bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transformer‐based Deep Learning Architecture Improves Detection of Associations between Spontaneous Speech Language Markers and Cognition
Authors: Mobtahej Pooyan , Gouron Sam , Javaheri Rojan , El‐Khoury Annelisse , Leiby Anne‐Marie C , Sultzer David L , Sajjadi S. Ahmad 
Published: January 2025

AbstractBackgroundSpontaneous speech is easily obtainable and has the potential to become an accessible and low‐cost marker for cognitive function. The time‐consuming and labor‐intensive nature of speech analysis has been a major obstacle to utilizing this promising tool. This study uses a novel transformer‐based methodology to explore associations between spontaneous speech language features and global cognition.MethodSpeech recordings were obtained from participants with clinical diagnoses of mild cognitive impairment (MCI), dementia, and cognitively unimpaired, from the Alzheimer’s Disease Research Center (ADRC) at University of California, Irvine. Audio samples were denoised and transcribed using our transformer‐based model (Figure 1). The model conducted association analyses between global cognition, measured by Montreal Cognitive Assessment (MoCA) scores, and 5 linguistic features pre‐specified based on popularity in language analysis. Features were extracted from transcripts via Bidirectional Encoder Representations from Transformers (BERT), which assigns each word a special “token” (tokenizing), translates “tokens” into a numerical format for computer comprehension (encoding), and generates unique identifiers (embeddings) that mathematically capture meanings and relationships between words. A linear regression (LR) model was then trained on power‐transformed BERT‐extracted linguistic features and its performance was tested against both untransformed and power‐transformed data without BERT feature extraction using the same linguistic features.ResultsIn a cohort comprising 73 healthy controls and 12 individuals with MCI or dementia, our analysis, utilizing pre‐specified linguistic features and leveraging BERT for enhancing feature extraction, revealed that vocabulary richness (p = 0.009), average word length (p = 0.005), and semantic coherence (p = 0.047) were significantly associated with MoCA scores (Table 1). The non‐BERT untransformed model found no significant associations with MoCA while the non‐BERT power‐transformed model found only one significant association: average word length (p = 0.016).ConclusionOur study's novel approach of employing BERT for linguistic feature extraction in association analysis increased the number of pre‐selected speech features significantly associated with global cognition, namely vocabulary richness, average word length, and semantic coherence.This improvement in association detection highlights the potential for better deep‐learning dementia detection methods and might lead to increased utility of spontaneous speech as an easily obtainable and scalable cognitive measure.
A genome‐wide association study for clinical dementia phenotypes in an ethnoracially diverse cohort
Authors: Sapkota Shraddha , Almeida Marcio A , Jin Lee‐Way , Farias Sarah Tomaszewski , Decarli Charles 
Published: January 2025

AbstractBackgroundGenome‐wide association studies (GWAS) for established clinical dementia phenotypes is often limited to Caucasians from European ancestry. Although the incidence of dementia is higher in African Americans and Hispanics, the inclusion of these groups in GWAS research is less common. We examine GWAS for clinical dementia phenotypes in an ethoracially diverse cohort of older adults.MethodWe used an older‐adult cohort from the University of California, Davis‐Alzheimer’s Disease Research Center (n=1014; mean age= 74.54 (7.50) years; 59.6% female). Participants were diagnosed as cognitively normal (CN; n=329), Mild Cognitive Impairment (MCI; n=448) and Demented (n=234) at final visit. Samples were genotyped and imputed using 1000 genomes as reference. Genetic admixtures components were calculated assuming three original populations resulting in a set of 6078198 high‐quality imputed single nucleotide polymorphisms (SNP) from 1000 participants. Our outcome tested GWAS across clinical dementia phenotypes (CN, MCI, Demented). We further stratified our genetic dataset into non‐synonymous and non‐synonymous high‐deleterious genetic variants. Finally, we performed a replication analysis of the top Alzheimer’s disease‐related genetic risk loci identified in the literature. All genetic association analyses included age, sex, admixture components and education as covariates.ResultAdmixture analysis detected primarily European/Caucasian genetic ancestry and African and Amerindian minorities (Figure 1). We did not observe significant associations for any panel (overall, non‐synonymous, high‐deleterious). Some findings were of interest. Our top genetic risk loci associated with clinical dementia phenotype in the (1) overall panel was rs62344486 (p<4.84e‐07) (Figure 2) known to play a protective role against cognitive decline, (2) non‐synonymous panel was rs17832998 in the DACT1 gene (p<1.0e‐05), and (3)non‐synonymous high‐deleterious panel were rs41267154 (p<3.4e‐03) and rs72697000 (p<1.5e‐03) in the FLG gene. We also identified rs17668923 located downstream of TMEM106B gene (p<2.8e‐05) and rs56220430 (p<1e‐04) near SIGLEC11 gene from our literature review.ConclusionWe identified two novel SNPs near TMEM106B and SIGLEC11 genes which have been associated with frontotemporal dementia and immunosuppressive signals, respectively. Preliminary GWAS for clinical dementia phenotypes in diverse populations may (1) identity novel genetic risk loci underlying the dynamic neurobiological underpinnings across the dementia continuum and (2) be further validated in larger minority population studies.
Digital Cognitive Screening and ML‐Enabled Random Forest Modeling for the Detection of Cognitive Impairment
Authors: Banks Russell , Thompson Karl , Jannati Ali , Tobyne Sean , Showalter John , Bates David , Pascual‐Leone Alvaro 
Published: January 2025

AbstractBackgroundMild cognitive impairment (MCI), is characterized by cognitive dysfunction not severe enough to affect one’s activities of daily living (ADLs)1. Annually, approximately 15‐20% adults 65 and older will present with MCI 1. MCI is considered a significant risk factor and a robust predictor for developing dementia. The time course for progression to dementia can vary substantially between individuals and is impacted by the specific pathology underlying the MCI, and the cognitive deficits associated with cognitive impairment (CI) subtypes 2,3. Despite the conversion risk of MCI to dementia and the effectiveness of early lifestyle interventions to mitigate the conversion risk, many investigations do not account for MCI in their CI prediction models. This research investigates binary and 3‐class ML‐enabled modeling to classify CI status leveraging multiple modalities of cognition extracted from the Digital Clock and Recall (DCR), a brief digital cognitive assessment.MethodData from 983 participants in the Bio‐Hermes‐001 multi‐site study (age mean±SD=72±6.7; 56% female; years of education mean±SD=15±2.7; primary language English), a priori classified as cognitively unimpaired (CU; n=417), mild cognitively impaired (n=309), or probable Alzheimer’s dementia (n=257) based on expert consensus clinical diagnosis and neuropsychological evaluation were analyzed. A random forest model was trained on DCTclock and word recall data to classify cognitive impairment using a binary (CI and CU) and 3‐tier (CI, Indeterminate, and CU) prediction thresholding schemes.ResultThe 3‐tier model predictions performed well (AUC=0.887; accuracy=0.834; NPV=0.801; PPV=0.859) outperforming the binary predictions (AUC=0.865; accuracy=0.79; NPV=0.737; PPV=0.837) when measured on Biohermes’ cohort diagnosis. The sensitivity and specificity of the 3‐tier predictions were 0.858 and 0.8, respectively.ConclusionThe DCR, a 3‐minute digital cognitive assessment can be used to classify MCI and probable Alzheimer’s dementia with high accuracy, NPV, and PPV.
Impact of race/ethnicity and area deprivation index on Alzheimer’s disease and related disorders biomarkers in the POINTER Cohort
Authors: Toledo Jon B. , Pascual Belen , Landau Susan M. , Harrison Theresa M. , Finn Quentin , Demesie Yitbarek N , Yu Melissa , Decarli Charles , Vemuri Prashanthi , Baker Laura D , Lovato Laura , Snyder Heather M , Masdeu Joseph C. , Pavlik Valory 
Published: January 2025

AbstractBackgroundThere is limited information on biomarker‐defined Alzheimer’s disease (AD) pathology in community‐recruited individuals of diverse racial and ethnic groups. Here, we assessed the association of race/ethnicity with baseline biomarkers and cognitive measures and hypothesized a lower impact of AD pathology in non‐Hispanic White (nHW) participants in the U.S. POINTER study clinical trial, which evaluates the benefits of a multidomain lifestyle intervention on cognition of community‐recruited at‐risk elderly individuals.MethodWe included 879 participants from the U.S. POINTER study enrolled in the imaging substudy (∼50% of participants) who self‐identified as nHW (n=644), African American/Black (AA/B, n=132), Asian (n=29), and Hispanic (n=74). Outcomes measures included global Aβ pathology (18F‐florbetaben), temporal meta‐ROI tau (18F‐MK‐6240), MRI‐measured white matter hyperintensity volume (WMH) volumes, global clinical dementia rating (gCDR), and global, executive, and memory standardized cognitive scores. The nHW participants were selected as reference. Power transformations normalized skewed distributions. Multivariable linear regressions include age, sex, education, Framingham risk score, and area deprivation index (ADI) as covariates.ResultAA/B harbored lower WMHs, and meta‐temporal tau ROI values. In the full model, 0.5 gCDR participants had higher centiloid values, were more likely to be AA/B and Hispanic, and had a greater burden of WMHs. In addition, higher centiloid values were associated with a 0.5 gCDR in all groups except for the AA/B participants. Hispanic, AA/B, and Asian participants had lower cognitive scores than the nHW group. A higher ADI score was associated with lower global and executive cognitive scores but not with WMH volume or Aβ centiloid score.ConclusionThe analyses of the community‐based U.S. POINTER indicate that AA/B individuals present differences in AD and related dementia biomarker values. nHW had higher fully adjusted cognitive scores. These results indicate a significant heterogeneity in the cognitive testing performance and biomarker values of individuals at risk for dementia in the community.
Physical activity and sex modify the influence of diabetic‐level insulin resistance on cognitive function in a cohort enriched with risk for Alzheimer’s disease
Authors: Mamlouk Gabriella M , Edmunds Kyle J , Breidenbach Brianne M. , McIntyre Clayton Connor , Lose Sarah R , Johnson Sterling C. , Okonkwo Ozioma C 
Published: January 2025

AbstractBackgroundPhysical activity (PA) and peripheral insulin resistance are two promising targets for delaying the onset of cognitive impairment in preclinical Alzheimer’s disease (AD). Understanding how these factors interact and whether their influence on cognitive outcomes is sex‐dependent may be crucial for designing effective lifestyle interventions to protect aging brain health. This study examines whether PA and sex modify the relationship between diabetic‐level peripheral insulin resistance and cognitive function in a sample enriched with risk for AD.MethodOur sample featured n=1,125 cognitively unimpaired participants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP). An index for PA was defined from self‐reported weekly minutes of moderate and vigorous physical activity, where active participants were defined by reporting at least 150 weekly minutes of moderate or vigorous aerobic exercise. Cognitive composite scores were based on neuropsychological examination by trained technicians, and outcomes were standardized to z‐scores across the WRAP database and indexed. The Homeostatic Model Assessment for Insulin Resistance (HOMA‐IR) quantified insulin resistance status, and scores exceeding 3.63 were classified as diabetic‐level. Cross‐sectional linear regression models were assembled in a moderation framework to test the three‐way interaction between PA, sex, and diabetic‐level insulin resistance on cognitive outcomes, and all model covariates included age, years of education, Apolipoprotein E4 (APOE‐ε4) carriage, and familial history of AD.Result73.6% of participants had family history of AD and 37.9% were APOE‐ε4 carriers. The sample was 69.9% female, and participants had a mean age of 64.5 ± 7 years and a mean of 16 ± 3 years of education. Models demonstrated significant interactions between PA, sex, and diabetic‐level insulin resistance on three cognitive composite scores: executive function (β=‐0.73, SE=0.30, p=0.017), preclinical Alzheimer's cognitive composite 3 (PACC3) (β=‐0.66, SE=0.27, p=0.014), and PACC4 (β=‐0.50, SE=0.24, p=0.037). In individuals with diabetic‐level HOMA‐IR, the effect of sex on cognitive performance was moderated by PA, with female sex being more strongly linked to higher cognitive performance in physically active subjects.ConclusionIn older adults at risk for AD, sex differences in cognitive function in individuals with diabetic‐level insulin resistance may depend on physical activity.
A Comparison of DTI‐Based Measures as Predictors of Future Cerebrovascular Degeneration
Authors: Reid Robert I. , Przybelski Scott A. , Lesnick Timothy G. , Fought Angela J. , Raghavan Sheelakumari , Kamykowski Michael G. , Graff‐Radford Jonathan , Knopman David S. , Petersen Ronald C. , Jack Clifford R. , Vemuri Prashanthi 
Published: January 2025

AbstractBackgroundThere has been a recent proliferation of various quantities based on single shell Diffusion Tensor Imaging (DTI) for capturing overall cerebrovascular health, especially Small Vessel Disease (SVD) as a single number. The existing literature has gaps in the comparison of these measures, so we evaluated them as predictors of both current and future White Matter Hyperintensity (WMH) as a proxy of SVD.MethodUsing baseline 3T MRI examinations (T1, DTI, FLAIR scans) of 598 participants (>60 years, amyloid negative, to limit to those on the SVD pathway) from the Mayo Clinic Study of Aging, we calculated five summary DTI quantities with varying degree of complexity. We estimated their partial R2 from cross‐sectional linear regression models and computed semi‐partial R2 from mixed models predicting WMH volume as a percentage of Total Intracranial Volume (TIV). All models were adjusted for age, sex, and scanner model, and the WMH and based were log‐transformed before fitting. We also conducted subset analyses with high (WMH+) vs. low (WMH‐) baseline WMH load (cut at 1.62%) to compare the performance of the measures in early vs. widespread WMH.ResultAll MD‐based quantities computed on the whole brain, including FWF, performed similarly to each other, but Genu FA underperformed at explaining WMH variance (Table 1). PSMD (skeletonized or atlas based) was more sensitive in WMH+ but dramatically less so in WMH‐. In WMH‐ and overall cohort, FWF and JHU MD (which is a simple averaging of MD in the JHU atlas) performed well.ConclusionAll MD‐based measures performed similarly to each other in the whole dataset but dichotomizing by WMH load showed interesting differences. PSMD measurements are driven by the extreme end (95th percentile) of the MD distribution and therefore more sensitive in WMH+. Genu FA, a regional measure, was not useful for predicting whole brain WMH change. We noticed some variation in how the quantities are affected by scanner model (GE Signa HDxt and Discovery 750) which needs broader investigation. Simply averaging MD over WM avoids additional processing and is more sensitive to WM degeneration that is not (yet) severe.
Complex relationship between serum IGFBP2 and FGF21, dysmetabolism and cognitive function across the clinical spectrum of Alzheimer’s disease
Authors: Dallaire‐Théroux Caroline , Denis Hélèna , Provencher Amélie , Valentin‐Escalera Josue , Leclerc Manon , Tremblay Cyntia , Loiselle Andréanne , Tournissac Marine , Belleville Sylvie , Picard Frédéric , Zetterberg Henrik , Duchesne Simon ,   CIMA‐Q Group, Calon Frederic 
Published: January 2025

AbstractBackgroundChronic conditions such as obesity, dyslipidemia, insulin resistance and metabolic syndrome are found with increased prevalence in older adults and are investigated as risk factors of Alzheimer’s disease (AD). The exact mechanisms by which such conditions may act on the brain to promote AD pathogenesis are still unknown. We aimed to investigate the relationship between peripheral markers of metabolic function, including adiponectin, fibroblast growth factor 21 (FGF21) and insulin‐like growth factor binding protein 2 (IGFBP2), and AD‐associated cognitive decline.MethodSamples from 288 participants (mean age, 74 [SD, 6] years; 37% males) were obtained through the CIMA‐Q cohort. Participants were classified as cognitively healthy (CH; n=56), subjective cognitive decline (SCD; n=115), mild cognitive impairment (MCI; n=86), and AD (n=31). Demographics, APOE4 status, cognitive testing, anthropometrics, blood and cerebrospinal fluid biochemical parameters and MRI data were compared between clinical groups using one‐way ANOVA and Kruskal‐Wallis tests. Multiple linear regressions were performed to evaluate the association between serum levels of adiponectin, FGF21 and IGFBP2 and clinical, biochemical and neuroimaging data, with adjustment for age and sex.ResultHigher levels of FGF21 (p=0.01) and IGFBP2 (p<0.001) were found with increasing age. Amongst the three metabolic markers, IGFBP2 was significantly elevated in SCD and AD (p=0.04) compared to CH. Clinical and biochemical evidence of obesity, insulin resistance and dyslipidemia were associated with lower serum adiponectin and IGFBP2, but higher FGF21 (p<0.0001). Significant inverse associations between metabolic markers and clinical scores included FGF21 with Rey Auditory Verbal Learning Test (RAVLT; p=0.002) and IGFBP2 with Montreal Cognitive Assessment (MoCA; p=0.02). Adiponectin levels were not linked to clinical diagnosis nor cognition but were higher in women. Finally, IGFBP2 also correlated with markers of neurodegeneration including higher plasma pTau181 and lower amygdala volume (p<0.05).ConclusionWhile FGF21 and IGFBP2 exhibit opposing trends in metabolically impaired individuals, both markers increase with age and are inversely associated with cognitive performance. However, serum IGFBP2 is more strongly associated with clinical diagnosis and cognitive function than FGF21. These results unveil complex relationships between fluid‐based biomarkers of dysmetabolism and AD, offering new opportunities for diagnosis and treatment.
Mapping alterations in the hippocampal subfields in the ARTE10 mouse model of Alzheimer’s disease using dMRI derived soma and neurite density maps
Authors: Shahid Syed Salman , Jarvis Erin E , Butch Elizabeth R , Snyder Scott E , Wu Yu‐Chien 
Published: January 2025

AbstractBackgroundThe hippocampus, a region vital for memory and cognition, is prone to abnormal deposition of beta‐amyloid (Aβ) during the early stages of the Alzheimer’s disease. Aβ‐associated pathophysiological mechanisms instigate dendritic deficit, neuronal loss, and neuroinflammation, leading to abnormal functional and behavioral changes. These factors directly impact tissue microstructures. Histological studies suggest subfield specific Aβ accumulation. The aim of the current study is to map the changes in soma‐ and neurite‐density in the hippocampal‐subfields due to Aβ load in the ARTE10 mouse‐model of AD using a compartment‐specific diffusion model.MethodTen‐month‐old male ARTE10 (N=8) and age‐matched littermate controls underwent MRI on a 9.4T scanner (Bruker BioSpin, GmbH, Germany) equipped with an 1H cryogenic surface coil. DWIs were acquired using a 2D multi‐shot spin‐echo echo planar imaging sequence using the following parameters: TE/TR=26/4000 ms; voxel size=125x125x400 µm3, 20 slices, 4 segments, b values= (500, 1000,2500, 4000,5500, 7000) s/mm2 and 5 b0 per b‐shell. The averaged b0 image was non‐linearly registered to Badhwar hippocampus atlas. The hippocampal‐subfields (CA1, CA2, CA3, CA4‐DG, subiculum, and stratum‐granulosum of hippocampus (SGH)) in atlas space were inverse transformed to diffusion space. Multi‐compartment microstructural estimation was performed using soma and neurite density imaging (SANDI) model. This model provided volume fractions for extra‐cellular (fec), intra‐neurite (fin), and intra‐soma (fsoma) compartments (Figure 1). The regional mean values of microstructural parameters of individual subfields were extracted and used in a general linear model for group comparisons.ResultThe group‐level differences (WT vs. ARTE10) in fin were observed in CA1 (p=0.02; d=‐1.25), CA2 (p=0.03; d=‐1.20), CA4‐DG (0.01; d=‐1.34), and SGH (p=0.03; d=‐1.22) Figure 2. Group differences in fsoma were significant in CA1 (p=0.04; d=1.08), CA2 (p=0.02; d=1.22), CA4DG (p=0.04; d=1.10), and SGH (p=0.03; d=0.99). In general, the ARTE10 AD mice had reduced neurite density and elevated soma density in the hippocampal subfields.ConclusionThe results demonstrated that the SANDI model is sensitive to the microstructural alterations in the hippocampal subfields of the animal model with profound AD pathologies.