bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new tool for fusioning PET and omics data
Authors: Povala Guilherme , De Bastiani Marco Antônio , Brum Wagner Scheeren , Ferreira Pamela C.L. , Bellaver Bruna , Zatt Bruno , Zimmer Eduardo R. 
Published: February 2022

AbstractBackgroundPositron emission tomography (PET) imaging plays a key role in the diagnosis of Alzheimer’s disease (AD). The idea of integrating PET and omics data can provide blood‐new insights about AD pathophysiology. In this context, state‐of‐the‐art techniques such as transcriptomics hold much potential for discovering altered biological processes in AD and other neurodegenerative diseases. However, tools for integrating PET and omics data are still unexplored. Here, we developed a new method that combines blood transcriptomics with PET data. We hypothesized that our method will allow advancing our understanding of AD neurobiology and potentially unravel clinically relevant new peripheral biomarkers.MethodImaging and transcriptomics data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Microarray gene expression profiling from blood samples of 69 cognitively unimpaired (CU) and 158 mild cognitively impaired (MCI) individuals were submitted to differential expression (DE) analysis using the limma R package. Genes obtained from DE analysis were selected to undergo integration with [18F]Fluorodeoxyglucose (FDG)‐PET images using voxel‐wise generalized linear regression (GLR) models adjusted for age, gender and APOEε4 (RMINC package).ResultThe DE analysis resulted in 1232 differentially expressed genes (DEGs) in CU vs MCI individuals (p‐value<0.05). For each DEG, the GLR computed the voxel‐wise association between FDG‐PET and gene expression, resulting in t‐value maps. Afterward, only gray matter voxels presenting absolute t‐values higher than 2.3 were preserved. Then, the volumes of interest (VOI) presenting at least 30% of voxels with a significant correlation obtained from the t‐value map were filtered, which resulted in 268 significantly correlated genes throughout the brain. Next, a map of DEG counts in each VOI (Figure 1a) and maps with the proportion of up‐ (Figure 1b) and downregulated DEGs (Figure 1c) were created. An overall map of altered VOIs is displayed in Figure 1d. Finally, we identified that the putamen was the VOI with the most significantly correlated DEGs.ConclusionHere we presented a pipeline for integrating PET neuroimaging with transcriptomics data. This method allows for the identification of potential novel biomarkers and altered peripheral biological pathways.
Cognition and hippocampal volume in amyloid positive clinically normal adults: Results from the A4 study
Authors: Petersen Kellen , Grober Ellen , Lipton Richard B. , Sperling Reisa A. , Buckley Rachel F. , Ezzati Ali 
Published: February 2022

AbstractBackgroundRecent studies suggests that sex and genomic differences influence the earliest neuropsychological and pathologic manifestations of Alzheimer’s disease (AD). The relationship between individual cognitive status and the degree of neurodegeneration, and the influence of sex and APOE4 status on these relationships, remains uncertain. Using cross‐sectional data, we investigate the association between cognition in preclinical AD and adjusted hippocampal volume (HVa), a proxy for neurodegeneration related to memory.MethodParticipants included 1169 cognitively unimpaired individuals aged 65 to 85 screened in the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s (A4) Study, who were amyloid positive, completed neuropsychological evaluations, and had APOE4 genotyping and structural MRIs. HVa was obtained by residual correction to account for sex differences in hippocampal and intracranial volumes. Association between HVa and cognition was evaluated using a two‐sample t‐test for the whole sample and subsamples stratified by sex and APOE4 status.ResultIn the whole sample, lower HVa was linked with worse performance on measures of memory (Free Recall (FR): p=0.030; Total Recall 96 (TR96): p= 0.032), mental status (Mini‐Mental State Examination (MMSE): p= 0.048), executive functioning (Digit Symbol Substitution (DSS): p<0.001), and the Preclinical Alzheimer’s Cognitive Composite (PACC: p<0.001) (Figure 1, Table 1). Based on comparison of effect sizes between groups, DSS and PACC were the most impacted measures to early neurodegeneration‐related cognitive decline while PACC and TR96 were the most impacted tests to advanced neurodegeneration‐related cognitive decline (Table 1). In multiple regression, adjusting for age and education, all cognitive measures except MMSE were associated with HVa among women (FR: p=0.012; TR96: p=0.007; Delayed Logical Memory (DLM): p=0.014; DSS: p<0.001; PACC: p<0.001), but not men. Among APOE4 positive individuals, all cognitive measures except MMSE were associated with HVa (FR: p=0.016; TR96: p=0.006; DLM: p=0.009; DSS: p=0.002; PACC: p<0.001), however, there was no association between cognition and HVa in APOE4 negative individuals.ConclusionWhile all cognitive measures were significantly associated with neurodegeneration in amyloid positive individuals, cognitive tests show different sensitivities in detecting early or late signs of neurodegeneration as measured by HVa. There are clear differences in association between cognition and neurodegeneration based on sex and APOE4 status.
Diffusion models reveal white matter microstructural changes with aging, pathology, and cognition
Authors: Pillai Sheela Kumari Raghavan , Reid Robert I. , Przybelski Scott A. , Lesnick Timothy G. , Graff‐Radford Jonathan , Schwarz Christopher G. , Knopman David S. , Mielke Michelle M. , Machulda Mary M. , Petersen Ronald C. , Jack Clifford R. , Vemuri Prashanthi 
Published: February 2022

AbstractBackgroundWhite matter microstructure undergoes progressive changes during the life‐span but the neurobiological underpinnings related to aging and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging (NODDI), to investigate the microstructural alterations due to demographics, common age‐related pathological processes (amyloid, tau, and white matter hyperintensities (WMH)), and cognition. We also compared NODDI findings to the older Diffusion Tensor Imaging (DTI) model based findings.Method328 participants (264 cognitively unimpaired, 57 mild cognitive impairment (MCI), and 7 dementia with a mean age of 68.3 ±13.1 years) from the Mayo Clinic Study of Aging with multi‐shell diffusion imaging, FLAIR‐MRI as well as amyloid and tau PET scans were included in this study. White matter (WM) tract level diffusion measures (Figure 1) were calculated from DTI and NODDI. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, WMH, amyloid, and tau as predictors. Analyses were also performed with each dMRI measure as a predictor of cognitive outcomes.ResultAge and WMH were the strongest predictors of all WM diffusion measures with low associations with amyloid and tau (Figure 1). However, neurite density decrease from NODDI was observed with amyloidosis specifically in the temporal lobes (Figure 2). WM integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures (Table 1). All diffusion measures provided information about WM aging and WM disease processes and were associated with cognition.ConclusionNODDI and DTI are two different biophysical models that provide distinct information about variation in WM microstructural integrity. NODDI provides additional information about synaptic density, organization, and free water content which may aid in providing mechanistic insights into disease progression.
Development and evaluation of an unbiased, high‐resolution and detail‐preserving structural T1‐weighted template for use in studies on older adults
Authors: Ridwan Abdur Raquib , Wu Yingjuan , Niaz Mohammad Rakeen , Qi Xiaoxiao , Bennett David A. , Arfanakis Konstantinos 
Published: February 2022

AbstractBackgroundConstruction of an optimal T1‐weighted template for older adults often requires selection of a target anatomy for nonlinear‐registrations, however a poorly chosen template may introduce bias due to large structural discrepancies between template and subjects used for template construction resulting in misregistration and thereby loss of important anatomical details. Moreover, large structural brain differences across older adults may introduce registration errors. Here, a minimum‐deformation T1‐weighted template specifically for studies on older‐adults was developed using a sparse, patch‐based, detail‐preserving template construction method that minimizes biases.MethodThis work used T1‐weighted brain MRI data obtained from 400 non‐demented older adults (65‐95 years of age, male:female=1:1) participating in longitudinal cohort studies of aging. The template construction scheme involved a reference‐free groupwise normalization utilizing affine transformations followed by ANTs SyGN groupwise normalization utilizing a weighted average scheme in every iteration. Finally a patch‐based tissue‐guided sparse‐representation approach was employed to generate the new template (Fig. 1). The new template was compared to the weighted average template and other 0.5mm templates through visual inspection, in terms of image sharpness by means of the normalized power spectral density and also inter‐subject spatial normalization accuracy achieved when used as references for normalization of T1‐weighted data from 202 ADNI3 participants. Normalization accuracy was assessed for each template by means of the average pairwise normalized cross correlation, average pairwise Jaccard index and average log‐Jacobian determinant of ADNI3 participants.ResultThe new template maintained anatomical consistency and exhibited higher image sharpness as demonstrated by larger high spatial frequency content in the normalized power spectra compared to other templates (Fig. 2 & Fig. 3A,C). Detailed cortical features were better resolved with the new template compared to MCALT_0.5mm (Fig. 3A,B). Visual inspection revealed atypical anatomical structures in the cortex of MCALT_0.5mm (red circles) (Fig. 3B), absent in other templates. Inter‐subject spatial normalization accuracy was higher when using the new template compared to the other templates (Fig. 4A,B,C). The new template required lower deformation for spatially normalizing ADNI3 dataset (Fig. 5).ConclusionThe new template is an unbiased high‐resolution detail‐preserving structural template of the older adult brain that requires lower deformation and provides higher spatial normalization accuracy for older adult data than other templates.
Early detection of Alzheimer's disease using 3D convolutional neural networks
Authors: Pan Dan , Zeng An , Zou Chao , Rong Huabin , Song Xiaowei 
Published: February 2022

AbstractBackgroundAs one of the most common neurodegenerative disorders which progress slowly over time, Alzheimer's Disease (AD) could be effectively managed by delaying the disease process through early detection and intervention at present. Mild Cognitive Impairment (MCI) is the prodromal state of AD. At present, in‐vivo structural magnetic resonance imaging (MRI) has been widely used for computer‐aided diagnosis of neurodegenerative disorders noninvasively owing to its sensitivity to morphological changes caused by brain atrophy like AD. Plus, the rapid progress of deep learning (DL), especially deep convolutional neural networks (CNNs) has improved MRI analysis thanks to its superiority in the generalization capability.MethodAn ensemble learning (EL) model which combines genetic algorithm (GA) with 3D‐CNN based on region of interest (ROI) was proposed to identify AD/MCI subjects (Figure 1). We firstly used the 3D‐CNN model to train a candidate base classifier for each a ROI (here a brain region). Then, the GA algorithm was employed to search for the best base classifier combination with the optimal generalization ability and based on it, the whole‐brain MRI classifier ensemble was built to detect AD/MCI. Owing to the one‐to‐one correspondence relationship between the base classifiers and the brain regions, we further identified those brain regions with significant classification capabilities.ResultIn three binary classification tasks, i.e., classification between 1) AD vs. NC (Normal control), 2) MCIc (MCI patients who will convert to AD) vs. NC and 3) MCIc and MCInc (MCI patients who will not convert to AD), the testing results revealed accuracy rate of 0.89±0.03, 0.88±0.03, and 0.71±0.08, with a stratified fivefold cross‐validation method, respectively. In a data‐driven way, the brain regions that greatly contributed to AD and MCI classifications (Figure 2), e.g., medial amygdala, rostral hippocampus, caudal hippocampus, were ascertained. They were linked to emotion, memory, language, and other key brain functions impaired early in the AD staging.ConclusionCompared with the 2D‐CNN models, the proposed classifiers ensemble could make full use of the effective information embedded in MRI to discover more discriminative MRI features/biomarkers. Additionally, the advocated method could also be valuable to detect the potential neuroimaging biomarkers for other brain disorders.
18F‐MK6240 longitudinal tau PET in ageing and Alzheimer’s disease
Authors: Krishnadas Natasha , Dore Vincent , Mulligan Rachel S. , Tyrrell Regan , Bozinovski Svetlana , Huang Kun , Lamb Fiona , Burnham Samantha C. , Villemagne Victor L. , Rowe Christopher C. 
Published: February 2022

AbstractBackgroundLongitudinal tau PET may prove useful for clinical trials, through its ability to detect patterns and rates of in vivo tau accumulation in ageing and Alzheimer’s disease (AD). Clinical trials are increasingly targeting the preclinical phase of AD. Flortaucipir studies estimate a 3% annual increase in global cortical tau SUVR in amyloid‐β positive (Aβ+ve) cognitively impaired (CI) cohorts, whereas either no change, or low rates of increase (0.5%), have been demonstrated in Aβ+ve cognitively unimpaired (CU) cohorts. F‐18 MK6240 is a novel tau tracer, with high target to background binding. We aimed to evaluate regional rates of 18F‐MK6240 accumulation in ageing and the AD continuum.MethodWe performed PET acquisition 90‐100 minutes post‐injection of 185MBq (±10%) 18F‐MK6240 at baseline and 12 months for 67 Aβ‐ve CU, 20 Aβ+ve CU and 19 Aβ+ve CI participants. SUVR (standardized uptake value ratio) for the entorhinal cortex, amygdala, hippocampus, parahippocampus and composite regions of interest (ROI) (Me, mesial temporal; Te, temporoparietal cortices) were generated using the cerebellar cortex as the reference region.ResultAge did not significantly differ between the groups (mean age 74 ± 4.4 Aβ‐ve CU, 76.2 ± 5.7 Aβ+ve CU, 72.5 ± 6.4 Aβ+ve CI). Aβ+ve participants (CU and CI) had higher baseline tau SUVR and higher annual percentage increases in tau SUVR compared to Aβ‐ve participants in all regions examined (Table 1) (Figure 1). CU Aβ+ve participants had larger increases in Me vs Te (1.6% vs 0.7%), while CI Aβ+ve participants had larger increases in Te vs Me (4.3% vs 1.9%). Compared to Aβ‐ve CU participants, Aβ+ve CU participants had higher increases in the amygdala (2.9% vs 1.8%) and entorhinal cortex (1.9% vs 0.7%).ConclusionLongitudinal tau imaging using 18F‐MK6240 discriminates between ageing and stages of AD. Rate of accumulation in preclinical AD (Aβ+ve CU) was highest in mesial temporal regions, while in CI individuals, rates were highest in the temporoparietal cortex. The amygdala and entorhinal cortex may be early regions to discriminate tau accumulation between Aβ‐ve CU and Aβ+ve groups. However, as the variance is large, the precision of these estimates may be refined with a larger sample size. Recruitment is ongoing.
Identifying precision AD biomarkers with varying prognosis effects in genetics driven subpopulations
Authors: Lee Brian N. , Wang Junwen , Shen Li 
Published: February 2022

AbstractBackgroundImaging, cognitive and fluid data have been widely studied to identify quantitative biomarkers that can help predict the status and progression of Alzheimer’s disease (AD). However, it is still an underexplored topic whether there exist subpopulations with different genetic profiles across which the biomarker‐based prediction models may vary. We propose to use the Chow test (Chow 1960 Econometrica 28(3)) to perform genetically stratified analyses for identifying SNP‐based subpopulations coupled with precision AD biomarkers with varying effects on future diagnosis in these subpopulations. The investigation of such SNPs and precision biomarkers may eventually pave the way for increased customization of AD care.MethodParticipants included 1,324 subjects from the ADNI cohort with both AD biomarker and genotyping data available (http://www.pi4cs.org/qt‐pad‐challenge). 30 significant (P < 1.5E‐278) AD SNPs were sourced from (Jansen 2019 NatGen). Chow tests were performed to determine whether each of baseline visit measures of 16 AD biomarkers predicted AD diagnosis at the three‐year visit with varying slopes when stratifying upon the allelic dosage of each of 30 chosen SNPs. Bonferroni correction (P < 1.04E‐4) was employed to correct for multiple comparisons.ResultMultiple SNP‐biomarker pairs showed significant genetically driven deviations in the regression coefficients when predicting diagnosis in three years using baseline biomarkers (Figure 1). Top SNP hits involved rs769449 (Chr 19, APOE) and rs7561528 (Chr 2, LOC105373605), and almost all 16 studied biomarkers demonstrated differential slopes in different genotype groups to predict diagnosis in three years. To examine the details of these top findings, the regression coefficients calculated for each of the five most significant biomarkers of both SNPs were bootstrapped and plotted in Figure 2.ConclusionGenetic analysis of AD candidate SNPs in conjunction with AD biomarker data via the Chow test identified several SNPs coupled with precision AD biomarkers with varying prognosis effects in the corresponding genotype groups. These findings provide valuable information to reveal disease heterogeneity and help facilitate precision medicine.
Development and evaluation of MIITRA T1w and DTI brain templates at 0.5 mm resolution
Authors: Wu Yingjuan , Niaz Mohammad Rakeen , Ridwan Abdur Raquib , Qi Xiaoxiao , Bennett David A. , Arfanakis Konstantinos 
Published: February 2022

AbstractBackgroundAn MRI multimodal older adult brain atlas with high spatial resolution is in high demand. As part of the Multichannel Illinois Institute of Technology & Rush university Aging (MIITRA) atlas project, this work aimed to develop high quality 0.5mm resolution T1‐weighted (T1w) and diffusion tensor imaging (DTI) multimodal templates using data from a large, diverse, community cohort of non‐demented older adults and quantitatively evaluate the new templates.MethodTemplate construction: T1w and DTI data were collected on 400 non‐demented older adults (50% male;64.9‐98.9 yrs;54% white,43% black) participating in longitudinal cohort studies of aging (Bennett et al.,2018; Barnes et al.,2012) with two 3T MRI scanners.First, a recently introduced approach for the development of high quality multimodal templates (Wu et al., 2019) was conducted in a 0.5mm isotropic resolution space to maximize the quality and anatomical consistency of the templates. Then an approach for the development of high spatial resolution templates based on principles of super‐resolution (Niaz et al., 2019) was used to eliminate interpolations in conventional template building methods. The resulting templates are named MIITRA templates. Evaluation: T1w and DTI data on 202 non‐demented older adults from ADNI3 were used for evaluation. The MIITRA templates were compared to other standardized templates in terms of the accuracy of inter‐subject spatial normalization and inter‐modality spatial matching of ADNI3 data achieved when each template was used as a reference for normalization.ResultVisual inspection shows that the MIITRA T1w and DTI templates with 0.5mm isotropic voxels exhibit high sharpness and are free of artifacts (Fig. 1, 2). Figures 3 and 4 demonstrate that the MIITRA templates provided higher inter‐subject spatial normalization accuracy of older adult data and required less deformation than other templates. Finally, the MIITRA T1w and DTI templates exhibited high spatial matching to each other and allowed higher inter‐modality spatial matching of normalized older adult data (Fig. 5).ConclusionThis work developed high quality 0.5mm resolution T1w and DTI templates for the MIITRA atlas using data from a large, diverse, community cohort of non‐demented older adults. The new templates allowed higher inter‐subject and inter‐modality spatial normalization of older adult data compared to other templates.
Cultural adaptation of the Reducing Disability in Alzheimer’s Disease (RDAD) protocol for intervention to reduce behavioral and psychological symptoms of dementia (BPSD) in Thailand
Authors: Thongsiri Sirinart , Chuengsatiansup Komatra , Sihapark Siranee , Wisetpholchai Bussabong , Luechai Rusamee , Teri Linda , Wandersman Abraham , Lamont Andrea , Fritz Stacy , Marques Andrea Horvath , Hinton Ladson , Gallagher‐Thompson Dolores , Chen Hongtu , Levkoff Sue E 
Published: February 2022

AbstractBackgroundThe Reducing Disability in Alzheimer’s Disease (RDAD) protocol is an evidence‐based intervention (EBI) found to be feasible for implementation in community settings, and effective for reducing behavioral and psychological symptoms of dementia (BPSD) and improving caregiver outcomes (Teri et al., 2003; 2008). This study adapted the RDAD for delivery in Thai community settings, so that core components of the intervention—knowledge, communication, pleasant events, behavioral modification and physical activity—could be implemented through home‐based care by community‐based health personnel.MethodAdaptation processes followed, sometimes recursively, 10 adaptation steps identified by Escoffery et al., (2018): (1) assess the current care delivery system and resources in the community, (2) understand relevant EBIs, (3) select appropriate EBI, (4) consult with experts in intervention design and implementation, (5) consult with stakeholders in implementation settings, (6) work with experienced implementers to decide what needs to be adapted, (7) adapt original intervention program, (8) train staff for implementation of modified intervention, (9) test adapted materials, and (10) implement and evaluate intervention.ResultBased on qualitative data collected through consultation and discussion meetings with experts in intervention and implementation research and stakeholders of care delivery from local communities (Steps 1‐7), a modified Thai‐RDAD protocol was developed to fit into the clinical, social, and cultural practice of Thai local settings. Protocols for training were pilot tested with 20 providers (Steps 8); and the Thai‐RDAD protocol was tested with persons with dementia and their family caregivers at four participating families (Step 9). Findings from the pilot study were incorporated into the intervention protocol, particularly concerning comprehension of the intervention activities, realistic goal setting, and improving communication among all parties involved, prior to implementation (Step 10) of the adapted intervention protocol in an NIMH‐funded large clinical trial in the long‐term care system of Thailand.ConclusionA systematic approach to adaptation of EBIs by synthesizing perspectives from both intervention and implementation researchers, as well as stakeholders of care delivery from local communities, and pilot testing the modified EBI protocol, are critical for effective adaptations to local clinical and cultural contexts.