bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diabetes and brain MRI measures: Results from SOL‐INCA MRI study
Authors: Gonzalez Kevin A , Tarraf Wassim , Banks Sarah , Bangen Katherine J. , Pa Judy , Gallo Linda C , Stickel Ariana M , Filigrana Paola , Isasi Carmen R , Daviglus Martha L , Testai Fernando Daniel , Decarli Charles , Gonzalez Hector M 
Published: January 2025

AbstractBackgroundHispanic/Latino communities in the US are rapidly growing and aging and are at two‐fold risk of Alzheimer’s Disease and Related Dementia’s (ADRD) compared to non‐Hispanic Whites. This additional risk could be, in part, due to increased risk of cardiovascular disease. Hispanics/Latinos also have higher rate of diabetes compared to non‐Hispanic Whites and nearly 2 out of 5 individuals with diabetes go undiagnosed. While diabetes has been linked to white matter hyperintensities (WMHs), less is known about the magnitude in diverse Hispanics/Latinos and even fewer studies have considered links to other neurobiological endpoints in this population. This work aims to clarify these associations in a deeply characterized diverse middle‐aged and older Latino cohort.MethodWe used data from the Study of Latinos‐Investigation of Neurocognitive Aging (SOL‐INCA) MRI study which is an ancillary study of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The goal of SOL‐INCA MRI is to understand cerebrovascular pathology and ADRD etiology using MRI. A total of N=2400 middle aged and older (50+ years) and N=400 younger (35‐49 years) participants were recruited from the parent HCHS/SOL study. Diabetes was assessed using both continuous HbA1c % and recommended ADA cutoffs to assess prediabetes and diabetes status. Outcomes included structural (i.e., volumes; WMHs, total brain, regional and total gray matter volumes, lateral ventricles, hippocampus) and diffusion MRI (free water=FW, fractional anisotropy=FA). Survey weighted linear models (covariate adjusted by important confounders) were used to test the associations between our exposures and outcomes of interest.ResultCompared to no diabetes, diabetes was linked to smaller total brain and occipital volumes, larger WMHs, larger lateral ventricles, lower FA, and higher FW (Figure 1). Higher levels of HbA1c % (treated continuously) were additionally associated with smaller occipital and larger hippocampal volumes (Figure 2). These findings were consistent across men and women. We did not find significant differences with the pre‐diabetes group.ConclusionOur findings suggest that elevated HbA1c and diabetes status are linked to cerebrovascular pathology and brain atrophy. Findings with hippocampal volume could be due to maladaptive process or hypertrophy. Further research is needed to understand different neurobiological mechanisms in understudied communities.
Gender differences in the associations of circadian function with cognition among older persons with sleep disturbances
Authors: Pan Yulu , Cho Gawon , Doyle Margaret , Polisetty Lakshmi , Burzynski Chase , Iannone Lynne , Knauert Melissa , Yaggi Henry , Gill Thomas M. , Miner Brienne 
Published: January 2025

AbstractBackgroundCircadian function, characterized by circadian strength, timing, and fragmentation, has been shown to correlate with cognitive function; however, little is known about in these associations, particularly among older adults who may have more variability in their sleep‐wake schedules.MethodAmong 60 community‐living adults aged 60‐90 years with self‐reported sleep complaints ≥once/ week, we investigated gender differences in the associations between circadian and cognitive function. Participants completed seven days of actigraphy and one night of polysomnography. Circadian function variables were calculated using activity counts from actigraphy and included: circadian strength (amplitude, interdaily stability [IS; degree of consistency of activity patterns from day‐to‐day], mesor); timing (activity peak time, sleep midpoint [midpoint between sleep onset and offset]); and fragmentation (intradaily variability [IV: fragmentation of the rhythm relative to its 24‐h amplitude]). Cognitive function was assessed by the Montreal Cognitive Assessment (MoCA; higher scores indicate better cognitive function). Pearson correlation coefficients examined the associations between circadian variables and cognitive function in men, women, and all participants, adjusting for age, race, and total sleep time (from polysomnography).ResultThe mean age was 74 (±6.4) years, with 65% women and 33.3% minority race. Among women, higher IS (r=0.62, p<0.001) and lower IV (r=‐0.44, p=0.006) significantly correlated with higher MoCA score. Among men, earlier sleep midpoint (r=‐0.53, p=0.02) and earlier activity peak time (r=‐0.53, p=0.02) significantly correlated with higher MoCA score. The amplitude‐MoCA score association was non‐significant in women (r=0.31, p=0.07) and men (r=0.30, p=0.21) but significant in all participants (r=0.32, p=0.01). No significant associations were found between mesor and MoCA score in any group.ConclusionAmong community‐dwelling older adults with sleep disturbances, circadian strength and fragmentation [IS, IV] correlated with cognitive function only in women, while circadian timing [activity peak time, sleep midpoint] correlated with cognitive function only in men. These differences may be related to sex differences in other sleep‐related factors, such as time in slow‐wave‐sleep and melatonin amplitude, which are higher in women than men. Further investigation is needed to understand biological differences that may explain the differential association of circadian variables with cognition in older men and women.
Susceptibility to smartwatch based interventions in patients with MCI or dementia in a controlled environment
Authors: Goerss Doreen , Köhler Stefanie , Teipel Stefan 
Published: January 2025

AbstractBackgroundPromising elements of assistive technologies are available to help people with cognitive impairment in their daily lives. However, there has been limited research on how smartwatches can directly interact with persons who have cognitive impairments. We looked at the factors that affect the effectiveness of interventions provided via a smartwatch.MethodWe designed two tasks delivered by smartwatch: A) drinking water and B) circling bells on a worksheet. We created two modes of intervention‐intensity affecting vibration, alarm sounds, text sizes and images. In case of failure, interventions were repeated up to three times. We observed n = 40 patients’ reactions to interventions via cameras and rated success on a 5‐step scale from 0 to 2. Patients were assigned (n = 20/20) either to mode “regular” or mode “intensive”. All patients received both tasks in the mode corresponding to the group. In both groups, half received the tasks in order AB, the other half in BA. Participants answered questionnaires and underwent neuropsychological testing. People were diagnosed with MCI (n = 12) or dementia (n = 28). We hypothesized that the mode, the sequence of tasks, age, affinity for technology (ATI) and cognitive status (MMSE) would influence the success of the interventions. We expected that intensive interventions would be more successful than regular interventions.ResultsThe groups statistically did not differ with respect to mean age, MMSE or ATI. The intensive group was more successful than regular group, for each task and global success score (mean 1.7, SD 0.3 vs. 1.2, SD 0.7., Welch t‐test p = 0.03). Testing of linear regression models showed that “mode” significantly increases model fit: H1 age + sex + MMSE + ATI + task sequence + mode p = 0.045, adjusted R² = 19%, compared to H0 age + sex + MMSE + ATI + task sequence p = 0.399, adjusted R² = 0.8%. Controlling for covariance (ANCOVA) showed only for “mode” a significant influence on success (p = 0.003) not of MMSE, sex or diagnosis (p<0.05, respectively).ConclusionTasks delivered from a smartwatch as intensive interventions are more likely to be solved by patients than regularly delivered ones. Intensity is more critical than the patient’s baseline status with respect to cognition and technology‐affinity.
Novel biomarkers in LBD ‐ Implications for clinical trials
Authors: Vermunt Lisa 
Published: January 2025

AbstractThe lack of an in‐vivo pathology marker for synuclein pathology has been a long standing challenge for dementia for Lewy bodies (DLB) research. This issue is critically important for phase II trials, which are often small, requiring the precise measurement of the biological effects, whether disease modifying or symptomatic. Recent advances have enabled the determination of alpha‐synuclein pathology status with CSF measurements, using aggregation assays [RT‐QUIC]. Additionally, several proteomic studies have identified candidate markers for disease progression. This presentation focusses on these breakthroughs, and how they will influence selection of clinical trial participants and detection of treatment response. In clinical trial research, these markers could be instrumental in understanding the treatment effects in early stage trials (phase I‐II) and generate novel read‐outs for later stage trials (phase II‐III).This talk will provide overview of novel CSF biomarkers, including Dopa Decarboxylase [DDC] and several others, exploring their potential implementation in clinical trials. The presentation will summarize converging evidence, offer forward‐looking perspectives, and present novel data on longitudinal cohort measurements for these markers, including DDC, to provide a comprehensive understanding of the potential applications of these biomarkers in the context of dementia research.
Network‐based prediction and real‐world patient data observation identifies Doxycycline and Irbesartan as candidate medicines for Alzheimer’s disease and Traumatic Brain Injury
Authors: Bykova Marina , Danziger Michael , Rosen‐Zvi Michal , Pieper Andrew A. , Cheng Feixiong 
Published: January 2025

AbstractBackgroundTraumatic Brain Injury (TBI) is one of the most common nonheritable causes of Alzheimer’s disease (AD). However, there is lack of effective treatment for both AD and TBI. We posit that network‐based integration of multi‐omics and endophenotype disease module coupled with large real‐world patient data analysis of electronic health records (EHR) can help identify repurposable drug candidates for the treatment of TBI and AD.MethodWe have collected a total of 69 TBI‐related genes and 144 AD‐related genes derived from human genetic and multi‐omics findings. We conducted network proximity analysis to prioritize likely drug candidates under the human protein‐protein interactome model. Using the MarketScan patient database (∼170 million individuals), the drugs were further investigated for association with a reduced incidence of AD against two patient cohorts: A cohort comprised of all patients over 70, and a TBI cohort comprised of patients who have sustained a traumatic brain, head, or facial injury.ResultUsing network proximity‐based analysis of drug‐target networks and disease‐associated genes under the human protein‐protein interactome network, we identified 66 and 53 candidate drugs for AD and TBI, respectively (z‐score ≤ ‐3, p‐value ≤ 0.01). Drugs present within or related to drugs within both candidate lists, such as irbesartan and doxycycline, were especially prioritized for further real‐world patient data validation. In the subsequent Marketscan analysis, we found that doxycycline is significantly associated with a reduced incidence of AD in the over‐70 cohort as compared to cohort patients taking other anti‐infective and antibacterial drugs (OR = 0.92, adjusted 95% CI 0.88‐0.97, p‐value = 0.002). In addition, prescription of irbesartan was significantly associated with a reduced incidence of AD as compared to all cohort patients taking cardiovascular (OR = 0.82, adjusted 95% CI 0.71‐0.95, p‐value = 0.002) and renin‐angiotensin agents (OR = 0.82, adjusted 95% CI 0.71‐0.97, p‐value = 0.002).ConclusionWe demonstrated that combining network‐based predictions and large real‐world patient data observations has identified doxycycline and irbesartan as candidate treatments for AD. Further functional and clinical validation is warranted to examine a causal relationship between doxycycline and irbesartan with a future risk of developing AD.
NV23non‐verbal communication
Authors: Scharler‐Plotnik Pia Moana ,   Demedarts
Published: January 2025

AbstractImagine a visit in the care taking home by the grandchild. The communication might be challanging. What if there was an option that offers a different way to interact without language? The NV23 is a table designed for non‐verbal and cross generational communication. Sensoric play without the possibility of doing it wrong, as there are no rules of usage. The NV23 has a surface of a landscape and can be filled with various objects. Objects should be selected by the haptic memory, to trigger positiv associations, but could simply be chosen by the appealing shape or color. Everyone who sits at the NV23 can choose or bring objects to the table. The curation of the object depends on the people using it, as well as the use of it. We have experienced ball games, sound checks of materials, storytelling to the different characteristics of the objects in the landscape, and many more. The artistic table have been designed and tested that encourage people with very advanced dementia to interact. The NV23 object has designed in different versions of appearances to test if this also has influence and make it appealing. It is still in development, so the session is an open conversation, a testing and a showing of findings. Pia Scharler is member of the DEMEDARTS project team, which is a artistic‐research team in Vienna.
Differential item functioning of patient‐reported outcomes measures of anxiety and depression by severity of cognitive impairment
Authors: Russell Andrea , Lovett Rebecca , Vogeley Abigail , Batio Stephanie , Opsasnick Lauren , Graham Eileen , Mroczek Daniel , Bonham Morgan , Yoshino‐Benavente Julia , O'Conor Rachel , Wolf Michael S 
Published: January 2025

AbstractIntroductionPatient‐reported outcomes are increasingly being utilized in clinical settings to identify psychological symptoms and track fluctuations over time. Some clinicians and researchers have expressed concerns about the validity of symptom questionnaires cognitively impaired populations. We sought to determine if differential item functioning (DIF) is present based on cognitive impairment using the patient reported outcomes measurement information system (PROMIS) inventories of anxiety and depression.MethodsData from a longitudinal aging cohort study in Chicago, IL. Mild impairment was defined as a z‐score between ‐1 and ‐1.49 and moderate/severe impairment was a z‐score of ≤‐1.5 on 2+ tests in one of 5 cognitive domains. PROMIS Anxiety and Depression measures were utsed. Due to sample size and response variation, the anxiety model examined DIF with participants categorized using three categories (none, mild, or moderate/severe cognitive impairment). The depression model used 2 categories (none/mild vs moderate/severe impairment). Analyses were conducted in R (lordif package with Monte Carlo simulations). Effect sizes for DIF were calculated by test item. Test characteristic curves (TCCs) examined measure performance by cognitive impairment.ResultsData was available for 396 participants, who were 71 years old, mostly female (n = 285) and about half were non‐Hispanic White (n = 214). Approximately 72% had no cognitive impairment, 14% had mild cognitive impairment and 13% had moderate/severe impairment. There was evidence of DIF in 4 of 7 anxiety items and 3 of 8 depression items. The direction of DIF varied, suggesting the response patterns of moderately/severely impaired participants resulted in psychometric overestimation of anxiety and underestimation of depression. Effect sizes of DIF were relatively small for anxiety (0.016‐0.034) and depression (0.014‐0.043) and TCC curves were similar by cognitive impairment category for both measures.ConclusionThere is psychometric measurement bias on PROMIS anxiety and depression measures by severity of cognitive impairment. Researchers and clinicians should be aware that cognitive impairment may impact responses when using assessments, however, this study showed this bias is small and results in negligible change when examining the measures overall. These findings support the conclusion that PROMIS Anxiety and Depression are reliable assessments when used among individuals with different levels of cognitive impairment.
Microhemorrhages and Siderosis in Cognitive Impairment
Authors: Alotibi Hessah A 
Published: January 2025

AbstractBackgroundMicrohemorrhages and superficial siderosis (SS) have been reported in patients with mild cognitive impairment (MCI) and dementia, especially in the context of Alzheimer’s disease (AD) and cerebrovascular disease. Cerebral amyloid angiopathy and small vessel disease (SVD) have both been implicated in microhemorrhages and SS but their prevalence in those with MCI and dementia and their relationship to SVD is unknown.MethodWe conducted a retrospective chart review of patients with MCI or dementia that had undergone MRI scans from 2014 To 2023. We calculated prevalence of microhemorrhages and SS, analyzed the distribution patterns, and assessed small vessel ischemic changes using the Fazekas scale. Exclusions included traumatic brain injury, large stroke, and subjective cognitive impairment.Result2852 individuals underwent underwent an MRI for CI and 120 (4.2%, 95% CI: 3.5‐5%) patients had microhemorrhages and/or SS and fulfilled inclusion criteria (mean age 80.52 (9.1) years; 55.8% male). Of the patients with hemorrhagic findings, 73% had microhemorrhages (mean 7.51 microhemorrhages patient), 7.5% had SS and 19.2% exhibited both. The microhemorrhages were deep (25.8%), lobar (35.0%), and mixed (39.2%). Fazekas rating scale was 0 in 0.8% of patients with microhemorrhages, 1 in 34.2%, 2 in 44.2%, and 3 in 20.8%. The most prevalent diagnoses in patients with hemorrhagic findings were mixed vascular and AD dementia (34.2%), vascular CI (25.0 %), AD (18.3%), vascular MCI(7.5%), MCI due to possible AD (4.2%), Parkinson/Lewy Body dementia (5.0%), dementia NYD (4.2%), frontotemporal dementia (0.8%), and progressive supranuclear palsy (0.8%). Common comorbidities included hypertension (70.0%), dyslipidemia (50.0%), and ischemic heart disease (25.8%).Conclusion4% of patients investigated for cognitive impairment have microhemorrhages and/or SS. Most of the patients had CI due to AD and/or vascular disease. Mild or moderate SVD as indicated by Fazekas scale was common as was hypertension. The etiology of microhemorrhages and SS is likely multi‐factorial and SVD may contribute to their presence, but hypertension may be an independent risk factor that warrants further investigation.
Advanced brain age prediction using 3D convolutional neural network on structural MRI
Authors: Ahmadi Babak , Morshedizad Zohreh , Gahrooei Mostafa Reisi , Babajani‐Feremi Abbas 
Published: January 2025

AbstractBackgroundPredicting brain age from neuroimaging data is an emerging field. The age gap (AG), the difference between chronological age (CA) and brain age (BA), is crucial for indicating individual neuroanatomical aging. Previous deep learning models faced challenges in generalizability and neuroanatomical interpretability. We incorporated patients with different dementia types, including dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), alongside mild cognitive impairment (MCI) and cognitive normal (CN) individuals. This inclusive strategy is essential for comprehensive mapping of neurocognitive trajectories and understanding distinct aging patterns across various cognitive conditions.MethodUtilizing T1‐weighted MRI images of n = 3,859 subjects (Table 1) from the CamCAN, NACC, and ADNI databases, this study aimed to predict brain age in four groups (CN, MCI, AD, and DLB). Structural MRI data were spatial normalized and skull‐striped. Then a 3D Convolutional Neural Network (CNN) based on the skull‐striped data was used for age prediction. The model’s architecture includes three convolutional layers with ReLU activation, max‐pooling, batch normalization, and dropout for regularization, ending with global average pooling and dense layers. The model was trained and validated on CN subjects. The trained model was used to predict age in MCI, DLB, and AD patients as well as the test set of CN subjects.ResultThe 3D CNN model accurately predicted brain age in the CN test set with an AG of 0.64 ± 2.74 years and an absolute AG of 1.86 ± 2.11 years (Figure 1 and Table 1). In DLB and AD patients, the average AG was 3.81 and 2.90 years, respectively, and significantly larger than 0 (P < 10‐5), indicating accelerated aging patterns in these groups. The average AG of MCI was 0.09 years which was significantly smaller than that of both DLB and AD (P < 10‐3), indicating the early stage of impairment in MCI patients.ConclusionOur 3D CNN model accurately predicted brain age in cognitively normal individuals and identified accelerated aging in DLB and AD patients. The model's precision highlights its potential for early detection and understanding of neurocognitive trajectories, contributing to advancements in neurological research and clinical diagnostics.
A Convolutional Neural Network to Identify Parkinson’s Disease using Multimodal Retinal Images
Authors: Richardson Alexander , Kundu Anita , Henao Ricardo , Lee Terry , Scott Burton L. , Grewal Dilraj S , Fekrat Sharon 
Published: January 2025

AbstractBackgroundRetinal structure and microvasculature may be used as a surrogate for parallel processes in the brain. Previous studies have revealed differences in retinal structure and microvasculature in patients with Parkinson’s disease (PD) compared to cognitively normal controls[1]. Previous work developed a convolutional neural network (CNN) trained on multimodal retinal images that was able to identify Alzheimer’s dementia with an area under the curve (AUC) of 0.841[2]. Herein, we developed a CNN to identify PD.MethodsBuilding upon a pretrained VGG19 architecture, we trained a CNN to receive inputs of ganglion cell‐inner plexiform layer (GC‐IPL) thickness color maps, optical coherence tomography angiography (OCTA) superficial capillary plexus 6x6mm en face images centered on the fovea obtained using the Zeiss Cirrus HD‐5000 with AngioPlex (v11.0.0.29946), ultra‐widefield (UWF) fundus color and autofluorescence (FAF) scanning laser ophthalmoscopy (Optos California) images from patients with a clinical diagnosis of PD and from controls without PD or cognitive impairment. The model consisted of a shared convolutional encoder, parallel image modality specific feature transformations, and prediction heads for all modalities that converge into a single output.Results1,784 images from 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for development and testing. 296 (80%) control eyes were used for training, 36 (10%) for validation, and 39 (10%) for testing. 59 (79%) PD eyes were used for training, 6 for validation (8%), and 10 (14%) for testing. Our best performing model had an AUC of 0.918. UWF color images proved to be the most effective imaging input (highest AUC 0.838), and GCL‐IPL color maps were the least effective (highest AUC 0.630).ConclusionsThis CNN was able to successfully identify individuals with a clinical diagnosis of PD. Further work is ongoing to expand the number of images and the diversity of the population to improve translatability.Ma, J.P., et al., Retinal vascular changes in Parkinson’s disease on ultra‐widefield retinal imaging. Investigative Ophthalmology & Visual Science, 2021. 62(8): p. 1779‐1779.Wisely, C.E., et al., Convolutional neural network to identify symptomatic Alzheimer’s disease using multimodal retinal imaging. British Journal of Ophthalmology, 2020. 106: p. 388 ‐ 395.