bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractBackgroundDriving 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.MethodsThis 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.ResultsBoth 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).ConclusionsDuring 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

AbstractBackgroundTo 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.MethodWe 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.ResultWe 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.ConclusionWe 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

AbstractBackgroundCognitive 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.MethodsHealthy 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.ResultsFor 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).ConclusionsThese 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

AbstractBackgroundSubjective 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.Methods603 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.ResultsSCD 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).ConclusionAs 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

AbstractBackgroundRecent 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.MethodGenetic 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.ResultWe 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.ConclusionOur 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

AbstractBackgroundGenetics, 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.MethodA 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.ResultSeven 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.ConclusionWe 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

AbstractBackgroundWe 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.MethodsAdults 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.ResultsThe 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.ConclusionDeploying 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

AbstractBackgroundPredicting 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.MethodUsing 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).ResultThe 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.ConclusionThese 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

AbstractBackgroundMost 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.MethodThe 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.ResultThe 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.ConclusionThe 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.