bluuebunny/crossref_metadata_2025_split huggingface dataset:
4D flow MRI of CSF dynamics: relations to CSF morphology and Aβ status
Authors: Vikner Tomas P. , Johnson Kevin M. , Hudson Sarah C. , Pompa Christian C. , Cadman Robert V. , Bevis William B. , Betthauser Tobey J. , Chin Nathaniel A. , Eisenmenger Laura B. , Johnson Sterling C. , Rivera‐Rivera Leonardo A. 
Published: January 2025

AbstractBackgroundCerebrospinal fluid (CSF) dynamics are increasingly studied to understand potential pathologic coupling with neurological disorders. In Alzheimer’s disease (AD), CSF dynamics may be altered secondary to AD‐related atrophy and enlarged CSF spaces. Additionally, animal studies suggest that altered CSF dynamics could impair clearance of metabolic waste, leading to accumulation of amyloid‐beta (Aβ). However, assessment of human CSF dynamics has typically been limited to the cerebral aqueduct using 2D phase contrast MRI. In this work, we used 4D flow MRI to enable volumetric characterization of CSF flow dynamics in the human brain, to study its associations with CSF morphology and Aβ burden from 11C‐PiB PET.MethodCardiac‐resolved 4D flow MRI was used to assess CSF motion in N=69 participants (age 69±8 years; 17 Aβ+) from the Wisconsin Registry for Alzheimer’s Prevention, using a clinical 3T scanner (GE Premier) and a velocity encoding of 5 cm/s. Region‐averaged velocity waveforms were acquired in the spinal canal (SC), fourth ventricle (4V), cerebral aqueduct (CA), third ventricle (3V), the foramens of Monro (FMo), and the lateral ventricles (LV), segmented from T1‐weighted volumes. The waveforms were characterized by root‐mean‐square (RMS) velocity and were evaluated in relation to total CSF volume and Aβ status from an established PiB global distribution volume ratio threshold >1.16.ResultVelocity waveforms demonstrated cardiac‐pulsatility in all regions of interest (ROIs) (Figure 1), with RMS velocities ranging from high in the SC (6.56±2.29 mm/s) to low in the lateral ventricles (right LV 0.31±0.15 mm/s). RMS velocities were related to total CSF volume for the SC (R=‐0.24; p=0.043), CA (R=0.27; p=0.034), 3V (R=‐0.28; p=0.018), left LV (R=‐0.39; p=0.002), and right LV (R=‐0.35; p=0.009) (Figure 2). In preliminary analysis, RMS velocities were higher in Aβ+ compared to Aβ‐ for the SC (p=0.048) and right LV (p=0.024) (Figure 3).Conclusion4D CSF flow MRI is feasible for the assessment of CSF dynamics throughout the ventricular system. Total CSF volume was related to RMS velocities in 5/8 ROIs, highlighting the influence of CSF morphology on CSF dynamics. Finally, Aβ status was related to RMS velocity in 2/8 ROIs, indicating that altered CSF dynamics could play a role in incipient AD.
Metabolic Hyperconnectivity in Underrepresented Individuals with long‐COVID
Authors: Machado Luiza Santos , Silveira Maiele Dornelles , de Bastiani Marco Antônio , Povala Guilherme , Borelli Wyllians Vendramini , Senger Joana Emilia , da Silva Ana Paula Bornes , da Ros João Pedro Uglione , de Mello Guilherme Bastos , Jotz Arthur Viana , Kadan Matheus Fakhri , Peixoto Guilherme G. Schu , Radaelli Graciane , Pascoal Tharick A. , Rosa‐Neto Pedro , Matushita Cristina Sebastião , Soder Ricardo Benardi , Schumacher‐Schuh Artur Francisco , Souza Diogo O. , Lourenco Mychael V , de Paula Faria Daniele de Paula , Coutinho Artur Martins , da Costa Jaderson Costa , de Souza Débora Guerini , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundLong‐COVID is characterized by persistent symptoms post‐infection with SARS‐CoV‐2. This condition includes neurological manifestations and has been proposed as a potential risk factor for the development of dementia. Individuals presenting with dementia due to Alzheimer's disease have dysfunctional brain metabolism, including metabolic brain network (MBN) hypoconnectivity. However, whether long‐COVID alters brain metabolic architecture remains elusive. Here, we aimed to evaluate the brain metabolic connectivity in a Brazilian cohort of individuals presenting with long‐COVID.Method[18F]FDG‐PET images were acquired from 52 community‐dwelling Brazilians above 50 year old. Standardized uptake value ratio (SUVr) parametric maps were processed to a common 8 mm FWHM and generated using the pons as the reference region (Figure 1). We extracted the mean values of regions of interest using the ICBM152 atlas. [18F]FDG‐PET MBNs were constructed using a novel multiple sampling scheme, which assembles a stable group representative MBN based on bootstrap (n = 2000). Adaptive Synthetic Sampling Approach for Imbalance (ADASYN) was used to account for group imbalance and generated the ADA‐MBNs. Graph measures, including density, global efficiency, average degree, and assortativity coefficient were computed. Data were corrected for multiple comparisons using the False Discovery Rate (FDR) method (p<0.05).Result41 individuals with long‐COVID and 11 healthy controls (HC) were included (Table 1). We observed that long‐COVID individuals present PET hyperconnectivity in both MBN and ADA‐MBN. (Figure 2a‐b). The long‐COVID group presented increased density, global efficiency and average degree whereas assortativity coefficient were reduced in both MBN and ADA‐MBN.ConclusionOur findings showed that individuals with long‐COVID presented a brain metabolic hyperconnectivity, which is supported by increased density and average degree and may indicate a potential compensatory mechanism within the brain. In addition, the increase in global efficiency indicates that the brain of long‐COVID individuals exchanges metabolic information more efficiently, but the decreased assortativity coefficient suggests vertices with different properties connect to each other. Further longitudinal studies should follow these individuals for assessing microstructural and cognitive changes.
Longitudinal beta‐amyloid measured with [C‐11]PiB accumulates at an accelerated rate in Down syndrome compared to neurotypical populations
Authors: McVea Andrew K , DiFilippo Alexandra H , McLachlan Max , Bettcher Brecca , Zammit Matthew D , Betthauser Tobey J. , Converse Alexander K , Murali Dhanabalan , Stone Charles K , Hartley Sigan L , Johnson Sterling C. , Tudorascu Dana , Laymon Charles M , Cohen Annie , Minhas Davneet S , Mathis Chester , Ances Beau , Zaman Shahid , Mapstone Mark , Head Elizabeth , Klunk William E , Handen Benjamin L , Christian Bradley T. ,   ABC‐DS Investigators
Published: January 2025

AbstractBackgroundTrisomy 21 in Down syndrome (DS) is associated with an earlier accumulation of beta‐amyloid (Aβ) plaques and a higher rate of Alzheimer’s Disease due to the triplication of the amyloid precursor protein gene. In this study we compare accumulation rates of Aβ measured with [C‐11]PiB PET between large longitudinal cohorts of DS and neurotypical (NT) participants at a single site.MethodsParticipants imaged at the University of Wisconsin with ≥2 PiB scans and ≥2 years between scans were included in this study. DS participants were included from the Alzheimer’s Biomarker Consortium–DS (ABC‐DS) study (n = 57) and NT participants from studies enriched with familial risk for AD (n = 162) with several participants having 10+ years of PiB data. An identical imaging procedure was conducted for all subjects and reconstructed PET images were processed using a standardized pipeline and converted into SUVR images using the cerebellar grey matter reference region. Global PiB SUVR, defined by grey matter regions from the AAL atlas, was used as a metric for Aβ deposition and input into a trajectory model (Betthauser, 2021) to estimate time from Aβ(+) (SUVR ≥ 1.40) for each scan (Figure 1). Using this model the PiB data were normalized in time and then fit using the logistic growth curve () with t50 set to 0. Nonspecific binding (NS), found by taking the average SUVR of all Aβ(‐) subject scans (n=491) at our site, and carrying capacity (K), represented by the highest total binding seen in any participant scanned, were set to 1.12 and 2.40 respectively. Aβ accumulation rate (r) was calculated by fitting all normalized participant data across each population.ResultsRate of Aβ accumulation for DS was 0.25±0.02 and 0.17±0.01/yr for NT, corresponding to 0.065 and 0.038 SUVR/yr respectively when becoming Aβ(+). No overlap in 95% CI between the DS (0.21‐0.29) and NT (0.15‐0.19) populations was observed.ConclusionsThere was a faster rate of Aβ accumulation observed in our DS cohort compared to NT by 47%. However, the high variability in Aβ rates requires further investigation towards understanding how genetic and lifestyle factors contribute to this process.
Target trial emulation analysis linking dampened circadian rest‐activity rhythms and dementia
Authors: Sun Haoqi , Yang Jingyun , Vialle Ricardo A , Gao Chenlu , Cai Ruixue , Haghayegh Shahab , Gao Lei , Rutter Martin K , Hu Kun , Li Peng 
Published: January 2025

AbstractBackgroundPrevious studies have linked disrupted circadian rest‐activity rhythms (RARs) with increased risks for Alzheimer’s disease and related dementias. However, these findings may be confounded due to the observational nature and offer limited insights into causality. We aimed to employ target trial emulation to estimate potential causal effects of RARs on dementia development.MethodUsing the UK Biobank data, we emulated a target randomized trial including 86,802 participants with ≥6 days of actigraphy. Cycle‐by‐cycle amplitudes of the ∼24‐h RARs were computed from actigraphy using uniform‐phase empirical mode decomposition. Two emulated trial arms were defined (1) a ‘robust RARs’ arm within which all participants had higher than cohort median amplitudes averaged over six consecutive days, and (2) a ‘dampened RARs’ arm where all participants had lower (than cohort median) values in six consecutive days. The primary outcome was time to all‐cause dementia, and the secondary outcome was time to death. Covariates considered included age, sex, race, college education (yes/no), body mass index, socioeconomic status (Townsend deprivation index), number of active medications, history of circulatory (yes/no), nervous system (yes/no), and mental or behavioral health conditions (yes/no). In the emulated trial, the baseline was defined as the beginning of actigraphy. The g‐formula and cause‐specific Cox proportional hazards model, complemented by bootstrapping for 95% confidence intervals (CI), were used for analysis.ResultOver an 8‐year follow‐up, 467 participants developed all‐cause dementia, and 2,769 died. The ‘dampened RARs’ arm exhibited a significantly higher risk of death than the ‘robust RARs’ arm. Also, a higher cumulative incidence of dementia was observed in the ‘dampened RARs’ arm after accounting for death as a competing risk. At 8 years post‐baseline, the cumulative incidence of dementia was 0.84% (95% CI: 0.71%–1.1%) in the ‘dampened RARs’ arm, versus 0.56% (0.46%–0.70%) in the ‘robust RARs’ arm (i.e., dampened RARs lifestyle had a 1.5‐fold increased risk compared to a robust RARs lifestyle).ConclusionThe findings from this multivariable‐adjusted target trial emulation suggest that perturbed RARs may be causally associated with increased risk of all‐cause dementia. These results underscore the importance of maintaining robust RARs for dementia prevention.
Patterns in the Diagnosis and Management of Early Alzheimer’s Disease in Community‐Based Settings in the United States
Authors: Juday Timothy R , Holub Ashley A , Betts Keith A. , Mattke Soeren , Kitchen Sophie A , Liu Hongjiao , Malik Amyn , Batrla Richard , Frech Feride H , Khachaturian Ara S 
Published: January 2025

AbstractBackgroundWith the changing care landscape for early Alzheimer’s disease (AD), optimizing the diagnostic and management process is key. This study assessed the correlation between patient and physician characteristics and outcomes related to the diagnosis, referral, and treatment process for early AD in community‐based settings.MethodThis cross‐sectional study conducted between August and September 2023 abstracted medical chart data for patients aged 50‐89 years who were diagnosed with early AD (mild cognitive impairment [MCI] or mild AD) within the past 2 years and had a clinic visit within the past year. Generalized estimating equation models accounting for clustering at the physician level were conducted to determine the association between patient and physician characteristics and the use of biomarker/imaging assessments, referrals for cognitive/behavioral concerns, and beta‐amyloid therapies.ResultThe study included 1,284 patient charts (817 MCI and 467 mild AD). Factors positively correlated with receiving biomarker/imaging assessments included younger age (<65 vs. 65+ years, adjusted odds ratio (aOR: 1.73; 95% CI: 1.22, 2.44), and physician type (neurologist vs. PCP [aOR: 2.55; 95% CI: 1.49, 4.38]). Non‐Hispanic ethnicity was also associated with lower odds of biomarker/imaging assessments (aOR = 0.58; CI = 0.37‐0.91). Patients with mild AD had increased odds of referral (aOR: 1.27; 95% CI: 1.05, 1.53) compared to patients with MCI. Younger patients (aOR: 2.82; 95% CI: 1.32, 6.06) also had increased odds of receiving beta‐amyloid therapy. While not significant, receipt of biomarker/imaging test (aOR: 2.04; 95% CI: 0.85, 4.93) and female sex (aOR: 1.44; 95% CI: 0.94, 2.21) were associated with increased odds of receiving beta‐amyloid therapy (p = ns). Likewise, patients with urban/suburban residence had increased odds of a referral (aOR: 1.31; 95% CI: 0.89, 1.94) and beta‐amyloid therapy (aOR: 1.41; 95% CI: 0.48, 4.13).ConclusionAs the care environment continues to evolve, future research should explore better measures and impact of insurance coverage policies, physician education and health care system readiness as it relates to the management of early AD, and other chronic disorders affecting memory, movement and mood.
The Effect of Loneliness on Plasma AD Biomarkers in Cognitively Normal Elderly
Authors: Plaska Chelsea Reichert , Bruno Davide , Cejudo Jaime Ramos , Osorio Ricardo S. , Zetterberg Henrik , Blennow Kaj , Pomara Nunzio 
Published: January 2025

AbstractBackgroundSocial isolation (SI) and loneliness are related to several negative health outcomes, including cognitive decline and dementia. While both males and females experience increased SI as a function of age, studies have found that females experience greater loneliness than males, despite males reporting more physical isolation and smaller social networks. Females, independent of SI‐status, are also at increased risk of Alzheimer’s disease (AD). These sex‐related differences in SI and AD‐risk prompted us to examine whether sex and loneliness influenced the plasma AD biomarkers in a cohort of elderly individuals.MethodParticipants were enrolled in the Memory Education and Research Initiative (MERI) program. MERI participants completed a neuropsychological battery, clinical and psychiatric evaluation, and blood draw. Plasma Aβ40, Aβ42, and PTau231 concentrations were determined using single‐molecule array (SIMOA) platform. Plasma Aβ42/Aβ40 ratio was calculated. MERI participants were included if they gave blood, were cognitively‐normal defined by MMSE>27, age 50 years older, and completed the Profile of Mood States scale (POMS). POMS‐Loneliness item was coded to a dichotomous variable (0‐Not Lonely; 1‐Lonely).ResultA total of 459 MERI participants (mean age=71 years; 59% females) were included. There were no significant differences between males and females for education, HAM‐D total score, or MMSE, only age. A 2x2 analysis of covariance (ANCOVA) with sex (male, female) and loneliness (not lonely, lonely), and age as a covariate, revealed a significant interaction between sex and loneliness with Aβ42 (p=0.014). Lonely females had significantly lower Aβ42 than males who are lonely. The same analysis revealed a significant main effect of sex on Aβ40 (p=0.014) and PTau231 (p=0.007); females had higher Aβ40 and lower PTau231, respectively. There were no significant main effects or interactions for Plasma ratio Aβ42/Aβ40.ConclusionCognitively‐normal females who reported being lonely, as compared with males, had lower levels of plasma Aβ42 but not lower levels of plasma Aβ42/Aβ40 ratio, a better predictor of a positive amyloid PET scan. Future studies should examine if the results reflect increased brain amyloid deposition in lonely females. If so, interventions that mitigate loneliness may reduce overall risk for AD in females.
Automated Thigh Tissue Segmentation by Vision Transformers: Application for Early Alzheimer's Diagnosis
Authors: Kasani Sara Hosseinzadeh , Dolatshahi Mahsa , Naghashzadeh Mahshid , Commean Paul K. , Rahmani Farzaneh , Liu Jingxia , Lloyd LaKisha , Nguyen Caitlyn , Hantler Nancy , McBee‐Kemper Abby , Ly Maria , Yu Gary Z , Ippolito Joseph E. , Mittendorfer Bettina , Sirlin Claude , Morris John C. , Benzinger Tammie L.S. , Raji Cyrus A. 
Published: January 2025

AbstractBackgroundWithin the research field of neurodegenerative disorders, unbiased analysis of body fat composition, particularly muscle mass, is gaining attention as a potential biological marker for refining Alzheimer’s disease risk. The objective of this study was to employ a deep learning model for fully automated and accurate segmentation of thigh tissues, potentially contributing to early Alzheimer's diagnostics.MethodIn an IRB‐approved study, 49 participants underwent thigh Dixon MRI scans with a TR=9.99s, TE=2.46s, flip angle=10°, and slice thickness= 5mm. The Dixon Fat/Water images were semi‐automatically segmented by an expert operator in all available slices to obtain the bone, intermuscular fat (InterFat), intramuscular fat (IntraFat), Subcutaneous Adipose Tissue (SAT), Muscle, and Gluteus. We trained and compared the performance of baseline and state‐of‐the‐art deep neural networks, namely, UNet, VNet, and two vision transformers (ViTs): UNETR and SwinUNETR. The performance of the trained models was tested on all data sets using a 3‐fold cross‐validation scheme.ResultWe found SwinUNETR outperformed the others with a mean dice similarity coefficient 96.20 (± 0.51), 80.91 (± 0.55), 50.56 (± 1.43), 95.26 (± 0.80), 98.70), 86.72 (± 1.12) in Bone, InterFat, IntraFat, SAT, Muscle, and Gluteus, respectively. Bland–Altman analysis and scatter plot (Figure 1) indicated that the differences between manual annotations and predictions by the SwinUNETR model were relatively minor for Bone volume, Intramuscular Fat volume, Muscle volume, and Gluteus volume classes. The overall mean difference is ‐88.8cm3 with a 95% confidence interval (CI) of [‐159.53, ‐18.13]. Biases [95% CI] for each tissue class were 5.44cm3 [−8.61, 19.50] for Bone volume, 51.74cm3 [−22.72, 126.20] for InterFat volume, 11.15cm3 [2.76, 19.53] for IntraFat volume, ‐191.09cm3 [−309.54, ‐72.64] for SAT volume, 15.16cm3 [−6.37, 36.70] for Muscle volume, and 18.76cm3 [2.20, 35.32] for Gluteus volume.ConclusionThis study highlighted the use of ViTs for the automated segmentation of thigh tissues in MR which may allow for the detection of subtle changes in muscle mass and fat composition, that are of increasing interests in their associations with the neurodegenerative processes in Alzheimer's disease.
Predicting postmortem neuropathology of Alzheimer’s disease and related dementias using widely accessible clinical data
Authors: Ren Yueqi , Shahbaba Babak , Stark Craig EL 
Published: January 2025

AbstractBackgroundFaced with a rapidly aging population and the rising prevalence of Alzheimer’s disease (AD) and related dementias, the field needs to urgently consider screening tools that utilize widely accessible data modalities. We have previously shown that lower‐cost data, operationalized as data modalities accessible at primary care visits, can indeed accurately predict AD clinical diagnosis and that clustering these data can provide useful information. Here, we apply a similar approach to predicting histopathological status.MethodWe first applied our previously‐developed feature extraction method based on a supervised encoder (SE) to transform potentially noisy input features while maintaining or amplifying relevant information. We next performed classification and clustering to stratify subjects by their neuropathology. Here, we compared two traditional classification methods with a novel Bayesian clustering‐classification algorithm called an Infinite Mixture Classifier (IMC). We identified distinct trajectories of subjects based upon changes in cluster assignment over time. Data for this study come from the National Alzheimer’s Coordinating Center, funded by NIA/NIH Grant U24 AG072122 and contributed to by NIA‐funded ADRCs.ResultWe found that relatively high classification accuracy of neuropathologic lesions was possible using widely accessible, lower cost clinical data. In addition, the supervised clusters, derived from using the SE’s latent features and from the IMC, held meaningful clinical diagnostic information that differentiates subjects along the clinical and pathologic continuum. When clusters were derived using longitudinal clinical data, we further observed distinct trajectories of subjects across time as their cluster assignments changed. These trajectory subgroups have significantly different risk of showcasing each type of neuropathologic lesion obtained from postmortem neuropathology.ConclusionOur framework benefits from the combined strengths of clustering and classification methods while avoiding drawbacks of unsupervised methods. By using lower cost features, which could be obtained at Medicare annual wellness visits, this work is broadly generalizable and has direct implications for screening of neuropathologic lesions of AD and related dementias for the public. As blood biomarkers become more accessible, our framework can be easily extended to include additional data to improve screening for neuropathology using widely accessible data.