bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of delay correction methods to estimate 18F‐florbetaben kinetics in the brain using an image derived input function on total‐body EXPLORER PET
Authors: Bhattarai Anjan , Holy Emily Nicole , Li Elizabeth , Wang Yiran , Spencer Benjamin A. , Wang Guobao , Decarli Charles , Fan Audrey 
Published: December 2023

AbstractBackgroundAbsolute quantification of β‐amyloid binding from 18F‐florbetaben PET requires compartment kinetic modeling and an input function. This study uses non‐invasive Image Derived Input functions (IDIFs), derived using a new total‐body EXPLORER PET/CT scanner (Spencer et al., 2021). For accurate kinetic modelling, it is imperative to correct for time delay from the IDIF location to tracer arrival to the tissue of interest. The standard approach of Joint Estimation (JE) fits for delay time in tandem with other kinetic parameters and is computationally extensive (Feng et al., 2021). This study investigates the utility of Leading Edge (LE) ‐a novel and computationally efficient pulse timing method to estimate delay (Li et al., 2021) compared to JE in estimating 18F‐florbetaben kinetics in the brain grey matter regions, known to be vulnerable in Alzheimer’s Disease (AD).MethodThe study cohort included 14 individuals (9 cognitively unimpaired, 2 MCI, and 3AD; age = 66‐86 years) of the UC Davis Alzheimer’s Disease Research Center. Dynamic total‐body 18F‐florbetaben PET images were acquired for 110min using the uEXPLORER, and reconstructed (resolution = 2.344 mm isotropic voxels, framing protocol:30×2s, 12×10s, 7×60s, 20×300s).The dynamic brain‐cropped images were motion corrected (Jenkinson et al., 2002), and linearly registered to individual subject’s T1W (Jenkinson & Smith, 2001). Individual T1W were segmented using DKT atlas to obtain cerebral grey matter ROIs (Desikan et al., 2006). Dynamic time activity curves for each ROI were fit to the two‐tissue compartmental model, using a subject‐specific IDIF (population metabolite‐corrected) derived from the descending aorta (Figure1). Kinetic parameters were estimated and compared between JE and LE methods.ResultStrong associations were observed between JE and LE estimated 18F‐florbetaben kinetic parameters, including vb(r = 0.84, p<0.001), K1(r = 1, p<0.001), k2(r = 0.9, p<0.001), k3(r = 0.93, p<0.001), and k4(r = 0.86, p<0.001), in all the brain regions (Figure2). Quantitative kinetic results averaged across the selected grey matter regions (N = 11) are reported in Figure3.ConclusionThis study demonstrated the utility of IDIF extracted from total‐body dynamic PET to estimate 18F‐florbetaben tracer kinetics in the brain grey matter regions. Furthermore, the results show that LE can be an efficient surrogate for JE based approach in grey matter focused PET kinetic modelling of 18F‐florbetaben.
Pain experience in immigrants living with dementia in residential aged care homes: A study from an Australian dementia behavior support service
Authors: Atee Mustafa , Morris Thomas , Whiting Daniel , Chejor Pelden , Cain Trish , Laging Bridget , Porock Davina 
Published: December 2023

AbstractBackgroundUntreated pain in aged care residents living with dementia has been linked to delirium, hospitalizations, inappropriate pharmacotherapy, disrupted activities of daily living, and reduced quality of life. Pain experiences and expressions are subjective and to a large extent culturally driven. As dementia advances, older immigrants living with dementia may revert to their culture and language of origin and may experience pain differently. Further, there is limited studies on the pain experience of immigrant residents living with dementia. This study aims to describe the pain experience in immigrants living with dementia in Australian residential aged care homes (RACHs).MethodsA sample of residents who were referred to Dementia Support Australia (DSA, a national behavior support service) for the period 01/06/2018‐30/06/2022 was eligible to be included in the study. Pain was assessed using PainChek®, a validated point‐of‐care medical device for non‐verbal adults living with dementia. PainChek® consists of six domains: Face (9 items), Voice (9 items), Movement (7 items), Behavior (7 items), Activity (4 items) and Body (6 items). The sum of domain scores produces a final score that match the following pain levels: 0‐6 (no pain), 7‐11 (mild pain), 12‐15 (moderate pain) and 16‐42 (severe pain). Immigration status was defined as country or place of birth reported other than Australia.ResultsThe eligible sample [n = 8607, 84.5 (7.0) y, 55.4% female, Alzheimer’s disease = 37.8%, vascular dementia = 12.9%, mixed dementia = 9.5%] had 4411 pain assessments completed, with an average total pain score of 9.4 (5.1). The prevalence of pain was 68.4% in the sample, with over one‐third of episodes represent mild pain (36.0%), and almost one‐third in moderate‐severe pain (20.1% and 12.2%, respectively). Among other factors, pain was the primary contributing factor (60.1%) in causing neuropsychiatric symptoms of dementia, such as agitation/aggression and depression. Top five countries of birth for DSA referrals were England, Italy, Greece, Scotland, and Germany.ConclusionPain is a prevalent clinical problem in immigrants living with dementia in Australian RACHs, who were seeking external behavior support for their neuropsychiatric symptoms.
Social relationship factors, cognitive activity engagement and cognitive decline across 25 years in the Maastricht Ageing Study.
Authors: Duffner Lukas , Deckers Kay , van Boxtel Martin P.J. , de Vugt Marjolein , Köhler Sebastian 
Published: December 2023

AbstractBackgroundSocial relationship factors and cognitive activity engagement have been suggested as protective for cognitive decline and dementia. However, relatively few studies have addressed the risk for reverse causation with extended follow‐up periods. This study therefore aimed at examining associations between those factors and cognitive decline across up to 25 years.MethodWe used data of 1,823 participants of the Maastricht Ageing study, who received baseline measurements of social relationship factors (social network size, social activity engagement and loneliness) and cognitive activity engagement (e.g. hours per week spent with reading books) from 1993 to 1995. Associations with baseline levels and change in memory, processing speed and executive functioning until the fifth Wave of follow‐up (2019 to 2022) were assessed using linear‐mixed models, controlling for age, sex, education, and a summary score of ten risk and protective factors for cognitive decline and dementia (the LIfestyle for BRAinhealth/LIBRA score).ResultMean age at baseline was 51.1 years (SD = 16.4) and 49.9% of participants were female. Participants were followed up for 17.3 years on average (SD = 9.1). At baseline, more frequent social activity engagement was positively associated with processing speed (p<.001). Moreover, larger social network size (p = .023), and more frequent cognitive activity engagement (p = .037) were associated with better executive functioning. Longitudinal analyses showed that people with smaller social network size (p<.001) and higher levels of loneliness had a steeper decline in processing speed from baseline to the 25‐year follow‐up (p = .020). People with higher levels of loneliness additionally showed a steeper decline in memory (p = .006).ConclusionSocial relationship factors were associated with cognitive function and decline across 25 years in a sample representative of the general Dutch adult population. Our findings support their role as potential targets for the maintenance of optimal cognitive functioning.
Cerebral microbleeds and amyloid pathology
Authors: Oomens Julie Elisabeth , Jansen Willemijn J. , Freeze Whitney M. , Maserejian Nancy Nairi , Verhey Frans R.J. , Vos Stephanie J. B. , Visser Pieter Jelle ,   the Amyloid Biomarker Study group
Published: December 2023

AbstractBackgroundCerebral microbleeds (CMBs) are common in the elderly population and are associated with cognitive decline. The aim of the current study was to examine the association between CMBs and cerebral amyloid pathology in cognitively unimpaired (CU) and cognitively impaired (CI) individuals included in the Amyloid Biomarker Study.MethodWe included 2550 CU and 1002 CI participants from 12 cohorts of the Amyloid Biomarker Study. Presence of amyloid pathology was determined based on Aß42 levels in CSF (n = 2873) or amyloid‐PET (n = 679) using data‐driven or center‐specific cutoffs. CMBs were assessed on in vivo MRI and were classified as present (≥1) or absent. The association of amyloid pathology with CMBs and its dependency on age, sex, APOE‐e4 carriership and hypertension was assessed using generalized‐estimating‐equations.ResultThirty‐seven percent of participants were amyloid positive, 38% were APOE‐e4 carrier, 54% were female, 30% had hypertension and 17% had CMBs (20% of the amyloid positive and 16% of the amyloid negative group, respectively). The mean age was 66.7 years (SD9.2). In CU participants, there was no association between amyloid pathology and CMBs. In CI individuals, CMB prevalence was associated with amyloid pathology depending on age (p = 0.044). At younger ages (<75 years), CMBs were more common in amyloid positive rather than amyloid negative participants while at older ages, amyloid negative participants had CMBs more often. However, the interaction between amyloid status and age became insignificant when hypertension was considered. CMB prevalence was associated with amyloid pathology depending on hypertension (p = 0.006). In CI participants without hypertension, CMBs were more common in those that were amyloid positive rather than amyloid negative. In CI participants with hypertension, there was no significant difference in CMB prevalence between the amyloid positive and negative groups (Figure 1). Sex and APOE‐e4 status did not affect the observed associations between amyloid pathology and CMBs in participants with CI.ConclusionCMB prevalence is associated with amyloid pathology and this association is dependent on hypertension. Knowledge on the background prevalence of CMBs considering AD biomarker and vascular risk factor status is needed to inform clinical trial design and safety evaluation of AD therapies.
A composite endpoint using version 3 of the Alzheimers’ Disease Research Centers’ neuropsychological test battery
Authors: Chan Kwun Chuen Gary , Saykin Andrew J. , Barnes Lisa L. , Kukull Walter A. , Dodge Hiroko H 
Published: December 2023

AbstractBackgroundVersion 3 of the Uniform Data Set (UDS) has been collected by the National Alzheimer’s Coordinating Center (NACC) since 2015, which contains neuropsychological test scores on multiple domains. However, its potential utility as prevention trial outcomes needs to be examined, in comparison with other previously validated outcomes.MethodThe analytical sample was extracted from the December 2022 data freeze from NACC, which includes baseline and follow‐up visits from participants who were cognitively normal during initial visits. Standardized z‐scores for each cognitive domain (attention/working memory, episodic memory, semantic memory/language, executive function, visuospatial function) were calculated. Linear mixed effects models with random intercepts and slopes were used for studying longitudinal changes comparing participants who progressed to MCI or AD (decline subgroup) and those who remained cognitively normal for up to 4 years of follow‐up (stable subgroup), controlling for baseline age, sex, race, education and APOE‐e4 carrier status. We then constructed an approximate effect size weighted average of domain‐specific scores as a global composite measure, obtain effect size estimates using the mini‐mental state examination (MMSE), the Montreal Cognitive Assessment (MoCA) and the composite measure as outcomes, and perform sample size calculations using the following assumptions: a significance level of 5%, 80% power, an equal proportion randomized in a hypothesized intervention and a placebo control arms, in which the intervention group has a 10% to 30% reduction of progression to MCI during the study period, each participant has a baseline visit and up to four annual follow‐up visits, a dropout rate of 10% per year.ResultThe estimated difference of the annualized rate of change in z‐scores between the decline and stable subgroups ranged from 0.028 (attention) to 0.105 (executive function) standardized units for the domain‐specific scores, and 0.068 standard deviation units for the composite score. The composite score requires a smaller sample size for detecting the same level of reduction in progression compared to MoCA and MMSE.ConclusionUsing a global composite score of UDS version 3 neuropsychological tests as a prevention trial outcome would result in about 30‐50% reduction of sample size needed compared to MMSE and MoCA.
Facilitating Clinical Use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative Data and Diagnostic Cutoff Values
Authors: Postema Merel C. , Dubbelman Mark A. , Verrijp Merike , Visser Leonie N.C. , Visser Pieter Jelle , Zwan Marissa D. , van der Flier Wiesje M. , Sikkes Sietske A.M. 
Published: December 2023

AbstractBackgroundThe Amsterdam Instrumental Activities of Daily Living Questionnaire(A‐IADL‐Q) measures impairments in everyday functioning in the context of dementia. To facilitate interpretation and clinical implementation of the A‐IADL‐Q, we aimed to generate demographically‐adjusted norm scores and a clinical cutoff value.MethodCross‐sectional data were used from Dutch Brain Research Registry(N = 1,064; mean (M) age = 62±11year; 69.5% female) European Medial Information Framework‐Alzheimer’s Disease(EMIF‐AD) 90+ (N = 63; Mage = 92±2year; 52.4% female), and European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study(EPAD‐LCS)(N = 300; Mage = 62±7year; 71.7% female)(Table 1). Norm scores were generated using a I) linear regression model approach; and II) Box‐Cox Power Exponential(BCPE) model approach (the latter falling within the generalized model for location shape and size(GAMLSS) framework). For both methods, different combinations of the variables age, sex and education were tested (including polynomial age and age*education interaction) and the best model was selected based on the lowest Aikake Information Criteria(AIC) value. Next, diagnostic contrast was generated using an independent memory clinic sample (the Amsterdam Dementia Cohort(ADC)(N = 2,123, Mage = 64±8year, 46.1% female)(Table 1), including people with dementia (both Alzheimer’s Disease(AD) and non‐AD). The optimal cut‐off value for cognitively healthy versus dementia was determined, and corresponding diagnostic accuracy was calculated using area under the receiver operating curves(AUC‐ROC). This optimal cutoff was then applied to different diagnostic contrasts, including mild cognitive impairment(MCI) and subjective cognitive decline(SCD).ResultThe best suitable normative model was the linear regression model, including cubic polynomial for age, education, and age*education interaction. This model was considered superior to the best BCPE model, which had infinitely large residuals due to bad model fit. The optimal cutoff to distinguish normal functioning from dementia, based on bootstrap average estimates of the Youden Index, was 1.82 standard deviation(SD) below the population mean, resulting in an AUC of 0.97[95%CI 0.98‐0.98]). Normative data are made publicly available in an online tool(Fig.1).ConclusionWe established demographically adjusted norm scores for the A‐IADL‐Q using a large representative sample, and provided an optimal cutoff value for cognitively healthy versus dementia, with outstanding accuracy. Our results, captured in the online tool, can help clinicians to interpret A‐IADL‐Q scores more easily by allowing comparison of functioning against a reference population.
Dementia and Dental health among attendees at a Geriatric outpatient clinic in Nigeria
Authors: Majekodunmi Mofoluwake , Elugbadebo Olufisayo Oluyinka 
Published: December 2023

AbstractBackgroundThere is a relationship between cognitive function and dental health as cognitive impairment may lead to poor tooth brushing activities as well as poor dental care and use of medications.MethodA cross sectional study was carried out on three hundred patients attending a geriatric clinic in Nigeria. The diagnosis of dementia was made using ICD 10 diagnostic criteria. Dental health was assessed using a dental chart that shows the number of teeth present and absent in the four quadrants of the mouth.ResultBlessed Dementia Rating Scale (BDRS) and Instrumental Activities of Daily Living (IADL) showed statistical significant association with tooth loss. BDRS (p = 0.001), IADL (p = 0.007). There was positive correlation between tooth loss and BDRS. Pearson correlation = 0.19, p = 0.001. The mean number of tooth loss was significantly higher in patients with dementia compared to those without cognitive impairment.ConclusionThe finding of higher tooth loss among people with dementia may support the evidence of ascending infection leading to inflammation in the brain might be responsible.
Personalised activity cards, goal‐directed daily (physical) activity and rest‐activity rhythm in institutionalised persons living with dementia
Authors: Wolf Evelien , Sikkes Sietske A.M. , Milders Maarten V. , Drent Madeleine , Scherder Erik J.A. 
Published: December 2023

AbstractBackground(Physical) inactivity and disturbances in the rest‐activity rhythm (e.g. more daytime sleeping and more night‐time restlessness) are common in institutionalised persons living with dementia (iPwD). Both are associated with worse quality of life (QoL), which underlines the need for non‐pharmacological interventions. The purpose of this study was to examine if the implementation of personalised activity cards (PACs) could enhance the amount of time spent on goal‐directed daily (physical) activities and improve the rest‐activity rhythm in iPwD.MethodThis prospective longitudinal quasi‐experimental multicentre study was conducted on 11 wards in 8 nursing homes in the Netherlands. The intervention consisted of PACs, a structured method for offering personalised activities based on input from (professional) caregivers. The PACs were implemented on 5 wards. The other 6 wards received care as usual (control group). Intervention compliance was checked every two weeks with an online survey. Outcome variables included minutes spent on goal‐directed daily (physical) activity as measured with an adjusted version of the Physical Activity Survey in Long‐Term Care (PAS‐LTC; observations by researchers), and rest‐activity rhythm was assessed using Actiwatch activity monitors. Outcomes were assessed at baseline (T0), and after 12‐15 weeks (T1).ResultWe analysed the data of 84 iPwD; intervention group: n = 35, control group: n = 49 (Table 1). Intention‐to‐treat mixed model analyses showed no significant interaction effects of the intervention (group × time) on goal‐directed daily (physical) activity and rest‐activity rhythm variables (Table 2). One ward in the intervention group did not implement the PACs. The four wards that did implement the PACs reported diverging percentages of the execution of planned activities, even as low as 33.5% (range 33.5‐74.2%). The most reported reason for not executing activities on the PACs was that employees were too busy.ConclusionContrary to our expectations, implementing PACs did not show any effect on goal‐directed daily (physical) activity in iPwD. The lack of improvement in activity level might be an explanation for the lack of improvement in rest‐activity rhythm. These findings support previous research that it can be difficult to implement new methods in the nursing home environment, and underline the importance of more implementation research
The effect of local versus remote Beta‐Amyloid on functional connectivity mediated Tau‐Spread in Alzheimer’s Disease
Authors: Wanger Fabian , Steward Anna , Biel Davina , Dewenter Anna , Roemer Sebastian Niclas , Ewers Michael , Dehsarvi Amir , Franzmeier Nicolai 
Published: December 2023

AbstractBackgroundIn Alzheimer’s disease (AD), Amyloid‐beta deposition (Ab) is associated with tau spreading from epicenters to connected regions. Yet, Ab and tau accumulate in strikingly different patterns, and it is unclear how Abdrives tau spreading. We envision two mechanisms, i.e. i) that cortical Ab exerts remote effects and “pulls” tau out of connected epicenters or ii) that tau is pushed out of epicenters, once Ab has reached the tau epicenters (Fig.1A). We tested both mechanisms in AD spectrum patients and determined the predominant mechanism by which Ab drives tau spreading.MethodsWe included 313 ADNI participants (n = 110 CN Ab‐, n = 98 CN‐Ab+, n = 70 MCI Ab+, n = 35 Dementia Ab+), with baseline amyloid‐ and longitudinal tau‐PET, based on which we calculated tau‐PET rates of change (ROC) for 200 cortical ROIs (Schaefer‐atlas; Fig.2). Using the same atlas, a resting‐state fMRI‐connectivity template was obtained in 42 healthy controls. For each subject, we determined tau epicenters as 5% of ROIs with highest baseline tau‐PET and assessed epicenter connectivity. Using ROI‐wise regression, we tested whether faster tau‐PET increase was explained by the simultaneous occurrence of i) higher tau‐PET in the epicenter, ii) stronger connectivity to the tau epicenter and iii) higher Ab in the target region (i.e. pull‐effect) or the tau epicenter (i.e. push‐effect). Lastly, we assessed ROI‐wise main effects of local Ab vs. Ab in the tau epicenter on tau‐PET changes, to determine which mechanism predominates.ResultsHigher tau and Ab in the epicenter explained faster tau‐PET increase in connected temporo‐parietal and frontal regions (Ab‐push‐effect, Fig.1B). In contrast, Ab‐pull‐effects were found also for late‐Braak regions (Fig.1C). Congruently, we found that Ab push effects dominated over Ab‐pull effects in temporo‐parietal brain regions, whereas Ab‐pull effects dominated in late‐Braak regions (Fig.1D‐F). Lastly, patients in which Ab and tau converged locally showed fastest subsequent tau accumulation (Fig.3).ConclusionAb exerts local and remote effects on tau spreading, yet local convergence of Ab and tau in the epicenter may predominantly drive tau spreading. Thus, local convergence of Ab and tau is a key event in AD that gives rise to accelerated tau spreading, previously referred to as cataustrophy.