bluuebunny/crossref_metadata_2025_split huggingface dataset:
Longitudinal characterization of white matter degeneration in early stages of AD
Authors: Kim Jeongchul , Jung Youngkyoo , Barcus Richard A. , Lipford Megan E. , Hughes Tim M. , Lockhart Samuel N. , Baker Laura D , Craft Suzanne , Whitlow Christopher T. 
Published: June 2023

AbstractBackgroundWhite matter (WM) degeneration in older adults is often characterized unimodally by diffusion tensor imaging (DTI), T1w and T2‐FLAIR MRI. However, approaches integrating multimodal MRI information will be necessary for better understanding WM degeneration in early stages of AD.MethodThis study characterizes the longitudinal changes of WM structure among 66 participants with marked baseline white matter hyperintensity volume (WMHv > 2000 mm3). 42 cognitively normal [CN, 72±7.2y, F = 25] and 24 mild cognitive impairment [MCI, 74±5.7y, F = 14]) adults underwent longitudinal MRI (scan interval: 2.8±0.85 years) within the Wake Forest AD Research Center. DTI metrics were estimated using FMRIB’s Diffusion Toolbox, voxel‐wise volumetric changes of WM (Jacobian Determinant [JD]) were estimated from fractional anisotropy (FA) maps using SPM CAT12, and WMHv were quantified using SPM’s Lesion Segmentation Toolbox. We segmented WM into WMH and normal‐appearing WM (NAWM) to characterize different degeneration patterns by integrating multimodal MRI information. We performed a linear regression analysis of %WMHv change with cognitive status, scan interval, and baseline WMH volume as covariates.ResultDecreased FA and increased mean diffusivity (MD) were observed in WMH regions compared with NAWM. Within WMH, interestingly, volume and FA increased, while MD decreased (p <0.001) during the longitudinal follow‐up (Figs. 1 and 2). Baseline WMHv was not significantly associated with cognitive status, but trended toward higher WMHv in MCI compared to CN (p = 0.07). Adjusted for baseline WMHv, cognitive status was associated with WMHv change (p = 0.036, MCI faster than CN, Fig. 3). While FA (p = 0.99) and MD (p = 0.16) did not change in the NAWM, volumetric change rate (JD) indicated volumetric contraction (p<0.001, Figs. 4 and 5). WMH progresses with time in older adults at risk of AD dementia by occupying neighboring NAWM regions, resulting in relative increases in FA and decreases in MD.ConclusionIn NAWM, WM degeneration can be better characterized by volumetric contraction than white matter microstructural integrity. This observation could implicate that secondary or peripheral WM fibers may reveal more vulnerable tracts in early stages of AD. Further investigation incorporating imaging makers of blood perfusion and metabolism could better explain structural mechanisms of WM degeneration.
Understanding paradoxical lucidity: Family caregiver interpretations of lucid episodes among people with late‐stage Alzheimer’s disease and related dementias
Authors: Griffin Joan M. , Finnie Dawn , Kim Kyungmin , Lapid Maria I. , Biggar Virginia S. , Frangiosa Theresa , Gaugler Joseph 
Published: June 2023

AbstractBackgroundParadoxical lucidity is characterized by spontaneous mental clarity with verbal or non‐verbal communication or behavior among people with AD who had previously been thought to lack the cognitive capacity to do so. With a paucity of data available, family caregivers (FCGs) provide critical perspectives on characteristics of these episodes of lucidity (EL).MethodTwenty‐four FCGs who witnessed at least one EL were interviewed about the experience. Qualitative data were transcribed, double coded, and analyzed to help characterize ELs.ResultFCGs described ELs as indelible moments, possibly triggered by music, food, holidays, or impending death. The spontaneity of the EL, its duration, the connectedness with FCGs, and coherence of communication varied. Instead of seeing them as cognitive fluctuations, FCGs often assigned metaphysical meaning to ELs.ConclusionFCG descriptions of ELs had few common patterns, suggestions they are heterogenous. Their impact on FCGs, however, was enduring.
Decreased functional integration in higher‐order association areas in individuals with autosomal‐dominant Alzheimer’s disease: Findings from the Colombia‐Boston (COLBOS) biomarker study
Authors: Castrillon Gabriel , Bustamante Catalina , Guzman‐Velez Edmarie , Langella Stephanie , Aduen Paula , Vila‐Castelar Clara , Fox‐Fuller Joshua T , Oliveira Gabriel , Baena Ana Y. , Lopera Francisco , Quiroz Yakeel T. 
Published: June 2023

AbstractBackgroundThe disruption of brain functional connections has been linked to cognitive impairment in Alzheimer’s disease. The functional architecture of the brain can be modeled using graph theory, which provides metrics to describe both local and global network properties. Autosomal dominant Alzheimer’s disease (ADAD) provides a unique opportunity to study brain changes in preclinical AD since carriers of ADAD mutations are destined to develop dementia at young age and have a well‐characterized disease trajectory. Here, we applied a graph theory approach to examine the functional network topology in Presenilin‐1 (PSEN1) E280A carriers and non‐carriers.MethodsA total of 43 cognitively‐unimpaired individuals (18 PSEN1 carriers, 25 non‐carriers; mean age = 36.6±5.2) from the Colombia‐Boston biomarker study were included. Participants underwent resting‐state fMRI in a 3T scanner, and a comprehensive multi‐domain neuropsychological battery. fMRI data were preprocessed using C‐PAC. Global functional connectivity (FC) was derived from the nodal strength of the Pearson correlation matrix between time‐series of 400 ROIs [Shaeffer‐2016] within 6 different functional networks [Yeo‐2011]. The strength of within‐ (z) and between‐network (h) functional integration was derived from a consensus modularity analysis [Dwyer‐2014]. Differences in cognitive variables were evaluated using Mann‐Whitney tests. Differences in FC were assessed using permutation methods: global FC using FSL‐randomize and integration parameters were based on a consensus analysis applied to random controlled matrices. Both methods were repeated 1000 times, with a p<0.05 significance threshold.ResultsThere were no group differences in cognitive variables. Compared to non‐carriers, carriers exhibited decreased local FC in higher‐order association areas, and increased connectivity in somatosensory areas. There were no group differences in the within‐network integration. Carriers showed decreased between‐network integration, especially in higher‐order association networks, mainly the default mode network (Fig.1).ConclusionsFindings support the hypothesis that alterations of functional network topology are evident in preclinical ADAD, primarily in the higher‐order cognitive networks. Early identification of abnormalities within these networks may be useful in understanding how FC changes may lead to subsequent cognitive decline in individuals at increased risk for AD. Future work with larger samples should investigate the relationships between FC changes and the neurobiological factors underlying AD.
Chromosome 19 polygenic risk scores defined by deep learning model predict the rate of Alzheimer’ disease progression
Authors: Bae Jinhyeong , Sanjay Apoorva Bharthur , Nho Kwangsik , Saykin Andrew J. , Penchev Valentin , Apostolova Liana G. 
Published: June 2023

AbstractBackgroundGenetic variants invariably play a crucial role in rate of disease progression. The effects of Alzheimer’s disease (AD) genetic risk factors on disease progression remains poorly understood. We developed a novel deep learning framework that could identify a personalized polygenic risk score (PRS) at the individual chromosomal level. We further used the individual’s PRS to predict the rate of cognitive decline and cerebrospinal fluid (CSF) biomarker changes.MethodWe applied a novel deep learning model to the 266,161 SNP chromosome 19 data of 313 AD and 457 cognitively unimpaired (CU) ADNI patients. The top 35 AD‐determining SNPs were determined and validated in an external dataset. Next, we used multiple regression to analyze the contribution of each combination of 3/35 SNPs to predict the rate of CSF accumulation (amyloid beta (Aβ), tau, phosphorylated tau (pTau), Aβ /tau, and Aβ/pTau) and the rate of cognitive decline in the memory, language, executive functioning, and visuospatial domains while controlling for age, sex, and education.ResultAll regression models were significant at the 0.05 level. The R‐squared adjusted values for each of the CSF models listed above were 0.917, 0.845, 0.902, 0.939, and 0.878, respectively. The majority of SNPs selected for the CSF regression models mapped to APOC1, APOC2, and TOMM40. The R‐squared adjusted values for the cognitive models were 0.188, 0.215, 0.436, and 0.266, respectively. The selected SNPs mapped to APOC1, TOMM40, VRK3, and NECTIN2. Given the less robust predictive power for cognitive change over time we increased the search to the top 150 AD SNPs. This produced R‐squared adjusted values of 0.426, 0.437, 0.607, and 0.557, respectively. The selected SNPS mapped to TOMM40, APOC1P1, ERCC1, IGSF23, ZNF556, CKM, APOC2, ZNF473, and CEACAM22P.ConclusionOur deep learning model PRS can successfully predict the AD cognitive and biomarker changes over time. Our model’s capability in deciphering the impact of SNPs on AD development could prove critical for early presymptomatic risk assessment and personalized therapeutic strategy approaches.
School‐based adult social support in segregated schools and late‐life cognitive function in the study of healthy aging in african americans (STAR)
Authors: Gutierrez Sirena , Whitmer Rachel A. , Soh Yenee , Peterson Rachel L. , George Kristen M , Lor Yi , Barnes Lisa L. , Mayeda Elizabeth Rose , Glymour M. Maria , Gilsanz Paola 
Published: June 2023

AbstractBackgroundIt is unclear how school segregation, a form of structural racism, affects late‐life cognition. Though segregated schools were often under‐resourced, they likely shielded Black students from interpersonal racism present at integrated schools. Social support may mediate and/or modify the relationship between school segregation and cognition.Method467 STAR participants aged 50+ reported if they attended a segregated school in 1st and 6th grade and received grade‐specific school‐based social support (feeling cared for by an adult at school). Baseline executive function (EF), semantic memory (SM), and verbal episodic memory (VEM) were measured using the Spanish and English Neuropsychological Assessment Scales. For each grade, linear regression models evaluated the association of segregated school attendance with each cognitive domain, sequentially adjusting for demographics, school‐based social support, and education (in pooled models). Effect modification by school‐based social support was examined through stratified models and interaction terms.ResultThe mean age was 68.2 (Table 1). Participants attending a segregated school in either grade were more likely to report school‐based social support than those in integrated schools (both p‐values<0.05; Figure 1). For each grade, attending a segregated vs integrated school was not associated with EF or VEM but was associated with lower SM (β(95% CI): ‐0.28(‐0.48, ‐0.08); Figure 2); results were similar when adjusting for social support. For each grade, effect estimates of attending a segregated school on SM and EF were smaller among those with social support than those without, though differences were not statistically significant (interaction p‐values>0.05;Table 2). In first grade, attending a segregated school was associated with SM among those without social support (β(95% CI): ‐0.40(‐0.78, ‐0.02)) but not among those with social support (β(95% CI): ‐0.10(‐0.36, 0.16)). The effect of attending a segregated school on VEM was not lower among those with social support in 1st or 6th grade than their counterparts without it. Stratified results were similar when adjusting for education.ConclusionHaving attended a segregated school may have both harmful and protective effects on late‐life cognition. Our findings suggest that potential harmful effects of having attended segregated schools may be diminished by the increased likelihood of school‐based social support.
Reduced temporal lobe cerebral blood flow is associated with regional white matter damage among apolipoprotein E ε4 carriers
Authors: Moore Elizabeth E. , Khan Omair A. , Shashikumar Niranjana , Pechman Kimberly R. , Liu Dandan , Houston Michelle L , Archer Derek B , Gifford Katherine A. , Landman Bennett A. , Hohman Timothy J. , Jefferson Angela L. 
Published: June 2023

AbstractBackgroundReduced cerebral blood flow (CBF) is a risk factor for white matter damage. Apolipoprotein E (APOE) e4, a genetic risk factor for Alzheimer’s disease and vascular health modifier, is associated with lower CBF. However, it remains unknown if lower CBF is associated with more severe white matter damage among APOE‐e4 carriers. We examine whether associations among regional CBF, cerebrovascular reactivity (CVR), and white matter integrity are modified by APOE‐e4 status in older adults.MethodVanderbilt Memory and Aging Project participants (n = 313, 73±7 years) underwent 3T brain MRI. Resting grey matter CBF and CVR (CBF response to hypercapnic stimulus, ΔCBF/100*ΔCO2) were quantified in regions of interest from pseudo‐continuous arterial spin labeling (ASL). Fractional anisotropy (FA) was quantified from diffusion tensor imaging in tracts of interest derived from tractography templates. Linear regressions related regional CBF and CVR to tract‐specific FA, adjusting for demographic and vascular variables, regional tissue volume, cognitive status, and APOE‐e4 status. Follow‐up models tested an APOE‐ε4 status x ASL variable interaction term.ResultIn main models, lower temporal lobe CBF was associated with lower FA in the occipital lobe, suggesting compromised white matter integrity (p = 0.02). Lower CBF and higher CVR in the occipital lobe were associated with compromised lower FA in all lobes (all p‐values<0.04). Temporal lobe CBF and CVR interacted with APOE‐e4 status, such that lower temporal lobe CBF related to lower FA in the frontal lobe (p = 0.01) and higher temporal lobe CVR related to lower FA in the frontal (p = 0.001) and temporal (p = 0.007) lobes among APOE‐e4 carriers only. Frontal lobe CVR interacted with APOE‐e4 status, such that higher CVR related to lower FA in the frontal (p<0.001) and temporal lobes (p = 0.008) in APOE‐e4 carriers only.ConclusionAmong older adults, APOE‐e4 modifies associations between cerebral hemodynamics and white matter integrity, such that reduced CBF and greater CVR in the temporal lobe are associated with greater white matter damage in APOE‐e4 carriers. APOE‐e4 carriers may be more susceptible to changes in temporal lobe hemodynamics, and vascular changes in the temporal lobe may have a stronger impact on brain health outcomes for carriers compared to non‐carriers.
MicroRNA in Alzheimer’s Disease: Results of interaction analysis
Authors: Yashin Anatoliy I , Wu Deqing , Arbeev Konstantin , Bagley Olivia , Duan Matt , Ukraintseva Svetlana 
Published: June 2023

AbstractBackgroundMiRNA are multifunctional molecules involved in regulating posttranscriptional expression of genes. Several studies showed that miR‐107 influences expression of the BACE1 gene in the process of Aβ production. Extracellular accumulation of Aβ is well‐known hallmark of Alzheimer’s disease (AD). The product NLRP3 gene is involved in inflammasome that induces neuroinflammation a pathological process accompanying AD. The role of miR‐107 in regulation of NRLP3 is unknown.MethodWe used logistic regression model with the interaction term to test the hypothesis that interaction of SNPs from miR‐107 with SNPs from the NLRP3 gene is associated with AD. For this we used data on genotyped individuals collected in Framingham Health Study (FHS). The Cardiovascular Health Study (CHS) was used to replicate the research findings. Linkage Disequilibrium test and clumping procedure were used to reduce the number of tests and make Bonferroni correction less conservative.ResultIn the analysis of FHS data we found that the association of the rs2926616 SNP from the miR‐107 miRNA with the rs10754557 SNP from the NLRP3 gene is positive and statistically significant. The effect size β = 0.356, p‐value 6.71E‐03. This value is smaller than the p‐value threshold calculated in accordance with Bonferroni correction 7.14E‐03. This result is replicated in the analysis of CHS data with β = 0.289, and p‐value 4.76E‐02. Using FHS data we also tested the role of interaction of SNPs from the miR‐107 with SNPs from the TLR2 gene in AD and found a pair of SNPs whose interaction has statistically significant association with AD. This finding was replicated in the analysis of the CHS data where an interaction of a pair of SNPs also showed significant association with AD. Detected SNPs from this pair were in linkage disequilibrium with the pair SNPs from the CHS data.ConclusionThe results of these analyses indicate that one miRNA may influence several components of AD pathological development. This property may have important implications for developing efficient medications against AD. More studies are needed to evaluate miRNA involvement in different pathways of AD regulation, as well as in identifying factors capable in affecting such miRNA.
Sex differences in cerebrospinal fluid testing for Alzheimer disease biomarkers: A seven‐year retrospective analysis of real‐world experience in a dementia clinic
Authors: Krivanek Taylor , Gale Seth A 
Published: June 2023

AbstractBackgroundAt the Brigham and Women’s Hospital Alzheimer Center, approximately 1100 patients are seen annually for diagnostic evaluation. Testing cerebrospinal fluid (CSF) via lumbar puncture (LP) for biomarkers of Alzheimer disease (AD) is one of the easiest and most definitive ways to make an AD diagnosis.MethodA retrospective review of records at the Alzheimer Center was conducted for patients who underwent LP for CSF AD biomarkers from 2010 to 2017. During this period, 166 patients had their CSF tested. Demographic information was available for 163 patients, and test results were available for 155 patients.ResultLPs for CSF biomarkers were more likely to be performed on men than women. 56% percent of patients tested were men, though men made up only about 47% of all patients seen at our center over this period. There was no significant difference in average age between men and women who were tested. Compared to women, tested men were more than twice as likely to identify as Hispanic or Latino and over four times as likely to identify as Black. Additionally, 41% of women received test results consistent with AD, compared to 30% of men. Between sexes, there was no significant difference in documented rationale for obtaining the test (i.e., exploratory testing vs. differentiating between neurodegenerative dementias or confirming a diagnosis).ConclusionPrevalence rates suggest that two‐thirds of individuals with AD in the United States are women. Thus, it is unsurprising that LP results for women at our center were more likely to indicate AD. However, since most individuals with AD or all‐cause dementia and patients treated at our center are women, the higher rate of men receiving the test was unexpected. It is unclear what is driving more men than women to be tested at our center and why women of color are much less likely to be tested. More research is needed to understand the factors behind these trends and to ensure an equitable approach to diagnostic testing for dementia.
Association of acoustic perturbation measures with dementia status: A Framingham Heart Study
Authors: Xue Chonghua , Karjadi Cody , Miller Matthew I. , Cordella Claire , Au Rhoda , Kolachalama Vijaya B. 
Published: June 2023

AbstractBackgroundDiagnosis of dementia due to Alzheimer’s disease (AD) is a resource‐intensive process, often requiring clinical assessments and neuroimaging which may fall beyond the reach of patients in remote settings. Given the challenges of traditional dementia work‐up, identification of disease‐relevant signatures from low‐cost modalities has attracted interest as a means to improve disease detection. Here, we describe a machine learning strategy to associate acoustic perturbations in voice with dementia status using digital recordings of neuropsychological exams administered in the Framingham Heart Study (FHS).MethodWe used 118 voice recordings from 79 participants to extract jitter, shimmer, and haronics‐to‐nosie ratio (HNR) from the ComParE 2016 dataset. Each of these acoustic perturbation measures are commonly used in the clinical context to characterize pathological voice. For each voice transcript, these features were computed and and fed as input to a one‐dimensional convolutional neural network to predict dementia status. The data was randomly split in the ratio of 1:1 (at the participant‐level), where 50% of the cases were used for training and the remaining for testing. This process was repeated 10 times and the average model performance was reported. For each model run, the neural network was trained for 500 epochs without early stopping and the model obtained after the final epoch was used for testing.ResultThe mean (±std) age of the participants included in the study was 82.6±8.3 years, and 21 of them were ApoE positive. The mean (±std) area under the receiver operating characteristic curve and the mean (±std) area under the precision‐recall curve of the model classifying participants with dementia from those with normal cognition was 0.673±0.043 and 0.791±0.052, respectively (Fig. 1). Also, the mean accuracy (±std) was 0.615±0.025, the mean precision (±std) was 0.714±0.121, and the mean sensitivity/recall (±std) was 0.721±0.179.ConclusionOur study presents a proof‐of‐principle that deep learning on acoustic perturbation measures can predict dementia status. Further studies with additional participants and an expanded set of acoustic, speech and linguistic features are needed to evaluate the full potential of machine learning to derive disease‐relevant signatures from audio data, and potentially expand the reach of early detection and intervention efforts.