bluuebunny/crossref_metadata_2025_split huggingface dataset:
Symptomatic Concussions, Depression, and Impulsivity in Fighters with a Family History of Dementia
Authors: Esagoff Aaron I. , Bray Michael J.C. , Shan Guogen , Narapareddy Laren , Bernick Charles B. , Peters Matthew E. , Narapareddy Bharat R. 
Published: December 2022

AbstractBackgroundProfessional boxers and Mixed Martial Arts (MMA) fighters regularly sustain blunt force trauma to the head leading to Traumatic Brain Injury (TBI) and concussion; therefore, fighters are at high risk for chronic traumatic encephalopathy and neurodegenerative disease. Family history of dementia (fhD) may significantly modulate fighter response to TBI. Genetic polymorphisms associated with dementia have been reported to modulate an individual’s response to brain injury and to influence emotionality, risk‐taking, and cognition. A recent study in male college athletes found that fhD predisposed athletes to poorer recovery after concussion and to decrements in impulse control following injury. Furthermore, in a population‐based cohort study from the Netherlands, researchers found that parental history of dementia led to increased rates of depression in mid‐life. The present study aims to investigate how fhD in fighters may relate to history of symptomatic concussions, depression, and impulsivity.MethodsUsing data from the Professional Fighters Brain Health Study (longitudinal cohort study of fighters), we included active, professional boxers and MMA fighters (n = 198) for cross‐sectional analyses. The latest study visits with available data were utilized to generate multivariate regression analyses examining the number of self‐reported symptomatic concussions in a fighter’s career, Patient Health Questionnaire (PHQ)‐9 scores, and Barratt Impulsiveness Scale Version 11 (BIS‐11) scores, adjusting for age, sex, education, and number of professional fights.ResultsFhD was significantly associated with more symptomatic concussions during one’s career (p<0.05), higher PHQ‐9 scores (p<0.01), and higher BIS‐11 scores (p<0.01).ConclusionsOur findings suggest that professional boxers and MMA fighters with fhD may be more likely to experience symptomatic concussions, depression, and impulsivity during their active fighting career. We postulate that fhD may predispose fighters to experience a higher burden of neuropsychiatric symptoms after sustaining a TBI, potentially due to differences in the brain’s response to injury. These findings may be used to advise those with fhD to minimize possible exposures to TBI and to educate fighters with fhD regarding risks of chronic neurotrauma exposure. Future research should evaluate potential genetic and/or molecular mechanisms behind these trends and behavioral outcomes associated with the sequelae of TBI in those with fhD.
Gabapentin utilization among older adults with different cognitive status: Results from the National Alzheimer’s Coordinating Center database (2006‐2019)
Authors: Oh GYeon , Moga Daniela C , Abner Erin L 
Published: December 2022

AbstractBackgroundAlthough gabapentin use has been increasing among older adults, its safety is understudied. We aimed to examine gabapentin utilization trends among older adults with different cognitive status and investigate potentially inappropriate concurrent medication use with gabapentin.MethodsData were extracted from National Alzheimer’s Coordinating Center Uniform Data Set (2006‐2019). We included participants (age 65+) with any medications reported at the time of visit. Within each study year, we estimated the prevalence of gabapentin use among the participants at each visit, both overall and within subgroups defined by cognitive status [normal, mild cognitive impairment, and dementia] and demographics [age and sex]. Additionally, we assessed prevalence of concurrent use of gabapentin with Central Nervous System (CNS) depressants, including opioid, combined opioid and benzodiazepine, antidepressant, and antipsychotic.ResultsOverall, participant‐reported gabapentin use nearly tripled (2.2 to 6.1%) from 2006 to 2019. Further, prevalence increased over this period in every participant subgroup. Among gabapentin users, the concurrent use of antidepressants or antipsychotics was more common in participants with a dementia diagnosis than those with normal cognition. Over half of gabapentin users with dementia also reported use of antidepressants. The concurrent use of gabapentin with opioids ranged from 10 to 30%, and the concurrent use of gabapentin, benzodiazepine, and opioids was below 8% throughout the study period.ConclusionsGiven increasing use among older adults, including those with cognitive impairment and dementia, rigorous studies are needed to examine the safety of gabapentin in this population, especially when using gabapentin with other CNS medications concurrently.
Why Bayesian analysis of clinical trials may be a good idea– a worked example of the aducanumab studies
Authors: Teipel Stefan , Ly Alexander , van Doorn Jonny , Wagenmakers Eric‐Jan , Tang Yi , Lutz Michael W. , Temp Anna G.M. 
Published: December 2022

AbstractBackgroundThe FDA decision to approve Aducanumab has sparked controversy. Here, we argue that some of these controversies are rooted in the conclusions facilitated by classical frequentist analysis. We suggest that a Bayesian analysis would answer some of the burning questions the aducanumab trials have posed to the field.MethodWe applied Bayesian analysis of model plausibility and effect sizes based on simulated data (the original data are not publicly available) of the two phase 3 trials (trials 301 and 302) of aducanumab in prodromal and mild dementia stages of Alzheimer’s disease.ResultExcluding the rapid progressors from our simulated data did not produce decisive evidence supporting aducanumab’s treatment effect. The Bayesian analysis suggested that the study indicator (study 301 or study 302) was irrelevant as a predictor of treatment effect (Bayes Factor (BF)exc = 42.16). Our simulated data further offered overwhelming evidence against Biogen’s assumption that rapid progressors affected any treatment arm of either study disproportionately (i.e., BFexc = 9910.74). We also found anecdotal evidence against the treatment arms (i.e., BFexc = 2.65) as a predictor of CDR‐SB change (Figure 1). The prior and posterior plots show that if the effect existed, it would be very small, likely ranging from Kendall’s tau 0.007 to 0.052, with a median of 0.029 (Figure 2).ConclusionBayesian analysis can quantify evidence in favor of the presence or absence of an effect, as well as assess the strength of the effect. In the case of our simulated data, Bayesian analysis quantified evidence that studies 301 and 302 do not provide diverging evidence, and that the treatment arm‐specific rapid progressor effect was absent. The Bayesian analysis thus provides more informative and nuanced conclusions than the reported frequentist tests, which leave many questions unanswered.
CenTauRz: A standardized quantification of tau PET scans
Authors: Dore Vincent , Bullich Santiago , Bohorquez Sandra Sanabria , Leuzy Antoine , Shimada Hitoshi , Rowe Christopher , Bourgeat Pierrick , Lopresti Brian J , Huang Kun , Krishnadas Natasha , Fripp Jurgen , Takado Yuhei , Stephens Andrew W , Weimer Robby , Higuchi Makoto , Hansson Oskar , Villemagne Victor L 
Published: December 2022

AbstractBackgroundOver the past decade, several PET tracers were developed to visualise and quantify tau pathology in vivo. However, all these tracers have distinct off‐target binding, different dynamic ranges and likely different levels of non‐specific binding resulting in large variability in semiquantification. We propose to standardise the sampling and the quantification across all available tau tracers.Method549 participants underwent tau scans with either 18F‐FTP (Cognitively Unimpaired (CU)=54/AD=14), 18F‐MK6240 (CU=186/AD=89), 18F‐PI2620 (CU=17/AD=21), 18F‐PM‐PBB3 (CU=30/AD=28), 18F‐GTP1 (CU=7/AD=38) or 18F‐RO948 (CU=35/AD=30). All CU individuals were Aβ‐ and all AD were Aβ+. The tau scans were spatially normalized using CapAIBL and the cerebellar cortex was used as reference region. We constructed a “universal” tau mask from the intersection of all the specific tau tracer masks, after subtracting AD from CU. All tau PET studies were sampled with a Mesial Temporal (MTL) and a Meta Temporal (MetaT) composites constrained by the universal mask. For each tracer and in composite, the mean and standard deviation of the Aβ‐ CU SUVR for each tau tracer were used to generate z‐scores (CenTauRz).ResultUsing a threshold of 2 CenTauRz in the MetaT regions, all tracers highly discriminated Aβ+ AD from Aβ‐ CU (ACC=[0.94‐1], sens=[0.84‐1], spec=[0.96‐1]) with mean CenTauRz for the different AD cohorts ranging from 8 to 14. Lower accuracy was observed in the MTL (ACC=[0.78‐1]) due to lower sensitivity in some cohorts [0.65‐1] however, the specificity was similar to that in the MetaT composite (spec=[0.94,1]).ConclusionAll tracers exhibited comparably high discriminative power to separate Aβ+ AD from Aβ‐ CU, where AD Aβ+ displayed a consistent range of CenTauRz across tracers. However, there were some differences between cohorts. For example, different PET scanners, with different sensitivities were used. For some cohorts, scans were selected as extreme representative cases, while for others the scans were more representative of clinical settings, with AD patients at early stages (with low or negative tau scans) or with suspected hippocampal sparing subtype that likely explains the lower accuracy in the MTL for some cohorts. Further studies with larger cohorts to validate the universal mask and CenTauRz scale are ongoing.
The Greek Dementia Helpline: experience and impact
Authors: Blekou Patra , Sakka Paraskevi , Leonti Antigoni Athina , Megagianni Sotiria , Zoi Panagiota 
Published: December 2022

AbstractDuring the Covid‐19 pandemic in Greece, quality and quantity of support for people with dementia and their family carers varies widely and depends on the availability of resourses in different areas. The pandemic forced an urgent transition from face‐to‐face to telehealth services.The Greek Dementia Helpline 1102 was launched on February 2021, as a telephone service staffed by psychologists, neurologists and social workers with expertise in dementia care and is provided by Athens Alzheimer Association. The aim of the helpline is to offer counseling about dementia, to support and guide family carers and link them with community support services and programs. Between February 2021 and October 2021, more than 8000 people have called Helpline 1102 and more than 2500 people have contacted the line via email or live webchat (68% women and 32% men). The beneficiaries of the helpline were family carers (75%), health professionals (15%) and lay people requesting information on dementia prevention, care and treatment. Especially, carers were interested in availability of dementia services in different areas of Greece, disability allowances, legal issues, and how to choose residential homes or paid caregivers.After using the Dementia Helpline, carers have reported increased sensitivity to the challenges faced by their loved ones with dementia, and more able to make the needed adjustments to their care‐giving task.Dementia Helpline 1102 provides dementia carers with a reliable a framework through the implementation of appropriate counseling by trustworthy healthcare professionals. Athens Alzheimer Association offers this service free of charge.
A social robot to support people with dementia at mealtimes: Design, implementation and preliminary evaluation
Authors: Astorga Misael , Favela Jesus , Cruz‐Sandoval Dagoberto , Riek Laurel D 
Published: December 2022

AbstractBackgroundPeople with dementia often experience difficulties eating at mealtimes, which is a common cause of concern to informal caregivers. These difficulties include becoming agitated or angry at mealtimes, taking food from someone else’s plate, or refusing to eat the food being offered. These behaviors manifest differently in each individual, are context‐dependent, and vary overtime.MethodWe engaged in a user‐centered design process to create a social robot that can serve as an eating companion for people with dementia (PwD). Interviews with caregivers (n=6) helped identify common problematic behaviors during mealtimes, and strategies to address them. Additionally, we conducted iterative, interaction co‐design sessions to define the robot's behaviors. The EVA robotic platform was used to implement the PwD‐robot interaction and to create videos of short interactions to show how the social robot can help to deal with these problematic behaviors. These videos were shown to informal caregivers (n=14) who were asked to assess user adoption using the Almere model (Heerink et al. 2010). A machine learning model was trained to detect disruptive eating behaviors from images captured by the social robot and to trigger appropriate responses.ResultCaregivers found the scenarios to be realistic and perceived the robot to be enjoyable and easy to use. A 92% precision was obtained from the trained Machine Learning model in detecting that an individual is eating (83% sensitivity and 96% specificity), using a capture rate of one frame per second.ConclusionThe results provide evidence of the technical feasibility of implementing a social robot to act as an eating companion for people with dementia and the positive perception of caregivers regarding its potential adoption.
Intricate connections between aging and Alzheimer’s diseases molecular trajectories revealed by plasma proteomics
Authors: kosti idit , Nanasi Tibor , Ruiz Agustin , Boada Mercè , Marquié Marta , Cano Amanda , Braithwaite Steven P , Lehallier Benoit 
Published: December 2022

AbstractBackgroundAging is the greatest non‐genetic risk factor for many diseases including Alzheimer’s Disease (AD) and its mechanisms are thus increasingly recognized as potential therapeutic targets.Since the molecular relationships between normal aging and AD are still largely unknown, we took advantage of recent methodological developments to measure more than 7k proteins in plasma to identify common but also different molecular mechanisms involved in aging and AD.MethodRecently, we developed new data‐driven bioinformatics approaches that uncovered marked non‐linear alterations in the human plasma proteome with age (Lehallier et al. Nature Medicine 2019). We applied these machine learning approaches to 271 patients at different stages of AD (61 subjects with Subjective Cognitive Decline: SCD, 106 with Mild Cognitive Impairment: MCI and 104 with AD) and compared these changes to those observed in a healthy aging cohort (370 subjects, 18‐69 years).ResultThe analysis of proteomics trajectories confirmed marked non‐linear alterations in the human plasma proteome with aging and uncovered the complex molecular choreography of AD. By analyzing trajectories of groups of proteins, we identified multiple clusters of proteins that are differentially affected during aging and AD. These clusters were enriched for various biological processes relevant for AD progression such as the complement and coagulation cascades, axon guidance and angiogenesis (q<0.05)ConclusionAltogether, our results reveal the intricate connections between aging and AD at the molecular level. The integrated proteomics trajectories analysis performed in this study allows the identification of new targets possibly playing a key role in AD but also pinpoints when these proteins are deviating from their normal trajectories and thus when they should be targeted by new therapies.
Prediction of Progression Along the Transition Points within the Continuum of Cognitive Impairment from Subjective Cognitive Complaints to Dementia Using Dyadic Patterns of Subjective Reporting
Authors: Pérez‐Blanco Lucía , Felpete Alba , Mallo Sabela C. , Campos‐Magdaleno Maria , Nieto‐Vieites Ana , Lojo‐Seoane Cristina , Juncos‐Rabadán Onésimo , Pereiro Arturo X 
Published: December 2022

AbstractBackgroundThe validity of Subjective Cognitive Complaints (SCCs) from dyadic patterns to predict progression to dementia is not yet clear. Some studies suggest that the validity of informant report in predicting dementia increases as cognitive function and awareness of symptoms decline. Our aim was to compare validity of informant and participant reports, and its agreement, to predict progression to MCI and conversion to dementia.MethodA total of 226 participants from the CompAS study were longitudinally assessed (intervals 18–24 months). The sample consisted of SCD (198) and MCI (28) participants. SCCs from participants and informants were assessed at baseline, 1st and 2nd follow‐ups using the QAM questionnaire. Informant‐participant total score agreement (disagreement, over‐ and under‐estimation, were identically considered) were calculated. Logistic regressions were separately performed to distinguish SCD stables and participants progressing either from SCD to MCI/dementia or from MCI to dementia using SCCs reports from participants, informants and agreement as predictive variables.ResultsInformant report at baseline significantly predicted conversion to dementia from baseline to the 3rd follow‐up (β = .173; SE = .048; p<.001; OR = 1.189, CI = 1.082‐1.306), similarly to that observed for informant reports at 1st follow‐up to predict conversion from 1st to the 3rd follow‐up (β = .225; SE = .050; p = .001; OR = 1.252; CI = 1.135‐1.381), and at 2nd follow‐up to predict conversion from 2nd to the 3rd follow‐up (β = .271; SE = .083; p<.001; OR = 1.313; CI = 1.110–1.552) (see Table 1).Informant report at baseline also successfully predicted the progression to MCI from baseline to the 3rd follow‐up (β = .139; SE = .057; p = .017; OR = 1.49; CI = 1.025‐1.288), similarly to that observed for informant reports at 1st (β = .124; SE = .056; p = .026; OR = 1.132; CI = 1.015‐1.264), and the 2nd follow‐ups (β = .195; SE = .073; p = .008; OR = 1.216; CI = 1.053‐1.404) (see Table 2).Conversion to dementia (β = .113; SE = .048; p = .018; OR = 1.119; CI = 1.020‐1.229) and progression to MCI (β = .138; SE = .054; p = .011; OR = 1.148; CI = 1.032‐1.277) using Self‐report were only significantly predicted using 1st follow‐up reports.Agreement did not significantly predict the progression to MCI or dementia at any of measurement points.ConclusionsInformant report successfully predicted progression and conversion at any transition point. Self‐reports were only predictive at 1st follow‐up. Agreement did not significantly predict progression and conversion.
Otiom ‐ Developing a Dementia tracking solution in collaboration with people living with dementia and their family caregivers
Authors: Thoft Diana Schack 
Published: December 2022

AbstractBackgroundThe study focused on developing a dementia tracking solution for people with dementia and their family caregivers that were user‐friendly and had a high user‐acceptability. Thus, high reliability, precision and low battery use. The study was a public private innovation project involving users, municipalities, universities and private companies.MethodThree workshops with people with dementia and their family caregivers, were held to ensure their experiences and needs were addressed (two groups of people with dementia and one group of relatives). The workshops focused on: Introduction and brainstorm for developing a new tracking solution, Introduction to possible new and existing tracking solutions and Evaluation and presentation of the developed tracking solution. Each workshop was planned in relation to the wishes and resources of the participants and involved creative and innovative methods.ResultThe workshops made it possible for people with dementia and their caregivers to contribute to the development of the tracking solution. People with dementia found the involvement exciting but also challenging, here peer‐support were experienced positively. The family caregivers wanted to participate as they found the involvement important but often, they had difficulties in finding time due to their caring role.ConclusionThe participants evaluated the involvement positively. Most of them also found the developed solution user‐friendly and ‐acceptable as it was nice, simple to use and had different mounting options. Several were keen to test it. However, some were also critical towards it and expressed a need for more features, another design and the possibility of individualisation.
Quantifying AD‐related brain amyloid with linearised progression models: model‐based vs. data‐based.
Authors: Wink Alle Meije , Shekari Mahnaz , Dicks Ellen , Collij Lyduine E. , Salvadó Gemma , García David Vállez , Gispert Juan Domingo , Tijms Betty M. , Alves Isadora Lopes , Yaqub Maqsood , Barkhof Frederik 
Published: December 2022

AbstractBackgroundBrain amyloid‐β (Aβ) is the pathological hallmark of Alzheimer's disease (AD). In logistic disease models, Aβ accumulation is a sigmoid function of time‐since‐disease‐onset (TSDO) (figure 1). Previous positron emission tomography (PET)‐based models vary accumulation onset(t50) and duration(r) globally; capacity(K) and baseline(NS) regionally (Whittington2018). We confirm existing approaches and propose a more powerful ICA‐based approach to quantify disease severity and estimate TSDO.MethodWe used 1071 18F‐florbetapir standard uptake value ratio (SUVR) images from the ADNI‐2 study (adni.loni.usc.edu/data‐samples/data‐types/pet). Images were mapped into MNI space. Averages were extracted using the Harvard‐Oxford brain‐atlas. Whole‐brain tracer‐specific sigmoid parameters (Jack2013) obtained from the literature were used to estimate TSDO.Of 16 models of regional Aβ accumulation (each of the 4 regional sigmoid parameters varied either regionally or globally), the optimal Bayesian information criterion was found with global t50 and r, and regional NS and K (figure 1) with global values r=6.16y and t50=4.10y.Linearised maps of NS and K were obtained by regressing the SUVR maps onto the global sigmoid. We also estimated these maps as independent components, using a 2‐component ICA on the SUVR maps. Both outcomes were used to quantify Aβ accumulation from SUVR images as weighting factors of the accumulation map.We compared the weights from the logistic model and the ICA model in ADNI, using effect size measured with Hedges' g between cognitively normal (CN), subjective memory complaints (SMC), mild cognitive impairment (EMCI/MCI/LMCI) and AD groups. We compared 3 longitudinal visits (N=112) in the OASIS‐3 study (see www.oasis‐brains.org) with both methods, global SUVR and Centiloid (Klunk2015) using 11C‐PiB PET SUVR images.ResultMaps of accumulation capacity from both models had spatial correlation of 0.86 (figure 2); baseline maps had spatial correlation of 0.95. Hedges' g between ADNI groups was 2.25 for K, and 2.42 for ICA (1.46 for global SUVR). In OASIS‐3, Hedges' g between visits was 1.24 for K, 1.46 for ICA (global SUVR 0.15, Centiloid 0.4).ConclusionWe demonstrate that linear accumulation models can be used to quantify brain Aβ with PET; maps obtained by ICA yield larger effect sizes than the logistic method for differentiating groups and measuring changes between visits.