bluuebunny/crossref_metadata_2025_split huggingface dataset:
The retina in Alzheimer’s disease: Anatomy and Pathology
Authors: Koronyo‐Hamaoui Maya 
Published: December 2023

AbstractGrowing evidence suggests that Alzheimer’s disease (AD) pathology is not confined to the brain but also appears in the neurosensory retina. Perceived as an extension of the brain, the retina is visible directly and noninvasively at the vascular, cellular and molecular resolution. Aβ accumulation, vascular amyloidosis, hyperphosphorylated (p)tau, microgliosis, astrogliosis, Müller glia changes, abnormal electroretinography patterns, and neuronal degeneration have all been described in the postmortem retina of AD patients, including in early‐stage cases. Although a few studies were unable to consistently repeat these findings, increased proportion of positive studies from subsequent histological, biochemical, and imaging studies validated the original findings. Several studies further provided data suggesting that the brain and retina follow a similar trajectory during AD progression, potentially due to their common embryonic origin and anatomical proximity. A host of retinal vascular abnormalities including reduced blood flow, foveal avascular zone and arteriole‐to‐venule ratio were observed by in vivo fundus imaging, optical coherence tomography (OCT) or OCT‐angiopathy in preclinical, prodromal and clinical AD patients. Further, retinal vascular platelet‐derived growth factor receptor‐β (PDGFRβ) expression and pericyte loss have been associated with retinal vascular amyloidosis and cerebral amyloid angiopathy in mild cognitively impaired (MCI) and AD patients. A recent proteomics profiling study revealed common molecular signatures in the AD retina and brain, with enriched pathways related to inflammation, vascular and photoreceptor vulnerability. Novel imaging techniques, such as curcumin fluorescence, have correlated increases in retinal amyloid burden with brain amyloid PET load and hippocampal atrophy in patients, and together with vascular changes predicted cognitive deficits. This perspective provides an overview on the evidence of AD pathology in the retina and current gaps in knowledge. Future studies exploring retinal pathology in AD and other neurodegenerative diseases may fundamentally contribute to the development of validated retinal biomarkers for improved noninvasive disease diagnosis and monitoring.
Modelling of Alzheimer’s disease pathology in the retina ‐ what can we learn from animal models?
Authors: De Groef Lies 
Published: December 2023

AbstractRetinal neurodegeneration and visual dysfunction have been reported in the majority of Alzheimer’s patients, and, in light of the quest for novel biomarkers, the retina has been receiving increasing attention as an organ for diagnosing, monitoring, and understanding disease. Thinning of retinal layers, abnormalities in vasculature, and protein deposition can be imaged at unprecedented resolution, which offers a unique systems biology view on the cellular and molecular changes underlying AD. It makes the retina not only a promising target for biomarker development, but it also suggests that novel fundamental insights into the pathophysiology of AD can be obtained by studying the retina–brain axis.This presentation will show how the visual system, together with retinal imaging techniques and available animal models of AD, constitutes a unique toolbox for fundamental and preclinical studies of AD; and explore how the retina‐brain axis has the potential to revolutionize preclinical research of AD. We will discuss the strengths of the rodent retina as a model organ to investigate Alzheimer’s disease mechanisms. Studies in different AD mouse models will be presented to showcase how retinal imaging can reveal information about the structure, function, and molecular composition of the retina and thereby aid in diagnosing, monitoring, and understanding AD. Furthermore, we will touch upon how the organotypic heterogeneity of the retina versus the brain may hold clues to understanding AD, and how the unique anatomical organization of the visual system offers opportunities to investigate disease propagation along neural connections.
Critical Next Steps for Translation of Preclinical and Ex Vivo Human Retinal Studies for Alzheimer’s Disease Diagnosis
Authors: Rissman Robert A , Ngolab Jennifer 
Published: December 2023

AbstractRetinal ganglion cells are part of the CNS and as such many years of research has focused on determining and documenting how changes in retinal cells and layers may inform on disease diagnosis, display neuropathology and neurodegeneration. As noted in this session, degeneration of the retina and visual dysfunction occur in the majority of AD, with new and exciting imaging techniques being employed in both animal models and human ex vivo retinal tissues that suggest that the retina can act as a mirror to the brain. Unfortunately, consistency of methods and access to viable tissues has hampered the generalizability and speed of translatability of these exciting fundings. In addition, to date, no studies have directly linked observed neuropathology or neurodegeneration in the retina with more established biomarkers in CSF or blood which is ultimately needed to understand whether retinal imaging or analysis is viable for clinical use. Retinal imaging in live participants could serve as a minimally invasive biomarker to detect neurodegenerative disease. From recent anti‐amyloid studies, identifying individuals at risk for AD in the asymptomatic stage is critical for efficacy of treatments. If validation and establishment of retinal analyses continue as they have and consistency and consensus is obtained in retinal targets to focus on and these prove to mirror changes ongoing in brain, the timing of these changes in relation to brain changes will need to be determined so that a window of when retinal imaging and analysis is viable will need to be determined. Once the conditions, methods and timing of retinal imaging is determined, the ultimate goal would be to screen aged individuals in a primary care setting annually and then refer to specialty clinics or centers for follow up, confirmation and consideration for therapeutic trials.
The relationship between Structural, PET, and Plasma Alzheimer’s Disease Biomarkers and neuropsychological composites among Latino bilinguals and monolinguals
Authors: Rosselli Monica , Velez‐Uribe Idaly , Asken Breton M. , Barker Warren W , Coombes Stephen A. , Duara Ranjan , Smith Glenn E. , Curiel Rosie E , Loewenstein David A. , Adjouadi Malek 
Published: December 2023

AbstractBackgroundEstimates from the Alzheimer’s Association indicate that approximately one in ten older adults in the US have Alzheimer’s disease (AD) dementia while 15 to 20% have mild cognitive impairment (MCI), projecting that about a third of those will develop dementia within five years. The Cognitive Reserve/ Resilience (CR/R) theory postulates that complex mental activity throughout the lifetime builds resilience to cognitive decline despite the biological risk (a neurodegenerative disease). Emerging evidence shows that bilingualism may be one of these neuroprotective factors in the aging brain, but results in bilingualism and CR/R remain inconsistent.MethodsWe studied 238 Latino older adults (age 71.8 ± 8.0 years old, 65% female) from the 1Florida Alzheimer’s Disease Research Center who were diagnosed as clinically normal, MCI, or dementia. Of these, 158 were Spanish‐English bilinguals and 80 were Spanish monolinguals. Bilingualism was assessed by a proficiency questionnaire. Six cognitive composites were developed (Figure 1). We investigated cognitive test correlates of plasma P‐tau181 (Quanterix simoa), hippocampal volume, and amyloid PET (Centiloid) biomarkers in both language groups, using 2×3 ANCOVAs (controlling for age and level of education). We analyzed correlations between biomarkers and cognition and performed multiple regression analyses within each language group.ResultsThe main effect of the diagnostic group was significant for all biomarkers (ps < .001). There was no main effect of the language group and no significant interactions. The correlations between biomarkers were equally significant for both language groups but differences in the association of biomarkers with cognitive tests emerged between language groups. Bilinguals showed a stronger negative association between hippocampal volume and cognitive test scores than monolinguals. In bilinguals, higher PTau181 was associated with lower visuomotor composite scores, while amyloid PET positivity and lower hippocampal volume were associated with cognitive stress and working memory. In monolinguals, higher PTau181 was associated with worse inhibitory control, and amyloid PET positivity and lower hippocampal volume were associated with worse memory.ConclusionResults demonstrated discrepancies between bilingual and monolingual Latinos in the associations between cognitive composites and AD biomarkers. Further exploring the relationship between bilingualism, cognitive test scores, and biomarker patterns is warranted, especially longitudinally.
Automatic analysis of skull thickness, scalp‐to‐cortex distance and association with age and sex in cognitively normal elderly
Authors: Zhang Junhao , Treyer Valerie , Gietl Anton F. , Hock Christoph , Razansky Daniel , Nitsch Roger M. , Ni Ruiqing 
Published: December 2023

AbstractBackgroundTranscranial brain stimulation has been a potential treatment for many brain diseases. The skull compartment exerts a strong influence on the delivery of stimulation, implying the need to accurately examine its geometry and alterations in the population. In addition, personalized neurostimulation requires better understanding of skull geometry and scalp‐to‐cortex distance for optimization.MethodHere, we developed an open source efficient pipeline BrainCalculator based on BrainSuite Skullfinder for automatically computing the skull thickness map to locate the position of minimum thickness, SCD, and brain volume based on structural T1‐weighted MRI 3T data. We examined the influence of age and sex cross‐sectionally in 407 cognitively normal older adults (71.93±7.98 years, 60.2% female) from the ADNI on the minimum skull thickness, SCD and brain volume of the whole‐head MR data. We demonstrated the compatibility of BrainCalculator with available preprocessing toolboxes, including FSL Brain Extraction Tool (BET) 2 and Statistical Parametric Mapping (SPM)12‐based unified segmentation, and compared their performance.ResultOur pipeline was able to compute automatically the skull thinkness, SCD and brain volume using T1 MR data in 8 minutes. We found that the sphenoid bone and temporal bone were thinnest among the skull regions. There was no increase in minimum skull thickness at different regions with increasing age except in the female sphenoid bone. No sex difference in minimum skull thickness or SCD in different age groups was observed. Positive correlations between age and SCD in both males and females were observed, and the slope was higher in the temporal cortex SCD in females (0.307%/y) than in males (0.216%/y) but comparable in other regions. A negative correlation between age and whole brain volume computed based on brain surface in both males and females was observed, and the slope was slightly higher in females (‐1.031%/y) than in males (‐0.998%/y).ConclusionWe developed a pipeline for MR‐based skull thickness and SCD brain volume automatic analysis and demonstrated the sex‐dependent association between minimum regional skull thickness, SCD and brain volume with age. This pipeline is potentially useful for personalized neurostimulation.
White matter hyperintensity features differentially contribute to dementia subtypes as shown by machine learning
Authors: Campo Cecilia Gonzalez , Guardia Camila Sol , Saracco María Antonia , Fraile‐Vazquez Matias , Altschuler Florencia , Ibanez Agustin , Moguilner Sebastian 
Published: December 2023

AbstractBackgroundWhite matter hyperintensities (WMH) are frequently observed on MRI scans of older people. Usually interpreted as a sign of cerebrovascular disease (CVD), they are also associated with an increased risk of cognitive impairment and dementia. WMH are prevalent in Alzheimer’s disease (AD) and in behavioral variant frontotemporal dementia (bvFTD). However, the total and spatial differential burden of WMH between these two pathologies is not well understood. Furthermore, the differential contributions of WMH across both conditions is unknown.MethodHere we assessed the total and tract‐specific WMH burden features to classify the two dementia subtypes. We used a gradient boosting (GB) machine learning algorithm with a multi‐feature approach (demographic, neuropsychological, and magnetic resonance imaging (MRI) data). A total of 66 patients (43 AD, 23 bvFTD) and 66 controls (CN) from the ReDLat consortium underwent high‐resolution brain MRI and neuropsychological examination. WMH were extracted from Flair images using automated segmentation algorithms (LST) to perform analyses of total volume and white matter (WM) tract spatial distribution using the JHU atlas. As input data, three different approaches were tested: cognitive data only (MoCA and IFS scores), structural data only (total and tract‐specific WMH, from 20 different WM tracts) and mixed data (cognitive, structural, and demographic data (age, gender and years of education)). Then, we employed the GB algorithm to classify between AD vs CN, bvFTD vs CN, and AD vs bvFTD.ResultThe model using only total and tract‐specific WMH data yielded the highest area under the curve classification values between AD and bvFTD (0.92 (±0.8)), in comparison with only cognitive (0.86 (±0.1)) and mixed inputs (0.81 (±0.12)). Feature importance analysis revealed specific tract affectation for each dementia subtype. Demographic features were not relevant for the classification, discarding possible sample biases.ConclusionThe model using only WMH data accurately identified dementia subtypes using readily available measures in patients with dementia. We gained biological insights into WMH pathophysiological profiles differentiating AD and bvFTD. If confirmed and replicated in future studies, this approach could be of complementary use for diagnosing and phenotyping dementias.
Predicting early Alzheimer’s disease from diffusion tensor imaging using machine learning techniques
Authors: Bezerra Thais Maria Santos , Balthazar Marcio Luiz Figueredo , Rizzi Liara 
Published: December 2023

AbstractBackgroundThe major current challenge in Alzheimer’s disease (AD) is the identification of individuals likely to develop dementia. Non‐invasive imaging techniques that reflect the integrity of the brain’s white matter are potential parameters to study the pathogenesis of AD. Diffusion tensor imaging (DTI) provides tools to analyze AD‐related brain changes despite the large amount of data generated. To process large amounts of data, computational methods capable of finding relationships among large and complex data sets are needed. In this context, machine learning (ML) techniques have been extended to address this issue. This study aimed to evaluate whether DTI features could predict future AD in non‐AD patients using ML techniques.MethodSixty‐two subjects, with mild cognitive impairment and healthy controls, were enrolled and followed for approximately eleven months. At the first assessment, MRI, neuropsychological tests, and cerebrospinal fluid analysis were performed. At the second assessment, ten had progressed to Alzheimer’s dementia. The clinical diagnosis was used to train the ML classification models. Five data sets were generated with different missing data strategies. Feature selection strategies measured by information gain were implemented and compared. Twenty ML algorithms distributed over several mathematical approaches were used in model training. A deep learning model was also implemented for comparison with ML results.ResultThe best model for predicting AD by DTI was Random Forest with kNN missing data imputation and features selected by information gain. This model achieved a test accuracy of 88.72%. The most relevant DTI tract that contributed to the prediction of the model was the fractional anisotropy (FA) of the cingulum ‐ hippocampus . When other categories of data, such as neuropsychological tests, were included in the model, the accuracy hardly changed (90.26%), suggesting that DTI alone is a good predictor of Alzheimer’s disease. When the deep learning model was run, the constructed neural network achieved 92.31% accuracy.ConclusionOur results suggest that DTI measures analyzed by ML models, such as Random Forest, may predict AD in non‐AD patients. Furthermore, DTI measure of cingulum ‐ hippocampus tract may be a sensitive marker of early AD pathology.
A Rule‐Based Framework to Identify Severity of Dementia from Unstructured Electronic Health Record Data
Authors: Prakash Ravi , Dupre Matthew E. , Ostbye Truls , Xu Hanzhang 
Published: December 2023

AbstractBackgroundThe severity of Alzheimer’s disease and related dementias (ADRD) is mostly documented in unstructured textual data in electronic health records (EHR). This information is important for clinical decision‐making yet is often “hidden” in free text fields and then not as readily available as information in the structured fields for clinicians to act upon. This study assessed the feasibility and potential bias in using keywords and rules‐based matching for obtaining information about severity of ADRD from EHR.MethodWe used EHR data from a large academic healthcare system that included patients with a primary discharge diagnosis of ADRD based on ICD‐9/10 codes between 2014 and 2020. The severity of ADRD was determined using clinicians’ notes based on (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment, and (2) explicit terms for ADRD severity (e.g., “mild dementia”, “advanced AD”). A list of common ADRD symptoms, cognitive test names and diagnosis stage terms was compiled and iteratively refined based on prior literature and clinical expertise. We used the list together with rule‐based pattern matching to identify the context in which the word/phrase was mentioned. The algorithm was developed in python 3.8 using spaCy and pandas library. We assessed the prevalence of the documented ADRD severity and used logistic regression to examine whether the severity varies by patient characteristics.ResultA total of 9,115 patients with over 65k providers’ notes were evaluated. Overall, 16.85% (N = 1,536) of patients were documented with mild ADRD, 17.95% (N = 1,636) were documented with moderate or severe ADRD, and 65.20% (N = 5,942) did not have any documentation of the severity of their ADRD. Compared with patients with mild ADRD, those documented with more advanced ADRD were older, more likely to be female, black, and receive their diagnoses in a primary care or in‐hospital setting. Relative to patients with undocumented ADRD severity, those documented with ADRD severity had a similar distribution regarding sex, race, and rural/urban living environment.ConclusionThis study demonstrated the value of unstructured EHR data and the feasibility of using pattern matching algorithm in identifying severity of ADRD. Still, differences in the documentation may introduce bias in the algorithm.
Remote and in‐clinic digital cognitive screening tools outperform the MoCA to distinguish cerebral amyloid status among cognitively healthy older adults
Authors: Dion Catherine , Kunicki Zachary J. , Emrani Sheina , Strenger Jennifer , De Vito Alyssa N. , Britton Karysa J. , Harrington Karra D , Roque Nelson A , Sliwinski Martin J. , Salloway Stephen , Correia Stephen , Jones Richard N , Thompson Louisa I 
Published: December 2023

AbstractBackgroundExisting cognitive screening measures fall short in capturing preclinical AD, and cognitive impairment is unrecognized or misdiagnosed in 27‐81% of older adult primary care patients. Digital assessment technology has the potential to deliver more efficient and sensitive cognitive screening, but requires rigorous validation first. We aimed to determine the accuracy of remote (M2C2 mobile app) and clinic‐based (TabCAT and DCTclockTM) digital tests to distinguish between older adults with and without AD pathologic change. We used the Montreal Cognitive Assessment (MoCA) as a reference standard comparison test.MethodsWe recruited 73 cognitively normal participants with Aß PET status (Aß+, n = 25; Aß‐, n = 48, determined by clinical read) from the Butler Hospital Alzheimer’s Prevention Registry (mean age = 69.2 and education = 16.5; 71% female; 89% White). Participants completed M2C2 tasks at home 3 times per day for 8 days, followed by the TabCAT tasks, DCTclockTM, and MoCA at an in‐person study visit. We calculated the area under the curve (AUC) to compare cognitive task accuracy to distinguish Aß status. Multi‐day learning curves were used to examine differences in M2C2 task performance by Aß status over time.ResultsAmong the M2C2 tasks, average performance on the Prices task (episodic memory) over 8 days showed the highest accuracy (AUC = .77) to distinguish Aß status. The Aß+ group tended to perform worse than the Aß‐ group on the Prices task over time (Figure 1). On in‐person screening measures (single time‐point), accuracy to distinguish Aß was greatest for the TabCAT Favorites task (AUC = .76), relative to the DCTclockTM (AUC = .72) and the MoCA (AUC = .71).ConclusionsWe showed that several brief digital screening approaches (memory‐specific tasks on the M2C2 and TabCAT) outperform the MoCA in distinguishing between cognitively healthy individuals with and without elevated cerebral Aβ. Although further validation in community and clinic‐based samples is needed, these results suggest that these digital cognitive assessments may be suitable for more widespread screening to detect early pathological changes in neurodegenerative disorders.
Figure 1. M2C2 task performance over time (3 sessions daily for 8 days) by Aβ status.
Glomerular filtration rate, neurofilament light, and phospho‐tau 181 in a community‐based cohort: the Shanghai Aging Study
Authors: Wu Jie , Xiao Zhenxu , Wu Wanqing , Wang Mengjing , Ma Xiaoxi , Liang Xiaoniu , Zheng Li , Ding Saineng , Luo Jianfeng , Hong Zhen , Chen Jing , Zhao Qianhua , Ding Ding 
Published: December 2023

AbstractBackgroundThe blood‐based biomarkers are approaching the clinical practice of Alzheimer’s disease (AD). Chronic kidney disease has a potential confounding effect on peripheral protein levels. It is essential to characterize the impact of renal function on AD markers.MethodPlasma phospho‐tau181 (P‐tau181), and neurofilament light (NfL) were assayed via the Simoa HD‐X platform in 1189 dementia‐free participants from the Shanghai Aging Study (SAS). The estimated glomerular filter rate (eGFR) was calculated. The association between blood markers and renal function was analyzed.ResultPlasma NfL significantly increased as the eGFR descended (median = 12.27,15.50,20.70pg/mL in the high, medium, and low eGFR groups, respectively, P<0.001). The same trend was observed for plasma p‐tau181, but insignificant after adjustment (P = 0.485). A significant interaction between age and eGFR was found for plasma NfL (Pinteraction< 0.001). Among participants older than 70 years old, the association between plasma NfL and eGFR was stronger (B = ‐0.222, P < 0.001).ConclusionIn a community‐based cohort, renal function differentially impacted blood NfL and P‐tau 181 levels. Plasma NfL level was markedly influenced, whereas P‐tau 181 was a relatively robust marker to comorbidities.