bluuebunny/crossref_metadata_2025_split huggingface dataset:
Older adults dancing their way to health: a systematic review of the effects of dance interventions on cognition, anxiety, and depression
Authors: Jaldin Michelle A , Balbim Guilherme Moraes , Pinto Juan M , Negrete Miguel , Castillo Crystal , Khanna Sonam , Brunskill Amelia A , Marquez David X. 
Published: December 2023

AbstractBackgroundDance is a form of physical activity that is a complex sensorimotor rhythmic activity integrating multiple physical, cognitive, and social elements (Merom et al, 2013). Many studies have showed the benefits of physical activity on older adults; however, no studies have showed the impact of formal and traditional dance (salsa, bachata, and ballroom dancing) on older adults which makes this review unique. This systematic review (SR) will synthesize dance interventions that include formal and traditional dance and their influence on cognition, anxiety and/or depression.MethodOur focus is on dance alone; or dance combined with other behavioral strategies (e.g., cognitive, or social program). Three reviewers screened titles, abstracts, full texts and completed data extraction. If disagreements occurred from screening, all reviewers would meet and vote on whether to include or exclude article. If disagreements occurred in data extraction, two extractors would discuss and agree upon a conclusion. If agreement was not met, a senior author would vote. Articles included were 1) published/peer‐reviewed 2) randomized controlled trials (RCT) of a dance intervention that contain at least one of our three outcomes 3) included only dance as a physical component and no other physical components. Participants were individuals cognitively unimpaired or with mild cognitive impairment aged 60 and over.ResultSixteen studies were included in the SR for qualitative review. Twelve articles examined cognition, one article examined depression, one article examined cognition, anxiety, and depression and two articles included cognition and depression. Our SR showed that dance interventions can potentially influence cognition. There were no changes in anxiety and although two of three articles examining depression showed significant effects, there is not enough evidence in literature to suggest that dance interventions can potentially influence anxiety and/or depression as there is a small number of articles that studied anxiety and/or depression.ConclusionWhile literature suggests dance interventions potentially influence cognition, depression, more dance interventions are needed on depression, anxiety, and overall mental health. It would also be interesting to examine/compare if different dances affect certain cognitive domains or mental health outcomes more than others.
Baseline diet quality of participants in AU‐ARROW trial based on adherence to the MIND diet recommendations
Authors: Chen Juliana , Castro Carolina B , Fernando Malika , Fuller Stephanie Jane , Jahan Farzana , Brown Belinda M , Loo Ruey , Sohrabi Hamid R , Martins Ralph N , Gardener Samantha L 
Published: December 2023

AbstractBackgroundThe Mediterranean‐Dietary Approaches to Stop Hypertension (MIND) diet is rich in leafy green vegetables, legumes, berries, wholegrains, fish, nuts, and olive oil and low in red and processed meats and discretionary foods (high in saturated fat, added sugar and added salt). Following a MIND diet is associated with a reduction in risk of cognitive decline and dementia. The lifestyle intervention known as the AUstralian multidomain Approach to Reduce dementia Risk by prOtecting brain health With lifestyle intervention study (AU‐ARROW) is undergoing recruitment of participants. Here we report on the dietary quality of participants to date, based on their adherence to the MIND diet.MethodA self‐administered MIND diet questionnaire was completed by participants in the online REDCap platform as part of the screening surveys to determine participant eligibility into the AU‐ARROW trial. Eligible participants were required to have a ‘poor diet’, which was a MIND diet score of ≤9 out of a maximum MIND diet score of 14. Comparative statistical analyses were conducted to determine the differences in dietary quality between eligible and ineligible participants.ResultFrom the 39 participants who were screened to date, 16 participants were ineligible, and 23 were eligible. Ineligible participants had a higher total MIND diet score (mean = 10.5(SD = 0.6)) compared with eligible participants (mean = 7.5(SD = 1.8)). For ineligible participants, there was greater proportion adhering to MIND diet recommendations for fish, nuts, and poultry. No eligible participants adhered to MIND diet recommendations for leafy green vegetables, berries, and wholegrains. There was general compliance to recommendations for red meat and butter intake among both groups, but a smaller proportion of eligible participants adhered to the MIND diet recommendations for fried foods, sweets, and cheese, compared to ineligible participants.ConclusionOverall, improvements to the diet quality of both eligible and ineligible participants are required. Particularly for participants eligible to the AU‐ARROW trial, a focus on improving adherence to core food group recommendations are required not only for cognitive health but also for general health and prevention of other chronic diseases. Education and counselling from dietitians are critical for supporting participants in dietary behaviour change.
Deep learning‐based SWAT‐TAB approach for Identifying Genetic Variants using Whole Genome Sequencing
Authors: Jo Taeho , Nho Kwangsik , Saykin Andrew J. 
Published: December 2023

AbstractBackgroundDeep learning has been employed in various genetic studies, but use with large‐scale datasets, such as whole genome sequence (WGS) data, can be challenging due to the high‐dimension low‐sample‐size (HDLSS) problem. In such studies, the number of features (genetic variants) may be orders of magnitude larger than the number of samples, which can impact the applicability of deep learning.MethodsIn this study, we aimed to identify genetic variants associated with Alzheimer’s disease (AD) using a modified version of the SWAT‐CNN1 approach called SWAT‐TAB that addresses the HDLSS problem. The main modification to the original SWAT‐CNN approach was the incorporation of the Tabnet2 algorithm in the second step (Fig. 1A). This algorithm determines associations between features and selects the most relevant ones for a given label using a concept called sequential attention. This calculates the importance of a feature at each step and create a trainable mask based on this information. We applied this modified approach to ADSP WGS data for Chromosome 19, using 7,416 samples (4,266 AD and 3,150 cognitively normal) and 204,486 SNP features.Resultsrs429358, rs7256200, rs1969899716, and rs10414043 were the most important features on Chromosome 19 reflecting known AD genes such as APOE and APOC1, as well as several novel genetic features (Fig. 1B). The SWAT‐TAB approach demonstrated improved execution speed, with a reduction in processing time per SNP by 11.58%. The SWAT‐TAB also demonstrated improved reproducibility and ease of implementation as it only required one layer for execution, compared to the original approach which required as many layers as the number of features in the window.ConclusionOur deep learning‐based SWAT‐TAB approach showed significant improvement in execution speed, reproducibility, and ease of implementation compared to the original approach. This method appears very promising for identifying genetic variants associated with AD and constructing accurate classification models. Ongoing studies will determine the potential utility of this approach in larger analyses of WGS data beyond the initial results obtained for Chromosome 19.
Pathways to selective vulnerability in neuromodulatory subcortical structures during the early pathogenic stages of Alzheimer’s disease
Authors: Ehrenberg Alexander J. , Pereira Felipe Luiz , Suemoto Claudia Kimie , Leite Renata Elaine Paraizo , Rodriguez Roberta Diehl , Paes Vitor Ribeiro , Ferretti‐Rebustini Renata Eloah de Lucena , Ferrioli Eduardo , Nitrini Ricardo , Jacob‐Filho Wilson , Pasqualucci Carlos Augusto , Grinberg Lea T. 
Published: December 2023

AbstractBackgroundThe two main catecholaminergic members of the isodendritic core nuclei are the substantia nigra (SN) and the locus coeruleus (LC). Despite cytoarchitectural and neurochemical similarities between the two nuclei, the LC is significantly more vulnerable to early degenerative changes in tauopathies like Alzheimer’s disease (AD) as compared to the SN. Here, we seek to use the gradient of vulnerability in these catecholaminergic nuclei to identify pathways associated with vulnerability in early AD pathological stages.MethodThis cohort includes individuals with no neuropathologic entities and Alzheimer’s disease proteinopathic stages. Individuals with TDP‐43, Lewy Body, or Chronic Traumatic Encephalopathy pathologic entities were excluded. The LC and SN was dissected from frozen human brainstem tissue using macroscopic anatomic features. To compare the LC and SN, only cases below Braak III with sufficient RNA from both nuclei were included. The Nanostring nCounter platform was used to probe specific pathways related to neurodegenerative processes. DBH and SLC6A3 served as confirmatory readouts of accurate anatomic sampling of the LC and SN. Differential gene expression analysis was used to evaluate patterns of gene expression that differentiate the two nuclei. Annotated gene sets were evaluated using global significance scores (GSS), a representation of cumulative t‐statistics for relevant genes.ResultThe LC and SN were collected from 26 cases (Figure 1) eligible for this analysis. Accurate dissections were confirmed by differential expression of DBH (p = 1.38e‐9) and SLC6A3 (p = 4.5e‐16). Among the differentially expressed genes (Figure 2), genes associated with microglia activation (GSS = 2.35), oxidative stress (GSS = 2.17), and lipid metabolism (GSS = 2.00) significantly differentiated the LC and SN.ConclusionThe LC and SN serve as examples of differentially vulnerable nuclei at the earliest neuropathologic stages of AD. Cell intrinsic factors, physiological demands, vascular contributions, and exposure to extrinsic factors like toxins are thought to contribute to the LC’s early vulnerability. Here, we show that neuroinflammation and oxidative stress pathways are likely central to differential vulnerability at the earliest stages of Alzheimer’s disease and may represent targets for intervention in prodromal stages.
The role of biofluid markers in predicting near‐term cognitive impairment
Authors: Yakoub Yara , Ashton Nicholas J. , Karikari Thomas K , Montoliu‐Gaya Laia , Schöll Michael , Spreng R. Nathan , Rosa‐Neto Pedro , Soucy Jean‐Paul , Breitner John C.S. , Zetterberg Henrik , Blennow Kaj , Poirier Judes , Villeneuve Sylvia ,   PREVENT‐AD Research Group
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) has been defined as a biological construct that can be determined using in vivo biomarkers. Recent studies confirmed the clinical value of the AT(N) framework when using Aβ and tau positron emission tomography (PET) scans in predicting progression from cognitively unimpaired to MCI. Our study aims to investigate if plasma biomarkers can yield similar predictive performance to PET biomarkers, and to also compare against cerebrospinal fluid (CSF) biomarkersMethodIndividuals from the PREVENT‐AD cohort were included in the study. Participants had Aβ and tau plasma measurements and at least one year of clinical follow‐up thereafter (n = 278). A subset of participants had CSF measurements (n = 102) and/or PET scans (n = 133) also with 1‐year clinical follow‐up. Participants were cognitively unimpaired at the time of all biomarker measurements. MCI diagnoses were made by clinical consensus among expert clinical and research staff blind to plasma, CSF, PET, MRI and APOE genotype. We established thresholds of plasma and CSF Aβ and tau positivity based on >80% specificity in identifying individuals with significant levels of Aβ‐PET (plasma Aβ42/40 = 0.057; plasma pTau181 = 7.53 pg/mL; CSF Aβ42 = 961.37; CSF pTau181 = 66.59 pg/mL). Aβ ([18F]NAV4694) and tau ([18F]AV1451)PET thresholds were defined by the mean plus 2 standard deviations from young participants (Aβ SUVR cut‐off = 1.17; tau SUVR cut‐off = 1.24).Result36.8% (7/19) of the A+T+ plasma group progressed to MCI, 50% (4/8) of the A+T+ CSF group progressed to MCI, and 46.6% (7/15) A+T+ PET group progressed to MCI (Fig. 1).We found differences in MCI progression status between the A+T+ group compared to the A‐T‐ and A+T‐ groups across all biomarkers’ measurements, with the A+T+ group always showing a higher rate of progression than the other two groups (post‐hoc chi‐square analyses; p<0.05).ConclusionAbnormal levels of Aβ and tau plasma biomarkers can help predict near‐ term progression to MCI. When plasma thresholds are derived based on PET data, however, plasma biomarkers have a slightly lower specificity than PET and CSF biomarkers, and both plasma and CSF biomarkers are less sensitive than PET biomarkers.
NetPRISBI‐AD: Network‐based Polygenic Risk Scoring model with Brain Imaging for Alzheimer’s Disease
Authors: Lee Dong‐gi , Jung Sang‐Hyuk , Park Sunghong , Shen Li , Shin Hyunjung , Kim Dokyoon ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: December 2023

AbstractBackgroundThe amount of gray matter can measure the structural characteristics of the brain, and the deterioration of brain function can be evaluated accordingly. In particular, the lower the amount of gray matter in areas involved in memory, the lower the cognitive function, eventually affecting the onset of dementia, such as Alzheimer’s disease (AD). Although there are various factors for changes in gray matter volume, genetic factors are also closely related. In addition, genetic factors are not only independently involved in biological activities, but also have complex interactions.MethodIn this study, we construct a SNP network that reflects interactions between SNPs and propose a model that can predict the risk score for AD and the gray matter volume of the brain by region from the SNP network. First, in the SNP network, interactions between SNPs are measured as similarities based on the linkage disequilibrium score. Then, the proposed method constructs a regression model to predict brain volume and risk scores for AD using feature propagation, which is conducted to refine existing genetic variant information.ResultThe proposed method was applied to data including both genetic and brain imaging information among UK Biobank (UKBB; 8,273 cases and 25,353 controls, total 33,626 participants) and Alzheimer’s Disease Neuroimaging Initiative (ADNI; 202 cases and 720 controls, total 922 participants) data. The results were cross‐validated from the two datasets and compared with other models including the conventional polygenic risk score with pruning and thresholding (PRS‐PT) model. Compared to the other methods, the proposed method showed outstanding performance and the discovery of genetic variants and brain regions affecting Alzheimer’s disease.ConclusionWe proposed the framework for predicting the volume of the brain with refined features of SNP that reflects the interactions between elements. The results revealed that genetic relations and inferring brain volumes are crucial for enhancing AD risk prediction. Furthermore, through the proposed method, it is expected that understanding and interpreting the phased mechanism of the onset of AD through brain lesions from genetic variants will be possible.
Mapping Medial Temporal Lobe Longitudinal Change in Preclinical Alzheimer’s Disease
Authors: Yushkevich Paul A. , Xie Long , Wisse Laura EM , Dong Mengjin , Ravikumar Sadhana , Ittyerah Ranjit , de Flores Robin , Das Sandhitsu R. , Wolk David A. 
Published: December 2023

AbstractBackgroundThe medial temporal lobe (MTL) is a site of early tau pathology and neurodegeneration in AD. In recent work [1], we reported that rates of change in MTL subregion volumes on longitudinal structural T1‐MRI differ significantly between cognitively unimpaired individuals with and without amyloid pathology (A+CU vs A‐CU), suggesting that these measures are effective biomarkers for tracking disease progression in preclinical AD. Here, we develop a specialized pipeline that quantifies pointwise longitudinal change across the MTL, revealing more informative spatial patterns of early neurodegeneration than summary volumetric longitudinal measures.MethodsOur pipeline, SkelDBM, combines skeletonization and deformation‐based morphometry to generate maps of annualized rate of atrophy on the skeleton of an MTL template (Figure 1). SkelDBM samples subjects’ atrophy maps along the skeleton of an MTL template, increasing robustness to subject‐to‐template registration errors. We evaluate SkelDBM using a subset of ADNI data studied in [1] (Table 1). Two preclinical AD groups (A+CU and tau‐positive A+CU) are compared to a control group (A‐CU) using general linear models (GLM) with age as nuisance covariate; permutation testing is used to derive statistically significant clusters.ResultsThe full A+CU vs A‐CU comparison shows significant differences in rates of change in right anterior hippocampus, right entorhinal cortex, right transentorhinal cortex, and left posterior hippocampus (Figure 2). Limiting the A+CU group to tau‐positive individuals yields more widespread differences, including most of the right anterior MTL cortex and both hippocampi. These patterns align with volumetric longitudinal data reported in [1] but provide a richer characterization of spatial atrophy patterns.ConclusionSkelDBM is sensitive to longitudinal atrophy occurring over relatively short periods (∼2 years) in preclinical AD and characterizes patterns of atrophy more comprehensively than summary volumetric measures. Summary measures are also subject to biases, since larger structures are typically associated with less measurement error and hence may show exaggerated statistical effects compared to smaller structures even when the underlying neurodegeneration rates are the same. Regional maps of longitudinal change may be more robust to this bias and may help discovery of “signature regions” associated with specific pathological drivers of neurodegeneration.[1] Xie et al., Human Brain Mapping, 2020.
Analytical Choices of Timescale in the Presence of Practice and Cohort Effects in Longitudinal Cognitive Aging Research: A Simulation Study Using Linear Mixed‐Effects Models
Authors: Wang Jingxuan , Chen Ruijia , Ackley Sarah F , Zimmerman Scott C , Sims Kendra D , Hayes‐Larson Eleanor , Mayeda Elizabeth Rose , Glymour M. Maria 
Published: December 2023

AbstractBackgroundUnderstanding determinants of cognitive decline relies on several modeling assumptions. Alternative longitudinal modeling strategies–such as accounting for aging using time‐on‐study (estimated based on within‐person comparisons), baseline age (estimated based on between‐person comparisons), or current age (estimated based on both within‐ and between‐person comparisons) –may produce qualitatively different results. Using simulations, we evaluated robustness of effect estimates for predictors of cognitive decline with current age (vs. time‐on‐study) as the timescale, in the context of practice and/or cohort effects.MethodOur simulation strategy approximates the data structures of several cohort studies of cognitive aging (figure 1). For scenarios with only practice effects and with both practice and cohort effects, we performed 1,000 simulations of a cohort of 2,000 individuals with annual cognitive assessments over 3 years. Longitudinal models were fit using three timescale specifications (with and without adjustment for practice effects for each): (1) time‐on‐study adjusted for baseline age; (2) current age without adjustment for baseline age; and (3) current age adjusted for baseline age. We evaluated the bias in estimated effect associated with each specification by comparing to known simulation parameters.ResultWhen practice effects were present and not accounted for, time‐on‐study and current age underestimated rate of cognitive decline (figure 2) but bias was smallest for current age as the timescale. When cohort effects were present, current age as the timescale led to biased estimates when baseline age was not adjusted for. With a correctly specified model, the variance in the estimate of the coefficient for current age was 36.3% smaller than the variance in the estimate of the coefficient for time‐on‐study with baseline adjustment. All estimates for the effect of education on cognitive decline were unbiased regardless of timescale, but 95% CIs were narrowest for current age as the timescale.ConclusionIn this simulation, current age as the timescale that combines within‐ and between‐person estimates led to more precise estimates than time‐on‐study with baseline age adjustment. Failure to account for practice effects can bias estimates of longitudinal within‐person age effects.
Heterogeneity in Modifiable and Non‐Modifiable Risk Factors among Different Dementia Types in UK Biobank
Authors: Luo Renhao , Patel Chirag J 
Published: December 2023

AbstractBackgroundDementia is heterogenous and includes many subtypes, including Alzheimer’s disease (AD), frontotemporal dementia, and vascular dementia, whose risk may be expressed at different ages in life. Therefore, the modifiable antecedent risk may weigh differently in different dementia subtypes, but this heterogeneity has yet to be understood. In 2020, the Lancet Commission reported the risk for all‐cause dementia for 12 modifiable risk factors. Here, we compare the risk among these risk factors reported in the Lancet Commission 2020 to assess the heterogeneity in the association between the non‐modifiable risk factors in dementia subtypes and age groups in 450,707 participants of the UK Biobank. We also compare the risk to non‐modifiable but causal risk factors, including AD family history and the ApoE genotype.MethodWe analyzed participant data from the UK Biobank. We divided the participants into two age groups, midlife (age of diagnosis 45‐65) and late‐life (age of diagnosis greater than 65). We built four different logistic regression models corresponding to each dementia subtype in each age group, including univariate, gender‐adjusted, ethnicity‐adjusted, and gender‐ethnicity‐adjusted models. We reported the odds ratio (OR), p‐values, and 95% confidence interval. Furthermore, we compared the ORs for different risk factors from the Lancet report with those from the UK Biobank. Lastly, we ran correlations between different dementia subtypes and unknown‐cause dementia.ResultWe identified age‐dependent risk factors in different dementia types, such as ApoE genotype, hearing loss and hypertension in all‐cause and AD, and diabetes in vascular dementia. We also illustrated risk factors that are not age‐dependent risk factors, such as AD family history, air pollution, TBI, current smoking, being underweight, excessive alcohol consumption, and physical inactivity. Additionally, TBI has the greatest difference in OR compared to the Lancet report. Lastly, unknown‐underlying dementia cases have a modifiable risk profile that is highly correlated with AD and vascular dementia in both age groups.ConclusionHere, we illustrated the heterogeneity of dementia risk factors in different subtypes of dementia. Taken together, our results highlight that different dementia subtypes share/do not share the extent of risk, which could contribute to identifying patient populations for future clinical research.