bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between polygenic risk for Alzheimer’s disease and brain structure in children and adults
Authors: He Xiao‐Yu , Wu Bang‐Sheng , Yu Jin‐Tai 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD), a complex and polygenic disease with a considerable hereditary component (60–80%), is a progressive neurodegenerative disorder characterized by concealed onset, and individuals often have significant cognitive impairment and histopathological changes in the brain before overt clinical diagnosis. However, the correlations between genetic risk for Alzheimer’s disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages.MethodThis study used large existing genome‐wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ∼ 23 000) and Adolescent Brain Cognitive Development Study (N ∼ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed‐effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life.ResultCompared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle‐aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD).ConclusionIn conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age‐specific change is consistent with the classical pattern of brain impairment observed in AD patients.
Evaluating Large Language Models (LLMs) in Information Extraction: A Case Study of Extracting Cognitive Exam Dates and Scores
Authors: Zhang Hao , Jethani Neil , Jones Simon , Genes Nicholas , Major Vincent J , Jaffe Ian S , Cardillo Anthony B , Heilenbach Noah , Ali Nadia Fazal , Bonanni Luke J , Clayburn Andrew , Khera Zain , Sadler Erica C , Prasad Jaideep , Schlacter Jamie , Liu Kevin , Silva Benjamin , Montgomery Sophie , Kim Eric J , Lester Jacob , Hill Theodore M , Hill Theodore M , Avoricani Alba , Chervonski Ethan , Davydov James , Small William , Chakravartty Eesha , Grover Himanshu , Dodson John , Brody Abraham A. , Aphinyanaphongs Yindalon , Masurkar Arjun V. , Razavian Narges 
Published: January 2025

AbstractBackgroundLarge language models (LLMs) provide powerful natural language processing capabilities in medical and clinical tasks. Evaluating LLM performance is crucial due to potential false results. In this study, we assessed ChatGPT and Llama2, two state‐of‐the‐art LLMs, in extracting information from clinical notes, focusing on cognitive tests, specifically the Mini Mental State Exam (MMSE) and Cognitive Dementia Rating (CDR).MethodWe compiled a dataset consisting of 765 clinical notes mentioning MMSE and CDR. 22 medically trained experts provided the ground truth. ChatGPT (GPT‐4, version “2023‐03‐15‐preview”) and Llama2 (“Llama‐2‐70b‐chat”) were used to extract MMSE and CDR instances with corresponding dates. Inference was successful for 742 notes. We used 20 notes for fine‐tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 assigned to two reviewers simultaneously. Precision, sensitivity, true/false negative rates, and accuracy were calculated. For double‐reviewed notes, we qualitatively assessed the errors.ResultThe patient and note characteristics can be found in Table 1. For MMSE information extraction, ChatGPT (vs. Llama2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true‐negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true‐negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and Llama2 on double‐reviewed notes. Llama2 errors included 27 cases of total hallucination, 19 cases where other scores were reported instead of MMSE, 25 missed scores, and 23 cases where the wrong date was reported for the right score. In comparison, ChatGPT’s errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date.ConclusionChatGPT exhibited high accuracy in extracting MMSE scores and dates, with better performance compared to Llama2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.
Examining early cerebral amyloid beta and tau accumulation in association with cognition in a predominantly middle‐aged cohort
Authors: Gonzales Mitzi M. , O’Donnell Adrienne , Ghosh Saptaparni , Thibault Emma G , Tanner Jeremy A. , Satizabal Claudia L , Decarli Charles , Fakhri Georges El , Johnson Keith A , Beiser Alexa S , Seshadri Sudha , Pase Matthew P. 
Published: January 2025

AbstractBackgroundIn older adults, greater amyloid (Aβ) and tau positron emission tomography (PET) binding is associated with cognitive decline and dementia. However, the association of early amyloid and tau PET accumulation with cognition at midlife remains unclear. The goal of the current study was to evaluate the associations of Aβ and tau PET with cognition in a predominately middle‐aged community‐based cohort, as well as to examine the factors that may modify these associations.MethodParticipants from the observational Framingham Heart Study completed cognitive assessments and underwent 11C‐Pittsburgh Compound B (PiB) Aβ and 18F‐Flortaucipir tau PET imaging. Associations of standardized quantitative measures of Aβ (global composite) and tau (five early regions of interest) PET retention with cognition were evaluated using linear regression models. Interactions with age, apolipoprotein ε4 status, and education were examined and stratified analyses were performed where interactions were significant. All statistical tests were two‐sided and were FDR‐corrected to account for multiple comparisonsResultThe sample included 423 participants (mean age 57±10 years, 50% female, Table 1). There were no significant associations between Aβ or tau and cognition in the overall sample after correcting for multiple comparisons (Table 2). Stratified analyses indicated a significant association between higher fusiform gyrus tau and poorer visuospatial function in those younger than 55 years (b=‐0.27, 95% CI ‐0.46 to ‐0.07, p=0.007) and associations between higher inferior and middle temporal tau with poorer visuospatial function in those without a college degree (inferior temporal b=‐0.25, 95% CI ‐0.02 to 0.41, p=0.01, middle temporal b=‐0.25, 95% CI ‐0.22 to 0.0, p=0.02).ConclusionThe continuous range of amyloid beta and tau accumulation at midlife was not cross‐sectionally associated with cognition. However, the findings suggest that higher tau accumulation at younger ages or in the context of lower cognitive reserve may increase vulnerability for poorer cognition as early as midlife.
Novel MRI method enables in vivo quantification of pre‐atrophic neurodegeneration in a mouse model of Alzheimer’s Disease
Authors: Tomaszewski Michal R , Sukstansky Alexander L , Haley Hyking D , Meng Xiangjun , Miller Corin O , Yablonskiy Dmitriy A. 
Published: January 2025

AbstractBackgroundRobust methods are needed for preclinical evaluation of novel Alzheimer Disease (AD) therapies to accelerate drug discovery. Quantitative Gradient Recalled Echo (qGRE) MRI shows significant promise to provide insight into neurodegeneration in AD prior to atrophy development in humans, with qGRE R2t* metric (tissue‐specific subcomponent of R2*) highlighting areas of low neuronal density (doi:10.3233/JAD‐210503). In this study a novel qGRE method is shown to non‐invasively measure the longitudinal neuronal loss in the hippocampus of a mouse model of AD tauopathy Tg4510.MethodTg4510 (n=15) and wild type (WT, n=7) mice underwent MRI (7T field strength) at 3, 5, 6, 7 months old (WT at 3 and 5mo only). Mice were isoflurane‐anesthetized to minimize BOLD contribution to R2* signal. 3D Multi‐GRE sequence was used to generate R2* maps. Light‐sheet microscopy of the brains stained with NeuN and MBP was used to visualize neuronal nuclei and myelin content respectively.ResultSignificant decrease in NeuN staining between 3mo and 6mo was observed in the hippocampus of Tg4510, indicating a decrease of over 30% in neuronal density (p<0.001, Figure 1), validating the mouse model. Longitudinal analysis showed clear changes in R2* values in the Tg4510 hippocampus undergoing neurodegeneration between 3 and 5 months old (Figure 2). Histogram analysis revealed patterns of increase in low R2* value (Dark Matter, DM), and broadening of R2* distribution. These were quantified as significant increase in both DM Volume Fraction (DMVF) and R2* Standard Deviation (SD) in Tg4510 mice (Figure 3, p=0.004/p=0.016 DMVF/SD) but not in WT controls (p>0.25). Further monotonical increase was also observed in both metrics in time. A significant negative correlation was observed between myelin content and the DMVF (p=0.01, r=‐0.76) in the hippocampus, suggesting sensitivity of the technique to the loss of myelinated axons.ConclusionIn this study a method is proposed for using R2* relaxometry as a direct biomarker of the neuronal loss. Low R2* volume fraction (DM) and the heterogeneity of R2* both show high sensitivity for capturing longitudinal neurodegeneration. The presented technique, together with accompanying histological validation can be readily applied in preclinical models of neurodegeneration for pharmacodynamics and mechanism of action assessment.
The Association Between Neuroticism and Global Cognition is Mediated by Negative Affect: Findings from the I‐CONECT Study with Socially Isolated Older Old
Authors: Yu Kexin , Wu Chao‐Yi , Gatchel Jennifer R. , Dodge Hiroko H 
Published: January 2025

AbstractBackgroundPersonality traits are long‐standing characteristics of behavior and emotion. Personality might influence cognitive health in later life by affecting responses to stressful events and engagement in cognitively stimulating activities, such as interpersonal interaction. The current study examined associations between personality traits and cognition in late life, and potential mediation by positive and negative emotion, depression, and social connectedness.MethodWe used baseline data from the Internet‐Based Conversational Engagement Clinical Trial (I‐CONECT) study. The I‐CONECT study recruited socially isolated older adults who were 75+ and were either cognitively unimpaired (CU) or had mild cognitive impairment (MCI). Big‐5 personality traits (neuroticism, extraversion, agreeableness, conscientiousness, and openness) were measured with the NEO‐FFI Personality Inventory. Global cognition was assessed with the Montreal Cognitive Assessment (MoCA); depressive symptoms with the 15‐item Geriatric Depression Scale, social connectedness with the 6‐item Lubben Social Network Scale (LSNS‐6), and emotional characteristics (negative affect, social satisfaction, and psychological wellbeing domains) with the NIH Toolbox‐emotion battery (NIHT‐EB). We ran general linear models to identify which Big‐5 personality traits were related to the MoCA score and to establish relationships between the Big‐5 personality trait and proposed mediators. Then, we ran mediation analyses. All analyses controlled for age, sex, and education. Sensitivity analysis included MCI status as a covariate.ResultThe analytical sample included 146 participants (female 69.2%, mean age 81.1 years, mean education 15.2 years, MCI 56.2%). Out of the five personality traits, only Neuroticism was related to MoCA (Path c, β = ‐0.08, p = 0.05). The mediation analysis showed that negative affect mediated the association between Neuroticism and MoCA (coefficients in Figure 1). Controlling for MCI did not change the model results. All the other mediators, including GDS‐15, LSNS‐6, and social satisfaction and psychological wellbeing did not mediate the relationship.ConclusionThe neuroticism trait in socially isolated older adults was associated with worse global cognition, and this relationship was mediated by symptoms of negative affect. Results suggest that the NIHTB‐EB may capture symptoms of negative affect distinct from the GDS‐15. Behavioral health interventions aimed at delaying cognitive decline could consider addressing negative affect for individuals with high neuroticism traits.
Independent and interactive contributions of cerebrovascular disease, neuroinflammation, and tau pathophysiology to Alzheimer’s disease‐related diagnostic conversion in adults with Down syndrome
Authors: Edwards Natalie C. , Lao Patrick J. , Alshikho Mohamad J. , Rizvi Batool , Aguilar Lisi Flores , Petersen Melissa , O'Bryant Sid E. , Tudorascu Dana , Handen Benjamin L , Gutierrez Jose , Wilcock Donna M. , Head Elizabeth , Brickman Adam M. 
Published: January 2025

AbstractBackgroundBy age 40 years, adults with Down syndrome (DS) develop Alzheimer’s disease (AD) pathology and progress to dementia in their 60s. Despite minimal systemic vascular risk factors, individuals with DS have MRI evidence of cerebrovascular injury that progresses with AD severity, suggesting an intrinsic vascular component to DS‐AD that may interact with neuroinflammatory processes to promote tau pathology and cognitive decline. In the current study we examined whether cerebrovascular disease (CVD) burden and inflammation/astrocytosis independently and interactively were associated with incident diagnosis among adults with DS.MethodThis study included 149 participants from the Alzheimer Biomarkers Consortium – Down Syndrome (baseline mean age[SD]=44.6[9] years) with available baseline MRI, plasma biomarker data, and at least two time‐points of clinical consensus diagnosis data (i.e., cognitively stable, mild cognitive impairment [MCI], and clinical AD) who were classified as cognitively stable or MCI at baseline. Logistic regression models assessed if baseline small vessel CVD, operationalized as white matter hyperintensity (WMH) volume, and plasma glial fibrillary acidic protein (GFAP), Aβ42/Aβ40, p‐tau217, and neurofilament light (NfL) concentrations are associated with conversion from a milder diagnosis to a more severe clinical diagnosis. Mediation models examined relationships between biomarkers and diagnostic conversion. All models adjusted for study site, sex/gender, latency between visit dates, and age group (below or above/equal to the median age of the sample).ResultDiagnostic conversion occurred in 26% of the sample. Higher baseline WMH volume (OR 1.08 [1.01, 1.81]), GFAP (OR 1.006 [1.003, 1.01]), and p‐tau217 (OR 20.56 [5.01, 112.43]), but not NfL nor Aβ42/Aβ40 concentrations were associated with higher odds of conversion to more severe cognitive impairment. GFAP concentration mediated the relationship between WMH and diagnostic conversion (ACME 0.05 [0.01, 0.1], p=0.006). P‐tau217 concentration mediated the relationship between GFAP and diagnostic conversion (ACME 0.13 [0.05, 0.23], p=0.004).ConclusionOur findings suggest that among individuals with DS, CVD promotes AD‐related clinical progression by increasing astrocytosis which, in turn, promotes tau pathophysiology and downstream MCI and AD incidence. The results implicate CVD and its interface with inflammation as a core feature of AD in DS.
Effect of Smart Ambient Bright Light (SABL) in Nursing Home Residents with Dementia: A Pilot Study
Authors: Jao Ying‐Ling , Wang Julian , Liao Yo‐Jen K , Berish Diane , Boltz Marie , Calkins Margaret 
Published: January 2025

AbstractBackgroundNeurobehavioral distress significantly impacts people with dementia. Lighting interventions have shown positive effects in this population, yet most did not accommodate natural daylight. This study developed an innovative, bright light system, the smart ambient bright light (SABL), that provides auto‐controlled, consistent indoor lighting and accommodates natural daylight. This study evaluated the effect of the SABL on neurobehavioral symptoms and affect in nursing home (NH) residents with dementia.MethodThis crossover, cluster randomized control trial recruited residents with dementia and agitation from two NHs in Pennsylvania. The study had five phases: baseline, intervention, post‐intervention/washout, control, and post‐control/washout. The intervention and control phases were four weeks each, and the intervention sequence was randomly assigned. The SABL was installed in residents’ bedrooms and common areas. Neurobehavioral symptoms and affect were measured at baseline and biweekly. The Neuropsychic Inventory (NPI) was calculated as a total score and four subscales: psychosis hyperactivity, mood, and frontal. Repeated measures analysis of variance were conducted.Result27 NH residents completed the study. Participants averaged 87.4 years and were primarily female (74.1%). After controlling for age, dementia stage, and gender, participants’ scores on NPI total (p = 0.007), hyperactivity (p = 0.04), mood (p = 0.03), and psychosis (p = 0.02) were statistically significantly lower during the intervention period as compared to the control period. Participants’ affect did not show a significant difference. No intervention‐related adverse effects were reported. Moreover, NH staff reported favorably that they would like the SABL to be used in their facility with an average rating of 4.1 on a 1‐5 rating scale.ConclusionOverall, results suggest that the SABL is well‐accepted and has a positive impact on most neurobehavioral symptoms in NH residents with dementia. Future studies may replicate the study in a larger, more diverse population, examine the intervention fidelity, and establish implementation strategies in NHs.
Decreased entropy of functional MRI signals in individuals with mild cognitive impairment compared to cognitively normal controls
Authors: Neumann Dara L , Tozlu Ceren , Razlighi Qolamreza R , Luchsinger José A. , Stern Yaakov , Devanand Davangere , Kuceyeski Amy 
Published: January 2025

AbstractBackgroundMild cognitive impairment (MCI) is a clinical cognitive deficit that is not severe enough to meet the threshold for Alzheimer's Disease (AD); however, MCI patients have an increased risk of developing AD. Therefore, a diagnosis of MCI may represent a critical turning point in the trajectory of developing AD. Establishing neurological signatures of MCI using network control theory (NCT) may allow more informed diagnosis, and an understanding of its underlying mechanisms could pave the way for novel treatments.MethodFunctional MRI (fMRI) metrics were collected in MCI patients (n = 57, mean age = 66.68) and healthy controls (HC) (n = 500, mean age = 72.25). The average structural connectivity matrix was obtained using age‐matched controls from the Human Connectome Project‐Aging dataset. Commonly recurring brain states were identified via k‐means clustering of activation matrices over 200 regions using the Schafer atlas. NCT was used to compute the transition energy (TE): the minimum energy required to transition between each pair of brain states. The entropy of each region’s activity was calculated using SampEn, and was then correlated with TE using Pearson’s correlation. Pairwise/global (average of all brain state pairs) TE and global entropy (average of all regions) were compared between MCI and HC using ANCOVA with age and sex as covariates.ResultThe brain states identified via k‐means clustering were high and low amplitude activity in the visual, somatomotor, and limbic networks (Figure 1). While there were no significant differences in pairwise or global TE between MCI and HC (Figures 2‐3), MCI had significantly lower global entropy than HC. ANCOVA revealed that increased age is associated with increased entropy. Pearson correlation showed a significant inverse relationship between global TE and entropy across individuals (r = −0.13) (Figure 3).ConclusionEntropy of brain activity measured with fMRI is a promising neuroimaging biomarker of MCI, which is often underdiagnosed or diagnosed with delay. Future work will investigate regional entropy reduction patterns between MCI and AD patients to establish the use of these metrics in disease progression, and to get a more detailed picture of brain activity changes in individuals with these diagnoses.
Biological Factors Influencing [18F]MK6240 and [18F]FTP Correlation in Target Regions
Authors: Tissot Cécile , Tsai Hsin‐Yeh , Therriault Joseph , Tudorascu Dana , Rahmouni Nesrine , Servaes Stijn , Stevenson Jenna , Lussier Firoza Z , Pezzoli Stefania , Ziontz Jacob , Gordon Brian A. , Pascual Belen , Lowe Val J. , soleimani‐meigooni David N. , Oh Hwamee , Klunk William E , Rosa‐Neto Pedro , Jagust William J. , Pascoal Tharick Ali , Baker Suzanne L. 
Published: January 2025

AbstractBackgroundThe association between [18F]Flortaucipir (FTP) and [18F]MK6240, two commonly used tau‐PET tracers in Alzheimer’s disease (AD), varies due to distinct binding properties and off‐target signal regions. Our study aims to elucidate the biological factors influencing this association and evaluate the applicability of a common equation across different on‐target regions.Method113 individuals from the HEAD dataset (11 young, 58 cognitively unimpaired elderly, and 44 cognitively impaired) underwent [18F]MK6240, [18F]FTP and Aβ‐PET scans. Images were processed using the inferior cerebellar grey (CG) as the reference region, with subjects categorized by Aβ status. Stepwise linear fitting identified off‐target (OFF) regions impacting tracer associations within the whole cortex, Braak and temporal‐meta regions of interest (ROIs). OFF regions included were a composite mask (pallidum, putamen, caudate ‐ PPC) to address collinearity in subcortical nuclei, as well as the region with the greatest partial volume effects (PVE) on the ROI (meninges, choroid plexus, and the hotspot medial to entorhinal cortices (EC)). Akaike information criteria (AIC) analyses tested linear models with various off‐target regions.ResultPPC and PVE regions influenced tracer association in on‐target regions. PPC binding is associated with age. PPC and meninges retention, along with Aβ status interaction, demonstrated the best model fit for the whole cortex (Figure 1). Weighting factors derived from the whole cortex model were applied to Braak regions and temporal meta‐ROI. Braak III‐VI and temporal‐meta ROI showed optimal models including OFF regions and Aβ status interaction (Figure 2). Forcing the whole equation gave optimal results. For Braak I (EC), adding the OFF covariates only was the best model. For Braak II (hippocampus), the best model included OFF regions and the interaction with Aβ status (Figure 2). Forcing the whole cortex equation onto Braak I and Braak II yielded suboptimal fits.ConclusionHarmonizing tau‐PET tracers involves considering significant covariates. Integrating meningeal and age‐related off‐target regions enhances [18F]MK6240 and [18F]FTP association in on‐target regions, further improved by Aβ status inclusion. While the whole cortex model may not suit early ROI, it proves effective for Braak III and beyond. Creating an equation for tracer translation necessitates incorporating meningeal and subnuclei off‐target retention.