bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cluster‐Derived Psychosocial Phenotypes, Cognitive Outcomes, and AD Plasma Biomarkers of Black and Hispanic/Latino Older Adults Enrolled in HABS‐HD
Authors: Clark Alexandra L. , Thomas Kelsey R. , Haley Andreana , Duarte Audrey 
Published: December 2023

AbstractBackgroundAlthough Black and Hispanic/Latino older adults are at increased risk for Alzheimer’s disease (AD), there is considerable within‐group heterogeneity in psychosocial factors of risk and resiliency amongst these community members. The present study (1) used cluster analysis on measures of economic/social resources and psychiatric distress to identify distinct subgroups and (2) compared cognitive and AD biomarker outcomes of these groups in an effort to understand pathologic aging among racially/ethnically diverse older adults.MethodBaseline data from 816 Black and Hispanic/Latino participants without dementia enrolled in HABS‐HD were included. Variables in the cluster analysis included: annual income, state area deprivation index, social support, occupational complexity, chronic stress, and symptoms of depression and anxiety. A discriminate function analysis (DFA) tested the extent to which the individual variables predicted cluster‐group membership. ANOVAs and chi‐squared tests compared the groups on demographics. ANCOVAs controlling for age, education, sex, and race/ethnicity examined group differences in subjective cognitive concerns, objective memory and executive functioning composite scores, and plasma AD biomarkers (AB42/40, t‐tau, NfL).ResultThe cluster analysis identified 3 distinct groups: Group 1 was low resource/high distress (n = 215); Group 2 was high resource/low distress (n = 248); and Group 3 was low resource/low distress (n = 353; Figure 1). The DFA correctly classified 95.3% of participants. Results revealed significant group differences in education and the proportion of women and Black adults across the groups (see Table 1). ANCOVAs revealed significant group differences in subjective memory concerns, executive functioning composite, and plasma AD biomarkers; Group 1 consistently displayed poorer cognitive outcomes and more severe AD biomarker patterns relative to Groups 2 and 3, but there no differences in outcomes between Groups 2 and 3. No group differences on the memory composite were observed.ConclusionRisk for AD among racially/ethnically diverse older adults may differ as a function of current levels of resources and psychiatric distress. Group 1 represents individuals with low resources and high levels of distress that may be at increased risk for poor outcomes in late life. Future work should further clarify precise mechanisms of resiliency, such as social support, in Group 3 and explore longitudinal trajectories of cognitive decline across the groups.
User experience and biomarker associations of remotely administered and unsupervised smartphone‐based cognitive testing in a community sample of older adults
Authors: Öhman Fredrik , Bosch Iris , Berron David , Srikrishna Meera , Heeman Fiona , Skoog Johan , Zettergren Anna , Papp Kathryn V. , Blennow Kaj , Zetterberg Henrik , Kern Silke , Skoog Ingmar , Schöll Michael 
Published: December 2023

AbstractBackgroundRemotely administered, unsupervised smartphone‐based cognitive tests have the potential for scalable and cost‐effective screening of Alzheimer’s disease (AD) in clinical trials and primary care. Previous work has provided initial evidence for the feasibility and validity of these tools in clinical and community settings. However, more studies investigating the utility of these cognitive tests are warranted, particularly regarding user experience and the association with AD biomarkers.MethodTo date, 198 participants (CDR 0 = 85%, CDR 0.5 = 15%, Mini‐Mental State Examination = 29, average age = 77) have enrolled in this smartphone sub‐study, a part of the Swedish population‐based Gothenburg H70 Birth Cohort Studies. After participating for at least 12 months, the user experience of smartphone‐based cognitive tests (two memory tests, MDT‐OS and ORR‐LDR) was assessed. Furthermore, we investigated their associations with volumetric brain changes based on magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) biomarkers (Aβ42/Aβ40 ratio, T‐tau, P‐tau, NFL, and neurogranin). The latter will be analyzed in a subsample (n = 45 for CSF and n = 110 for MRI) of all participants. Voxel‐based analyses are being performed in SPM12.ResultMost study participants found the app easy and enjoyable to use, the instructions easy to understand, and they generally favored performing tests in their home environment. Low CSF Aβ42/Aβ40 ratio at age 70 was associated with worse performance on the ORR‐LDR at age 77 (R = .43, p<.002), while measures of t‐tau, p‐tau, NFL, and neurogranin at age 70 were not. Voxel‐wise associations between smartphone‐based cognitive tests and MRI measures will be reported at the conference.ConclusionOur results suggest that using smartphone‐based cognitive tests in a remote unsupervised setting has the potential to identify aspects of early AD in a community‐based setting. The positive user experience is promising for acceptance of these novel tools for cognition assessment. Next steps include analyses of the relation with neuroimaging‐based biomarkers.
Early‐life bilingualism could protect from age‐related gray matter volume loss in the left caudate – preliminary results
Authors: Schumann‐Werner Beate , Molloy Eóin N. , Fischer Larissa , Schwarck Svenja , Hochkeppler Anne , Vockert Niklas , Büchel Anna‐Therese , Sick Florian , Werner Cornelius Johannes , Kreißl Michael C. , Düzel Emrah , Maass Anne 
Published: December 2023

AbstractBackgroundEarly‐life bilingualism (i.e., second language acquisition before the age of 30) has been shown to confer benefits in separate cognitive domains later in life, possibly contributing to the concept of cognitive reserve. However, the effects of early‐life bilingualism on cognitive performance and gray matter volume (GMV) in the elderly have been studied scarcely. In an ongoing cohort, we examined the influence of early‐life bilingualism on cognitive performance (e.g., phonemic fluency as tested by the Regensburg word fluency test (RWT), among others) as well as age‐related brain gray matter volume in older adults.MethodWe analysed 3‐Tesla T1‐weighted Multi‐Echo MPRAGE and cognitive data from 25 healthy adults (72.8±7.69 years, 10 female) in our new cohort established within the CRC1436 (https://sfb1436.de/). Assessment of bilingualism was conducted using the Lifetime of Experiences Questionnaire (LEQ). MRI data were preprocessed and analysed using FSL‐VBM. We performed a whole‐brain analysis looking for an interaction between age and bilingualism on GMV values, postulating that early bilingualism might contribute to a preservation of gray matter along the age trajectory. We extracted GMV values from significant regions of interest (ROIs) and looked for correlations with cognitive performance. Due to the small cohort and the preliminary nature of these analyses, we chose an exploratory threshold of p<0.001 (uncorrected) for the whole brain analysis. ROIs were defined by a threshold of p<0.1 cluster‐corrected (z>3.1).ResultParticipants with early‐life bilingualism (n = 7) had significantly more years of education (p<0.05) and performed better in the RWT (p<0.05) than monolinguals (n = 18). The age‐bilingualism interaction term across the whole brain (adjusted for education) identified the head of left caudate nucleus as being protected from age‐related GMV loss by early‐life bilingualism (Fig. 1). The age‐GMV‐relationship for both groups is shown in Fig. 2. We observed no significant relationship between GMV in the left caudate and performance in any cognitive tests.ConclusionThese preliminary results hint at a role of early‐life bilingualism in modulating GMV in older adults. How these relate to better cognitive performance remains to be elucidated. Moreover, considering brain areas not typically studied in cognitive aging, such as the basal ganglia, seems worthwhile.
Using machine learning of neuroimaging patterns combined with mitochondrial genomics to predict maternal inheritance of Alzheimer’s disease
Authors: Veatch Olivia J , Dutta Sreejata , Mansel Clayton , Townley Ryan A , Sardiu Mihaela E , Honea Robyn A 
Published: December 2023

AbstractBackgroundMaternal family history (FHm) contributes increased risk for developing Alzheimer’s disease (AD) compared to paternal. Family history is often unknown, making it difficult to identify these higher‐risk patients. Atrophy patterns indicative of AD relate to parent‐of‐origin effects. Risk factors may also include mitochondrial variation (mtDNA). Machine learning (ML) approaches could identify predictive traits for FHm, helping detect higher‐risk patients with unknown family history. We aimed to determine if structural MRI variables and mitochondrial haplogroups could classify FHm.MethodUsing data from the National Alzheimer’s Coordinating Center and NIA Genetics of Disease Storage Site, we determined if a feature set, comprised of structural MRI variables and mitochondrial haplogroups, could accurately predict FHm. Haplogroups were called with HaploGrep v2.4.0 based on the PT17‐FU1 reference. Two‐thirds of the dataset was used for training and one‐third for testing. For hyper‐parameter tuning and optimization of model performance, we used 4‐fold cross‐validation. To identify traits that aid in predicting FHm, feature importance was calculated for the best performing model (selected based on accuracy and AUC).ResultML models were initially trained on imaging variables for 961 individuals (60% female) with confirmed positive (69%) or negative (31%) FHm. Average age at MRI was 68±11 years old. XGboost emerged as the best method (Accuracy = 0.62, AUC = 0.56). Feature importance calculations indicated precuneus was the most important. Including haplogroups significantly reduced the sample size (n = 602) and model performance. Most of the dataset (87%) were assigned to mitochondrial haplogroups within the R branch. Notably, although model performance was reduced, precuneus remained one of the most important variables.ConclusionThis analysis was among the first to explore if structural or genomic traits predicted FH. Although performance for the initial models showed moderate accuracy, we are currently testing additional ML approaches to enhance predictivity. We identified the precuneus as a key‐AD‐vulnerable region associated with FHm. Our study contributes to a growing literature showing structural, neurodegenerative, cerebral metabolic and increased amyloid‐β burden in the precuneus of individuals with FHm. Analyses aimed at more comprehensive evaluation of mitochondrial genomic variation that expands upon haplogroup classification are ongoing to clarify the role mtDNA plays in maternally inherited AD.
Effects of hearing intervention on cognitive decline: Results of the Aging and Cognitive Health Evaluation in Elders (ACHIEVE) randomized trial
Authors: Lin Frank R , Pike James R. , Albert Marilyn S. , Arnold Michelle L , Burgard Sheila , Chisolm Theresa , Couper David , Deal Jennifer A , Goman Adele M , Glynn Nancy W. , Gmelin Theresa , Gravens‐Mueller Lisa , Hayden Kathleen M. , Huang Alison R , Knopman David S. , Mitchell Christine , Mosley Thomas H. , Pankow James , Reed Nicholas S , Sanchez Victoria A , Schrack Jennifer A , Windham B Gwen , Coresh Josef ,   ACHIEVE Collaborative Research Group
Published: December 2023

AbstractBackgroundHearing loss is associated with greater cognitive decline and incident dementia. Whether hearing intervention could reduce cognitive decline in older adults with hearing loss is unknown.MethodThe ACHIEVE study is a randomized trial (NCT03243422) of 70‐84 year‐old adults with untreated hearing loss and free from substantial cognitive impairment that took place at four U.S. sites. Participants were recruited from two study populations: 1) a group of adults participating in a longstanding observational study of cardiovascular health (Atherosclerosis Risk in Communities [ARIC] study), and 2) a group of healthy de novo community volunteers. Participants were randomised (1:1) to hearing intervention (HI; audiological counseling and provision of hearing aids) or a successful aging health education control intervention (SA; sessions with a health educator on chronic disease prevention). The primary intention‐to‐treat endpoint was 3‐year change in a global cognition standardized factor score from a comprehensive neurocognitive battery.Result977 participants (238 ARIC, 739 de novo) underwent randomisation; 490 were assigned to HI and 487 to SA control. Participants from ARIC were older, had more risk factors for cognitive decline, and had lower baseline cognitive scores than the de novo group. In the primary analysis combining the ARIC and de novo groups, 3‐year cognitive change (in S.D. units) was not significantly different between HI and SA control (HI: ‐0·200 [95% CI: ‐0·256,‐0·144]; SA: ‐0·202 [‐0·258,‐0·145]; Difference 0·002 [‐0·077,0·081],p = 0·96). However, prespecified analyses demonstrated significant differences in the effect of HI on cognitive change between the ARIC and de novo group (p interaction = 0·010). In the ARIC group, HI was associated with a 48% reduction in 3‐year cognitive change compared to SA control (HI: ‐0·211 [‐0·349,‐0·073]; SA: ‐0·402 [‐0·536, ‐0·267]; Difference 0·191 [0·022,0·360,p = 0·027]). In the de novo group, cognitive change was not significantly different between HI and SA control (HI: ‐0·213 [‐0·277,‐0·148]; SA: ‐0·151 [‐0·215,‐0·087]; Difference ‐0·061 [‐0·151,0·028,p = 0·18]). The rate of cognitive change among control participants was observed to be 2.7‐fold faster in the ARIC versus de novo group.ConclusionHearing intervention may have a significant effect on reducing cognitive change over 3 years in populations of older adults at increased risk for cognitive decline.
Cognitively Impaired Subjects with Normal Total Cholesterol Using Lipophilic Statins Undergo Accelerated Decline in Regions Associated with Dementia and Those Regions are Predictive of Further Decline Over the Subsequent Two‐Year Period in a Multi‐Center Longitudinal Study
Authors: Padmanabham Prasanna S. , Liu Stephen S. , Silverman Daniel H. 
Published: December 2023

AbstractBackgroundWe recently reported that lipophilic statins, known to penetrate the blood‐brain barrier, were associated with accelerated decline in cortical regions known to decline in early Alzheimer’s Disease, in subjects with early Mild Cognitive Impairment (eMCI) having normal cholesterol levels. We now report the predictive value of the pattern of regional metabolism in forecasting severity of decline over the subsequent two years.MethodSubjects were drawn from a consecutive series enrolled in the Alzheimer’s Disease Neuroimaging Initiative, identifying 70 with eMCI having serum cholesterol levels below the 206 mg/dl median of non‐statin users and with clear histories of time‐frames of prescriptions and types of statins. FDG‐PET scans acquired at baseline and two years later were compared for each group, and rates of regional metabolic decline were determined.ResultIn direct statistical comparisons on a voxel‐by‐voxel basis, subjects on lipophilic statins (LS) underwent significantly greater decline in posterior cingulate (PCC) and right parietotemporal cortex (rPTC) compared to those not on statins (p<0·0005). Moreover, baseline rPTC metabolism was positively correlated with magnitude of left inferior parietal metabolism two years later in LS subjects (p = 0.008), but not in nonS subjects (p = 0.95). Conversely, baseline parietal metabolism was not correlated with subsequent rPTC metabolism in either group of subjects. In addition, based on regional network analysis, LS subjects had greater functional connectivity in the medial temporal regions.ConclusionSubjects with eMCI and normal range cholesterol levels taking lipophilic statins suffered faster metabolic decline of brain regions affected by early Alzheimer’s disease, and baseline activity in those regions predicted further decline over the subsequent two years in associative cortex. Moreover, the pattern of functional connectivity with medial temporal regions was altered in subjects on these statins.
Interpretable deep learning framework towards understanding molecular changes associated with neuropathology in human brains with Alzheimer’s disease
Authors: Joshi Amogh Manoj , Shah Jay , Readhead Benjamin , Su Yi , Wu Teresa , Wang Qi 
Published: December 2023

AbstractBackgroundPostmortem brain tissues have been used to characterize some of the molecular networks and drivers associated with Alzheimer’s disease (AD). In this study, we extend our previous work of a deep learning approach to characterize the molecular changes associated with the severity of AD‐related neuropathology and cognitive impairment, by applying the framework to different brain regions with larger sample sizes.MethodWe trained multi‐layer perceptron (MLP) models for classification of neuropathologically confirmed AD vs. healthy controls (HC) using the transcriptomic data of three brain regions (dorsolateral prefrontal cortex (DLPFC, Total N = 1092), posterior cingulate cortex (PCC, Total N = 647), and head of caudate nucleus (HCN, Total N = 717)) respectively from the ROSMAP study. We performed z‐score normalization for each gene expression and randomly split data in ratio 80:20 with matching class (AD and HC) distributions for training and testing. We embedded the expression profiles of all the subjects to the same Uniform Manifold Approximation and Projection (UMAP) space using the final layer of the trained models and obtained progressive trajectories that mirrored AD pathological severity and cognitive impairment of the whole cohort. Interpretable technique SHapley Additive exPlanations (SHAP) was explored to explain model predictions and obtain significant genes contributing to AD. Network analysis was then carried out to identify key gene modules presented in the models underlying AD progression of different brain regions.ResultThe MLP models differed for each dataset with the number of layers ranging between 3‐5 and achieved the best AUC of 0.891, 0.940 and 0.776 on DLPFC, PCC and HCN datasets respectively. The AD severity indexes (SI) calculated from the trajectory were highly correlated with neuropathology biomarkers (R ∼ 0.6, p < 1e‐11) and global cognitive function (R ∼ 0.7, p < 2.2e‐16). Significant genes identified by the SHAP explainer revealed common and specific transcriptomic signatures from different brain regions implicated in AD.ConclusionThis study illustrates the potentials of deep learning methods to multi‐omic data to characterize the molecular networks associated with increasingly severe clinical and neuropathological stages of neurodegenerative diseases like AD. This offers the opportunity to discover drug targets and/or biomarkers.
Emerging Strategies for Developing Highly Engaging Dementia Care Intervention for Paid Care Workers in Long‐Term Care (LTC) Settings
Authors: Levkoff Sue E , Chen Hongtu , Cortes Dharma 
Published: December 2023

AbstractBackgroundThere is widespread agreement about the need for strengthening the dementia‐capable workforce to increase the accessibility of online dementia care training programs for paid care workers (PCW) in LTC. Through three NIH‐funded research projects, we are forming an approach to the development of digital dementia training interventions based on two main strategies: a) improving ‘engage‐ability’ by using an engagement design centering on the concepts of motivation (key needs of the user), satisfaction (positive experience with the intervention), and interactivity (connection from one session to another); and b) integrating domain‐specific employee training with a generic employee wellness program.MethodStudy 1 evaluates a self‐directed learning (SDL) digital dementia care training intervention to increase ‘engage‐ability’ by addressing PCWs’ needs for improved knowledge and competence for caregiving while maintaining a sense of control in the learning process (motivation); by enhancing the ease of use through scenario‐based audiovisual modules (satisfaction); and, by using post‐session quizzes and pre‐session reminder messages (interactivity). Study 2 developed a worksite digital health risk assessment combined with a wellness improvement program that enabled a workplace counselor to deliver health education materials to increase healthy lifestyle behaviors in preretirement adult workers aged 55 and above. Study 3 combines the SDL dementia care training with the wellness program for PCWs in LTC settings in order to achieve high ‘engage‐ability’ and acceptability of the intervention.ResultA pre‐post evaluation of the SDL program (Study 1) revealed a significant reduction in stress and improved self‐efficacy in providing dementia care among PCWs (Study 1). Formative research demonstrated moderate usability and acceptability from the pre‐retirement workers who participated in Study 2. In Study 3, focus groups with PCWs and managers from LTC organizations showed high endorsement of integrated dementia care training and wellness programs with organizational support for the delivery of the training intervention for PCWs in LTC settings.ConclusionA successful digital training program for PCWs providing dementia care in LTC systems requires an integration of dementia‐specific training and the tools for wellness critical for supporting PCWs’ overall well‐being while securing support from the LTC organizations for PCW participation in the program.
Influence of amyloid and diagnostic syndrome on non‐traditional memory scores in early‐onset Alzheimer’s disease
Authors: Bushnell Justin , Hammers Dustin B. , Apostolova Liana G. , Kramer Joel H. , Carrillo Maria C. , Dickerson Brad C. , Rabinovici Gil D. , Clark David G. 
Published: December 2023

AbstractBackgroundThe Rey Auditory Verbal Learning Test (RAVLT) is a neuropsychological test used for assessing episodic memory impairment. The sequence of the RAVLT consists of five learning trials, an intrusion list recall, a subsequent recall (short delay), and a delayed recall (approximately thirty minutes after the short delay recall). The RAVLT has been studied extensively in various presentations of dementia, but there is limited research for early‐onset Alzheimer’s disease (EOAD). We analyze the influence of amyloid and diagnostic syndrome on traditional and novel RAVLT scores in EOAD.MethodWe transcribed RAVLT recordings from 303 subjects in the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS). Subjects were group by amyloid status (control/EOnonAD vs EOAD) and syndrome: posterior cortical atrophy (PCA), primary progressive aphasia (PPA), amnestic, and non‐amnestic presentations. The traditional method of scoring the RAVLT is to count the total number of correct words recalled for each task (i.e., raw score). Two other count‐based scores were calculated by examining serial position effects (SPEs). SPEs describe the tendencies to remember words in the beginning (primacy) or end (recency) of the list. A relational SPE score, known as J‐curve, is examined here and is defined as primacy minus recency. Three timing‐based scores were analyzed: duration, stopping time, and speed. The seven scores were entered separately into linear mixed effects models as dependent variables. Amyloid status, syndrome, and nuisance variables (age, sex, education) were entered as independent variables.ResultCompared with amyloid‐negative subjects, amyloid‐positive subjects showed negative effects on raw score, primacy and recency. The presence of amyloid increased stopping time by 5%. Duration and speed were not found to be associated with amyloid. Significant differences among clinical syndromes were observed with recency, duration, and stopping time.ConclusionRAVLT measures are sensitive to the effects of amyloid and syndrome in early‐onset dementia. Stopping time is a novel score that may complement existing measures for describing amyloid‐positive individuals. Further work is needed to quantify the predictive value of these scores.