bluuebunny/crossref_metadata_2025_split huggingface dataset:
A comparison of machine learning based‐composite cognitive test scores to track cognitive decline in early stages of Alzheimer’s disease dementia: ADOPIC study
Authors: Shishegar Rosita , Lee Matthew , Cox Timothy , Chai Tze Young , Dore Vincent , Bourgeat Pierrick , Fripp Jurgen , Burnham Samantha C. , Lamb Fiona , Robertson Joanne , Laws Simon M , Porter Tenielle , Savage Greg , Hassenstab Jason J. , Morris John C , Aschenbrenner Andrew J. , Weiner Michael S. W. , Masters Colin L , Rowe Christopher C , Villemagne Victor L , Lim Yen Ying , Doecke James D , Sohrabi Hamid R , Maruff Paul 
Published: December 2023

AbstractBackgroundClinical trials of early Alzheimer’s disease (AD) dementia use cognition as a primary outcome and therefore cognitive measures that accurately reflect disease progression and represent multiple cognitive domains are required. Current cognitive endpoints are computed by averaging standardized change from baseline scores (i.e., preclinical Alzheimer cognitive composite (PACC)). Here we compare the PACC to composite scores for which the combination of multiple performance scores has been optimized using machine learning (ML) algorithms in a large, harmonised data set, namely ADOPIC.MethodsA dataset harmonised across various cognitive scores (Table 1) was used to construct composite scores using ML‐based algorithms: a manifold learning dimension reduction technique (UMAP), principal component analysis (PCA) and Latent variable analysis (LVA). Data from ADNI (n = 1470), AIBL (n = 1105) and OASIS participants (n = 412, Table 2) with ≥3 assessments ≤5 years before clinical progression/last visit were included. Participants were classified clinically as; stable cognitively unimpaired (CU), CU progressing to mild cognitive impairment (MCI) or dementia, stable MCI, MCI progressing to dementia, or AD dementia. For UMAP, all stable participants (including dementia) were used in 4‐fold cross‐validation training sets. However, for testing UMAP and unsupervised models (PCA and LVA) all clinical groups (except for dementia) were used. The validity of ML‐based composites was challenged by modelling mean (SD) change over time in the non‐demented clinical groups, using linear mixed model (LMM) analysis. Signal‐to‐noise ratios (SNRs) were calculated (mean change/SD change) for each composite. The mean change was measured in progressives relative to stable participants of each group.ResultsEach ML‐based cognitive composite showed sensitivity to cognitive decline in the progressor groups (Figure 1 A). For the MCI progressors, PCA and UMAP composites had significantly higher SNRs than PACC (P<0.01; Figure 1B), however, LVA performance was not significantly better than PACC. For the CU progressor group, SNRs for PCA and LVA did not show significant differences with PACC and UMAP performed significantly worse than PACC (P<0.01).ConclusionML‐based cognitive composite score computed using PCA provides a practical solution to track cognitive decline with improved performance in tracking cognitive decline in MCI progressors compared to PACC, while being comparable in CUs.
Will Changes in the COVID‐19 Brain MRI Signature Region Affect Subsequent Dementia Outcomes? Linking Pre‐Pandemic Brain MRIs to Long‐Term Dementia Incidence in UK Biobank Participants
Authors: Wang Jingxuan , La Joie Renaud , Kelly J. Daniel , Kornak John , Chen Ruijia , Ackley Sarah F , Glymour M. Maria 
Published: December 2023

AbstractBackgroundNeurocognitive sequelae of SARS‐CoV‐2 infection have been suggested as potentially leading to a higher incidence of dementia, but the underlying mechanisms remain unclear. By comparing brain magnetic resonance imaging (MRI) between participants with and without a history of SARS‐CoV‐2 infection, prior work identified infection‐related structural changes, including reduced cortical thickness and gray‐white matter contrast. We characterized the COVID‐19 signature region (COVIDsig) in MRIs taken prior to the pandemic and evaluated whether changes in this region predicted incident dementia.MethodUsing pre‐pandemic data on UK Biobank participants aged 55+, we examined the association between the COVID‐19 brain MRI signature region and dementia incidence up to March 2020 (N = 39,119 dementia‐free participants with a median of 1.8 years of follow‐up). The COVIDsig comprised the lateral orbitofrontal cortex and parahippocampal gyrus and the exposures included COVIDsig cortical thickness and COVIDsig gray‐white matter contrast. Dementia diagnoses were obtained from linked hospital, primary care, and death registry data. We used Cox proportional‐hazards models and adjusted for sex, APOE‐ε4, education, material deprivation, body mass index, and assessment center. To test if COVIDsig predicts dementia risk independently of global cerebral atrophy, we further adjusted for an Alzheimer’s disease signature region (ADsig) or a non‐COVID‐19 signature region (cerebral cortex except COVIDsig). In sensitivity analysis, we repeated the primary analysis on each component of COVIDsig (lateral orbitofrontal cortex and parahippocampal gyrus) separately.ResultThe mean age of the participants was 64.7 years (SD = 7.4; Table 1) and 65 dementia cases were identified during follow‐up. The age‐adjusted dementia incidence rate was 2.5 per 1,000 person‐years. One standard deviation decrease in COVIDsig cortical thickness and gray‐white matter contrast was associated with a 49% and a 52% increase in the hazard of dementia diagnosis (HR for cortical thickness = 1.49, 95% CI = 1.19‐1.88; HR for gray‐white matter contrast = 1.52, 95% CI = 1.20‐1.94; Table 2), respectively. After adjusting for ADsig or non‐COVID‐19 signature region, associations were attenuated (Figure 1).ConclusionThese results should be evaluated in independent samples and the persistence of post‐COVID‐19 structural brain changes assessed. If confirmed, findings suggest SARS‐CoV‐2 infection may increase dementia incidence via structural brain changes.
Gross measures of neurodegeneration from autopsy explain as much variance in dementia severity as measures from histology
Authors: Woodworth Davis C. , Nguyen Katelynn M. , Kawas Claudia H. , Corrada María M. M. , Sajjadi S. Ahmad 
Published: December 2023

AbstractBackgroundGross assessment of brain atrophy at autopsy provides important information about the degree of neurodegeneration. It complements information on misfolded proteins and other microscopic lesions provided by histological assessment. We examined how strongly these gross measures of neurodegeneration from autopsy were related to dementia severity as compared to measures from histology.MethodWe used data from participants in the National Alzheimer’s Coordinating Center database (09/2005‐05/2020) with relevant autopsy data and clinical assessment within a year before death. We used Clinical Dementia Rating sum‐of‐the‐boxes (CDR‐SB) or Mini‐Mental State Examination (MMSE, in subset where available) as outcome measures. Gross pathology measures included hippocampal atrophy, cortical atrophy, frontal/temporal lobar atrophy, and locus coeruleus hypopigmentation. Histology measures included Alzheimer’s disease neuropathologic change (ADNC), Lewy bodies, cerebral amyloid angiopathy, hippocampal sclerosis, arteriolosclerosis, infarcts, microinfarcts, and hemorrhages. We used dichotomized versions of all pathology variables (see Table‐1 for dichotomizations). We used multiple linear regressions with relative importance analyses for separate models (with either gross or histology measures) and a joint model (all measures), adjusting for sex, years of education, and months between clinical assessment and death. We report variance‐explained (R2) and relative importance of predictors.ResultParticipant characteristics are described in Table‐1 (N = 984). For models including gross or histology measures separately, the variance‐explained for CDR‐SB by the gross model (total = 40.8%, gross measures = 29.4%) was comparable to the histology model (total = 36.9%, histology measures = 23.9%, Figure‐1). Results for the separate models were similar when using MMSE (N = 238; gross model = 38.8%, histology model = 39.6% variance‐explained). For the joint model, variance‐explained for CDR‐SB increased to 47.3%, with 22.0% explained by gross and 15.2% explained by histology measures (Figure‐2). While ADNC was the most important variable in the joint model, this was followed in importance by three gross measures: hippocampal atrophy, cortical atrophy, and locus coeruleus hypopigmentation. Results for the joint model were similar when using MMSE (total = 50.3%, gross = 19.6%, histology = 25.6% variance‐explained).ConclusionWe found that measures of gross neurodegeneration were as strongly related to dementia severity as measures from histology. While histology provides important clues about dementia etiology, measures of neurodegeneration are just as vital in our pursuit towards better understanding, preventing, and treating dementia.
Music and humanoid robots aid in performing daily living activities in people affected with advance stage dementia
Authors: Khan Arshia A 
Published: December 2023

AbstractBackgroundAs the disease progresses the ability to perform daily living activities is gradually reduced. As dementia progresses music is one of the last things to go(Foo et al.2017, Beatty et al., 1988). Additionally many studies have shown music to be an effective tool to help people affected with dementia(Baird, & Samson, 2015). In order to help residents in nursing homes to perform their daily living activities we programmed a humanoid robot to sing, perform the daily living activity and instruct the resident to follow them in performing specific daily living activities. The activities that are performed are brushing teeth, washing face, putting on clothes and going from point A to point B.MethodHumanoid robots are programmed to sing the instructions to perform a daily living activity and also act out the activity. The robots are programmed to perform simple activities of daily living that the people affected with dementia are forgetting to perform. The music is composed and lyrics are written by Professors Rudy Perrault and Rachel Inselman.ResultThe robots have been successfully helping people affected with dementia in a short trial. There were three participants chosen for this case study where one person was in the advanced stage of dementia, one person was in the moderate stage of dementia and the third person was in the mild stage of dementia. Each of the participants was able to successfully perform the activities of daily living by mimicking the robots.ConclusionAs soon as the robot started to sing and enact the daily living activity the participants started to move to the music and follow the robot.
Podcasts as Open‐Access Knowledge Dissemination Tools for Researchers: Lessons from Three Years of AMiNDR Podcast (A Month in Neurodegenerative Disease Research)
Authors: Louadi Sarah , Koch Ellen T , Rowe Elyn M , Kuhlmann Naila , Kamesh Anusha , Ferreira Jacques , Onbasi Lara , Liang Joseph J.H. , Cheng Judy 
Published: December 2023

AbstractBackgroundResearchers are expected to manage various responsibilities whilst staying up‐to date with developments in their field. This is an arduous and time‐consuming task. In Alzheimer’s disease research alone, an average of 350 research articles are appearing every week on PubMed. We present the podcast AMiNDR (A Month in Neurodegenerative Disease Research), as a means of easing the burden on scientists. We offer an accessible channel where one can find all the primary papers sorted by topic in distinct episodes and summarised in an audio friendly format, complete with a bibliography of the papers covered as well as those not featured on the podcast.MethodsWe assess the success of this initiative on three levels: 1) We track our listenership and reach through podcast analytics on Simplecast including unique listeners, countries reached, and number of downloads per episode. 2) We evaluate the utility of our podcast based on listener retention, growth through time, and feedback collected through surveys and engagement indicators on social media. 3) We also consider the sustainability of our project through episode output, team growth, and internal interviews with volunteers to assess satisfaction.ResultsSince the launch of AMiNDR in June 2020, we have published over 350 episodes with accompanying bibliographies in 40 categories pertaining to Alzheimer’s disease research. Our podcast benefited from the contribution of 51 scientists worldwide at various stages of their training and reached over 6500 unique listeners in 91 countries. We present the results of the ongoing assessment of this initiative and measures we took to continue to refine our tool over the past three years, with a list of recommendations for digital knowledge dissemination tools like the AMiNDR podcast.ConclusionBased on our program evaluation, we find that AMiNDR is garnering a growing listenership worldwide and proving to be useful and accessible to researchers.
Antemortem MRI detects atrophy related to hippocampal sclerosis pathology in the oldest‐old but side matters: The 90+ Study
Authors: Woodworth Davis C. , Li Janice X. , Faramarzi Sina , Greenia Dana E. , Bukhari Syed A. , Montine Thomas J. , Kawas Claudia H. , Corrada María M. M. , Sajjadi S. Ahmad 
Published: December 2023

AbstractBackgroundHippocampal sclerosis of aging (HS) is the neuropathological finding of neuronal death and gliosis in the hippocampus. HS is more prevalent, and associated with dementia, in the oldest‐old (those ≥90 years), but cannot be diagnosed during life. HS is often unilateral and can be missed if only one brain hemisphere is assessed, which is standard neuropathological practice but may hamper the development of biomarkers for HS. Here we examined hippocampal volumes from antemortem MRI in relation to bilaterally‐assessed HS at autopsy, accounting for side, in oldest‐old participants from The 90+ Study.MethodWe included participants with both autopsy and antemortem MRI data. All participants had neuropathological assessment of HS for both hemispheres. We performed hippocampal segmentation of antemortem 3D‐T1w MRIs using FreeSurfer, adjusting by intracranial volume. We compared total average (average of left and right), left, and right, hippocampal volumes between those with and those without HS. For HS participants we combined the hippocampal volumes from each hemisphere with HS into a single group and from each hemisphere without HS into a separate group, and compared these to the average hippocampal volumes from those without HS. We used Welch’s T‐tests for comparisons and report Cohen’s d effect sizes.ResultParticipant characteristics (N = 89) are shown in Table‐1. All HS was unilateral (Right = 6, Left = 4). Average total hippocampal volume was not significantly different between those with and those without HS (d = 0.51, P = 0.11, Figure‐1a). However, right hippocampal volumes were significantly related to right‐sided HS with a large effect size (P = 0.002, d = 1.02) and left hippocampal volumes had a large effect size for left‐sided HS (P = 0.17, d = 0.90, Figure‐1c), whereas this was not the case when HS was on the opposite side (P>0.3, d<0.4, Figure‐1b,c). Combined HS participant hippocampal volumes for the side with HS (d = 0.98, P = 0.003), but not the side without HS (d = ‐0.14, P = 0.7), were different from volumes in participants without HS (Figure‐1d).ConclusionOur results suggest HS is detectable by atrophy on antemortem MRI, but most strongly when using hippocampal volumes from the same side as HS. Researchers should consider assessing and documenting the presence of HS in both hemispheres to accelerate development of neuroimaging biomarkers for HS.
Social Networks and MRI measures of Brain Injury in Racially and Ethnically Diverse Populations
Authors: Chen Ruijia , Pederson Annie , Wang Jingxuan , Hirst Andrew K , Hill‐Jarrett Tanisha G , Torres Jacqueline M , Gilsanz Paola , Decarli Charles , Corrada María M. M. , Whitmer Rachel A. , Glymour M. Maria 
Published: December 2023

AbstractBackgroundSocial networks may influence late‐life cognition directly via affecting brain structures or indirectly through enhancing cognitive resilience – i.e., the ability to maintain cognitive function in the presence of neurodegeneration. This study assesses the associations of social networks with markers of neurodegeneration, and determines whether social networks modify the associations between these markers and cognitive function.MethodWe pooled data from three harmonized cohorts of Kaiser Permanente Northern California members: the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE)study, the Study of Healthy Aging in African Americans (STAR), and the LifeAfter90 (LA90) Study. Social network sizes were calculated by summing the number of living children, spouse, and close relatives and friends. Neuroimaging markers included total brain volume (TBV), hippocampal volume (HCV), and white matter hyperintensity volume (WMH). Volumetric measures were normalized using a residual approach, which regressed intracranial volume (ICV) on volumetric measures to predict the ICV‐adjusted volume. Executive function and verbal episodic memory were measured by the Spanish and English Neuropsychological Assessment Scales and were z‐standardized. Linear regression models estimated the associations between social network size and each neuroimaging marker, adjusting for age, sex, race and ethnicity, education, cohort, and Activities of Daily Living (ADLs). We included an interaction term between social networks and neuroimaging markers to evaluate whether the relationships between imaging markers and cognitive function differed by social networks.ResultThe pooled sample’s average age was 76 (SD 11) years (59% female; 47% Black) (Table 1). In the fully adjusted models, the mean differences in injury markers did not differ by social network size (Figure 1). However, the associations of HCV and WMH with executive function (βsocial network*WMH = 0.09, 95% CI 0.01,0.18; βsocial network*HCV = ‐0.08, 95% CI ‐0.15, ‐0.01) were stronger among participants with smaller social networks (Figure 2).ConclusionIn this cross‐sectional study of racially and ethnically diverse older adults, larger social networks attenuated the associations between neurodegeneration markers and executive function. These findings suggest that larger social networks may promote cognitive resilience.
TELL: A web‐based app to capture speech and language markers of neurodegeneration
Authors: Garcia Adolfo M 
Published: December 2023

AbstractBackgroundOver the last decade, the search for scalable markers of neurodegeneration has been boosted by automated speech and language analysis (ASLA). Participants are asked to produce natural discourse, generating acoustic and linguistic data that can be digitally extracted and analyzed with statistical or machine learning methods. This approach has revealed subtle patterns that differentiate among disorders, predict core symptom severity, correlate with anatomo‐functional brain alterations, and identify autopsy‐confirmed pathology years before death. Accordingly, multiple studies have described ASLA as a promising resource for patient assessment. However, this translational potential remains unfulfilled, as the underlying technologies are unavailable for immediate clinical use. Aiming to tackle this issue, we developed the Toolkit to Examine Lifelike Language (TELL), a web‐based app for collecting, storing, analyzing, and visualizing speech data.MethodTELL version 1.0 was developed by an interdisciplinary team of language, brain health, and data science experts. We designed a speech elicitation protocol comprised of validated spontaneous, semi‐spontaneous, and non‐spontaneous tasks with different cognitive demands. Each task is analyzed via ASLA algorithms capturing word frequency, affective valence, and emotional magnitude, as well as rhythm‐ and pitch‐related features. On‐the‐fly results are offered through intuitive visualizations for each metric. The measures are complemented with a language profile survey. Audio files, transcriptions, and data matrices can be downloaded for offline analysis. The app runs on multiple devices, requires no installation, and offers multi‐language capabilities.ResultsTELL version 1.0 is now being used in 10 sites worldwide, mainly including low‐income countries. Preliminary results from persons with Alzheimer’s and Parkinson’s disease, as well as frontotemporal dementia, show that its metrics are sensitive for disease identification, discrimination, and monitoring. Updates have been implemented drawing from users’ feedback. Funds have been secured to develop TELL version 2.0, with novel metrics, improved speech‐to‐text algorithms, and a streamlined front end.ConclusionTELL turns basic language science methods into clinically usable tools, offering an objective, low‐cost, non‐invasive complement to classical assessments. More generally, it illustrates how digital technologies can foster equitable insights on brain health. Ongoing updates will further increase its capacity to establish scalable markers of diverse neurodegenerative diseases.
Associations between air pollution exposure and empirically derived subtypes of mild cognitive impairment in older women
Authors: Petkus Andrew J. , Wang Xinhui , Beavers Daniel P. , Espeland Mark A. , Millstein Joshua , Resnick Susan M. , Kaufman Joel D. , Gatz Margaret , Rapp Stephen R. , Chen Jiu‐Chiuan 
Published: December 2023

AbstractBackgroundExposure to air pollution in older adulthood is a novel risk factor for mild cognitive impairment (MCI). MCI is a heterogenous construct with different subtypes likely reflecting different disease etiologies. Understanding if long‐term exposure to air pollution is differentially associated with MCI subtypes may provide insight into the neurotoxic effects of air pollution exposure on the aging brain.MethodParticipants included women from the Women’s Health Initiative Memory Study. Data from 651 women without dementia who screened positive for cognitive impairment on the modified mini‐mental state examination (3MS) and a randomly selected comparison sample of 2,560 women who were cognitively intact based on the 3MS were utilized. Participants were administered the 3MS annually (1996‐2008). Those who screened positive for cognitive impairment on the 3MS completed the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) cognitive battery and the Trailmaking Test parts A and B. Average annual levels of particulate matter of aerodynamic diameter<2.5 (PM2.5) and nitrogen dioxide (NO2) were estimated at the participant’s residence using regionalized national universal kriging models and averaged over the 3‐year period before the cognitive assessment. Latent class structural equation models (SEM) were constructed to identify MCI subtypes. We then examined associations between air pollution exposure and latent class membership using multinomial regression while adjusting for demographic, lifestyle, and clinical confounders.ResultFour significant MCI subtypes were identified (Figure 1): multidomain MCI (n = 147; 23%), amnestic MCI (n = 141; 22%), dysnomic/dysexecutive MCI (n = 200; 31%), and subtle MCI (n = 163; 25%). Women living in locations with higher levels of PM2.5 (per interquartile range [IQR] = 3.93 µg/m3; OR = 1.28; p = .04) or NO2 (per IQR = 9.25 ppb; OR = 1.36; p = .02) were more likely to be classified as having subtle MCI compared to normal comparisons. One IQR increase in NO2 was also associated with increased likelihood of being classified with dysnomic/dysexecutive MCI (OR = 1.38; p = .003) compared to normal comparisons.ConclusionOur study illustrates that long‐term exposure to PM2.5 and NO2 may be associated with specific subtypes of MCI. Our findings provide further evidence that air pollution may exert neurotoxic effects on brain areas underlying executive function and language ability in older women.