bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection of cognitive impairment from eSAGE cognitive data using machine learning
Authors: Kawakami Ryoma A , Scharre Douglas W. , Ning Xia 
Published: December 2022

AbstractBackgroundAlzheimer’s disease is a neurodegenerative disease that affects many people worldwide. Many cognitive screening tests are available, with one example being eSAGE – a self‐administered exam (Scharre et al., 2017)1. eSAGE is a valid and reliable cognitive assessment tool to detect mild cognitive impairment or early dementia. It has been implemented as an app in BrainTest®, with sample screenshots in Figure 1. While eSAGE provides diagnostic results of its own, there is a potential to apply machine learning methods to the responses to obtain better diagnostic results.MethodeSAGE results1 and behavioral data from BrainTest® were obtained for 69 subjects, each with a diagnosis of normal cognition, mild cognitive impairment, or dementia. 84 metrics were extracted, including the time that subjects spent on each test page, and drawing speed and line straightness from the construction test problems (Table 1). The hypothesis was that cognitive impaired subjects would show different behavioral patterns (e.g., spend more time on questions, change answers often) compared to those of normal cognition. Different machine learning methods including logistic regression and gradient boosting are built over the metrics to classify between cognitive impairment and normal cognition.ResultLogistic regression classifies those with cognitive impairment vs normal cognition with a recall of 100% and ROC of 75.32% and classifies those with MCI vs normal cognition with a recall of 76% and ROC of 79.45% (Table 2). The most important features (those highest weighted metrics used to differentiate the diagnostic classification) for both these classifications were similar (Table 3). Individuals with normal cognition had, on average, longer connected line segments (not lifting their finger off the tablet) when performing the Modified Trails B task (Figure 1). They also spent less time completing their verbal fluency and picture naming tasks (Table 3). See Table 2 and 3 for other findings.ConclusionBehavioral features extracted from self‐administered eSAGE with the BrainTest® app are predictive of cognitive impairment diagnosis by logistic regression and gradient boosting. Important features identified suggest executive processing time lengthens as one progresses from normal to impaired cognition.Reference
1Scharre et al. (2017). Alzheimer’s Research & Therapy, 9, 44.
An examination of the usability of the Mayo Test Drive remote cognitive testing platform in older adults with and without cognitive impairment
Authors: Patel Jay S , Christianson Teresa J. , Karstens Aimee J , Stricker John L , Kremers Walter K. , Jack Clifford R. , Knopman David S. , Mielke Michelle M. , Petersen Ronald C. , Stricker Nikki H. 
Published: December 2022

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for self‐administered assessment for novel (Stricker Learning Span) and open access (Symbols Test) cognitive measures with previously demonstrated validity and reliability. Our study aim was to examine the usability of MTD, defined as the extent to which MTD was used to achieve the desired study goal of completing a testing session among participants willing to engage in an all‐remote study.Methods248 Mayo Clinic Study of Aging and Mayo Alzheimer’s Disease Research Center participants were invited to participate in this ancillary, uncompensated remote pilot study via email. To examine 1) willingness to participate in a remote cognitive assessment study and 2) usability of MTD, the initiation and completion rates of MTD were examined by cognitively unimpaired (CU; n = 142) and cognitively impaired (CI; n = 106) groups (see Figure 1). These rates and other factors associated with MTD feasibility were further explored across age in CU (i.e., 50‐69, 70‐79, and 80+ years) and specific diagnostic groups in CI (i.e., individuals with dementia, mild cognitive impairment (MCI), and possible MCI).ResultsSession completion rate (usability) was 98% across all participants who chose to participate, and, surprisingly, completion rates did not vary by age or diagnostic groups (p =.69; see Table 1). As expected, more individuals chose to participate in the CU (77%) versus CI (52%) group (p < .001). In terms of feasibility, individuals over 80 and with CI were more likely to have some contact with the study coordinator. Use of smartphones decreased with age, with most CU individuals over 70 using a personal computer (69‐73%; Table 2).ConclusionsThough the CI group was less likely to choose to participate than the CU group, out of those that did choose to participate the rate of completion was 98%. This demonstrates high usability of MTD. Study coordinator support and a flexible testing platform is critical for facilitating participation in remote testing across the aging population, and more supports and incentives may be needed for individuals with cognitive impairment to improve participation rates.
Fear and avoidance of memory loss is associated with lower levels of psychosocial functioning and quality of life in older adults
Authors: Farina Francesca R. , Pavithra Pavithra , Bennett Marc P , Lenaert Bert , Griffith James W 
Published: December 2022

AbstractBackgroundDementia is one of the most feared diseases associated with aging. Although some level of fear can motivate protective strategies, too much fear can be harmful and maladaptive. We examined fear and avoidance of memory loss and their relationship to psychosocial and lifestyle factors associated with dementia risk in a community‐dwelling sample of older adults.MethodParticipants were 230 adults (112 female) aged 55 years and older (61.3 ± 5.2). Questionnaires were administered to measure fear and avoidance of memory loss, perceived memory failures, depression, sleep disturbance, engagement in social activities and quality of life. We hypothesized that fear and avoidance of memory loss would positively predict perceived memory failures, depressive symptoms and sleep disturbance, and negatively predict social engagement and quality of life.ResultFear and avoidance of memory loss did not significantly differ by age or sex. People with a family history of dementia reported higher fear (ps < .02, ds = .4), but not avoidance scores. Fear scores were significantly associated with increased self‐reported memory failures and sleep disturbance, and lower quality of life and social functioning, after adjusting for anxiety and memory performance (βs > .8, ps < .03). Inclusion of avoidance scores improved the predictive power of all models except sleep; avoidance was associated with reduced quality of life and social functioning, and increased memory failures and depression (βs > .9, ps < .03).ConclusionFearful anticipation and avoidance of memory loss is associated with poorer psychosocial functioning and quality of life. Based on these results, we propose a model whereby excessive fear of memory loss motivates withdrawal from social and cognitively demanding activities that can protect against and compensate for dementia risk. Findings have practical implications for psychological and lifestyle interventions, which could allay multiple modifiable risk factor pathways.
Comparing cerebrovascular disease diffusion MRI markers using post‐mortem and longitudinal imaging data
Authors: Raghavan Sheelakumari , Przybelski Scott A. , Kamykowski Michael G. , Reid Robert I. , Lesnick Timothy G. , Murray Melissa E. , Reichard Ross R. , Graff‐Radford Jonathan , Nguyen Aivi T. , Knopman David S. , Mielke Michelle M. , Jack Clifford R. , Petersen Ronald C. , Vemuri Prashanthi 
Published: December 2022

AbstractBackgroundDiffusion MRI (dMRI) based quantification of microstructural changes in white matter (WM) are increasingly proposed to measure cerebrovascular disease (CVD) related changes. Our objective was to compare the proposed dMRI markers using postmortem neuropathologic CVD data and then evaluate the predictors of longitudinal dMRI markers using serial scans.MethodWe identified n=51 participants (mean age: 83.8 years, 63% males) with ante‐mortem dMRI and postmortem CVD evaluation and n=718 participants (mean age: 71.1 years, 56% males) with at least two dMRI scans from the population‐based sample of Mayo Clinic Study of Aging. We computed dMRI measures proposed for measuring CVD: Free Water (FW), Fractional Anisotropy (FA) adjusted for FW (FAadj), Peak width Skeletonized Mean Diffusivity (PSMD), and FA of the genu of corpus callosum (Genu‐FA). We used the FW and PSMD (PSMD release 1.8.1) kits from the MarkVCID consortium.Using weighted linear regression models with adjustments for MRI scan time to death, we evaluated associations between the baseline dMRI and two pathology CVD scores: Strozyk (represents the presence and number of macroscopic lesions) and Kalaria (represents the summary score of vessel wall modifications) scales. We ran linear mixed effect models with all available serial dMRI measures as outcomes and vascular risk (measured by the number of cardiovascular and metabolic conditions‐CMC), baseline imaging measures (amyloid and white matter hyperintensities (WMH)) and their interaction with time as key predictors.ResultMost dMRI markers were predictors of postmortem CVD pathology (Table 1). In longitudinal dMRI models, terms associated with amyloid explained little variability in dMRI (<1%) (Table 2). WMH was a significant predictor of baseline and decline in dMRI measures with WMH explaining a considerable percentage of variability in FW (34%) and PSMD (28%). Vascular risk measures were significant predictors of three out of the four dMRI measures.ConclusionThe diffusion measures proposed as surrogate markers of CVD map reasonably well to CVD pathological scales and do not change substantially as a function of baseline amyloidosis. Our findings shed light on variability in the proposed diffusion markers using postmortem data and longitudinal imaging data. Further work is needed to evaluate their clinical utility.
Isoaspartate as a biomarker for early diagnostics of neurodegenerative diseases
Authors: Wang Jijing , Zhang Ya‐Ru , Shen Xue‐Ning , Han Jinming , Cui Mei , Tan Lan , Dong Qiang , Zubarev Roman A. , Yu Jin‐Tai 
Published: December 2022

AbstractBackgroundThe formation of isoAsp via spontaneous asparagine (Asn) deamidation or aspartate (Asp) isomerization has been regarded as a molecular clock of aging that significantly correlates with neurodegenerative diseases (NDDs). Our earlier findings established a strong link between isoAsp and Alzheimer’s disease (AD). In this study, we aimed at validating the diagnostic value of blood isoAsp‐related biomarkers for NDD prediction.MethodsThe levels of isoAsp in plasma human serum albumin (HSA) and the immunoglobulin G (IgG) specific against artificially aged HSA were measured in two cohorts by indirect enzyme‐linked immunosorbent assay (ELISA). Cohort 1 contained patients with AD and controls, while Cohort 2 recruited subjects with mild cognitive impairment (MCI), vascular dementia (VD), frontotemporal dementia (FTD), Parkinson disease (PD) and healthy controls. Apart from the memory tests, other plasma biomarkers reported in literature were quantified as well, including amyloid beta (Aβ) peptides Aβ40 and Aβ42, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP) and phosphorylated Tau 181 (pTau181) protein.ResultsCohort 1 confirmed as expected a significant increase of isoAsp in HSA and reduction of IgG against aged HSA in AD group compared to controls (P < 0.0001). Cohort 2 revealed similar trends in comparisons between controls and subjects of different NDDs (P < 0.05). The most exciting finding was the best performance of isoAsp‐related biomarkers in MCI prediction among all blood biomarkers. In addition, the levels of our biomarkers were strongly associated with the memory test scores (P < 0.01).ConclusionWe demonstrated the diagnostic capacity of isoAsp‐related biomarkers in predicting NDDs at the early stage of disease. The biomarker levels correlated with cognitive decline, supporting their roles in NDD development. However, the specificity of these novel biomarkers seemed to be not limited to AD. Additional longitudinal studies with follow‐up data shall be conducted for validation of these findings.
pTau heterogeneity as a measure for disease severity in incipient Alzheimer's disease
Authors: Servaes Stijn , Lussier Firoza Z , Therriault Joseph , Tissot Cécile , Bezgin Gleb , Kang Min Su , Wang Yi‐Ting , Stevenson Jenna , Rahmouni Nesrine , Arias Jaime Fernandez , Benedet Andréa Lessa , Chamoun Mira , Pascoal Tharick A , Gauthier Serge , Rosa‐Neto Pedro 
Published: December 2022

AbstractBackgroundThe presence of p‐tau in biofluids has previously been proposed to be a response to neurofibrillary tangle pathology, one of the hallmarks of Alzheimer’s disease (AD). However, the increase of p‐tau in cerebrospinal fluid (CSF) precedes detectable neurofibrillary tangle pathology, as indexed by tau Positron Emission Tomography (PET), by up to a decade, suggesting that soluble tau could be an indication of early tau pathology. With this study, we investigated the heterogeneity of p‐tau species in CSF to assess the disease status of participants of the Translational Biomarkers of Aging and Dementia (TRIAD) cohort.MethodsSupport vector machines were used to determine cutoff values of p‐tau181, p‐tau217, p‐tau231 and p‐tau235 in CSF, identifying a group of participants that were amyloid positive (58 from a total of 165 participants). Amyloid positivity was determined by using an [18F]AZD4694 SUVR threshold value of 1.55 in the neocortex. Using these cutoff values, signatures were calculated on an individual level to identify the number of phosphorylated sites. Based on the number of phosphorylated sites, [18F]MK6240 SUVR maps and [18F]AZD4694 SUVR maps were calculated.ResultsWhen combining different CSF p‐tau species, the largest contribution in identifying amyloid positivity came from p‐tau217, followed by p‐tau231, p‐tau181 and p‐tau235. Achieving the cutoff for multiple p‐tau species was associated with more tau pathology particularly in the later Braak stages (fig 1) and increased amyloid‐β plaque accumulation (fig 2).ConclusionOur findings suggest that heterogeneity in p‐tau species carries predictive power in the identification of disease severity in incipient Alzheimer’s Disease.
Baseline White Matter Neuroinflammation Predicts Cognitive Decline in Alzheimer Disease
Authors: Wang Qing , Wang Wenshang , Schindler Suzanne E. , McKay Nicole S. , Chen Gengsheng , Liu Jingxia , Wang Sicheng , Sun Zhexian , Hassenstab Jason J. , Fagan Anne M. , Morris John C. , Wang Yong , Benzinger Tammie L.S. 
Published: December 2022

AbstractBackgroundNeuroinflammation, an intrinsic immune response of the nervous system, is important in an early phase of Alzheimer Disease (AD) pathogenesis. However, it is largely unclear how neuroinflammation promotes the clinical progression of AD. Extracellular water edema (ECW) from Neuro‐inflammation imaging (NII), a diffusion‐based MRI approach for neuroinflammation, has been utilized to asses white matter (WM) neuroinflammation in AD. This study aims to investigate the relationship between WM neuroinflammation and longitudinal cognitive changes in AD.MethodThis study included 286 participants who were enrolled in ongoing research at the Knight ADRC at Washington University in Saint Louis. The CSF levels of Aβ42/Aβ40 and ptau181 measured with the automated Lumipulse platform were used to categorize participants as being positive or negative amyloid (A) and tau (T), respectively (Table 1). As measures of cognitive performance, the Knight ADRC Preclinical Alzheimer Cognitive Composite (Knight ADRC‐PACC) and a Global composite score were employed for each individual. The rates of change of cognitive measures were calculated for each individual. The NII was acquired with a multi‐b value scheme (bmax =1400s/mm2 and 25 directions). The total WM neuroinflammation (NII‐ECW) was obtained using TBSS (http://www.fmrib.ox.ac.uk/fsl) based WM skeleton. Randomise in FSL was utilized to examine voxel‐wise relationships between NII‐ECW and the rate of change of cognitive measures. Non‐parametric Kruskal‐Wallis and Chi‐square tests were employed for comparing continuous and categorical variables of the participants’ demographics, respectively. The group differences of NII‐ECW were tested using one‐way ANOVA. All statistical tests were conducted in R (R Core Team, 2020). Age, sex, and APOE ε4 carrier status were adjusted as covariates.ResultTable 1 summarizes demographic data. When compared to the A‐T‐ group, the A+T+CDR0 and A+T+CDR0.5 groups had higher NII‐ECW (Figure 1). When the A+T+CDR1 group was compared to the other groups, there was no group difference in NII‐ECW. There were substantial negative associations between the NII‐ECW and the rates of change of cognitive measures in the widespread WM regions (Figure 2).ConclusionOur data suggest that neuroinflammation increases early in preclinical AD and decreases later in the disease. Furthermore, baseline neuroinflammation as measured by NII‐ECW predicts cognitive decline in AD.
Characterization of Longitudinal Brain Changes in a Community Cohort in Relation to Aging and Cognitive Impairment
Authors: Chwa Won Jong , Lopez Oscar L. , Kuller Lewis H , Raji Cyrus A. 
Published: December 2022

AbstractBackgroundBrain atrophy in aging and Alzheimer’s Disease (AD) involve numerous cortical and subcortical regions. Though there is much overlap in atrophy patterns between aging and AD, there may be regional differences between these two conditions (Fjell et al., 2019). Few studies, however, have explored these patterns in large community samples. This study attempts to investigate longitudinal changes in brain volumes and thickness in relation to aging and cognitive impairment in a well‐characterized community cohort.MethodClinical and neuroimaging data were compiled from 72 participants from the Cardiovascular Health Study‐Cognition Study, a community cohort study of both healthy aging and probable AD patients. Two time points were investigated for each participant with a mean follow‐up time of 5.36 years. MRI post‐processing, cortical and subcortical volume measurements, including hippocampal subfields, and statistical analyses were performed using FreeSurfer, Version 7.1.1. Longitudinal changes were adjusted for multiple comparisons through cluster‐wise correction via the Monte Carlo Z simulation.ResultCortical volume was significantly decreased in the bilateral superior frontal (left: ‐2.322%, p = 0.0002; right: ‐3.03%, p = 0.0002), left superior parietal (‐3.451%, p = 0.0002) and bilateral inferior parietal (left: ‐3.129%, p = 0.0002; right: ‐3.656%, p = 0.003) regions. Cortical thickness was significantly reduced in the bilateral superior frontal (left: ‐1.435%, p = 0.006; right: ‐1.615%, p = 0.0002) and left inferior parietal regions (‐1.581%, p = 0.025). Overall gray and white matter volumes and hippocampal subfields, including the cornu ammonis regions, also demonstrated significant reductions. Cortical volume and thickness trajectories between cognitively declined and cognitively stable subjects were not significantly different.ConclusionObserved volume reductions were consistent with previous studies investigating patterns of brain atrophy (Salat et al., 2004). The magnitude of atrophy was comparable with MCI/AD changes than normal aging (Bakkour et al., 2013). As patterns of atrophy did not significantly differ regardless of cognitive decline, there exists significant overlap between AD‐vulnerable regions and effects of normal aging. These findings help characterize patterns of cortical atrophy that may be reflective of the general population. Larger studies replicating these results are important to more definitively establish population norms of aging and AD‐related neuroimaging changes.
Effects of Public intervention for family caregivers of people with dementia in South Korea: 6‐month follow‐up
Authors: Seo Hyun‐Ju , Choi Eun Jeong , Choo Il Han , Kim Seong Min , Park Jeong Min 
Published: December 2022

AbstractBackgroundFamily caregivers of people with dementia are at higher risk for perceived burden of caregiving. The current studies has shown the effectiveness of providing community‐based interventions for caregivers of people with dementia to lessen their burden. In this study, the observed follow‐up effects after the intervention were examined using healthcare utilization as well as quality of life and perceived burden of the family caregivers.MethodCommunity indwelling people ranged from 18‐90 years old including Dementia Relief Centers were recruited in the study. The participants were non‐randomly allocated into control or experimental group. The experimental group received a 8‐week public intervention focused on increasing knowledge on dementia and abilities to caring for people with dementia, while the control group received no intervention. Resources and utilization in dementia (RUD), health‐related quality of life, and caregiver burden of family caregivers of people with dementia, were measured using a questionnaire at pre‐intervention, post‐intervention and 6‐month follow‐up. Eighty‐one family caregivers with valid data on caring for people with dementia were included in this study.ResultThe present study will show significant difference in the change of healthcare utilization, health‐related quality of life, and caregiver burden between groups at 6‐month follow‐up. The specific study findings will be demonstrated in poster presentation of the conference.ConclusionA public community‐based intervention focused on supporting family caregivers of people with dementia will be effective strategy for decreasing the frequency of healthcare utilization as well as improving health‐related quality of life and caregiver burden of family caregiver of people with dementia even beyond the period of intervention.