bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting progression in subjective cognitive decline (SCD) using a machine learning (ML) approach: The role of the complaint’s severity
Authors: Felpete Alba , Valladares‐Rodríguez Sonia , Mallo Sabela C. , Lojo‐Seoane Cristina , Facal David , Belleville Sylvie , Juncos‐Rabadán Onésimo , Pereiro Arturo X.
Published: December 2020

Background

Presence of significant subjective complaints about cognition (SCD) is considered the first behavioral manifestation of Alzheimer disease (AD). However, SCD has not yet overcome the challenge of becoming a reliable preclinical AD marker. Severity indices were proposed to improve the accuracy of complaints when predicting the risk of AD (Jessen et al., 2010).

Our aim was to compare the predictive accuracy of ML algorithms using a more (95%ile) or less (5%ile) restrictive cut‐off point in severity of complaints for classification in Low (LSC) and High (HSC) subjective complaints groups.

Method

One hundred and ninety‐nine participants from the Compostela Aging Study (ComPAS) were identified at baseline as SCDs and completed three follow‐up measurements (54‐72 months). Considering their total scores in the Subjective Memory Questionnaire, participants were classified as LSC and HSC following using two distinct cut‐off points: complaint scores above or below than 5%ile vs 95%ile. Participants were labeled as ‘worsening’ or ‘stable’ based on their progression or not to MCI or AD. ML classifier algorithms (Random Forest, Support Vector Machine, Extra Tree) were applied to forty‐one measures (socio‐demographic, time, health, cognitive, behavioral, cognitive reserve) collected at baseline.

Results

The best performing model was the Random Forest (95%ile: PPV =.87; Sensibility =.92; Specificity =.38; 5%ile: PPV =.66; Sensibility=.61; Specificity =.51). The confusion matrix (Figure 1) showed that: a) both, the more (95%ile) and the less (5%ile) restrictive criteria mostly classified the HSC‐stable participants as LSC‐stable; and b) criterion 5%ile, but not 95%ile, was able to differentially identify progressors based on their complaints. Episodic memory, executive functions, depression, cognitive reserve and progression timing measures assume the highest levels of importance in the ML algorithm that differentially predicts the progression of SCD to MCI and AD according to the level of complaints.

Conclusions

For both the criteria, algorithms failed to successfully identify stable SCD participants. The less restrictive criterion resulted in a better classification than the more lenient one to differentially identify LSC and HSC participants who get worse. Cognitive, affective, cognitive reserve and progression timing were the more prominent variables in conforming the predictive algorithm.
Effects of mild cognitive impairment on brain function during distracted driving
Authors: Talwar Natasha A. , Churchill Nathan W. , Hird Megan A. , Pshonyak Iryna , Fischer Corinne E. , Graham Simon J. , Schweizer Tom A.
Published: December 2020

Background

Driving is an integral part of daily life. The complex, multi‐faceted nature of driving makes it vulnerable to the effects of pathological aging on the brain. Studies have shown that patients with mild cognitive impairment (MCI) maintain safe driving in routine circumstances, however there is limited examination of the effects of MCI in more challenging driving scenarios, such as distracted driving. In addition, little is known about how distraction may disrupt the brain networks that are involved in safe driving for patients with MCI. This is the first study to combine functional magnetic resonance imaging (fMRI) with an MRI‐compatible driving simulator to study the effect of distraction on driving‐related brain activity in healthy and MCI cohorts.

Methods

This study used fMRI and an MRI‐compatible driving simulator to measure brain activity during driving in 30 patients with MCI and 30 age and sex‐matched control participants. Patients were diagnosed using the criteria defined by the National Institute on Aging‐Alzheimer’s Association. In the simulator, participants were required to respond to a distracting true or false question while driving. Parametric maps of brain activity were calculated using one‐sample t‐tests, which were cluster‐size thresholded to adjust for multiple comparisons.

Results

Both groups displayed consistent recruitment of the bilateral frontal, parietal, temporal and occipital lobes during the distracting condition (Figure 1). Patients with MCI exhibited more extensive positive activation in the bilateral superior temporal lobes and the middle and inferior frontal lobes. Although patients with MCI did not commit more driving errors, they displayed impairment by not answering the true or false questions (p < 0.001) and by taking longer to complete turns (p < 0.001).

Conclusions

During distracted driving, patients with MCI significantly altered their behaviour to safely complete the driving task. The observed increased recruitment of the frontal lobes among patients may be reflective of the compensatory cognitive effort exerted to maintain task performance. MCI may result in changes in driving behaviour, which are exacerbated in challenging driving situations. These findings suggest that subtle changes in measures of driving behaviour may be signs of significant MCI‐related alterations in driving networks of the brain.
Identifying genetic modifiers of APOE in Alzheimer’s disease using evolutionary information and regression analyses
Authors: Kim Young Won , Al‐Ramahi Ismael , Koire Amanda , Botas Juan , Lichtarge Olivier
Published: December 2020

Background

To date, the APOEɛ4 allele is the best known genetic risk factor of late‐onset Alzheimer’s Disease (AD), while the APOEɛ2 allele is known to protect against AD. However, some APOEɛ4 allele carriers remain AD‐free, and some APOEɛ2 carriers develop dementia.

Method

We focused on the whole‐exome sequences of patients with paradoxical genotype‐phenotype relationships (APOEɛ2 AD carriers and APOEɛ4 healthy individuals) and assessed the predicted impact of coding variants using Evolutionary Action equation. Then, for each gene, we measured the mutational EA burden given its background mutation rate and quantified the deviance in mutational EA burden of a gene against those with similar background mutation rate. We repeated this in the two populations of interest to identify outliers with apparent potential for protective or pathogenic qualities. The predicted modifiers of APOE were tested computationally through statistical analyses as well as experimentally by assessing neuronal dysfunction in a Drosophila AD model.

Result

We found ∼200 genes with differential mutational signatures in the paradoxical patient groups. These genes were differentially expressed in AD brains and shown to include or interact with known AD genes. Nearly half of the fly homologs tested modified motor performance deficits induced by tau or β42, both of which are responsible for pathological hallmarks of AD.

Conclusion

We conclude that this method successfully identifies protective or pathogenic nature of genes related to AD and allow for a tractable analysis of specific variants often missed by conventional methods, by quantitatively distinguishing genes under differential selection pressures. Identification of such dependencies and how they interact together would allow better understanding of epistatic relationships in Alzheimer’s disease and provide additional insight into novel drug targets for therapeutic exploration.
Automatic assessment with EEG‐based brain‐computer interface shows difference between healthy population, mild cognitive impairment and moderate cognitive decline patients
Authors: Maimon Neta , Molcho Lior , Loterrman Tomer , Pressburger Narkiss , Sasson Ady , Intrator Nathan
Published: December 2020

Background

Cognitive decline is still under‐diagnosed due to the lack of objective biomarkers. Brain‐ Computer Interface (BCI) combined with inexpensive mobile EEG devices have been used to assess mental workload revealed different neurological biomarkers to detect cognitive load. However, they are still scarcely implemented among the elderly and cognitively impaired populations. We introduce novel cognitive biomarkers obtained from a single‐EEG‐channel (by Neurosteer Aurora) to indicate cognitive decline. The assessment is fully automated using auditory verbal and musical stimulation.

Methods

Healthy participants (N=40) and mild‐to‐moderate cognitive decline (MMSE score 17‐23, N=10; and 24‐27, N=14) were tested in three conditions: an auditory detection task with two difficulty levels and a resting state task. Two biomarkers, previously extracted from healthy participants data through machine learning, VC9 and T2, were used in the statistical analysis.

Results

For the healthy population, VC9 activity was significantly increased for higher cognitive load (p<.001); for the mild‐to‐moderate cognitive decline group, VC9 activity was significantly higher than healthy group (p=.004), and remained similar for all tasks and levels (see figure 1 for the mean activity of VC9 biomarker per each condition: detection level 1, level 2 and resting state, for the healthy participants and cognitively impaired group). The biomarker showed a difference in mean activation during the cognitive and resting tasks (p=.022), separating between the two groups of cognitive decline (MMSE<24 & MMSE>24) (see figure 2 for the mean activity of T2 biomarker comparing the two cognitively impaired groups and the healthy group). Furthermore, the T2 feature was negatively correlated with participants’ MMSE score (p=.013, see figure 3 for correlation between MMSE score and T2 activity).

Conclusions

These initial results show two novel biomarkers of cognitive activity, which are able to differentiates between patient groups and detect MCI. The study shows different patterns of cognitive functions for patients with cognitive impairment, as well as difference in total activity between healthy subjects and mild cognitive impairment patients, to patients with moderate cognitive impairment. Results were obtained with an inexpensive BCI combined with an easy and automatic cognitive assessment, which provides more objective evaluation and decreases the need of highly trained personnel.
Subjective cognitive decline correlates with medial temporal lobe and hippocampal subfield volumetry in cognitively unimpaired participants
Authors: Operto Greg , Sánchez‐Benavides Gonzalo , Domingo Gispert Juan , Molinuevo Jose Luis ,   the ALFA Study
Published: December 2020

Background

Subjective cognitive decline (SCD) is a risk factor for cognitive decline and AD. SCD individuals display lower medial temporal lobe (MTL) volumes. Some features of SCD (SCDplus), such as specific worry and recent onset, have been proposed as more related to AD pathology. In this study we aimed to explore the differences in MTL volumes between SCD and control individuals and their associations with memory performance in a large sample of cognitively unimpaired subjects.

Methods

603 cognitively unimpaired individuals from a population‐based study (age: [47‐76], mean: 60.04∓6.57) underwent structural MRI, clinical and neuropsychological assessments. 184 were classified as SCD or 419 as Controls by their answers to the question “Do you perceive difficulties in memory or cognition as compared to how they used to be?”. A subgroup of 31 fulfilling two SCDplus criteria (Worry and Onset within last 5 years) was defined. Memory performance was assessed with the Free and Cued Selective Reminding Test (FCSRT).

MTL and HS were segmented using ASHS using T1 and IR sequences. We compared regional volumes across SCD groups using linear models. Age, sex, education, APOE carriership and total intracranial volume were included as covariates. SCD group*memory scores interactions were assessed.

Results

SCD subjects showed lower volumes than Controls in right CA1 (p=0.031) and subiculum (p=0.026) (Figure 1). SCDplus subjects showed larger BA36 volumes in both hemispheres (Figure 2). In models comparing Controls and SCDplus we found a significant interaction (p=0.021) between SCDplus status and parahippocampal (PHC) bilateral volumes on FCSRT free recall score, with positive association in Controls and negative in SCDplus (poorer performance associated with larger PHC) (Figure 3).

Conclusion

As previously reported in smaller and older samples (Schwarz et al, AAIC 2019) a reduction of CA1 and subiculum was found in SCD subjects. Moreover, PHC volume correlates negatively with memory performance in SCDplus: this may pinpoint a transitional phase in the AD continuum with increments of cortical volume, as described in (Gispert et al., 2015; Fortea et al., 2011). Further work including CSF biomarkers will assess how these associations may be mediated by AD pathology.
Identifying novel causal genes and proteins in Alzheimer’s disease
Authors: Robins Chloe , Wingo Aliza P. , Meigs Jacob , Duong Duc , Cutler David J. , De Jager Philip L. , Lah James J. , Bennett David A. , Seyfried Nicholas T. , Wingo Thomas S. , Levey Allan I.
Published: December 2020

Background

Recent large‐scale proteomic analyses have shown Alzheimer’s disease (AD) phenotypes (i.e., clinical manifestations of AD or AD pathology) to correlate with expression levels of hundreds of proteins in brain. However, it is currently unknown whether variation in these protein levels are causes or consequences of AD. Here, we use mediation analysis to identify brain proteins that are under genetic control and play a causal role in AD.

Method

Genetic variants were selected as instruments for causality testing if they were both associated with variation in cis brain protein levels in 144 cognitively unimpaired participants of the Religious Order Study (ROS) and Memory and Aging Project (MAP) (FDR<0.05) (Robins et al. 2019) and suggestive risk loci in a published large‐scale GWAS of AD (p < 0.00001) (Jansen et al. 2019; Kunkle et al. 2019). These variants, and their associated proteins, were tested via mediation analysis using matched protein expression data (MS), whole genome sequencing, and clinical and pathologic data from 345 cognitively impaired and unimpaired participants of ROS/MAP. For each genetic variant and protein pair, a regression framework was used assess if some of the causal effect of the genetic variant on AD can be explained by the associated protein. Separate analyses were performed for the following AD phenotypes: 1) amyloid beta protein levels; 2) neurofibrillary tangle density; 3) clinical diagnosis of dementia; 4) cognitive trajectory.

Result

We found the proteins coded by the following four genes to be consistent with causality in AD: 1) MADD; 2) RTFDC1; 3) SF3B3; 4) DOC2A. The protein coded by MADD was found to have a significant causal effect (p < 0.05) on neurofibrillary tangle density and clinical diagnosis of dementia. The protein coded by RTFDC1 was found to have a significant causal effect on neurofibrillary tangle density. The proteins coded by SF3B3 and DOC2A were found to have a significant causal effect on cognitive trajectory.

Conclusion

Our results suggest that proteins play causal roles in AD. Furthermore, our results suggest that different proteins may have a causal effect on different aspects of the disease. Future work clarifying the causal mechanisms of each protein will aid the development of drug interventions.
DNA and RNA deep sequencing and epigenetic characterization of two kindred cases affected by slow and fast decline dementia
Authors: Palmieri Ilaria , Gagliardi Stella , Valente Maria Luisa , Zucca Susanna , Davin Annalisa , Medici Valentina , Poloni Tino Emanuele , Guaita Antonio , Cereda Cristina
Published: December 2020

Background

Genetics, transcriptomic and epigenetics play an important interplay in influencing the clinical picture and the final diagnosis of patients affected by neurodegenerative diseases. Clinical diagnosis is often inaccurate compared to the neuropathological one, which remains the gold standard for neurodegenerative disease diagnosis so far. For this reason, it becomes necessary to strengthener molecular efforts to sustain clinical diagnosis. Here, we present the molecular characterization of two kindred cases (mother and son) initially diagnosed as slow and fast dementia and, after post‐mortem neuropathological examination, classified as Alzheimer’s Disease (AD) and Dementia with Lewy Body (DLB) respectively.

Method

A customized panel of over 6000 genes associated to mendelian disorders and to inherited neurodegenerative diseases was sequenced by NGS (SureSelectQXT Target Enrichment, Agilent Technology) on peripheral blood DNA of the two patients. Total RNA from hippocampus, parietal lobe, substantia nigra and basal ganglia was extracted and RNA‐Seq analysis was run (SENSE Total RNA‐Seq, Lexogen). Differentially Expressed Genes (DEGs) were identified via R package DESeq2. Kegg pathway and Gene Ontology (GO) analyses for biological processes were made using enrichR. AlphaLisa analysis was performed on brain homogenates to investigate the dimethylation status of Histone 3 Lysine 9 (H3‐K9) and Histone 3 Lysine 4 (H3‐K4) and the acetylation status of H3‐K9.

Result

Seven variants were found to be shared by patients (Table 1), while six variants were present only in the son and (Table 2). DEGs analysis revealed a dysregulation in the mother’s hippocampus and in the son’s substantia nigra, with a 10‐fold higher number of DEGs. GO analyses highlighted a deregulation in the epigenetic landscape. In particular, the substantia nigra of the son resulted hypomethylated and hyperacetylated respectively in the H3‐K9 residue, while the mother resulted globally hypermethylated in all the brain areas analysed at the H3‐K9 and H3‐K4 residues.

Conclusion

We identified genetic factors that may explain the AD and the DBL phenotypes and DEGs in AD‐ and DBL‐specific brain areas involved in the epigenetic spectrum. Our investigation highlighted either the importance of DNA and RNA sequencing and the relevance to study the epigetic lanscape to support differential diagnoses of complex neurodegenerative pathologies.
The Alzheimer's Prevention Registry: Success metrics and lessons learned in registry member retention through online engagement campaigns
Authors: Kettenhoven Cassandra L , High Nellie M , Salata Hayley , Reiman Eric M , Tariot Pierre N , Langbaum Jessica B
Published: December 2020

Background

We developed the web‐based Alzheimer’s Prevention Registry (APR) program to provide resources to the AD scientific community, aiming to accelerate recruitment and enrollment into studies and to complement local efforts. Considerable efforts have been given to strengthening retention and engagement of APR members over time, regardless of whether they enroll in an AD study.

Methods

Adults age 18+ are eligible to join the APR at www.endALZnow.org. At enrollment, individuals provide their name, email address, zip/postal code and year of birth; after enrollment they provide additional demographic information. Immediately after enrollment members begin receiving APR emails welcoming and orienting them to the APR. The APR sends members monthly newsletters and study opportunity announcements via email for engagement and retention purposes. We developed a 4‐part re‐engagement campaign for members who either have not opened an APR email within 6 months or fall below our engagement scoring model. If a member does not act on a re‐engagement email, then APR removes them from its email distribution list. Only “actively engaged” members remain on the APR email distribution list.

Results

The APR has enrolled >347,000 members since its launch in 2012. As of January 2020, 86,175 people are considered “actively engaged” members of the APR. In 2019, 12 monthly e‐newsletters were sent to APR members. The average e‐newsletter open rate was 45% (compared to nonprofit healthcare industry average of 16%); and the average e‐newsletter click rate (percentage of APR members who clicked on the email in relation to those who opened/viewed the email) was 24% (compared to the industry average of 1.6%). In 2019, 36 study opportunity emails were sent to APR members with similar open and click rates. To date, approximately 54,000 members have been added to the re‐engagement campaign, and nearly 10,000 (18.5%) have been successfully re‐engaged.

Conclusion

Deploying online engagement campaigns as a retention tool is a valuable resource for the APR. Thoughtfully engaging and re‐engaging APR members promotes increased likelihood of email opens and clicks when study opportunities are offered. Future work will examine the relationship between email engagement and responsiveness to study opportunities.
Relating occlusion maps obtained through deep learning to functional impairment in dementia of Alzheimer’s type
Authors: Bae Jinhyeong , Stocks Jane , Heywood Ashley , Jung Youngmoon , Katsaggelos Aggelos , Jenkins Lisanne M , Karteek Popuri , Beg Mirza Faisal , Wang Lei
Published: December 2020

Background

Predicting the conversion from Mild Cognitive Impairment (MCI) into Dementia of the Alzheimer’s type (DAT) and functional change is crucial to patient care and treatment. In order to visualize brain regions which are significant in the prediction, we implemented an occlusion map based on deep learning.

Method

Using T1‐weighted structural MRI data from ADNI, 3D convolutional neural network was trained to predict the conversion from MCI to DAT through a transfer learning pipeline. The model resulted in an 82.4% classification accuracy on an independent test set. An occlusion map was subsequently generated as follows. Each brain scan was occluded by 2x2x2 voxel patch iterated through every position in the brain. The model produced a prediction score corresponding to the location of the occlusion patch to identify important voxels for the model’s prediction at the subject level. Mean intensity value within the occlusion map was obtained in Gray Matter. This was used to correlate to clinical scores’ rate of change, which include Clinical Dementia Rating‐Sum of Boxes (CDRSB), Alzheimer’s Disease Assessment Scale – cognitive 11 item (ADAS11) and cognitive 13 item (ADAS13), Mini Mental State Exam (MMSE), Rey Auditory Verbal Learning Test (RAVLT) – RAVLT Immediate (IMD), RAVLT Learning (LRN), RAVLT Forgetting (FRG), RAVLT Percent Forgetting (PCF), and Functional Activities Questionnaire (FAQ).

Result

The occlusion map identified regions important to the prediction of conversion. Mean intensity values of the gray matter within the occlusion map showed significant correlation with behavior measures: decreased intensity (indicating gray matter loss) was associated with decreased memory performance as measured by RAVLT immediate recall performance, increased cognitive dysfunction as measured by ADAS11, ADAS13, and increased daily living deficits as measured by FAQ.

Conclusion

These results indicate brain regions associated with cognitive change during conversion from MCI to DAT. These regions provide validity of the deep learning model’s results and provide insights on patient functional changes during conversion.
Prediction of the MMSE up to 6 years ahead with cross‐cohort replications
Authors: Maheux Etienne , Koval Igor , Archetti Damiano , Redolfi Alberto , Durrleman Stanley
Published: December 2020

Background

Most of the studies that focus on the Alzheimer’s disease (AD) prediction forecast a dementia label: cognitively normal, mild cognitive impairments or AD. However these labels cannot always be compared across cohorts, are not perfectly accurate and describe the disease thanks to a limited number of stages (only 3) which, in practice, is a continuous progression. For these reasons, we predict a cognitive test (MMSE) that has a finer discretization and is similarly assessed across cohorts.

Method

The Bayesian mixed‐effects model introduced in (Schiratti et al. 2015, NIPS) allows to estimate the average temporal progression of a subset of scores from ADNI, out of the individual longitudinal data. This profile is personalized to individuals from the ADNI cohort but also AIBL and PharmaCOG. It allows to predict future values (1.5 to 6 years after the last seen visit). To measure the error of prediction, the value to predict is hidden and then compared to the prediction. This error is compared to the (i) constant prediction (i.e. no MMSE change), (ii) linear and (iii) logit regressions and (iv) the test‐retest MMSE noise ‐ whose standard deviation is 2.8 out of 30 (Clark et al, 1999, Archives of Neurology) ‐ which is the best achievable score.

Result

The constant prediction, while being trivial, is better than the linear and logit regressions [Figure 1]. Moreover, it is of the order of the noise (i.e. best possible prediction) for horizons of less than 3 years. In any case, our prediction is of the noise order or smaller than the constant prediction, allowing to predict accurately the MMSE at any temporal horizon. Moreover, the accuracy on the AIBL and PharmaCOG [Figures 2 and 3] datasets highlights the potential of the model to generalize to patients from other cohorts but also in real‐life applications.

Conclusion

The constant prediction outperforms standard regression techniques (linear and logit) due to the important noise level of the MMSE and the fact that the progression of the disease occurs over long periods of time. However, our model is able to beat this prediction for far enough temporal horizons.
The peculiar internal microstructure of retinal arteries as a biomarker for Alzheimer’s disease
Authors: Kotliar Konstantin , Ortner Marion , Bilirer Mahmut , Hauser Christine , Schmaderer Christoph , Grimmer Timo
Published: December 2020

Background

Vascular risk factors contribute to the development of Alzheimer's disease (AD). We demonstrated previously that functional retinal arterial reaction to flicker stimulation and retinal arterial vasomotion are altered in mild‐to‐moderate AD dementia. The spatial structure of the retinal artery blood column measured along the vessel axis is termed longitudinal arterial profile (LAP). It reflects configuration of internal retinal arterial wall and can be assessed with modern in‐vivo retinal imaging. Whether LAP is altered in retinal arteries of AD patients and whether it changes during vascular constriction/dilation is investigated.

Method

13 patients, 72.7(67.1 – 78,6) y.o. [median(1st quartile – 3rd quartile)], with mild‐to‐moderate dementia due to probable AD fulfilling the standard diagnostic criteria (ADD); and 15 anamnestic healthy control subjects 69.2(60.9 – 71.1) y.o. without subjective/objective cognitive impairment (HC) were examined with Dynamic Vessel Analyzer (IMEDOS Systems, Jena). In retinal arterial segments of 1280 µm in length vessel diameters were measured with a spatial resolution of 10µm in order to obtain LAP at different stages of arterial reaction to flicker. Differences in amplitude and frequency of spatial vessel diameter changes were analysed using Fast Fourier Transformation.

Result

The magnitude of spatial arterial diameter changes was higher in HC than in ADD during all stages of arterial response, p<0.01. At baseline it amounted to 40(32 – 54) µm in HC and to 24(20 – 29) µm in AD. Compared to the arteries of HC power spectra of LAP in AD patients showed pronounced waves with a period of ∼200 µm during baseline and vasodilation (parameter: area under the power spectra at certain frequency range), and pronounced waves with a period of ∼100 µm during vasodilation and relaxation (p <0.05).

Conclusion

Dynamic functional alterations of retinal arteries in AD dementia are shown using a non‐invasive in‐vivo technique. Retinal arteries of AD patients change their internal microstructure which is especially emphasized during vasodilation. Further research of retinal vascular function in AD dementia should reveal the reason for the reported structural vascular alterations. Since dynamic retinal vessel analysis provides a direct non‐invasive assessment of microcirculatory damage, it could allow for additional diagnostic characterization of AD.
Disclosure of individual‐specific results to participants of a dementia prevalence study: A qualitative study
Authors: Fadda Marta , Fiordelli Maddalena , Amati Rebecca , Ibnidris Aliaa , Albanese Emiliano
Published: December 2020

Background

Dementia research and innovation is action area 7 of the WHO Global action plan on the public health response to dementia. Effective progress in this area depends on the promotion of participation in research both for people with and for those without dementia. However, evidence suggests that current research ethics policies and norms take participants’ preferences in little account, and may hinder equitable opportunities to take part in epidemiological research. Before conducting an epidemiological study into dementia and its impact, whose primary outcome is prevalence of dementia based on a probabilistic dementia diagnosis, we conducted a qualitative study to explore participants’ understanding of the meaning of individual‐specific results and on their preferences regarding their return.

Method

Between July 2019 and January 2020, we conducted interviews with individuals who participated in the validation phase of an epidemiological study (N = 22). Participants were 65 years old or older and resident in the Italian‐speaking part of Switzerland. We elicited information on their participation experience in the validation study, their reasons for participation (including expectations and potential barriers), their opinion on the return of research results, their understanding of these results. We also explored the communication preferences and the personal justification in favor or against the and the anticipated feelings with respect to the return of results. Finally, we discussed their preferences regarding the provision of informed consent for the return of study results. All interviews were recorded, transcribed and analyzed using thematic analysis techniques.

Result

Most participants reported that researchers have a duty to share individual‐specific results with participants if requested to, and that these results must meet certain validity and reliability criteria. The main reasons in support for the return include the desire to know/ exclude oneself dementia diagnosis, and the need to establish a relationship of mutual trust between researchers and participants.

Conclusion

In the context of an epidemiological study on dementia, researchers should demonstrate the validity of individual results (including dementia diagnosis) and offer in advance to participants the opportunity to receive this information accounting for their preferences and values.
Genome‐wide association analysis of neurofibrillary tangle burden identifies novel risk loci in the adult changes of thought (ACT) and the religious orders study and memory and aging project (ROSMAP) autopsy cohorts
Authors: Mukherjee Shubhabrata , Fardo David W. , Gibbons Laura E. , Larson Eric B. , Schneider Julie A. , Montine Thomas J. , Keene C. Dirk , De Jager Philip L , Bennett David A. , Crane Paul K. , Thornton Timothy A.
Published: December 2020

Background

Neurofibrillary tangle (NFT) deposition is a hallmark for neuropathological diagnosis of late‐onset Alzheimer’s disease (LOAD). We were interested in identifying genetic variants associated with NFT burden in brain tissue from participants in the Adult Changes in Thought (ACT) and the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) autopsy cohorts.

Method

In ACT, NFT (paired helical filament (PHF)‐tau) is measured using histelide in frontal, temporal and parietal cortex. In ROSMAP, NFT burden is quantified by microscopic examination of silver‐stained slides from hippocampus and midfrontal, midtemporal, inferior parietal, and entorhinal cortex. In each cohort, we used Confirmatory Factor Analysis (CFA) to derive composites for NFT burden. We ran null models adjusting for sex and age at death; residuals were phenotypes for genetic analyses. We ran genome‐wide association studies (GWAS) of NFT burden using 1000G imputed data (NCBI; build 137) from European ancestry participants using Q‐ROADTRIPS which uses an empirical correlation matrix to account for population substructure. We performed meta‐analysis using the adaptively‐weighted Fisher’s method. Due to modest sample size and use of p‐values alone in meta‐analysis, we used conservative criteria for candidate loci, with minor allele frequency (MAF)> 0.05, meta‐analytic p‐value < 5 × 10−8, and p‐value < 0.2 in each cohort. We used generalized linear models for the top SNPs to investigate direction of effects.

Result

Mean death ages were 89 (ROSMAP, n = 1,225) and 87 (ACT; n = 388); and 66% (ROSMAP) and 53% (ACT) were female. A single factor CFA model fit in ACT. A bi‐factor model with a secondary factor comprising midfrontal, midtemporal, and inferior parietal cortices was needed for ROSMAP. We identified two novel loci at LRP1B (Chr 2: rs10496895; Pmeta = 1.54 × 10−8) and upstream of ZHX2 (Chr 8: rs56258958; Pmeta = 4.9 × 10−8) apart from SNPs near APOE.

Conclusion

LDL receptor related protein 1B (LRP1B) has restricted expression in human tissues and binds to several extracellular ligands, including fibrinogen and APOE‐ carrying lipoproteins. Zinc‐finger and homeodomain protein 2 (ZHX2) is specifically expressed in neural progenitor cells during cortical neurogenesis. Further functional enrichment analysis is needed to determine whether these novel loci may provide targets for interventions to ameliorate LOAD.
miR‐9 could mediate the relationship between childhood maltreatment and depression
Authors: He Cancan , Wang Qing , Xie Chunming
Published: December 2020

Background

Childhood maltreatment (CM) is regarded as an important risk factor for major disorder depression (MDD). However, the neural links corresponding to the process of early CM experience producing brain alterations and then leading to depression later remain unclear.

Purpose

To explore the neural basis of the effects of CM on MDD and the potential effects of microRNA‐9 (miR‐9) in mediating these processes.

Method

Forty unmedicated MDD patients and 34 healthy controls (HCs) completed resting‐state fMRI scans and peripheral blood miR‐9 tests. The neural substrates of CM, miR‐9, and depression, as well as their interactive effects on intrinsic amygdala functional connectivity (AFC) network were investigated in MDD patients. Two‐step mediation analysis was separately employed to explore whether AFC strength mediates the association among CM severity, miR‐9 levels, and depression. A support vector classifier (SVC) model of machine learning was used to distinguish MDD patients from HCs.

Result

MDD patients showed higher miR‐9 levels that were negatively correlated with CM scores and depressive severity. Overlapping effects of CM, miR‐9, and depressive severity on bilateral AFC networks in MDD patients were primarily located in the prefrontal‐striatum pathway and limbic system. The connection of amygdala to prefrontal‐limbic circuits separately mediated the effects of CM severity on the miR‐9 levels, and the impacts of miR‐9 levels on the severity of depression in MDD patients. Furthermore, the SVC model, which integrated miR‐9 levels, CM severity, and AFC strength in prefrontal‐limbic regions, had good power in differentiating MDD patients from HCs (accuracy 85.1%).

Conclusion

MiR‐9 may play a crucial role in the process of CM experience‐produced brain changes targeting prefrontal‐limbic regions and that subsequently leads to depression. The present neuroimaging‐epigenetic results provide new insight into our understanding of MDD pathophysiology.
Multiomics approaches reveal a link between the MS4A gene loci, TREM2, and microglia function
Authors: You Shih‐Feng , He June , Filipello Fabia , Brase Logan , Del‐Aguila Jorge L. , Mihindukulasuriya Kathie A. , Benitez Bruno A. , Cruchaga Carlos , Harari Oscar , Karch Celeste M.
Published: December 2020

Background

Soluble triggering receptor expressed on myeloid cells 2 (sTREM2) in cerebrospinal fluid (CSF) has been associated with Alzheimer’s disease (AD). TREM2 plays a critical role in microglial activation, survival, and phagocytosis; however, the pathophysiological role of sTREM2 in AD is not well understood. Understanding the role of sTREM2 in AD may reveal new pathological mechanisms and lead to the identification of therapeutic targets. We recently identified common variants in the membrane‐spanning 4‐domains subfamily A (MS4A) gene region that were associated with CSF sTREM2 concentrations. One variant (rs1582763) was associated with increased CSF sTREM2 and reduced AD risk, while a second variant (rs6591561) was associated with reduced CSF sTREM2 and increased AD risk. Using human induced pluripotent stem cell‐derived microglia, we found that MS4A4A and TREM2 colocalize on lipid rafts at the plasma membrane. Here, we sought to define the molecular mechanism by which variants in the MS4A gene region impact sTREM2, microglia function and AD risk.

Methods

To define the functional effects of MS4A variants, we used genotype and bulk RNAseq data from 579 human brain samples. We then evaluated microglia specific effects using single nuclei RNAseq data obtained from 70 human brains.

Results

Leveraging genotypic and transcriptomic data in human brain tissue, we found that rs1582763 and rs6591561 alter distinct molecular pathways. Rs1582763, which confers AD resilience, impacts pathways associated with cholesterol metabolism, while rs6591561, which confers AD risk, impacts pathways associated with chemokine regulation. Using single nuclei RNAseq data in human brain tissue, we found that variants in the MS4A gene region were sufficient to alter specific microglia populations.

Conclusions

These findings also provide a mechanistic explanation for the original GWAS signal in the MS4A locus for AD risk and indicate that TREM2 may be involved in AD pathogenesis not only in TREM2 risk‐variant carriers but also in those with sporadic disease.
A multifactorial training program for cognitive, metacognitive and social participation enhancement of self‐validated older adults from Argentina: Preliminary results
Authors: Feldberg Carolina , Tartaglini María Florencia , Hermida Paula , Natalia Irrazabal , Quian Rosario , Somale Maria Veronica , Garcia Claudia , Garcia Lydia Moya
Published: December 2020

Background

The prevalence of cognitive disorders in the elderly population increases with time. Cognitive intervention is a broad field that uses different means to increase or maintain these capacities. Multifactor programs work with several aspects involved in cognitive processing simultaneously incorporating cognitive, metacognitive and psychosocial strategies. The aim of the present study is to implement and asses the efficacy of a multifactorial program for cognitive and metacognitive enhancement of self‐validated older adults.

Method

Design: Comparative, pre‐post, longitudinal intervention study. Self‐validated subjects over 60 years. Mental Fit participants (n= 47) (M= 70,04 years old; ds= 8.07 years) and controls in the community(n= 57) (M= 69.92 years old; ds= 7.31 years) were assed at the begging and at the end of the program. Instruments: Questionnaire of Social Demographic Data (built ad hoc), Brief Neuropsychological assessment for memory and attention. Metacognition: Metamemory Questionnaire, Inventory of Verbal Episodic Memory Self‐efficacy. Procedure: All the participants of the Intervention group took part in the "Memory Fit" seminar. The “Memory Fit” seminar has a duration of 8 sessions of 90 minutes each, where theoretical and practical tools are provided to improve cognitive performance in everyday life and they were also invited to take part in other activities like academical, artistic workshops, and specialist's talks about healthy habits. Controls were assed twice with eight weeks of difference between the first and second evaluation.

Results

Results reveal that when comparing the groups, statistically significant differences can be observed in favor of Mental Fit participants in: memory self‐efficacy measures before intervention (M=7.5 , SD=1.93) and after (M=8.26 , SD =1.91 t (‐2.5)= ; p <.01, metamemory before (M=119,80, SD=16.96) and after (M=135,41 , SD =19.68 t (5.06)= ; p <.000, and delay recall of a word's list before(M=7.17 ;SD=2.4)and after (M=8.05 , SD =2.5 t (‐2.6)= ; p <.01.

Conclusion

Our findings concur with those presented in scientific literature and support the WHO recommendations regarding the need to implement intervention programs for older adults. The inclusion of cognitive training is an approach that can help cognitively vulnerable older adults and promote healthy cognitive aging.
The expression of P2X7 receptor in human induced pluripotent stem cell‐derived cellular model of Alzheimer’s disease
Authors: Francistiova Linda , Téglási Annamária , Kern Zsófia , Kobolák Julianna , Dinnyés András
Published: December 2020

Background

We aim to elucidate the role of the purinergic receptor P2X7 in the development and progression of the Alzheimer’s disease (AD) pathology in human induced pluripotent stem cell‐derived cellular model. Our model consists of iPSC‐derived mixed neuronal and astrocytic cells, together with human iPSC‐derived microglia‐like cells. Since jointly with amyloid‐β and tau pathology, neuroinflammation presents one of the main factors in AD. Thus, having microglia in the cellular model of AD is unavoidable.

Method

iPSC‐derived neuronal cultures were generated using the dual SMAD inhibition protocol, while microglial cells were generated through a 3D differentiation protocol, using bone morphogenetic protein 4 (BMP4), vascular endothelial growth factor (VEGF), and stem cell factor (SCF). Further differentiation was forced by adding macrophage colony‐stimulating factor (M‐CSF) and interleukin‐3 (IL‐3). Expression of cellular markers, as well as the studied P2X7R, was verified via ELISA, immunocytochemistry, western blot and RT‐qPCR.

Result

Our neuronal cell cultures obtained from AD patients have been previously reported to show major pathologic signs of AD, thus, suitable for AD modelling. The acquired microglia‐like cells expressed Iba1, CX3CR1, CD11c, and TMEM119, which confirms their suitability for further use. Both neuronal and microglial cells express the investigated P2X7 receptor. Moreover, based on our preliminary results, the expression of P2X7R in neurons seems to be diminished when co‐cultivated with microglia. This might suggest a temporary modulatory mechanism, activated to supplement the function of the receptor in the absence of microglia.

Conclusion

Based on our results, the presented cellular system offers an excellent model that can be used for studying the molecular basis of age‐related neurodegenerative diseases such as Alzheimer’s disease. Future experiments will explore the effects of P2X7R antagonism on AD pathology in our cellular model. P2X7R seems to be actively engaged in AD pathological processes, and thus, the modulation of the receptor’s activity with antagonists might present a good therapeutic approach for AD. Therefore, this research helps to validate the correlation between animal and human studies of P2X7R; and will be extended to the evaluation of the therapeutic potential of selected P2X7R antagonists in Alzheimer’s disease.
Label‐free X‐ray technique for distinguishing 5XFAD from wild‐type mice
Authors: Dahal Eshan , Crentsil Jasson , Ghammraoui Bahaa , Badano Aldo
Published: December 2020

Background

We recently proposed a label‐free method based on spectral small‐angle x‐ray scattering (sSAXS) technique that estimates brain amyloid load in vivo in small animals. Here we present the first results of amyloid load mapping for an Alzheimer’s disease mouse model without using contrast agents.

Method

A prototype sSAXS system was built to collect elastic x‐ray scattering data from an intact mouse head at angles less than 10° and energies between 30 and 45 keV. 5XFAD mice along with age‐matched wild‐type (WT) controls were used in the study. Fixed mice heads were scanned from eye to ear at three different heights in a sagittal plane with a scanning step size of 2 mm. Data were acquired at each position for 300, 600, and 1200 seconds with repositioning of the target. The relative amyloid load was estimated by taking area‐under‐the‐peak (AUP) in the scattering profile from momentum transfer (q) 3.6 to 8.4 nm‐1. AUP analysis covers the characteristic signal of amyloid plaques associated with its beta‐sheet structure.

Result

Out of 15 scanned locations in mice heads using 300 s exposure time, areas associated with the isocortex and the olfactory bulb showed significantly increased AUP values with respect to WT mice. Midbrain regions showed negligible differences in AUPs. These results were further reproduced using 600 and 1200 s exposure times. Compared to WT, 5XFAD mice exhibited significantly elevated levels of amyloid plaques in the isocortex near hippocampus (9.3 ± 3.7%) and the olfactory bulb (10.8 ± 0.82 %). Our method was sensitive enough to differentiate brain regions with and without plaques despite the strong background scattering signal from the skull and other brain tissues.

Conclusion

Our results showcase the capability of sSAXS based method to differentiate non‐diseased and diseased mice by estimating amyloid load in Alzheimer’s disease mouse model ex vivo or in vivo and without using any contrast agent. Intrinsic small‐angle x‐ray scattering signal from beta‐amyloid plaques in diseased mice with advanced Alzheimer's allows the differentiation of brain regions with a high amyloid load.
A longitudinal study of gait on fall risk and disease progression in patients with mild cognitive impairment and Alzheimer's disease
Authors: Nocera Joe R , Gregory Gregory J , Lah James J , Levey Allan I
Published: December 2020

Background

The evaluation of gait has gained popularity in clinical practice due to its sensitivity for identifying functional decline and fall risk in numerous patient populations. Specific to Alzheimer’s Disease (AD), previous research has shown higher risk of falls and increased disability when compared to their healthy counterparts. However, gait biomarkers that can predict fall risk, disability and disease progression in those with AD have not been adequately quantified. A better characterization of longitudinal gait changes in patients with dementia could have implications for future research and targeted interventions aimed to identify and lessen fall risk as well as the related disability.

Methods

Gait outcomes, including gait speed, step length variability and step symmetry were collected at two timepoints using an instrumented ProtoKinetics Gait Mat. Each gait collection was linked to the electronic medical record in order to obtain a disease diagnosis and fall history.

Results

The final sample included 70 patients that were classified as mild cognitive impairment (MCI) at gait timepoint 1 and remained MCI at gait timepoint 2 (MCI‐MCI), 84 patients that were classified as AD at gait timepoint 1 and remained AD (AD‐AD) and 17 that were classified as MCI at gait timepoint 1 and subsequently transitioned to AD at gait timepoint 2 (MCI‐AD). Across the groups, gait speed was significantly slower in those with a history of falls when compared to those without a fall history (p<0.05). Fall rates were highest in the AD‐AD group (41%). At gait timepoint 1, there was no significant difference in age or MOCA between the MCI‐MCI and MCI‐AD groups, however, fall rates were higher for those that transitioned from MCI to AD (24%) versus those that remained MCI from timepoint 1 to 2 (16%).

Conclusion

The results demonstrate there is a high risk of falls among patients with AD and those that transition from MCI to AD. A slower gait speed was associated with higher fall risk across groups. The significant rate of falls highlights the need for targeted interventions as falls are associated with increased disability, hospitalization and mortality.