bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accelerated cognitive decline and hippocampal volume loss in mild cognitive impaired carriers of Apolipoprotein‐E ε4
Authors: Sepehrband Farshid , Parasoglou Prodromos , Trotter Dinko Gonzalez , Parikshak Neelroop , Levy Oren , Harari Olivier , Avbersek Andreja 
Published: December 2022

AbstractBackgroundThe ε4 allele of the Apolipoprotein‐E (APOE) gene is the major genetic risk factor for late‐onset Alzheimer’s disease (AD). APOE ε4 carriers represent around 45% of AD population, with each copy of APOE ε4 allele conferring increased risk for AD. Here, we aim to quantify disease progression during the early clinical stage of AD using imaging biomarkers to stratify patient populations for therapeutic interventions.MethodsData used in the preparation of this abstract were obtained from the Alzheimer’s Disease Neuroimaging Initiative. CDR some of boxes (CDR‐SB), PET (amyloid load; Florbetapir), MRI (hippocampal volume) and APOE4 genotype were collected. Only MCI patients were included (MMSE=27.6±1.8 and global‐CDR=0.5±0.05). Available follow up data with at least 10 participants were included in the analysis.ResultsData from 1021 participants with baseline MCI status were included in the study (110 ε4 homozygotes, 397 heterozygotes ε4).At baseline, CDR‐SB of non‐carriers and ε4 homozygotes was not different (p=0.24). The annual rate of cognitive decline in ε4 homozygotes (0.7 points per year; p=7e‐8) and heterozygotes ε4 (0.56 points per year; p=4e‐8) was 3.7 to 4.6 times greater than non‐carriers (0.15 points per year; p=0.00005).At baseline, hippocampal volume was significantly (p<0.01) lower in ε4 homozygotes (6.50 mL) and heterozygotes ε4 (6.76 mL) compared to non‐carriers (6.98 mL). The annual rate of hippocampal loss in ε4 homozygotes (0.09 mL; p=0.032) and heterozygotes ε4 (0.13 mL; p=0.0006) was faster than non‐carriers (0.03 mL; p=0.16).At baseline, amyloid load was significantly (p<0.0001) higher in ε4 homozygotes (SUVR=1.37) and heterozygotes ε4 (SUVR=1.31) compared to non‐carriers (SUVR=1.13). The rates of progression were not significantly different across groups.ConclusionAPOE ε4 carriers showed notably greater rate of progression on CDR‐SB compared to non‐carriers. Greater amyloid load and hippocampal atrophy were observed in APOE ε4 carriers compared to non‐carriers at baseline although the CDR‐SB scores were comparable. The amyloid deposition appeared to plateau at MCI. Hippocampal volume loss continued in all groups, with a fastest progression rate in ε4 carriers. Result suggests that detection of therapeutic changes from interventions may be more efficient in this population than the broader population of MCI patients.
Microstructural diffusivity (DTI) changes underlying visual short‐term memory deficits in Alzheimer's Disease detected by digital screening
Authors: Toniolo Sofia , Attaallah Bahaaeddin , Veldsman Michele , Maio Maria R , Slavkova Elitsa , Dickson Shannon , Plant Olivia , Idris Imran , Tabi Younes Adam , Butler Christopher , Thompson Sian , Manohar Sanjay , Husain Masud 
Published: December 2022

AbstractBackgroundDeficits in visual short‐term memory (STM), rather than episodic long‐term memory, have recently been found to be associated with Alzheimer’s Disease (AD). However, the brain microstructural underpinning of selective abilities within visual STM remains poorly described.Method52 AD and 60 age‐matched healthy controls were recruited from the Oxford Centre for Cognitive Disorders. We used a novel digital visual STM task – the “What was where?” task (Figure 1) and extracted four metrics: Identification Accuracy (percentage of correctly identified items), Target detection (probability of correctly identifying the target), Misbinding (erroneously localizing an item to the remembered location of another item in memory) and Guessing (random guessing), (Figure 1). We then applied TBSS (Tract‐Based Spatial Statistics) to investigate in which areas of the brain microstructural disruption of physiological diffusivity correlated with behavioural performance.ResultIn AD patients, a core area including Forceps Major, left Optic Radiation (OR) and left Middle Longitudinal Fasciculus (MLF) was commonly associated with performance across all four metrics (Figure 2). Misbinding also involved the left MLF (part III), left inferior Fronto‐Occipital Fasciculus (IFOF) and left Vertical Occipital Fasciculus (VOF). Target detection additionally was associated with Superior Longitudinal Fasciculus (SLF), and Identification Accuracy with left Superior Thalamic Radiation (STR) in AD and also right OR in controls.ConclusionThere is a common shared area of altered diffusivity in AD patients that underlies a global impairment in visual STM, while distinct types of memory errors were associated with disruption of additional contribution of selective white matter fibers.
Prediction of network‐based neurodegeneration using deep graph recurrent neural network
Authors: Kang Sohyun , Kim Sung‐Woo , Seong Joon‐Kyung 
Published: December 2022

AbstractBackgroundAlzheimer's disease (AD) is a neurodegenerative disease characterized by the progressive loss of neurons. To predict such progression, deep neural network models with longitudinal data have been used recently. However, there is a limit that they didn't consider graph topology of brain network, while it is well‐known that neurodegeneration progresses along the large‐scale brain network. In this study, we proposed a graph neural network (GNN) based deep recurrent model to predict longitudinal progression of neurodegeneration in mild cognitive impairment (MCI) patients.MethodWe used 1,146 T1‐weighted magnetic resonance imaging from Alzheimer’s Disease Neuroimaging Initiative. The images were of 320 MCI subjects at baseline with longitudinal data at 2‐year intervals. The length of the data ranged from 3 to 7 years, and an average length is 3.58. We extracted cortical thicknesses using FreeSurfer v6.0 and Desikan‐Killiany atlas. Additionally, we acquired diffusion tensor imaging and resting‐state functional magnetic resonance imaging from Human Connectome Project to extract normative averaged structural connectivity (SC) and resting‐state functional connectivity (rsFC). We also used spatial connectivity of which the element for comparison. We adopted graph‐LSTM model in our problem setting: we used mean cortical thickness as node feature and connectivity as adjacency matrix. We performed 5‐fold cross‐validation and evaluated the performance using the test dataset.ResultWe compared performance of our model with two baseline models, which have been used to predict disease progression but couldn’t learn graph topology of brain, such as RNN and LSTM. The loss was significantly lower at our model than at both baseline models by about one seventh. We implemented additional studies using three distinct connectivities as mentioned above. When using SC and rsFC as adjacency matrix, model performances were increased by about one third compared to using spatial connectivity. In addition, raw cortical thicknesses between prediction and observation were not significantly different in most regions except right anterior cingulate in year 8.
Conclusions: We predicted progression of neurodegeneration using graph‐LSTM considering brain networks. We found substituting GNN for embedding layers of LSTM improved performance. We also identified utilizing SC and rsFC as underlying structure of neurodegeneration decreased the prediction error.
Predicting Conversions from Normal to MCI and from MCI to AD with Clinical Variables in the National Alzheimer’s Coordinating Center Uniform Data Set Version 3: Application of Big Data Analytics and Transition Probability Calculators
Authors: Pang Yijiang , Zhou Jiayu , Albin Roger L. , Sano Mary , Shen Changyu , Kukull Walter A. , Dodge Hiroko H 
Published: December 2022

AbstractBackgroundClinical trials are increasingly focused on pre‐manifest and early Alzheimer’s disease (AD). Accurately predicting those who transit from normal to MCI or from MCI to dementia/AD (true positives) versus non‐progressors (false positives) is challenging. Biomarker positivity and comorbidity information are often not sufficient to identify those who are destined to have symptom progressions. Accurate identification of progressors is important to avoid un‐necessary treatment as well as to improve trial efficiency.MethodUsing only clinical variables and big data analytics, we aimed to predict those who convert from normal to MCI and from MCI to AD, estimating probabilities of these conversions with a small set of selected clinical variables. This work updates our previous work using National Alzheimer’s Coordinating Center (NACC) Uniform Data Set Version 2. (1) We also generated a user‐friendly conversion probability calculator to estimate individual subject conversion. We used data from the NACC Uniform Data Set Version 3 and machine learning feature selection methods to identify clinical variables that predicted MCI and dementia conversion. We built a pipeline with machine learning techniques to test model performance.ResultUsing NACC data (frozen 3/2020), 577 subjects were used to predict conversions from normal to MCI within 4 years, and 538 and 882 subjects were used to predict conversion from MCI to AD within 2 years and 3 years, respectively. An exploratory analysis was also conducted for amnestic MCI (aMCI) conversion. For each prediction model, we selected 20 clinical variables optimizing predictions using Receiver Operating Characteristics Area‐under‐Curve (AUC). Our model achieved 82.1% prediction accuracy, 75.9% specificity, 85.1% sensitivity, and 80.5% AUC for MCI to AD conversion within 3 years. Similar results were obtained for normal to MCI conversion. The variables which ranked highest in predicting transitions include clinician’s judgement of subject’s meaningful cognitive impairment (conversion to MCI) and CDR community affairs item (conversion to AD in 3 years).ConclusionAlthough NACC UDS is extensive, our algorithm is based on a large sample size of potential trial participants who visited or are referred to Alzheimer’s Disease Centers. The probability calculator can be used to effectively pre‐screen and enrich trial participants.
Identification of circRNAs linked to Alzheimer’s disease and related dementias
Authors: Puri Sambhavi , Hu Junming , Yang Zhuo , Sun Zhuorui , Wolozin Benjamin , Zhang Xiaoling 
Published: December 2022

AbstractBackgroundAlthough linear mRNA transcriptome study has been well performed for Alzheimer’s disease (AD), little is known for the non‐coding RNA. Circular RNAs(circRNAs) are particularly notable because of their selective expression in the brain and differential regulation in neurological disease conditions, including AD. CircRNAs are formed from back‐splicing events, and they majorly function as sponges for microRNA and RNA‐binding proteins. Our study examines circRNA expression patterns associated with AD and related dementias in different brain regions.MethodTo detect and quantify cortical circRNAs, we used Mount Sinai Brain Bank (MSBB) rRNA‐depleted RNA sequencing(RNAseq) data derived from inferior frontal gyrus tissue of 278 subjects(216 AD cases, 62 controls) by applying DCC and Circexplor3. To detect hippocampal circRNAs, we generated rRNA‐depleted RNAseq data from 207 human postmortem hippocampal region samples(186 AD, 21 controls) obtained from the BU Alzheimer Disease Research Center. Differentially expressed circRNAs were identified using limma and DESeq2. Both cortical and hippocampal circRNA candidates were further validated using RT‐qPCR in a smaller independent cohort of patients.ResultWe identified over 300 circRNA significantly associated with AD condition (FDR<0.05). Interestingly, a major fraction of these circRNAs are derived from genes involved in excitatory synaptic transmission, axon guidance, and calcium signaling pathways. CircRNA from AD‐linked genes, including APP, PICALM and PSEN1, were identified as top candidates associated with AD in cortex. In the hippocampus, we find circSLC8A1 and circHOMER1 among the top differentially regulated circRNAs. We also find expression of circHOMER1, circMLIP, circATRNL1 among others to be correlated with AD traits of clinical dementia, BRAAK pathology, and amyloid plaque. Our analysis of AD‐only patients and AD patients with vascular or Lewy body dementia revealed differential expression of 39 circRNAs common among all three disease conditions.ConclusionCircRNAs are a novel class of regulatory RNAs but their role in AD pathogenesis is just beginning to be explored. Our study identified circRNA whose expression is linked to AD and are correlated with indices of tauopathy and cognitive loss in cortex and hippocampus. Furthermore, particular circRNAs were differentially expressed among patients with AD or other dementias, suggesting that circRNA might be useful disease biomarkers or targets for disease intervention.
Blood protein kinase activity regulating genes are associated with brain glucose metabolism
Authors: Povala Guilherme , De Bastiani Marco Antônio , Bellaver Bruna , Ferreira Pamela C.L. , Brum Wagner S. , Ferrari‐Souza João Pedro , Rosa‐Neto Pedro , Zatt Bruno , Pascoal Tharick A , Zimmer Eduardo R. 
Published: December 2022

AbstractBackgroundThe diagnosis of Alzheimer’s disease (AD) has been greatly improved due to the fundamental role of positron emission tomography (PET) imaging. Also, predicting PET brain imaging alterations using blood‐based biomarkers is of high interest. This way, integrating PET and omics data can provide new insights into AD pathophysiology. Here, we aimed to develop a framework that combines blood transcriptomics with PET data. We hypothesized that integrating omics and PET data will help advance our understanding of AD neurobiology and may reveal relevant new peripheral biomarkers.Method[18F]Fluorodeoxyglucose ([18F]FDG)‐PET imaging and transcriptomics data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Microarray gene expression profiling from blood samples of 99 Cognitively Unimpaired (CU) and 218 Cognitively Impaired (CI) individuals were submitted to differential expression (DE) analysis. Differentially expressed genes (DEGs) were submitted to Gene Ontology (GO) analysis. The GO terms were clustered by semantic similarity using the GOSemSim method for biological processes ontology. All computations analyses were performed in the R statistical environment. Gene clusters obtained in the previous step were selected to undergo integration with [18F]FDG‐PET images using voxel‐wise generalized linear regression (GLR) models adjusted for age, gender, years of education, and APOEε4 (RMINC package).ResultThe GO semantic similarity resulted in 16 GO clusters. The voxel‐wise correlation between [18F]FDG‐PET and GO clusters resulted in t‐statistical maps. Afterwards, only statistically significant correlated voxels (uncorrected t‐value > 2.0) were retained (Fig. 1A). A cluster related to the regulation of protein serine/threonine kinase activity showed a strong correlation with the [18F]FDG‐PET signal in the brain. At the voxel level, this cluster has a positive correlation in the precuneus’ gray matter (95.7% left, 81.1% right) as well in the medial frontal gyrus (79.6% left, 44.66% right) and cingulate region (76.9% left, 56.4% right) (Fig. 1B).ConclusionWe identified several peripheral biological processes associated with [18F]FDG‐PET metabolism in the brain of CU and CI individuals. Furthermore, the prominent enrichment of protein serine/threonine kinase activity‐related genes highlights its potential as novel AD biomarkers and as a proxy of [18F]FDG‐PET metabolism.
Are caregivers’ self‐rated health, preparedness, and burden associated with acute care utilization by people living with dementia enrolled in home palliative care?
Authors: Duan Lewei , Nguyen Huong Q , Borson Soo 
Published: December 2022

AbstractBackgroundUnderstanding family caregiver factors that influence patient outcomes could open new avenues for improving care. Our previous analysis showed lower risk of downstream patient acute care utilization among people living with dementia (PLWD) versus those without dementia following admission to home palliative care (HomePal). We aimed to examine the association between PLWD caregiver self‐rated health, preparedness, and burden with patient acute care utilization.MethodThe cohort was comprised of 183 PLWD‐caregiver dyads enrolled in a trial testing two models of HomePal. We measured caregiver self‐rated health (good/very good/excellent or fair/poor), preparedness (Preparedness for Caregiving Scale, above or below the CPS median score (23), and burden (Zarit‐12, low (≤12) or high (>12) at admission. We used a proportional hazard competing risk model to assess how the risk of acute care utilization (emergency department visits, observation, and inpatient stays) was associated with baseline caregiver factors. The model was adjusted for selected patient socio‐demographics, comorbidities, hospice enrollment, and caregivers’ age and relationship.ResultThe PLWD cohort had a mean age of 85±8 years, were mostly female (61%) and ethnically diverse (51%, non‐White). Caregivers had a mean age of 63±12 years and were mostly female (85%), adult children (69%). Caregivers residing with PLWD (OR = 3.2, 95%CI: 1.5‐7) and Spanish speakers (OR = 3.1, 95%CI: 0.9‐10.8) were more likely to report poor/fair health. Spousal caregivers scored lower on preparedness than adult children (OR = 2.4, 95%CI: 1.2‐5). Spanish‐speakers reported less burden than English‐speakers (OR = 0.3, 95%CI: 0.1‐1.1). The median follow‐up time was 78 (IQR:171) days. In adjusted models, accounting for the competing risk of death, only caregiver low preparedness (HR = 0.63, 95%CI: 0.39‐1.02, P = .06) but not self‐rated health (HR = 0.78, 95%CI: 0.47‐1.3, P = .34) and burden (HR = 0.99, 95%CI: 0.64‐1.54, P = 0.98) was associated with lower likelihood of acute care utilization. Other PLWD or caregiver factors associated with utilization included patient older age, co‐morbidities, hospice use, and receiving care from a spouse (p<0.1).ConclusionCaregiver factors and characteristics, in addition to patient’s comorbidities, play important roles in the intensity of end‐of‐life care for PLWD. Further qualitative study is warranted to better understand the counterintuitive association between low caregiver preparedness and decreased acute care utilization.
Interpretable Deep Learning in Computational Histopathology for refined identification of Alzheimer’s Disease biomarkers
Authors: Garay Gabriel Jimenez , Kar Anuradha , Ounissi Mehdi , Stimmer Lev , Delatour Benoit , Racoceanu Daniel 
Published: December 2022

AbstractBackgroundIn this research, an explainable deep‐learning based framework is proposed for the segmentation of tau protein biomarkers like plaques, and tangles in histopathological whole slide images (WSI). The concept is to integrate an explainable deep learning model with collaborative human feedback to improve the model precision of identifying tau biomarkers as well as making the results interpretable to human experts. The final goal of this project is to refine AD patients’ stratification.MethodA pilot study has been done on 6 WSIs stained with ALZ50 monoclonal antibody. The preliminary models have been extended to a larger database of 15 WSIs stained with an AT8 monoclonal antibody. All WSIs are post‐mortem brain samples corresponding to the frontal cortex. In the proposed system, firstly, for automatic segmentation of Tau aggregates, deep learning (DL) models are trained and tested using the initial database (6 WSIs). Secondly, we train deep learning models with interpretability features, such as Attention U‐Net, CA‐net and graph neural networks. With these models, two operational loops (Figure 1) are implemented. In the first loop, a technical expert looks at the predictions of the trained model and provides corrections. The corrected predictions are fed back to the training dataset to improve the model performance. In the second loop, a clinical expert looks at the explainability results from the model and provides further corrective feedback.ResultA preliminary version of our DL models achieved promising F1 scores for tangles and neuritic plaques as reported in [1]. Currently, a second larger dataset of 15 WSIs (AT8) is studied and used for training an evolved version of the DL model, by including explainable visual features. These new overall results will be presented and discussed during the conference.ConclusionThe proposed interactive and interpretable tau protein segmentation framework is envisioned to be impactful towards Alzheimer's patient stratification problems which requires high accuracy identification of AD biomarkers.[1] K Maňoušková, V Abadie, M Ounissi, G Jimenez, L Stimmer, B. Delatour, S. Durrleman, D. Racoceanu. Tau Protein Discrete Aggregates in Alzheimer's Disease: Neuritic Plaques and Tangles Detection and Segmentation using Computational Histopathology. SPIE Medical Imaging 2022, Feb 2022, San Diego, United States.
Comparability and usability of Paired Associates Learning task (PAL) delivered on a smartphone
Authors: Baker Elizabeth , Thorp Emily , Anwyl‐Irvine Alexander L , Cashdollar Nathan , Zavitz Kenton , Taptiklis Nick , Cormack Francesca K 
Published: December 2022

AbstractBackgroundDelivering episodic memory tests on a mobile device can support increased accessibility across diverse health settings. This is particularly important for increasing access to memory screening tools for participants at risk of memory impairment including Alzheimer’s and dementia.CANTAB PAL task is a short (8‐12 minutes) task of memory performance that is currently validated for delivery on a tablet, laptop or desktop device. Cambridge Cognition developed a prototype version of PAL that can be delivered on a smartphone (iPhone or Android). A key component of making the task available on smartphone is to understand the comparability to the original version of the task. For tasks delivered direct to patient, usability becomes even more important. This study was designed to evaluate the comparability and usability of PAL on smartphone device.MethodParticipants (N = 98, mean age 55 ± 5.59 years, gender balanced (50% (49)) were recruited through the Prolific platform to remotely complete both smartphone and desktop versions of CANTAB PAL on two consecutive days followed by a usability questionnaire. A cross‐over design was used with 50:50 participants smartphone first: desktop first to counterbalance for exposure to the task. Participants used their own devices for assessment and the device operating system was captured.ResultMost participants used Android smartphones (74.1% (69)) and Windows desktops (90% (81)). Smartphone and desktop versions of PAL were shown to be highly correlated (r = 0.61, p < 0.05). Average performance was better on the desktop device; an upwards bias estimated as 3.52 (±1.96 SD 27 to ‐20) adjusted errors. The usability questionnaire found that 76.6% (59) participants strongly agreed they found the instructions clear and easy to follow and 42.9% (33) strongly agreed that they could see the symbols easily on their mobile phone. Most participants preferred using PAL on a desktop device in terms of ease of use (70.1% (54)) and enjoyment 66.2% (51).ConclusionWe have shown good correlation between iPhone and computer versions of the test, but with some important caveats. The usability data collected here will inform improvements to ensure suitability of PAL on a smartphone device for near patient assessments.