bluuebunny/crossref_metadata_2025 huggingface dataset:
The role of Cognitive Reserve and depression on executive functions in older adults: a 10‐year longitudinal study
Authors: Frau Loredana , Jonaitis Erin M. , Koscik Rebecca Langhough , Zuelsdorff Megan , Okonkwo Ozioma C. , Bruno Davide
Published: December 2022

Background

Cognitive reserve (CR) refers to the use of compensatory strategies related to executive functions (EFs) to reduce the negative effects of age‐related brain changes. Depression represents a risk factor for dementia, but little is known about its role in the association between CR and EFs.

In the present study, we tested whether depression moderated the association between CR and EFs, and which variables among CR, depression and APOE‐e4 status (e4/non‐e4) predicted conversion to Cognitively Unimpaired‐Declining (CUD; Johnson et al., 2018) and Mild Cognitive Impairment (MCI) from a Cognitively Unimpaired‐Stable (CUS) baseline.

Method

416 participants were selected from the Wisconsin Registry for Alzheimer’s Prevention. These were native English speakers, aged ≥50, CUS at baseline, and with no history of neurological or psychiatric disorders. Of these, 290 were still CUS, 97 were CUD and 29 had MCI at the 10‐year follow‐up visit. Depression was assessed with the 20‐item Center for Epidemiologic Studies Depression Scale (CES‐D). A standardized composite score from three tests (CFL, Digit Span, Letter‐Number sequencing) estimated EFs. Another composite score, based on the WRAT‐3 Reading subtest (a measure of literacy or premorbid abilities) and years of education estimated CR. A hierarchical linear regression was performed to evaluate the effect of CR and depression on EFs at follow‐up. Baseline demographic variables (age, gender, APOE‐e4) and diagnosis at follow‐up (CUS, CUD, MCI) were entered in Model 1, CR and depression in Model 2, and the CR*Depression interaction in Model 3. Moreover, a multinomial logistic regression was used to predict conversion to CUD and MCI from baseline.

Result

Model 3 added significantly onto Model 2 (p = .024), indicating a moderation between CR and depression on EFs. Particularly, the beneficial effects of CR on EFs seem to be mitigated by increasing levels of depression. In the multinomial regression, depression predicted conversion to CUD from CUS while CR, depression and APOE‐e4 predicted conversion to MCI from CUS.

Conclusion

These findings support the hypothesis that CR, associated with EFs, may protect against cognitive aging, but also highlight the disruptive effect that depression has on long‐term cognitive health. Further studies may replicate these findings in clinical populations.
Building an Academic Infrastructure for Rigorous Testing of Online Platforms to Support Family Caregivers
Authors: Koeuth Sokha , Kales Helen C , Kern Vincent Joseph , Bouranis Nicole G , Webster Melinda J , Gitlin Laura N.
Published: December 2022

Background

With COVID‐19, online opportunities to support families living with dementia are becoming increasingly important. However, academic institutions are typically not prepared to develop and test online platforms. We present a case study to describe steps for creating an infrastructure to test an online platform, WeCareAdvisor (WCA). WCA provides caregivers disease education and tailored strategies to manage dementia‐related behavioral symptoms using the DICE Approach. WCA was previously tested in a small, randomized trial demonstrating positive caregiver outcomes. To advance its evidence‐base, WCA is being tested in a NIA‐funded Stage III efficacy trial with a national sample of 326 caregivers.

Method

To test WCA, an infrastructure in the academic institution had to be created to address HIPAA compliance, privacy considerations, integrate user and developer input, and support rigorous trial methodologies. Through key informant interviews and literature reviews, we established a six‐step process: 1) Identifying and engaging key stakeholders (legal, Information Technology offices, research team, software company); 2) Creating software development agreement with stakeholder input; 3) Detailing scope of work and an oversight structure of software company, 4) Developing formal agreements with the software company, 5) Conducting security assessments with university IT offices; and 6) Establishing formal vendor status of the software company. This also necessitated new roles and responsibilities of research team members.

Result

The six‐step process was labor intensive, transpired over 12 months, and involved over 15 iterative meetings with investigators, project staff, and stakeholders. Careful coordination of stakeholders to provide practical and iterative guidance at each of the six steps was essential. Deliberations resulted in app store access, URLs and domains, and compliance and privacy statements reviewed and approved by various university offices, and then launching WCA on app stores for access by study participants.

Conclusion

Researchers and academic institutions have varying levels of understanding and readiness to engage in infrastructure development to rigorously test online platforms. Our approach resulted in an effective infrastructure for testing WCA which can be used by other researchers. Development of an infrastructure requires new skills for investigators, engaging multiple stakeholders, appropriately budgeting for this activity, and allocating sufficient start‐up time.
Single‐Molecule Detection of Isoform‐Specific Tau Phosphorylation
Authors: Mallick Parag
Published: December 2022

Background

Post‐translational modifications (PTMs) contribute to the vast diversity of proteoforms that drive biological processes. Currently, very little is known about the molecular heterogeneity of proteoforms. This gap can be dominantly attributed to a lack of methods to analyze PTMs on intact single protein molecules on a proteome‐scale.

Method

Here, we introduce an antibody‐based single‐molecule analysis platform that includes novel biochemistry for single‐protein molecule immobilization, instrumentation for highly sensitive detection, and computation for data interpretation. We use this platform to study Tau proteoforms. Aberrant PTMs on Tau generate a wide diversity of proteoforms that lead to Tau misfolding and aggregation and are associated with neurodegenerative disorders collectively referred to as Tauopathies.

Result

First, we validate the platform by measuring defined mixtures of recombinant tau proteins. Next, we use the platform to examine tau protein enriched from human induced pluripotent stem cell (iPSC)‐derived neurons and tau‐expressing cell lines. As PTMs can be assigned to individual protein molecules, the platform is able to reveal the molecular heterogeneity of tau proteoforms that is missed by both bulk measurements and by peptide‐centric proteomics approaches.

Conclusion

Ultimately, quantitative analysis of proteoform molecular heterogeneity will lead to a more detailed view of the tau proteoform landscape towards advancing precision medicine of tauopathies, such as Alzheimer’s disease.
Distinct volumetric features of cerebrospinal fluid distribution in idiopathic normal‐pressure hydrocephalus and Alzheimer’s disease
Authors: Kang Kyunghun , Jeong Shin Young , Lee Sang‐Woo , Yoon Uicheul
Published: December 2022

Background

The imaging characteristics of ventriculomegaly, high‐convexity tightness, and Sylvian fissure dilation has been called “disproportionately enlarged subarachnoid space hydrocephalus”.

Objective

To investigate relationships in cerebrospinal fluid (CSF) volumes between the lateral ventricle, high‐convexity subarachnoid space, and Sylvian fissure region in patients with idiopathic normal‐pressure hydrocephalus (INPH) and Alzheimer’s disease (AD), and to evaluate differences in these volumes among INPH, AD, and healthy controls.

Methods

Forty‐nine INPH patients, 59 AD patients, and 26 healthy controls were imaged with three‐dimensional MRI.

Results

INPH patients had larger lateral ventricles and CSF spaces of the Sylvian fissure region and smaller high‐convexity subarachnoid spaces than other groups, and AD patients had larger lateral ventricles and CSF spaces of the Sylvian fissure region than the control group. The INPH group showed a negative correlation between lateral ventricle and high‐convexity subarachnoid space volumes, while the AD group showed a positive correlation between lateral ventricle volume and volume for CSF spaces of the Sylvian fissure region. The ratio of lateral ventricle to high‐convexity subarachnoid space volumes yielded an area under the curve of 0.990, differentiating INPH from AD.

Conclusions

Associations between CSF volumes suggest different mechanisms between INPH and AD to explain their respective lateral ventricular dilation. In AD, lateral ventricular enlargement might be associated with brain volume loss through cortical atrophy. In INPH, the lateral ventricles may expand superiorly into the high‐convexity area, and this may further instigate cortical thickening. The ratio of lateral ventricle to high‐convexity subarachnoid space volumes distinguishes INPH from AD with good diagnostic sensitivity and specificity. We propose to refer to this ratio as the VOSS (ventricle over subarachnoid space) index.
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

Background

The 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.

Methods

Data 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.

Results

Data 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.

Conclusion

APOE 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

Background

Deficits 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.

Method

52 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.

Result

In 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.

Conclusion

There 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

Background

Alzheimer'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.

Method

We 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.

Result

We 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

Background

Clinical 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.

Method

Using 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.

Result

Using 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).

Conclusion

Although 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

Background

Although 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.

Method

To 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.

Result

We 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.

Conclusion

CircRNAs 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

Background

The 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).

Result

The 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).

Conclusion

We 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

Background

Understanding 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.

Method

The 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.

Result

The 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).

Conclusion

Caregiver 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

Background

In 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.

Method

A 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.

Result

A 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.

Conclusion

The 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

Background

Delivering 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.

Method

Participants (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.

Result

Most 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).

Conclusion

We 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.
Three‐Objects‐Three‐Places Test: Association with Alzheimer’s Disease Biomarkers
Authors: Krug Sophie , Ribaldi Federica , Altomare Daniele , Garibotto Valentina , Tomczyk Szymon , Scheffler Max , Lovblad Karl‐Olof , Fernandez Aldara Vazquez , Chicherio Christian , Frisoni Giovanni B
Published: December 2022

Background

The Three‐Objects‐Three‐Places Psychological Test (3O3P) assesses episodic memory and has been developed to screen for Alzheimer’s Disease (AD). To perform the test, the clinician shows the patient three objects and hides them in three separate locations. The patient is later asked what the objects and hiding places were, and which object was hidden where, producing a score out of 9. The aim of this study was to investigate if 3O3P scores were associated with common AD biomarkers to test its potential efficacy in detection of AD pathology, and how these associations compared with other common AD neuropsychological tests.

Method

166 patients from the Geneva Memory Centre cohort (106 MCI, 19 dementia, and 41 unimpaired) who underwent amyloid‐PET, tau‐PET, MRI, as well as clinical and neuropsychological assessments, were included. The biomarkers tested included amyloid (A) and tau (T) load, and cortical thickness (N). Associations between these biomarkers and 3O3P scores, MMSE and delayed total recall scores (DTR; extracted from the 16‐item Free and Cued Recall) were tested. The associations between biomarkers and test scores were investigated using linear regression.

Result

Mean patient age was 71 years, 52% were female, and mean years of education was 14. When controlling for age and education, 3O3P scores were negatively associated with amyloid (b = ‐0.02, p<0.001), tau load (b = ‐4.14, p<0.001), and positively with cortical thickness (b = 7.21, p<0.001). Similar trends were seen between these biomarkers and MMSE (A: b = ‐0.02; T: b = ‐5.21; N: b = 8.18, p<0.001) and DTR scores (A: b = ‐0.03; T: b = ‐5.28; N: b = 10.2, p<0.001).

Conclusion

These preliminary findings indicate that 3O3P scores correlate significantly with the AD biomarkers tested, as do MMSE and DTR scores. However, the 3O3P test carries advantages, such as its short duration, easy administration, and not requiring the examiner to purchase equipment or score sheets. Further analysis is needed to clarify the relationship between 3O3P scores and AD biomarkers.
Modifying effect of AD pathology in the association between CSF synaptic biomarkers and brain function and structure in preclinical Alzheimer
Authors: Milà‐Alomà Marta , Cacciaglia Raffaele , Shekari Mahnaz , Operto Grégory , Brinkmalm Ann , Kvartsberg Hlin , Ashton Nicholas J. , Kollmorgen Gwendlyn , Wild Norbert , Suridjan Ivonne , Zetterberg Henrik , Blennow Kaj , Molinuevo Jose Luis , Gispert Juan Domingo , Suárez‐Calvet Marc
Published: December 2022

Background

Previous studies have shown that synaptic biomarkers are increased in CSF in the preclinical stage of Alzheimer’s disease (AD). However, little is known about how CSF synaptic biomarkers associate with structural and functional brain measures in preclinical Alzheimer. The aim of this study was to test the association of CSF synaptic biomarkers with brain metabolism and structure in preclinical Alzheimer and explore whether these associations were modified by AD pathology.

Method

We studied 328 cognitively‐unimpaired individuals of the ALFA+ cohort. Participants underwent lumbar puncture, [18F]‐FDG PET scan and structural MRI. The following synaptic biomarkers were measured in CSF: neurogranin (NeuroToolKit [a panel of automated robust prototype immunoassays], Roche Diagnostics International Ltd), GAP‐43 (ELISA), SNAP‐25 and synaptotagmin‐1 (IP‐MS). We performed voxel‐wise analyses to test the association of each synaptic biomarker with brain metabolism and grey matter volume, using a threshold of p<0.005 and a minimum cluster size of 100 voxels. Participants were categorized by AT groups (A+ if CSF Aβ42/40<0.071 and T+ if CSF p‐tau>24pg/ml) and interaction terms with each synaptic biomarker were evaluated.

Result

212 (64.6%) participants were A‐T‐, 91 (27.7%) were A+T‐ and 25 (7.6%) were A+T+. To focus the study on the AD continuum, A‐T+ participants (n=12) were excluded. When assessing all participants, positive associations between each CSF synaptic biomarkers and FDG PET uptake and grey matter volume were found in brain areas including the caudate, the insula, cingulate gyrus and inferior temporal and parietal gyrus (Fig 1A). AT status modified the associations with both FDG PET uptake and grey matter volume. Compared to A‐T‐, A+T+ participants showed a positive association of CSF neurogranin, GAP‐43 and synaptotagmin‐1 with FDG PET uptake in the precuneus. Conversely, CSF synaptic biomarkers were negatively associated with grey matter volume with progressive AD pathology (A‐T‐>A+T‐>A+T+) in several brain regions including the cingulate gyrus and middle and superior frontal gyrus (Fig 1B).

Conclusion

In preclinical Alzheimer, CSF synaptic biomarkers show associations with functional and structural brain measures, which are modified by AD pathology. Progressive AD pathology is associated with increased brain metabolism and decreased grey matter volume in several AD‐related brain regions.
Measurement equivalence of the NACC UDS neuropsychological battery across race/ethnic groups
Authors: Gaynor Leslie S , Lopez Francesca , Van Hulle Carol A. , Li Clara , Vasunilashorn Sarinnapha M , Simone Stephanie M. , Mungas Dan M.
Published: December 2022

Background

The National Alzheimer’s Coordinating Center (NACC) maintains a national database of clinical and cognitive data collected under a standard protocol set by the National Institute on Aging (NIA) called the Uniform Data Set (UDS). Development of factor scores unbiased by race/ethnicity will allow for comparison of other lifestyle and psychosocial factors within the NIA health disparities framework. The present study examined the dimensional structure of the comprehensive neuropsychological test battery and factorial equivalence across racial/ethnic groups.

Method

43,999 participants (Table 1; Age: Mean = 71.6, Education: Mean = 15.8; Latinx: N = 3549; Asian: N = 1141; Black: N = 5989; White: N = 32832) were assessed at baseline and included in the present study. Clinical status varied from cognitively normal to dementia. The sample was divided into learning and cross‐validation samples; the best fitting model was developed in the learning sample and tested in the cross‐validation sample. Multiple group confirmatory factor analysis (CFA) was used to evaluate measurement equivalence across race/ethnic groups. Measurement invariance models developed in the learning sample were tested in the cross‐validation sample.

Result

Results from the full sample identified a best fitting four‐factor model (Figure 1; episodic memory, attention, executive function, language). Multiple group analyses confirmed the presence of metric invariance (Table 2; invariant factor loadings). Results supported partial scalar invariance—all domains had invariant intercepts but also non‐invariant intercepts. Most memory factor indicators had invariant intercepts (5/7). Attention and language had more non‐invariant intercepts. Category fluency, which loaded onto language and executive function factors, was particularly non‐invariant.

Conclusion

Results showed that the UDS neuropsychological battery measures the same four factors across race/ethnic groups. Crucially, there are important differences in measurement parameters across race/ethnic groups that must be accounted for in studies using these neuropsychological tests as outcomes. The four‐factor model effectively summarizes the comprehensive neuropsychological test battery and could provide a useful tool for studies of cognitive aging and Alzheimer’s disease and related dementias. Importantly, these cognitive factor scores can be used to clarify the influence of lifestyle and psychosocial factors on cognition across these groups.
Default mode network and medial temporal lobe functional connectivity changes with Alzheimer’s disease severity and cognitive impairment in individuals with Down syndrome
Authors: DiProspero Natalie , Sathishkumar Mithra , McMillan Liv , Keator David B. , Doran Eric , Hom Christy , Nguyen Dana , Rosas H. Diana , Lai Florence , Brickman Adam M. , Schupf Nicole , Silverman Wayne , Lott Ira T , Yassa Michael A.
Published: December 2022

Background

By age 40, virtually all people with Down syndrome (DS) have sufficient beta‐amyloid and tau pathologies present to meet the criteria for pathological Alzheimer's disease (AD). Determining the timing and location of neurobiological changes caused by AD is critically important for effective intervention within this high‐risk population and more broadly in the general population, but little is known about the brain changes associated with clinical onset of AD in DS. Previous studies in sporadic AD suggest that functional connectivity of the default mode network (DMN) and medial temporal lobe (MTL) is vulnerable to AD pathology‐related impairment.

Method

Seventy‐five individuals with DS (mean age 49.9±6.6, 36% women) from a subset of the Alzheimer’s Biomarkers Consortium ‐ Down Syndrome study underwent MRI, including an MPRAGE scan and a resting state functional scan. Participants completed a battery of neuropsychological tests and were assigned a consensus diagnosis for the presence or absence of mild cognitive impairment (MCI‐DS) or AD dementia. Forty‐eight participants were classified as cognitively stable (CS) (mean age 48±6.1, 42% women), 16 participants had MCI‐DS (mean age 50.8±4.2, 19% women), and 11 participants had dementia (mean age 57.2±6.6, 36% women).

Result

Participants with dementia had lower functional connectivity between the posterior DMN and the MTL relative to the CS and MCI‐DS groups. MCI‐DS participants had greater functional connectivity within the MTL relative to the CS group. Functional connectivity differences in these networks tracked with memory performance as well.

Conclusion

Changes in DMN and MTL functional connectivity are related to the progression of clinical symptoms of AD‐DS and may reflect synaptic changes stemming from AD pathology.
A matrisomal proteomics network modulates tau and amyloid‐beta toxicity in fly AD models.
Authors: Li‐Kroeger David , Al‐Ramahi Ismael , Marella Aditi , Amoh Bismark K , Smith Nathaniel , Botas Juan , Seyfried Nicholas T. , Levey Allan I. , Shulman Joshua M
Published: December 2022

Background

Mounting evidence indicates Alzheimer’s disease (AD) has a complex etiology where insults in multiple pathways conspire to disrupt neuronal function, yet the molecular changes underlying AD remain poorly understood. To identify protein changes associated with AD, we previously performed mass‐spectrometry on post‐mortem brain tissue from longitudinal cohorts and identified >40 protein coexpression networks correlated to AD pathological and clinical traits. A small module (M42) consisting of 37 proteins linked to the extracellular matrix (ECM/matrisome) has the strongest correlation to AD pathology (r=0.75), and includes several proteins linked to AD (e.g. APP, APOE) including SMOC1 as the central hub. SMOC1 has conserved roles in modulating TGF‐Beta and wnt signaling during development, however, its role in the adult brain remains unstudied.

Method

We evaluate the SMOC1 associated protein network using the powerful genetics of Drosophila to characterize its role in neuronal function and uncover links to specific triggers of AD. We use a high‐throughput robotic screening platform and video assisted software which enables robust, quantitative assessments of neurological function to identify conserved members of M42 that modify Amyloid‐Beta‐ or tau‐induced neurodegeneration. Using a combination of loss‐of‐function alleles and RNA interference (RNAi) we tested over 200 transgenic fly lines, representing 47 Drosophila genes corresponding to 37 human proteins nominated from the M42 ECM network.

Result

We found that SMOC1 knock‐down modifies tau toxicity using RNAi and loss‐of‐function alleles, confirming a link to specific AD triggers. In addition, we identified 13 other genes with robust evidence of interactions with either tau or Aβ in our fly models including multiple wnt signaling components and TGF‐β ligands. Additionally, we begin characterizating the fly ortholog dSMOC1 showing expression in a small subset of neurons and many glial cells in the brain. Finally, we show human SMOC1 can rescue lethality caused by complete loss of dSMOC1 function, thus setting the stage for mechanistic analysis.

Conclusion

The M42 protein coexpression network contains multiple proteins with links to AD and our screening pipeline identified multiple conserved ECM proteins interacting with specific AD triggers. This analysis helps diversify the pool of potential therapeutic targets for the fight to cure AD.