bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating multimodal data to support Alzheimer’s disease target prioritization
Authors: Cary Gregory A. , Gockley Jake , Lehallier Benoit , Leal Karina , Greenwood Anna K , Shulman Joshua M , Longo Frank M , Levey Allan I , Mangravite Lara M , Logsdon Benjamin A , Carter Gregory W 
Published: December 2021

AbstractBackgroundThe limited success of therapeutics targeting the amyloid hypotheses of Alzheimer’s Disease (AD) suggests the need to examine additional and potentially complementary therapeutic approaches. Recent efforts to amass genome‐scale data from diverse cohorts have revealed thousands of new therapeutic targets and highlighted previously underappreciated molecular and cellular dysfunction associated with disease states. Our understanding of the mechanistic relevance of individual targets and the inter‐relationships of groups of targets remains limited. Here we combine evidence across multiple independent studies to prioritize specific targets for further resource and reagent generation in the Target Enablement to Accelerate Therapy Development for Alzheimer’s Disease (TREAT‐AD) consortium.MethodWe propose a gene‐level scoring method that synthesizes evidence from diverse studies and data modalities. Genetic scores are derived from genome‐wide association studies of large AD cohorts as well as genetic evidence from model systems. Multi‐omic scores combine evidence from transcriptomic and proteomic studies of human post‐mortem brain tissue in the Accelerating Medicine Partnership in Alzheimer’s Disease (AMP‐AD) cohorts. We also use scores based on high‐throughput functional screens in D. melanogaster and scores based on term associations from indexed publications. These subscores from each category are combined in a final target score that is used for target prioritization and hypothesis evaluation.ResultWe have scored over 17,500 targets using these scoring metrics including four pilot hypotheses implicated by recent work in the AMP‐AD consortium. The most significant functional annotations associated with high‐scoring targets include cellular respiration, regulation of trans‐synaptic signaling, neuron death, and activation of immune response. Using the highest‐priority targets within each pilot hypothesis, ten targets were further prioritized for immediate development of resources by the TREAT‐AD consortium.ConclusionThis prioritization provides an integrative and evidence‐based metric for the assessment of novel therapeutic targets and hypotheses. High‐priority targets will be considered for further assay development, structural biology, and medicinal chemistry studies in the TREAT‐AD consortium. These resources will be openly shared with the research community, which will help to de‐risk the evaluation of understudied candidate targets and therapeutic hypotheses.
Longitudinal study on subjective cognitive complains from patients and informants: Differences between stable and worsening SCD and MCI participants and prediction of cognitive decline
Authors: Pérez‐Blanco Lucía , Felpete Alba , Mallo Sabela C. , Campos‐Magdaleno Maria , Nieto‐Vieites Ana , Lojo‐Seoane Cristina , Facal David , Pereiro Arturo X 
Published: December 2021

AbstractBackgroundSubjective cognitive complaints (SCCs) are a risk factor to dementia. Self‐ and informant‐reports may have different predictive validity along the cognitive decline continuum. We aimed to analyze longitudinal differences between self‐ and informant‐reports in stable(‐s) and worsening(‐w) SCD and MCI participants, and the predictive value of these reports on cognitive worsening at different stages of cognitive decline.MethodA total of 216 participants with SCCs from the CompAS study were longitudinally assessed three times (intervals 18‐24 months). SCD (190) and MCI (26) participants were classified as stable or worsening at the last follow‐up.SCCs from participants and informants were evaluated through the QAM, and depressive symptoms through the GDS‐15. SCD participants were classified as Low (L‐SCD=119) and High (H‐SCD=71) complainers (below and above 5%ile in self QAM scores).Mixed‐ANOVAs tested differences between the stable and worsening groups (L‐SCD‐s; H‐SCD‐s; L‐SCD‐w; H‐SCD‐w; MCI‐s; MCI‐w) in the three QAM measurements for participants and informant. Logistic regressions were performed to analyze if QAM scoring of participants and informants at each assessment time predicts worsening.ResultsSignificant main effect of Group [F(1,5)=40.15, p< .001, ηp2
=.489] showed QAM self‐reports (Figure 1A) were lower for L‐SCD‐s than for H‐SCD‐s, H‐SCD‐w, MCI‐s, MCI‐w; and higher for H‐SCD‐s than for L‐SCD‐s H‐SCD‐w, MCI‐s, MCI‐w. For QAM informant‐reports (Figure 1B) significant Group*Measurement interaction was found, [F(10,294)=2.91, p=.002, ηp2
=.090] indicating (Bonferroni tests) that QAM‐3 was higher than QAM‐1 and QAM‐2 for MCI‐w; and only QAM‐3 was higher for MCI‐w than for L‐SCD‐s, H‐SCD‐s, and MCI‐s. Covariate GDS‐15 score did not alter the results.Neither self‐ nor the informant‐reports predicted L‐SCD‐w. Self‐report‐QAM‐2 [β=.300; SE=.088; p=.001; 95%CI=1.35(1.13–1.60)] and informant‐report‐QAM‐3 [β= .314; SE=.119; p=.008; 95%CI=1.36(1.08–1.73)] significantly predicted H‐SCD‐w and MCI‐w [self‐report‐QAM‐2: β=.197; SE=.087; p=.023; 95%CI=1.21(1.02–1.44); informant‐report‐QAM‐3: β=.301; SE=.109; p=.006; 95%CI=1.35(1.09–1.67)].ConclusionsCognitive complains from informants increased at the end of follow‐up for SCD and MCI participants who worsened compared to those who were stable, although significance was only achieved for the MCI‐w. Neither the self‐ nor the informant‐report showed predictive validity at baseline assessment. Self‐reports predicted progression earlier (QAM‐2) and informant‐reports later (QAM‐3).
A three‐dimensional neuroimaging based deep learning model to predict Alzheimer’s disease progression for clinical trial design
Authors: Gladstein Scott , Yang Liuqing , Wooten Dustin W , Florian Hana , Huang Xin , Comley Robert A. , Guo Qi 
Published: December 2021

AbstractBackgroundIn order to detect drug effects, in many instances, it is important to enroll patients in clinical trials whose symptoms will progress within the trial time frame. Here we aim to develop deep learning models combining multi‐modal imaging, demographic, genetic, and cognitive data to identify Alzheimer’s Disease (AD) patients who will progress within 2 years in terms of a change in diagnosis and/or cognition assessed by CDRSB.MethodSeparate models were trained to predict 1) whether a subject will progress from mild cognitive impairment (MCI) to AD, and 2) whether their CDRSB will increase by >=1 within 2 years. 5718 T1 MRI, 1537 [18F]AV45 amyloid, and 1958 [18F]FDG images from ADNI were used for model development. First, convolutional neural networks (CNNs) based on a modified 3D VGG16 architecture (Figure 1) were trained to classify AD vs healthy controls (CN) for the diagnosis model, and CDRglobal=0 vs >=1 for the cognition model using each imaging modality separately. Images from MCIs or subjects with CDRglobal=0.5 at baseline were fed into these pre‐trained CNNs to output probabilities for diagnosis and CDRSB changes respectively. Finally, these CNN‐predicted probabilities were combined with age, APOE4, ADAS13, CDRSB, FAQ, and MMSE at baseline into an ensemble model using “Super Learner” in R to predict diagnosis change or cognitive decline. Model development using tau PET images is ongoing.ResultFor the CNN, using imaging data only, the FDG model best discriminated AD vs CN, or CDRglobal of 0 vs >=1, with AUCs of 0.96 and 0.97 respectively followed by the MRI, and amyloid models. To predict progression, the amyloid CNNs performed best with AUCs of 0.82 and 0.78 for the diagnosis and CDRSB changes respectively (Figure 2). The ensemble model further improved progression predictions with the amyloid PET ensemble model performing best with AUCs of 0.92 and 0.86 for diagnosis and CDRSB change respectively (Table 1).ConclusionThe development of deep learning models to identify patients who will progress rapidly has the potential to be a valuable tool to support patient stratification, facilitate the design of future AD clinical trials, and treatment planning.
Cognitive functioning in Florida’s oldest old: A successful aging analysis of individuals living over the age of 90
Authors: Anton Stephen , Miller Amber H , Marra David E , Maraganore Demetrius M , Smith Glenn E 
Published: December 2021

AbstractBackgroundAs our population ages, many individuals are surpassing the age of 90 and living independent lives free of major diseases. It is critical to identify healthy lifestyle and genetic factors that contribute to “Successful Aging” (SA) in the population’s oldest‐old. This pilot study utilizes a unique dataset, the OneFlorida Clinical Research Consortium, to identify and recruit ‘Successfully Aged’ nonagenarians using electronic health records (EHR).MethodA consensus panel of experts created SA criterion, while taking EHR bioinformatics into consideration, to identify individuals 90 years and older who would be considered “Successful Agers”. To validate this cohort, researchers conducted a three‐phase recruitment period that included two contact letters and a third phone call attempt. Interested participants were screened by telephone and administered the Telephone Interview for Cognitive Status (TICS‐M) and the Geriatric Depression Scale (GDS).ResultUsing OneFlorida and EHR data from the University of Florida Health Systems, 1,901 individuals were identified as a virtual SA cohort. Of the 1,901 identified virtual cohort, 103 participants consented to the study (Mean Age = 92.11; 47.57% Female; 91.26% White). Of these, 18 individuals were excluded due to health conditions not captured in the EHR and thus were not administered the TICS‐M or GDS. Of the 85 participants who were administered the TICS‐M (Mean TICS‐M = 31.45, SD = 5.09), 16 participants scored below the cut‐off of ≤27/50 indicating cognitive impairment, 26 participants fell within the MCI range (28‐31/50), and 43 participants scored in the cognitively normal range (≥32/50). Of the 77 participants who were administered the GDS, 2 participant endorsed significant symptoms of depression (Mean GDS = 1.36, SD = 1.42). Together, this resulted in 69 (67%) participants who completed the full assessment battery and were classified as Successfully Aged.ConclusionUtilizing this three‐phase recruitment process, researchers were able to further investigate the demographic characteristic of our “Over 90s” population from the OneFlorida identified cohort. Future research is underway to conduct whole‐genome sequencing and identify a new Over 85 cohort. This study uniquely utilized an EHR registry to identify and assess a population of over 90‐year‐old persons considered SA, living independently and free of cognitive impairment.
The effect of microglial genes on network diffusion of pathology in mouse models of tauopathy
Authors: Anand Chaitali , Maia Pedro D. , Torok Justin , Mezias Christopher , Raj Ashish 
Published: December 2021

AbstractBackgroundMisfolded tau and amyloid, the hallmark pathologies of Alzheimer’s disease, undergo “prion‐like” trans‐synaptic transmission along the brain’s anatomic connectivity network (Frost, et al,2010). Our lab has previously shown that Alzheimer’s neuropathology progression is well‐approximated by a network diffusion model (NDM) (Raj, et al,2012). However, other processes in the brain may affect the neuropathology progression by mediating intraregional clearance/amplification and interregional spread. For instance, microglial activation is a common pathological feature of Alzheimer’s disease, with TREM2‐expressing microglia being integral to the inflammatory response. We show here that the NDM can be extended to include effects of microglial mediation of pathology.MethodWe modified the NDM to an extended NDM (eNDM) with additional model parameters related to microglia‐mediated spread/clearance and applied it to the 426×426 region mouse connectome. The eNDM included datasets of longitudinal spatiotemporal tau pathology in PS19 mice, expression levels of five microglial homeostatic signature genes (Cx3Cr1, C1qa, P2ry12, Fcrls, Olfml3) as well as TREM2 and CD33, microglial surface‐markers and phagocytosis regulators (Li, et al,2018). We compared the fits of the NDM to those of the eNDM using each gene individually as well as the first two principal components of spatial gene expression (Lein, et al,2007) after optimizing model parameters using a constrained nonlinear optimization algorithm.ResultsInclusion of microglial gene expression improved the predictive power of the model beyond that of the NDM (R2=0.46). Using the principal components of gene expression generally provided better model fits than individual genes. Notably, exclusion of TREM2 before performing PCA had a significant effect on model fit (p<0.001, R2=0.5) (Table 1, Figure 1), increasing the values of the optimal diffusion and spread parameters.ConclusionAlthough network spread models capture well the spread of pathology between brain regions, there are other factors that mediate this process. We demonstrated that an eNDM with microglial genes had an improved model fit even after accounting for differences in model parsimony. We also showed that exclusion of TREM2 from the model resulted in higher diffusivity parameter values, thus supporting the biological observation of TREM2‐knockout resulting in increased spread of pathology, putatively by affecting microglial phagocytic potential (Griciuc, et al, 2019).
Alterations in olfaction‐related brain regions in the ageing population: A structural MRI study
Authors: AlOtaibi Majed , De Marco Matteo , Venneri Annalena 
Published: December 2021

AbstractBackgroundAgeing plays a major role in neurodegenerative disorders such as Alzheimer’s disease. Olfactory dysfunction can be an early alteration heralding the presence of a neurodegenerative disorder in ageing. Studying alterations in olfaction‐related brain regions might help detection of neurodegenerative diseases at an earlier stage. Objectives: To assess the volume of olfactory cortex and areas associated with odour processing in different age groups in the cognitively healthy population using high resolution 3D structural MRI scans. To test whether normal ageing determines any volumetric alterations of olfactory cortex, hippocampus and other associated areas and whether there are any differential predictive values of these alterations for neurodegenerative disorders. To determine if atrophy of the olfactory cortex occurs prior to atrophy of the hippocampus and other areas involved in some aspects of odour processing in the healthy population.MethodNeurologically healthy participants were divided based on their age into three age groups: young (20–35 year; n=53), middle‐aged (36‐65 year; n=66) and elderly (66‐85 year; n=95). T1‐ weighted MRI scans acquired at 1.5T were processed using SPM12. Smoothed images were used to extract the volume of olfaction‐related regions including primary olfactory cortex, hippocampus, parahippocampus and amygdala.ResultOne‐way ANOVA analyses showed significant decreases in volume between age groups in all olfaction‐related brain regions (p≤0.0001). Post‐hoc multiple comparisons tests showed that significant differences were found between age groups except in the young vs the middle‐aged where no significant differences were found for hippocampal and amygdala volumes. Pairwise comparisons of groups following ANCOVA showed no significant difference in all olfaction‐related brain regions when controlling for age. Adjusting for education showed significant age‐group differences in olfactory cortex and parahippocampus volumes except for hippocampus in all groups, and for the amygdala for young versus middle‐aged groupsConclusionThese preliminary data showed significant reduction in the volume of olfaction‐related brain regions with ageing, but hippocampus and amygdala appeared to have a more limited volume reduction than primary olfactory cortex and parahippocampus. The results suggest that monitoring of volume of olfaction‐related brain regions in the ageing population may be valuable to detect risk of neurodegenerative diseases.
The relationship between beta‐amyloid accumulation and P3 event‐related potential in older adults: A pilot study
Authors: Devos Hannes , Gustafson Kathleen , Liao Ke , Ahmadnezhad Pedram , Brooks William , Mahnken Jonathan D , Burns Jeffrey M. 
Published: December 2021

AbstractBackgroundEmerging evidence from animal and human studies shows that beta‐amyloid (Aβ) may induce neuronal hyperexcitability in the early stage of Alzheimer’s disease (AD). Electroencephalography (EEG) offers unparalleled temporal resolution in tracing distinct neurophysiological processes even in preclinical AD. The aim of this pilot study was to determine the relationship of Aβ accumulation and event‐related potential (ERP) in cognitively normal older adults.MethodAβ accumulation was characterized with [18F] Florbetapir positron emission tomography (PET) scans. Global Aβ burden was assessed by taking the mean standard uptake value ratios (SUVR) of 6 predefined regions: anterior cingulate, posterior cingulate, precuneus, inferior medial frontal, lateral temporal, and superior parietal cortex. All participants completed a working memory task with three levels of difficulty (0‐,1‐,2‐back). ERP P3 peak amplitude and latency were extracted from the task effect (non‐target minus target). We used Pearson r to calculate associations between mean SUVR and P3 ERP at the midline channels Fz and Cz. Since the working memory task induced frontal cortical activity, we also explored the relationship between SUVR in the inferior medial frontal cortex and the frontal channels Fz, F3, and F4.ResultTwenty‐seven (age: 73.9 ± 5.5; MOCA: 27.37 ± 2.0) completed both PET and EEG scans. Mean SUVR ranged from 0.95 to 1.60 and correlated negatively with P3 latency of the 2‐back at Cz (r = ‐0.40; p = 0.04) and Fz (r = ‐0.58; p = 0.002). SUVR in the inferior medial frontal cortex correlated negatively with P3 peak latency of 1‐back at Fz (r = ‐0.42; p = 0.03) and F3 (r = ‐0.49; p = 0.009), and of 2‐back at Fz (‐0.54; p = 0.004) and F3 (r = ‐0.40; p = 0.045). No correlations were found between global Aβ burden and P3 peak amplitude.ConclusionThe negative relationship between Aβ accumulation and P3 latency provides preliminary support for the neuronal hyperexcitability hypothesis in preclinical AD. However, these findings need to be confirmed in a longitudinal cohort study with larger sample size.
The relationship between hippocampal volume and P3 event‐related potential in cognitively normal older adults without and with elevated amyloid: A pilot study
Authors: Devos Hannes , Brooks William , Gustafson Kathleen , Liao Ke , Ahmadnezhad Pedram , Mahnken Jonathan D , Burns Jeffrey M. 
Published: December 2021

AbstractBackgroundThe P3 event‐related potential (ERP) is typically reduced and delayed in patients with Alzheimer’s disease (AD). The hippocampus is a core regulator of the P3 ERP. The aim of this study was to investigate the relationship between hippocampal volume and P3 peak amplitude and latency in cognitively normal older adults with (Aβ+) and without elevated amyloid (Aβ‐).MethodAβ was assessed using a Florbetapir F‐18A positron emission tomography scan. No participants showed cognitive impairments based on performance on neuropsychological tests. Hippocampal volume was measured from T1‐weighted magnetic resonance imaging using Freesurfer. 256‐channel high‐definition electroencephalography (EEG) was used to record brain activity while completing a working memory (1‐back) test. The 1‐back test consisted of 120 non‐targets and 60 targets and lasted about 7 minutes. P3 amplitude and latency of the task effect (non‐target – target) was extracted using EEGlab software. Midline (Fz, Cz, Pz) and frontal (F3, F4) were identified a priori as channels of interest.ResultWe assessed 12 Aβ‐ participants (age 75.0 ± 6.4; MOCA 27.8 ±1.4; 6 (50%) women) and 11 Aβ+ participants (age 71.8 ± 5.6; MOCA 27.7 ± 2.0; 8 (72% women). Pearson r correlations for the total group (n = 23) demonstrated significant and positive correlations between both left and right hippocampal volume (L/R HV) and peak P3 amplitude at Fz (L HV, r = 0.55, p = 0.006); R HV, r = 0.52, p = 0.01), Cz (L HV, r = 0.55, p = 0.006; R HV, r = 0.53, p = 0.01), F3 (L HV, r = 0.50, p = 0.01; R HV, r = 0.44, p = 0.03), F4 (L HV, r = 0.57, p = 0.004; R HV, r = 0.55, p = 0.007), but not with Pz (L HV, r = 0.24, p = 0.26; R HV, r = 0.33, p = 0.12). Hippocampal volume did not correlate with any P3 peak latencies.ConclusionOur preliminary findings suggest a relationship between hippocampal volume and P3 peak amplitude in the frontocentral channels in cognitively normal older adults. Further research will reveal the putative mechanisms of neurophysiological changes in preclinical AD.
Association of peripheral blood DNA methylation levels with Alzheimer’s disease progression
Authors: Li Qingqin S. , Vasanthakumar Aparna , Davis Justin W. , Idler Kenneth , Nho Kwangsik , Waring Jeffrey , Saykin Andrew J. 
Published: December 2021

AbstractBackgroundIdentifying biomarkers associated with Alzheimer’s disease (AD) progression may enable patient enrichment and improve clinical trial designs. Epigenome‐wide association studies (EWAS) have revealed correlations between DNA methylation level at cytosine‐phosphate‐guanine (CpG) sites and AD pathology in the brain or diagnosis in peripheral blood.MethodWe performed cross‐sectional analysis of DNA methylation in peripheral blood as a predictor of AD progression in participants in the Alzheimer’s Disease Neuroimaging Initiative cohort.ResultThe rate of cognitive decline in participants from initial DNA sampling visit to subsequent visits was estimated by the slopes of the modified Preclinical Alzheimer Cognitive Composite (mPACC; mPACCdigit and mPACCtrailsB) and Clinical Dementia Rating Scale Sum of Boxes (CDR‐SB) plots using robust linear regression in cognitively normal (CN) participants and patients with mild cognitive impairment (MCI), respectively. Two differentially methylated positions were identified (P < 5.79 × 10−8 [Bonferroni correction threshold]): cg00386386 was associated with the slope of mPACCdigit in CN participants, and cg09422696 annotated to RP11‐661A12.5 was associated with the slope of CDR‐SB in MCI participants. No CpG sites significantly associated with diagnosis conversion status were identified. Genes involved in cognition and learning were enriched. A total of 70, 76, and 102 differentially methylated regions (DMRs) with Sidak‐corrected P < 0.05 were identified as associated with the slopes of mPACCtrailsB, mPACCdigit in CN participants, and CDR‐SB in MCI participants, respectively; these included DMRs annotated to HOXA4, HOXB6, and HOXB9. Overall, 75 and 123 DMRs were associated with conversion status in participants with CN and with MCI, respectively. The most significant DMR was annotated to an AD‐associated gene PM20D1 (chr1: 205,818,956 to 205,820,014 [13 probes], Sidak‐corrected P = 7.74 × 10−24), which was associated with both the slope of CDR‐SB and the MCI conversion status. The identified DMRs were enriched in CpG islands, promoter, and exons, including 5’ untranslated region and protein coding, whereas intergenic region, short and long interspersed nuclear element repeats, and introns were depleted.ConclusionCandidate CpG sites and regions in peripheral blood were identified as associated with the rate of cognitive decline in CN and MCI participants, respectively, in the longitudinal ADNI cohort.