bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lower in‐vivo locus coeruleus integrity is associated with lower cortical thickness in older individuals with elevated Alzheimer’s pathology
Authors: Engels Nina , Hsieh Stephanie , Schneider Christoph , Prokopiou Prokopis C. , Schultz Aaron P. , Riphagen Joost M. , Koops Elouise A. , Hanseeuw Bernard , Rentz Dorene M. , Sperling Reisa A. , Johnson Keith A. , Jacobs Heidi I.L. 
Published: December 2022

AbstractBackgroundCortical neurodegeneration is, along with Alzheimer’s Disease (AD) pathology, an important feature of AD, which correlates highly with tauopathy and cognitive decline. Post‐mortem data show that the locus coeruleus (LC) loses approximately 8.4% volume per cortical Braak stage. This work investigates whether LC integrity, as indexed by LC MRI signal intensity, is associated with cortical thickness, and whether this is exacerbated by the presence of AD pathology.MethodCross‐sectional data from 165 Harvard Aging Brain Study participants (age range=50‐94 years; female=∼60%; cognitively unimpaired: n=149, cognitively impaired: n=16; Table 1) who underwent 3T‐MRI, PiB‐PET (cut‐off=1.324 DVR) and T807‐PET scanning was selected. LC MRI signal intensity was assessed by normalizing the LC to the pontine tegmentum (reference region) for each individual and averaging clusters of 5 voxels with the highest intensities. Unilateral cortical thickness (CT) values were extracted utilizing the Desikan‐Killiany atlas (FreeSurfer 6) and averaged across hemispheres to obtain regional bilateral values. PET measures were corrected for partial‐volume effects. Associations between CT and i) LC intensity, or ii) LC intensity interacted with pathology (i.e., inferior temporal (IT) tau, entorhinal (EC) tau and global amyloid), were examined with bootstrapped linear regressions, while controlling for age, sex, and education, and applying FDR‐correction.ResultWe found a positive association between LC intensity and CT for medial and lateral temporal regions (p‐FDR<0.05; Figure 1). Models interacting LC intensity with IT or EC tau revealed that lower LC intensity was associated with lower CT especially in temporal regions. This effect was greater in individuals with elevated tau (p‐FDR<0.05; Figure 2‐3). Results still held after controlling for amyloid (p‐FDR<0.05). Similarly, interacting LC intensity with amyloid showed a relation between lower LC intensity and lower CT in temporal and some occipital regions, which was stronger in individuals with elevated amyloid (p‐FDR<0.05; Figure 4).ConclusionThis work demonstrates that lower LC integrity is associated with bilateral cortical thinning in regions aligning with the expected topographical distribution of tau in preclinical AD. Additionally, these spatial patterns are more widespread in individuals with elevated AD pathology. These results suggest that LC integrity is closely related to the downstream neurodegenerative sequela of AD pathology.
Comparison of plasma amyloid, tau, and astrocyte biomarkers to identify AD pathophysiology
Authors: Ferreira Pamela C.L. , Tissot Cécile , Ferrari‐Souza João Pedro , Bellaver Bruna , Brum Wagner S. , Leffa Douglas Teixeira , Therriault Joseph , Benedet Andréa Lessa , Servaes Stijn , Lussier Firoza Z , Chamoun Mira , Stevenson Jenna , Rahmouni Nesrine , Tudorascu Dana L , Klunk William E , Villemagne Victor L , Cohen Ann D , Zimmer Eduardo R. , Ashton Nicholas J. , Zetterberg Henrik , Blennow Kaj , Karikari Thomas K , Rosa‐Neto Pedro , Pascoal Tharick A 
Published: December 2022

AbstractBackgroundAlthough it has been already demonstrated that plasma amyloid‐β (Aβ), phosphorylated tau (p‐tau), and glial fibrillar protein (GFAP) can predict with high accuracy Alzheimer's disease (AD) pathophysiology, no previous study has compared their performance in the same set of individuals. Here, we compare the performance of plasma Aβ42/40, p‐tau, GFAP, and NfL against Aβ and tau PET across the AD spectrum.MethodWe used the ROC curve to test the predictive performance of Simoa plasma Aβ42/40, p‐tau (at threonine 181 and 231), NfL, and GFAP to identify Aβ and Pau PET positivity in 138 cognitive unimpaired (CU) and 87 cognitive impaired (CI) from the McGill TRIAD cohort. Pearson correlation and linear regression tested the association between markers.ResultWe showed that plasma p‐tau231, p‐tau181, GFAP, and NfL correlated with each other (Figure 1), while Aβ42/40 did not. In CU, voxel‐wise linear regressions (Figure 2A) showed that p‐tau231, p‐tau181, and GFAP concentrations were significantly associated with Aβ‐PET (Figure 2A). While for Tau‐PET (Figure 2B), there was a significant association only with p‐tau231 and p‐tau181 (Figure 2B). P‐tau231 outperformed the other plasma biomarkers to identify both Aβ‐ and Tau‐PET positivity (AUC 0.877 and 0.796, respectively) in CU individuals. In CI, Aβ‐ and Tau‐PET were significantly associated with p‐tau231, p‐tau181, and GFAP, whereas NfL was only associated with Tau‐PET. The discriminative accuracy of GFAP in identifying both Aβ‐PET and Tau positivity (AUC 0.936 and 0.944, respectively) outperformed the other plasma biomarkers in CI individuals (Figure 3, Table 1).ConclusionWe showed that plasma p‐tau231, a novel biomarker of early AD, best depicted AD pathophysiology in CU individuals. Interestingly, GFAP, an astrocyte reactivity marker, was better associated with brain Aβ and tau pathologies than plasma p‐tau and Aβ markers in CI individuals. Our results highlight that the performance of the novel plasma biomarkers of amyloid, tau and neuroinflammation to detect brain AD pathophysiology is disease stage specific.
Unique contributions of brain amyloid load and APOE4 Status to Regional Brain Volume
Authors: Boyle Christina P , Ching Christopher RK , Thomopoulos Sophia I , Thompson Paul M 
Published: December 2022

AbstractBackgroundBrain amyloid and tau, and volumetric atrophy on MRI, are defining features of AD (Jack et al., 2018). Despite growing interest in these component processes, their relationship is still poorly understood.We evaluated the unique contributions of APOE4 allele (E4+/E4−) and brain amyloid (Aβ) status (Amy+/Amy−) to brain volume in the elderly to identify associations with neurodegeneration in regions vulnerable to Alzheimer’s disease (AD).MethodAccelerated 3T T1‐weighted brain MRI scans (N=1,353) obtained at screening for three distinct phases of ADNI with available information on APOE status and/or Amyloid positive/negative status were identified for the following subjects: 561 healthy controls (CN) (age: 71.8±6.3 y, 330F/231M), 599 MCI (mild cognitive impairment; age: 71.9 ± 7.5 y, 264 F/335 M), and 193 AD (age: 74.4±8.2 y, 79 F/114 M). Amyloid status was based on standardized cut‐off values in amyloid sensitive PET scans (Royse et al., 2021).We applied unbiased tensor‐based morphometry (TBM; Leow et al., 2009; Hua et al., 2011) to each scan to measure relative volume differences between each subject and a common minimal deformation template (MDT), resulting in 3D Jacobian maps to represent relative expansion or contraction at each brain voxel.At each voxel, a linear regression model was fit to relate regional brain volumes to amyloid status, and APOE4, after adjusting for covariates. P‐value maps were generated to visualize the pattern of voxel‐wise model contributions and statistical significance while enforcing a standard 5% false discovery rate (FDR) correction for multiple statistical comparisons across the brain (Benjamini et al., 1995).ResultAmyloid positive status was associated with significantly lower regional volume in the temporal lobes and higher volume in the ventricles, i.e., ventricular expansion. APOE4 positive status was associated with lower volume in the temporal lobes (see Fig. 1).
ConclusionThe highly localized effects of APOE4 status and amyloid load on brain volumes are consistent with greatest effects of AD pathologic processes in the temporal lobes and similar associations found for Amy+ and E4+ status with cortical thinning (Li et al. (2017).Continued analysis of longitudinal ADNI data will offer further insight into effects of amyloid and APOE4 allele status on atrophy estimates.
Distinctive age‐related longitudinal dementia progression patterns using a machine‐learning‐based MRI biomarker
Authors: Ma Da , Popuri Karteek , Wang Lei , Lockhart Samuel N. , Craft Suzanne , Gurcan Metin Nafi , Beg Mirza Faisal 
Published: December 2022

AbstractBackgroundDementia of Alzheimer’s type (DAT) is an age‐related neurodegenerative syndrome caused by Alzheimer’s disease. The risk of developing dementia increases with age, but also depends on disease trajectory. In this study, we aim to evaluate the effect of normal aging on the MRI‐derived dementia risk score and investigate the distinctive age‐related longitudinal dementia progression patterns among individuals with different dementia trajectories.MethodsA total of 1609 participants with multi‐timepoint neuroimaging tests and diagnoses were recruited from the ADNI. Based on their dementia trajectories, the subjects were stratified into four groups: stable normal cognition (sNC=423), stable/progressive mild cognitive impairment (sMCI=535/pMCI=321), and stable dementia of Alzheimer’s type (sDAT=330). Brain structures were automatically segmented and harmonized to control the confounding effect of sex and scanner, and feed into an extensively‐validated kernel‐based ensemble classifier to derive the score, which we termed longitudinal MR‐DAT‐Score (MRDATS). The CSF‐based biomarker was derived as the ratio between the total tau and the amyloid beta (t‐tau/Aβ1‐42). We analyzed the effect of aging on the biomarkers’ distribution of each stratified group in terms of their empirical cumulative distribution function (ECDF). We also evaluated the difference in longitudinal MRDATS progression patterns among different groups.ResultsCompared to the CSF biomarker, the MRDATS revealed a more distinctive age‐related risk increase among all stratified groups (Figure 1). In addition, the effect of aging persisted across both the non‐progression groups (sNC and sMCI) and the progression groups (pMCI) except for the DAT group, where the age‐related effect is only observable for the 80‐90 year old group. Distinctive longitudinal dementia risk progression patterns (in terms of MRDATS) were also observed for each stratified group (Figure 2). Specifically, for patients with mild cognitive impairment (MCI), distinctive patterns were observed between sMCI and pMCI across subjects from different aged populations.ConclusionOur study demonstrated differential aging effect patterns as well as distinctive longitudinal patterns between AD‐progressive and non‐progressive subjects across different age groups. The results of this study emphasize the importance of disentangling the effect of aging with disease‐driven brain atrophy patterns.
Genetic and Structural Network Contributions to The Regional Vulnerability of Tauopathy
Authors: Wen Qiuting , Yu Meichen , Shahid Syed Salman , Zhao Yi , Risacher Shannon L. , Saykin Andrew J. , Wu Yu‐Chien 
Published: December 2022

AbstractBackgroundTau propagates in a highly stereotyped pattern in both normally aged and cognitively impaired individuals, with the medial temporal lobe (MTL) being first affected. Although substantial progress has been made in revealing its spreading pattern with the aid of network analysis, it remains unclear why tau first seeds in this specific region. We hypothesize that regional white‐matter connections exacerbate MTL’s vulnerability to tauopathy. Further, we speculate that tau candidate genes have higher expression levels in MTL. This study tested the two hypotheses in cognitively normal older brains using two independent imaging datasets, i.e., ADNI and Indiana ADRC (IADRC), along with gene expression data derived from the Allen Human Brain Atlas (AHBA).MethodWe identified 159 cognitively normal participants (72±7 y/o) in ADNI and 57 (70±7 y/o) in IADRC that have both diffusion MRI and tau‐PET (AV1451) data. We estimated network metrics and tau SUVR in 68 cortical regions of interest (ROIs) for each participant. All participants' data were then averaged (after controlling for age and sex) to produce ROI‐wise measurements. Gene expression data were extracted from the same 68 ROIs. We preselected eight tau risk genes, including MAPT, LRP1, PTK2B, MADD, CASS4, BIN1, EPHA1, and APOE. Spearman correlation was applied to examine their spatial correlations.ResultADNI and IADRC reveal a similar spatial pattern for structural connectivity and tau deposition, with MTL demonstrating lower connectivity and higher gene expression levels (Figure 1A‐B). This trend is also evident when summarized in Braak stages (Figure 1C‐D), with Braak I ROI (entorhinal) showing the most elevated tau deposition, lowest structural connectivity, and highest gene expression level. Figure 2 summarizes the Spearman’s Rho across the 68 ROIs. Consistently, higher tau deposition is associated with lower connectivity and higher the expression levels of BIN1 and APOE.ConclusionThe two independent datasets produced consistent results, supporting our hypotheses that both weaker structural connectivity and higher tau candidate gene expression contribute to the regional vulnerability in the MTL. These preliminary results encourage future studies to systemically examine the interaction between tau, gene expression, and axonal changes in healthy aging and across the AD spectrum.
Rural‐urban and gender differences in the prevalence of metabolic syndrome among older Indians
Authors: Sundarakumar Jonas S. , Stezin Albert , Lingegodwa Abhishek Mensegere , Ravindranath Vijayalakshmi 
Published: December 2022

AbstractBackgroundThe clinical significance of metabolic syndrome (MetS) as a risk factor for cardiovascular disease, cerebrovascular disease, and dementia is becoming increasingly prominent. However, there is a dearth of studies from India on the prevalence of MetS, especially in the aging population. We aimed to estimate MetS prevalence and associated comorbidities in two prospective, aging cohorts from rural and urban India.MethodOur study sample includes aging (≥ 45 years) individuals – 2171 from the rural (SANSCOG) cohort and 332 from the urban (TLSA) cohort. These older Indian adults have contrasting population characteristics, which echo the stark rural‐urban divide in the country. Data from baseline clinical and biochemical assessments were used to calculate the prevalence of MetS in both the above cohorts using two well‐established criteria, namely consensus criteria and NCEP‐ATP III criteria. Further, we compare rural‐urban, gender‐ and age‐wise differences in the prevalence of MetS as per the consensus criteria.ResultPrevalence of MetS was 46.2% and 54.8% as per consensus criteria in rural and urban subjects, respectively; corresponding numbers using NCEP‐ATP III criteria were 40.3% and 45.1%. Rural‐dwelling older adults had a significantly lesser prevalence of all individual MetS parameters except impaired triglycerides and high‐density lipoprotein levels. Rural women had a significantly higher prevalence than rural men, whereas there was no significant difference among urban subjects. We did not observe any consistent age‐wise trend when comparing both these cohorts. There was a high burden of comorbidities among both groups, mostly undiagnosed in rural subjects.ConclusionRoughly 1 in 2 older Indians have MetS, urban significantly more than rural, reaching an alarming 63.1% among urban subjects aged 65‐74 years. The very high prevalence of undiagnosed co‐morbidities in rural Indians is extremely concerning, calling for urgent public health measures including ramping up the primary health care system in this marginalized and health disparate population.
Cardiorespiratory Fitness, BMI, and Dementia risk: Findings from The Cooper Center Longitudinal Study
Authors: Gafni Tal , Weinstein Galit , David Leonard , Carolyn Barlow E. , DeFina Laura F. , Gabriel Kelley Pettee , Berry Jarett D , Shuval Kerem 
Published: December 2022

AbstractBackgroundCardiorespiratory fitness (CRF) and body mass index (BMI) are each independently related to premature mortality. Previous research indicates that high fitness is protective against premature mortality even among those who are overweight or obese. We aimed to explore the independent and joint associations of CRF and BMI with incident dementia among middle‐aged adults.MethodData on CRF, BMI and covariates were derived from the Cooper Center Longitudinal Study (CCLS). The sample included 6,458 middle‐aged adults (mean age 50.9±7.6 years; 85% men; mean CRF 11.6±2.3 METs and BMI 25.3±3.2 kg/m2), who attended the Cooper Clinic (Dallas, TX) for preventive medical exams twice between 1970‐1999 (mean time between exams 2.5±2.1 years), and had fee‐for‐service Medicare coverage after age 65 at any time between 1999 and 2009. Maximal metabolic equivalents (MET) levels of CRF were computed based on the final speed and grade on treadmills. BMI was calculated based on measurements of weight and height (kg/m2). The primary independent variables consisted of average CRF and BMI from the two exams and the change between exams. The chronic condition warehouse definition of Alzheimer’s disease and related dementias was based on the International Classification of Diseases, 9th Revision. Proportional hazards models were applied to investigate the independent and joint associations of midlife CRF and BMI and their change over time with dementia diagnosis adjusted for age, sex, education, systolic blood pressure and fasting glucose and cholesterol levels.ResultOver 40,975 person‐years of Medicare surveillance (average 6.3±3.6), 637 cases of dementia were identified. Average CRF and BMI were each related to incident dementia (HR = 0.95 per MET 95% CI 0.91‐0.99 and HR = 1.03 per kg/m2 95% CI 1.00‐1.06, respectively), but not the interaction. Change in CRF or BMI between exams did not predict incident dementia.ConclusionIn line with studies exploring the link of CRF and BMI with mortality, our findings suggest that baseline fitness is related to dementia incidence independently of body mass and that significant joint effects were not observed.
“Hablemos de Alzheimer”: Words Mexican Americans use to talk about Alzheimer’s disease genetic testing. Implications for precision medicine education.
Authors: Fong Jamie C , Chavez Fatima , Silos Karla , Castro Gabriela Castro , Arroyo‐Miranda Mirna L , Kunik Mark E , Shulman Joshua M , Medina Luis D 
Published: December 2022

AbstractBackgroundEffective Alzheimer’s disease (AD) diagnosis and therapy will require a tailored approach to each person’s unique AD manifestation, known as AD precision medicine (PM). PM‐associated comprehensive genetic testing is currently restricted to the research domain and not readily available to minoritized Hispanics/Latinos (H/L). Successful AD precision medicine programs benefitting everyone require culturally appropriate patient outreach/education materials. We investigated the words H/L use to talk about AD genetic testing.MethodWe conducted freelisting interviews with 14 Spanish‐preferring Mexican American adults (female, 85.7%; mean age, 49.3±16.8 years, range = 19‐73; completed less than or at most a high school education, 35.7%). Participants were presented with vignettes about a H/L father of varied ages (5th‐7th decade) and degrees of cognitive impairment (no memory deficits to severe memory loss) who received genome sequencing results. During freelisting, participants were given prompts about the vignettes, and were asked to list all words that came to mind when they thought about the prompts. Responses were reviewed for semantically related words. Discrepancies were resolved by consensus agreement. Among sociodemographic data collected, familism and perceived social status were measured by the Pan‐Hispanic Familism Scale and scored (1‐strongly disagree to 5‐strongly agree, max score 25), and the MacArthur Scale of Subjective Social Status (SSS; each item: 1‐lowest to 10‐highest), respectively.ResultsHighest frequency unique word/word clusters (n = 151) for one vignette (father: age 70 with cognitive slowing, prompt: father’s reaction to his own moderate risk result) included worry (17.2%), age/aging/older (15.9%), family (12.6%), high/medium/low chance (12.6%), Alzheimer’s (10.6%), and normal/well/healthy (7.9%). High frequency of family‐related worry aligned with high familism scores (M = 23.2±2.6, range = 15‐25). Moderate frequency words of participants, 78.6% of whom perceived they held social standing in the top half of the community (SSS>5), reflected action orientation: do (6.6%), seek (1.7%), inform (1.5%), and manage (1.1%).ConclusionSpanish‐preferring Mexican Americans respond robustly to information about family. Family‐centered educational materials about AD precision medicine, including information about healthy aging versus neurodegeneration, risk magnitude categories, and action words, could have high impact. Additional research to learn words used by diverse Spanish‐preferring H/L is needed.
Alzheimer’s disease status can be predicted using a novel fractal‐based metric computed from resting‐state EEG
Authors: Brookshire Geoffrey , Wu Yunan "Charles" , Quirk Colin , Gerrol Spencer , Merrill David A. , Caselli Richard J. , Lucero Ché 
Published: December 2022

AbstractBackgroundAlzheimer’s disease (AD) lacks a fast, easy, reliable, and inexpensive method of diagnosis. Currently, diagnosis is based on time‐consuming behavioral tests and the exclusion of other potential causes of impairment. Several biomarkers show good or promising diagnostic performance (e.g. CSF, tau PET, MRI, blood), but are either expensive, invasive, or still in development and while some can detect preclinical disease stages, all appear slow to change relative to the rate of cognitive decline. Here we develop a prototype diagnostic classifier based on novel metrics of brain activity in resting state electroencephalography (EEG) that correlates well with mental status.MethodArchival resting‐state EEG recordings of older adults (N=248) came from a memory clinic and university‐based clinic with a range of clinical diagnoses including subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and dementia, representing AD, vascular dementia, and Lewy body dementia, TBI, and depression. We developed XGBoost classifiers to detect AD using EEG, age, and sex under increasingly challenging conditions. We computed metrics of periodic and aperiodic brain activity using the FOOOF algorithm. Furthermore, we developed a novel technique called [Banded Fractal Variability], which yields a set of features based on fluctuations in the fractal dimension within canonical frequency bands. We trained classifiers using cross‐validation to avoid overfitting during hyperparameter selection.ResultAlong with ROCAUC, we report an optimal sensitivity, specificity, and accuracy for the point on the ROC curve that maximizes Youden’s j. The classification tasks were healthy vs. probable AD (ROCAUC = 98%, sensitivity = 90%, specificity = 99%, accuracy = 96%), SCI vs. mild AD (ROCAUC = 89%, sensitivity = 76%, specificity = 87%, accuracy = 84%), and AD vs. other pathologies (no AD diagnosis) in MCI and dementia patients (ROCAUC = 82%, sensitivity = 72%, specificity = 87%, accuracy = 80%). All ROCAUC values were stronger than would be expected by chance (ps < 0.001).ConclusionThese preliminary results suggest that AD could be diagnosed in the clinic on the basis of machine‐learning classifiers and resting‐state EEG. Furthermore, they demonstrate that [Banded Fractal Variability] carries clinically‐relevant information about AD.