐based protein biomarkers and MRI markers of AD, highlighting a high degree of concordance between these measurements. This suggests a possible advantage in combining multiple AD‐related markers to improve accuracy of early diagnosis, prognosis, progression monitoring and treatment response.Acknowledgement: This study is supported by funding obtained under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 ‐ Project code PE0000006, CUP D93C22000930002, “A multiscale integrated approach to the study of the nervous system in health and disease” (MNESYS).
Investigating the synaptic trafficking of AD Biomarker Neuronal Pentraxin 2: Implications for Interneuron and Cholinergic Circuit Dysfunction in Alzheimer’s Disease
Authors: Roh Seung‐Eon , Swartzlander Mira , Cheong Kathryn , Worley Paul F 
Published: January 2025

AbstractBackgroundDeficits in interneuron and cholinergic circuits are noted in AD pathology, yet the precise mechanisms of their contribution to cognitive decline in the disease remain elusive. Neuronal Pentraxin 2 (NPTX2), a sensitive marker for synaptic activity and AD progression, is an immediate early gene expressed by pyramidal neurons that functions at excitatory synapses on Parvalbumin interneurons (PV‐IN) to cluster AMPA receptors and strengthen circuit inhibition. NPTX2 is later shed from some synapses into the cerebrospinal fluid (CSF), where reduced NPTX2 levels inversely correlate with hippocampal volume and cognitive performance in individuals with AD/MCI. Recent studies employing NPTX2‐SEP (Super‐Ecliptic pHluorin) as a marker of its synaptic trafficking have suggested that the diurnal oscillation of NPTX2 trafficking is important for circuit refinement, which is crucial for ensemble formation and memory updating.MethodWe aim to investigate the mechanisms of NPTX2 synaptic trafficking by utilizing two‐photon imaging of NPTX2‐SEP in the somatosensory cortex while chemo‐ and optogenetically manipulating PV‐IN and cholinergic circuits, specifically the basal forebrain (BF) and cortical intrinsic cholinergic neurons.ResultWe found that reducing PV‐IN activity leads to rapid shedding of NPTX2, whereas increasing PV‐IN activity results in elevated accumulation of NPTX2 at excitatory synapses on PV‐IN. This suggests that PV‐IN activity is a critical determinant for NPTX2 synaptic exocytosis and shedding and supports a model akin to spike‐timing‐dependent plasticity, wherein synapses are strengthened with correlated pre‐ and postsynaptic activity and weakened when presynaptic activity is not associated with postsynaptic activity. Direct activation of BF cholinergic neurons had minimal effect on NPTX2‐SEP. However, consistent with the observation that activation of nicotinic and muscarinic ACh receptors (AChR) can respectively increase and decrease cortical PV‐IN activity, chemogenetic activation of BF cholinergic neurons with mAChR blocker induced NPTX2 shedding, while nicotine also moderately induced its shedding. Additionally, chemo‐ and optogenetic activation of cortical intrinsic cholinergic interneurons reduced PV‐IN activity and induced NPTX2 shedding.ConclusionThese results underscore the significant roles of the ACh‐PV pathway in regulating NPTX2 synaptic trafficking and implicate deficits in NPTX2 shedding and circuit refinement due to the failure of the ACh‐PV pathway as a mechanism for cognitive decline in AD.
Neuropsychiatric symptoms and associated structural neuroimaging correlates in Mild Cognitive Impairment
Authors: Leung Cheryl Cheuk Yan Y 
Published: January 2025

AbstractBackgroundNeuropsychiatric symptoms (NPS) are behavioural and psychological manifestations frequently present in dementia and mild cognitive impairment (MCI). NPS are known to be associated with adverse health, cognitive and functional outcomes and increasing needs for hospitalisation and institutionalisation. It is understood that MCI may be an intermediate stage between healthy and dementia states. Therefore, NPS‐related brain alterations found at this prodromal phase may provide insights into the pathophysiology of the symptoms and act as potential biomarkers for future prognosis.MethodBy conducting a single‐centre cross‐sectional study of thirty MCI patients from South London and Maudsley NHS services, participants have undergone demographic and clinical interviews, cognitive and neuropsychiatric assessments using the Clinical Dementia Rating, Standardised Mini‐Mental State Examination, and the Neuropsychiatric Inventory ‐ Clinician Rating Scale, as well as 3 Tesla MRI scans. Cognitive, clinical and demographic data for MCI participants with and without specific NPS symptoms have been compared and using a whole brain analysis approach neuroanatomical regions showing correlations between grey matter (GM) volume and NPS symptom severity scores have been identified.ResultThere is some evidence that higher delusion and hallucination severity scores correlate with reduced GM volume in crus I/II of the cerebellum bilaterally, potentially indicating Dementia with Lewy Bodies (DLB) pathology. Furthermore, it has been found that higher aggression severity scores are significantly correlated with greater GM volumes in the calcarine and pallidum regions, and higher irritability severity scores are significantly correlated with greater GM volumes in bilateral anterior cingulate cortex (ACC) and right middle frontal gyrus. An unexpected finding is that the majority of the GM regions of interest (ROIs) appear to have positive correlation with NPS severity scores.ConclusionStructural magnetic resonance imaging (MRI)‐based neuroanatomical correlates for a variety of NPS symptoms were found, even as early as the MCI stage of dementia. The unexpected positive correlation between NPS severity and GM for many NPI‐C domains points to possible GM hypertrophy or relative GM sparing associated with NPS. Such volumetric changes may form a distinct recognisable pattern, with potential to increase the specificity of structural MRI biomarkers in predicting future clinical outcomes in MCI.
Cognitive Frailty as a Distinct Construct: Differential Risk Factors and Reversibility
Authors: Tay Laura , Tay Ee Ling , Mah Shimin , Ng YeeSien 
Published: January 2025

AbstractBackgroundCognitive frailty (CF) has been proposed as a state of reduced cognitive reserve that is intermediate between age‐related cognitive changes and neurodegenerative diseases. We aim to examine (i) whether cognitive frailty presents with risk factors distinct from isolated phenotypes of physical frailty and cognitive impairment, (ii) reversibility of cognitive frailty subtypes.MethodsParticipants were older adults aged >60 years from a community frailty screening programme. Physical frailty was assessed using Fried phenotype. Cognitive status was assessed based on response to a single question on memory symptoms and performance of the modified Chinese version of Mini‐Mental State Examination (CMMSE). All participants completed assessments for social vulnerability, mood, comorbidities, functional performance, sensory problems (hearing, vision), nutritional status, and sarcopenia. Each participant was classified into one of the diagnostic groups: reversible cognitive frailty (rCF) defined as subjective cognitive decline with prefrailty/ frailty; potentially reversible cognitive frailty (pCF) defined by mild cognitive impairment co‐existing with prefrailty/ frailty; (iii) isolated prefrailty/ frailty (PF); isolated cognitive impairment (CI); robust‐cognitively intact. Participants were followed at 1 year for frailty and cognitive status.ResultsAmong 961 participants, 351 (36.2%) were robust‐cognitively intact, 112 (11.7%) had rCF, 56 (5.8%) had pCF, 279 (29.0%) had isolated PF, and 163 (17.0%) had isolated CI. Depression increased risk for both CF subtypes (rCF: RRR 17.59; pCF: RRR 7.97; p<0.001), isolated PF [RRR 4.72 (2.76, 4.31)], and isolated CI [RRR 4.85 (2.11, 11.12)]. Sarcopenia was significantly associated with CF (rCF: RRR 15.12; pCF: RRR 14.20; both p<0.001) and isolated PF [RRR 16.13 (5.61, 46.40)]. Visual impairment was significantly associated with rCF [RRR 2.40 (1.26, 4.59)] and isolated CI [RRR 2.01 (1.16, 3.45)], while hearing impairment was associated with isolated PF [RRR 1.75 (1.00, 3.76)]. Education was protective against CF and isolated CI, while participation in social/ community activities was protective against PF only. At 1‐year, 25% of rCF and 12.5% of pCF reverted to being robust‐cognitively intact.ConclusionDepression is a common risk factor across cognitive frailty subtypes and isolated phenotypes of physical frailty and cognitive impairment. Sarcopenia may contribute to cognitive frailty through physical frailty.
Differential network‐based propagation of cortical thinning in atrophy subtypes in non‐demented individuals
Authors: Lorenzini Luigi 
Published: January 2025

AbstractTHIS IS A RESUBMISSION OF A PREVIOUSLY SUBMITTED ABSTRACT ALREADY ACCEPTED, NEEDED FOR FELLOWSHIP APPROVALBackgroundDifferent patterns of atrophy exist in the dementia stage of AD. However, little is known about the heterogeneity of atrophy patterns and the mechanisms that drive subsequent propagation of the disease in the preclinical stages.MethodFrom the AMYPAD‐PNHS cohort, we included a total of 1323 non‐demented individuals, including 1094 amyloid‐negative, and 229 amyloid‐positive participants (Table 1). Gray matter thickness in 100 regions of interest was measured using FreeSurfer. Global cortical amyloid burden was derived from amyloid PET scans in Centiloids (CL).Individuals were assigned to atrophy subtypes based on cortical thickness measures using Non‐Negative Matrix Factorization (Figure 1). Multinomial regression models were used to study differences between subtypes in age, sex, global CL and APOE E4 carriership. Linear regression models were used to investigate the effect of time on atrophy within each subtype. Coordinated deformation models (Figure 1, Shafiei et al. 2023) were used to estimate network‐based thickness changes in each region, by multiplying the effect of time in connected regions by the strengths of their connections. Connectivity strength was based on templates of functional, structural and morphometric similarity networks, and allowed us to evaluate differential mechanisms. Significance was tested using a permutation approach.ResultsThree significant subtypes of regional GM atrophy were identified:‘limbic predominant’, ‘typical AD’, and ‘diffuse cortical’ (Figure 2). Individuals assigned to the limbic predominant subtype were significantly older, while the typical AD subtype had more APOE E4 carriers compared to the others (all p<0.001). Both limbic predominant and typical AD subtypes had higher CL compared to the diffuse cortical atrophy subtype (all p<0.001). Significant longitudinal thickness changes per subtype are shown in Figure 2. In the typical AD subtype, all network architectures constrained longitudinal GM changes (p<0.001), while in the diffuse atrophy subtype, propagation was mostly determined by morphometric similarity (p<0.005). No significant prediction model was found for the limbic predominant subtype.ConclusionWe show that atrophy subtypes can already be detected in non‐demented individuals. Progression of atrophy within subtypes is determined by network‐based mechanisms.
Cognitive clusters in sporadic early‐onset Alzheimer’s disease patients from the LEADS study
Authors: Logan Paige E. , Lane Kathleen A. , Gao Sujuan , Eloyan Ani , Taurone Alexander , Thangarajah Maryanne , Touroutoglou Alexandra , Vemuri Prashanthi , Dage Jeffrey L. , Nudelman Kelly N. , Carrillo Maria C. , Dickerson Bradford C. , Rabinovici Gil D. , Apostolova Liana G. , Hammers Dustin B. ,   LEADS Consortium
Published: January 2025

AbstractBackgroundEarly‐onset Alzheimer’s disease (EOAD) occurs before age 65 and has more diverse disease presentations than late‐onset AD. To improve our understanding of phenotypic heterogeneity among EOAD individuals, we analyzed cognitive scores using data‐driven statistical analysis.MethodBaseline cognitive data from 286 sporadic EOAD individuals from the Longitudinal EOAD study (LEADS) were transformed to z‐scores using data from 95 cognitively normal (CN) individuals. Cognitive composites were generated for domains of memory, language, speed/attention, visuospatial, and executive function. Residuals from linear regression models on Z‐scores adjusted for age, sex, and education were obtained. Cluster analysis using the Ward method on the cognitive domain residuals was performed and scree plot using the pseudo T‐squared determined the optimal number of clusters for the EOAD sample. We also compared gray matter density (GMD) of each EOAD cluster to CN participants using voxel‐wise multiple linear regressions.ResultsThree clusters of cognitive performance were identified from the EOAD sample. Disease duration was not significantly different across clusters. Using a z‐score of ‐1.5 SD as the impairment threshold, all clusters were impaired across most domains (Table 1). Cluster‐3 was more impaired than cluster‐2 in all domains (Table 2; all p<.0001), and in all domains except episodic memory compared to cluster‐1 (all p<.01). Cluster‐1 (n = 71; 85.9% amnestic) was most impaired in executive function, visuospatial, and speed/attention. Cluster‐2 (n = 133; 88.7% amnestic) was most impaired in episodic memory. Cluster‐3 (n = 82; 69.5% amnestic) was most impaired in executive function, visuospatial, and speed/attention (Table 1). 3D‐comparisons showed all EOAD clusters had reduced GMD compared to CN. Cluster‐1 and cluster‐3 both showed widespread atrophy, with cluster‐3 being more severe. Cluster‐2 showed the most atrophy in the temporal and parietal lobes (Figure 1).ConclusionWe identified heterogeneity in cognitive patterns among sporadic EOAD individuals. Cluster‐3 appeared to reflect widespread impairment, and cluster‐2 represented an amnestic‐only presentation. Despite comparable disease duration, some EOAD patients progress faster, while some are more resilient. 3D‐comparisons showed neurodegenerative changes affecting brain regions responsible for respective impaired cognitive functions in each cluster (e.g., cluster‐2 is primarily amnestic‐impaired and has temporoparietal atrophy). Future work should explore amyloid‐PET and tau‐PET burden.
Machine Learning‐Driven Analysis of COVID‐19 Vaccination Effects on Alzheimer’s Disease Progression: A Retrospective Cohort Study
Authors: Fajar Rifaldy , Prihantini Prihantini , Elfiany Elfiany , Putri Sahnaz V 
Published: January 2025

AbstractBackgroundRecent evidence suggests a potential link between systemic inflammation induced by viral infections like COVID‐19 and the exacerbation of neurodegenerative processes in Alzheimer’s disease (AD). This study aims to explore the novel idea that COVID‐19 vaccines may offer neuroprotective effects against the progression of AD, leveraging machine learning techniques to analyze multimodal data sets.MethodWe conducted a retrospective cohort study involving 10,000 participants aged 65 and older, with an initial diagnosis of early‐stage AD, from five distinct geographic locations. Participants were divided into two groups: those who received a COVID‐19 vaccine (n = 5,000) and those who did not (n = 5,000), matched for age, sex, comorbidity, and baseline cognitive function. We utilized a combination of machine learning models, including deep learning neural networks and gradient boosting machines, to analyze longitudinal data encompassing genomic profiles, clinical assessments, and neuroimaging data collected over a two‐year period. The primary outcome was the rate of cognitive decline, measured semi‐annually using the Mini‐Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale‐Cognitive Subscale (ADAS‐Cog).ResultThe vaccinated group showed a statistically significant slower rate of cognitive decline compared to the unvaccinated group. Specifically, the annual MMSE score deterioration was 1.2 points less in the vaccinated group (95% CI: 0.8‐1.6, p<0.001). Additionally, the ADAS‐Cog progression rate was reduced by 30% (95% CI: 25%‐35%, p<0.001). The machine learning models identified a significant interaction between vaccine‐induced immune response markers and a slower progression of AD‐related neurodegenerative changes in neuroimaging data.ConclusionOur findings suggest that COVID‐19 vaccination has a neuroprotective effect in early‐stage Alzheimer’s disease, likely due to reduced systemic and neuroinflammation. This study reveals the potential therapeutic benefits of vaccination in older adults at risk of Alzheimer’s and urges further research to understand the biological mechanisms and long‐term outcomes.