bluuebunny/crossref_metadata_2025_split huggingface dataset:
General Anesthesia and Cerebral Function: Changes in Cognition and Regional Brain Metabolism in Patients following Surgery
Authors: Struble Helen , Liu Stephen , Silverman Daniel H 
Published: December 2022

AbstractBackgroundImproved understanding of relationships between general anesthesia and subsequent cognitive decline is critically needed. In this study, previously healthy and mildly impaired subjects were compared pre‐ and post‐surgery, with respect to longitudinal assessments of their neuropsychologic performance and regional brain metabolism.MethodWe screened records of 1,450 Alzheimer’s Disease Neuroimaging Initiative subjects to identify all those undergoing non‐cancer surgeries involving general anesthesia with a minimum of two pre‐surgical and two post‐surgical ADNI visits, yielding neuropsychological data for this analysis acquired during 256 visits by 64 subjects – 32 surgical patients meeting the above criteria, and 32 controls who were demographically matched but with no reported surgeries during enrollment. Cognitive domain scores were correlated with rates of decline in regional cerebral metabolism based on interval changes in pairs of FDG‐PET scans acquired pre‐ and post‐surgery; analogous calculations were performed in the non‐surgical group by setting “surgery” dates according to median elapsed time from first visit to surgery in the surgical group. Imaging data were quantitatively examined by both standardized volume of interest (sVOI) and statistical parametric mapping (spm) methods of analysis.ResultsVOIs included bilaterally 6 frontal, 5 temporal, 4 parietal, 2 occipital, and sensorimotor cerebral cortical volumes, as well as 7 noncortical volumes. Compared to the non‐surgical group, the surgical group showed greater post‐surgical decline in the two anterior temporal volumes measured (p=0.01), both medially (including the hippocampus and adjacent cortex) and laterally. Within the surgery group, comparing pre‐ versus post‐ timeframes also identified most significant accelerated decline in the two anterior temporal volumes. The spm analyses corroborated significant acceleration of metabolic decline postsurgically in temporal cortex, predominantly in the right hemisphere, with cluster size significant after statistical adjustment for multiple comparisons (P<0.0005, Pcorr = 0.047). Finally, pre‐ and post‐surgical differences in rates of temporal metabolic decline correlated with differences in executive function decline within the surgical group (r=0.785).ConclusionMajor surgeries involving general anesthesia were associated with significantly accelerated decline in temporal metabolism postsurgically. This in turn was correlated with greater acceleration of decline in cognitive function of surgical patients.
Mining CSF_tau associated genes with deep learning in AD patients
Authors: Wang Qi , Apostolova Liana G. 
Published: December 2022

AbstractBackgroundTau protein abnormalities have been attracting more and more attention in Alzheimer’s disease (AD) pathophysiology. Tau spreads in a hierarchical pattern in the AD brain, likely by trans‐synaptic propagation between neurons. Discovering genetic risk factors associated with tau pathology could be key for improving our ability to predict and treat AD. In this study, we leveraged the non‐linear analytic approach of deep learning to identify genes that are associated with cerebrospinal fluid (CSF) total tau levels.MethodWe selected 384 subjects [118 normal control and 266 mild cognitive impairment (MCI)] from ADNI cohort. We designed a deep neuron network model that predicted CSF total tau levels using peripheral blood transcriptomic data. More specifically, we developed an occlusion map approach to rank 2,364 transcripts, which were initially selected from 49,386 transcripts by dimension reduction. The occlusion map aimed to rank the transcripts and identified the critical ones.ResultOur deep neural network successfully predicted CSF total tau levels with an accuracy of 67% and the occlusion map identified the critical genes for the prediction. The list included genes like TTL5, AKIRIN2, CPEB1, PTPN7, CIRBP and RPS23, which have been previously reported to be related to AD. For example, TTL5 relates to missorting of tau protein. AKIRIN2 is essential for the formation of the cerebral cortex. CPEB is found to be an interacting factor of amyloid‐precursor protein (APP). PTPN7 belongs to protein tyrosine phosphatases family, among which PTPN1& PTPN5 has been reported to tau pathology and amyloid pathology. CIRBP is reported to exert protective effects against neuronal amyloid toxicity. RPS23 regulates amyloid level and tau phosphorylation.ConclusionUncovering the genes that have critical association with tau protein abnormalities is critically important for improving our understanding of the drivers of neurodegeneration in prodromal AD. Our deep learning algorithm has shown promising results in identifying the critical genes for tau pathology. This study has demonstrated the effectiveness of deep learning in genetic analysis for AD research.1. Bahureksa, L., et al. (2017)2. Wilcockson, T.D.W., et al. (2019)3. Tomoeda, C. K. (2001)4. Feldman, H. H., et al. (2008)5. Winblad, B., et al. (2016)6. Thomann, A. E., et al. (2018)
Capturing DNA methylation heterogeneity and examining its relationship to somatic mutations in Alzheimer’s disease
Authors: Markov Yaroslav , Thrush Kyra , Higgins‐Chen Albert , Morgan Levine 
Published: December 2022

AbstractBackgroundOne of the barriers to the identification of molecular targets in Alzheimer’s disease (AD) is its biological heterogeneity. However, computational approaches used to quantify heterogeneity in cohorts can be improved to produce more nuanced output. We develop novel and refine existing statistical methods to capture molecular heterogeneity and examine it on multiple ‐omic levels in the context of AD.
MethodAs AD is associated with aging, we consider key age‐associated molecular changes: somatic mutations and a novel DNAme biomarker of aging, Stochastic Epigenetic Mutations (SEM). SEM are outliers in DNAme values at genomic sites when compared with the rest of cohort and can quantitatively capture DNAme heterogeneity. However, it was unclear whether the existing SEM detection methods were optimal for capturing biologically meaningful signals. We test statistical approaches to optimize these methods using the strength of association between SEM burden and chronological age as our metric of choice in brain and whole blood. Furthermore, we test the reliability of these methods in technical replicates. Hypothesizing that SEM coming from different types of DNAme probes (e.g., normally unmethylated or methylated) may contain information on distinct processes, we develop machine learning methods to subcategorize SEM and test their associations with AD pathology. Finally, we assess the relationship between somatic mutations, SEM, and AD pathology in multi‐region brain datasets generated by our lab from the ROSMAP cohorts.ResultWe refined the existing approaches for SEM detection using the skewness adjusted Interquartile Range to correct for the non‐symmetrical DNAme distributions. Additionally, we subcategorized SEM and found that only certain SEM types are linked with the hallmarks of AD; we also observe similar associations between AD pathology and somatic mutations burden. Finally, we saw that these associations are present only in certain brain regions that are known to be affected by AD.ConclusionWe show that DNAme contains important heterogeneous signals associated with AD and somatic mutations. We will perform mediation and enrichment analyses to elucidate the crosstalk of the two age‐associated molecular changes in the context of AD and hope that this will bring us one step closer to identifying novel therapeutic targets for this debilitating disease.
Modulating T cell inflammation in a mouse model of Alzheimer’s disease
Authors: Dressman Dallin , Elyaman Wassim 
Published: December 2022

AbstractBackgroundRecent single cell transcriptomic studies using peripheral blood and post‐mortem brain samples from Alzheimer’s disease (AD) patients have suggested a significant role for T cells in modulating AD‐related neuroinflammation. Previous findings from our group and others show that T cells in AD patients and aged individuals are clonally expanded and upregulate genes related to inflammation, cytotoxicity, and senescence. We have found that TSC22D3, a putative anti‐inflammatory transcription factor, is upregulated in brain‐infiltrating CD8+ T cells and found in gene co‐expression modules in peripheral blood from AD patients.MethodWe are pursuing the mechanism of TSC22D3 function and its effect on T cell biology and AD neuropathology. We have generated mice that lack TSC22D3 in CD8 T cells on the 5XFAD background, a mouse model of AD. We used flow cytometry to measure populations of various immune cell subtypes, and sort T cell subtypes for downstream sequencing. We are also using immunocytochemistry to quantify markers of amyloid and tau pathology, microglial interaction with amyloid plaque, and neuronal and synaptic loss in the hippocampus. Finally, we are examining the induction of immune reactions to amyloid and tau protein in TSC22D3 conditional knockout mice without AD transgenes.ResultIn human tissue, TSC22D3 is highly expressed in brain‐infiltrating CD8+ T cells along with genes associated with a senescent, late‐differentiation phenotype. Flow cytometry analysis showed that late‐stage AD mouse splenocytes bore significantly higher percentages of dendritic cells and various cytokines in CD4+ T cells than non‐AD or younger AD mice. Most of these differences were independent of TSC22D3 conditional knockout status. Ongoing bulk sequencing may reveal TSC22D3‐dependent changes in T cell gene expression, with effects on key markers of AD progression in the hippocampus.ConclusionTSC22D3 is known to directly suppress NFkB and other pro‐inflammatory mediators. Its co‐expression with more pro‐inflammatory and cytotoxic genes in brain‐infiltrating CD8+ T cells may represent a compensatory mechanism in the face of neuroinflammation related to aging and AD. We expect that knockout of TSC22D3 in CD8+ T cells will increase expression of pro‐inflammatory genes in T cell subsets, and will manifest in advanced AD pathology in older adult mice.
Effect of Cognitive Decline on Facial Appearance Revealed by Artificial Intelligence
Authors: Kameyama Masashi , Kameyama Yumi Umeda , Iizuka Tomomichi , Ogawa Sumito , Iijima Katsuya , Akishita Masahiro , Toba Kenji 
Published: December 2022

AbstractBackgroundWe have demonstrated that Cognitive function has a stronger correlation with perceived age than with chronological age (Umeda‐Kameyama et al. 2020). Based on this study, we hypothesized that cognitive decline appears in one's face, and have demonstrated the possibility of artificial intelligence to detect cognitive decline using a facial photography (Umeda‐Kameyama et al. 2021). Perceived age can be a useful biomarker in the geriatric field, however it requires many specialists' judgement. We aimed to determine if artificial intelligence can produce reliable perceived age easily.MethodAI perceived age was acquired with Microsoft azure face API.ResultAI perceived age showed significant correlation with chronological age and perceived age judged by human. AI perceived age and chronological age showed significant correlation with MMSE in female and total, however,the difference was not significant. A logistic multiple regression analysis showed larger standardized partial correlation coefficient with chronological age than AI perceived age.ConclusionThe failure to show the superiority of AI perceived age over perceived age by human may be attributable to the weakness of azure system in old people and Asian ethnicity.
Naturally Derived Rho‐kinase 2 Inhibitors Acting as Mitophagy Inducer: A Novel Therapeutic Approach for Alzheimer’s Disease
Authors: Mani Shalini 
Published: December 2022

AbstractBackgroundThe aged and damaged mitochondria are removed through a selective mode of autophagy called mitophagy. The mitophagy may be triggered mainly via Parkin‐dependent and Parkin‐independent pathways. However, in different pathological conditions, this process is impaired in neuronal cells and lead to a variety of neurodegenerative disease (NDD) including Alzheimer Disease (AD). In case of AD, the pathological conditions such as accumulation of amyloid beta plaques and neurofibrillary tangles are also reported to inhibit the clearance of damaged mitochondria via the process of mitophagy. Hence triggering the mitophagy may be a promising therapeutic approach for the management of such conditions. In light of the significant role of impaired mitophagy in AD, further studies have also investigated the regulatory pathways/ protein involved in triggering mitophagy. Consequently, in the recent past Rho‐kinase 2 (ROCK) in suggested to negatively regulate the Parkin‐dependent mitophagy pathway in neuronal cells. Thus, inhibition of ROCK protein may trigger mitophagy in the treated cells and may alleviate the pathological conditions. Several synthetic compounds are studied for their ROCK2 inhibiting ability, but due to their side effects; immense attention is being diverted towards the use of natural compounds. We explored the ROCK2‐targeting ability of phytocompounds from the 7 medicinal plants (Allium sativum, Camellia sinensis, Centella asiatica, Nelumbo nucifera, Punica granatun, Rosmarinus officinalis and Triticum aestivum) used as neuro‐tonic.Material & MethodsSelected 350 phytocompounds and y‐27632 dihydrochloride (control) were docked against ROCK2 using GLIDE (Schrödinger). Drug‐likeness and ADMET properties were predicted using Molinspiration and ADMETlab, respectively. Selected phytocompounds were also checked for their toxicity using ProTox‐II. Following these results, Molecular dynamics and simulation (MDS) using GROMACS, were performed for the selected phytocompounds.ResultsBased upon the free energy, toxicity analysis, and drug likeliness predictions, 11 phytocompounds were selected. MDS (100ns) showed mean RMSD for ROCK2‐Isoscutellarein (1.12nm) and ROCK2‐Epicatechin (2.42nm), indicating these may tightly bind with ROCK2 in a stable conformation. Isoscutellarein and Epicatechin also showed a toxicity score of 6 and 5, respectively, indicating their safe usage.ConclusionIsoscutellarein and Epicatechin, may behave as a potential inhibitor of ROCK2 and hence may trigger mitophagy in neuronal cells.
Poorer dexterity is associated with underlying Alzheimer’s disease brain pathology
Authors: Gupta Lily Eliza , Kohli Akshay , Oh Jennifer M. , Yang Kao Lee , Betthauser Tobey J , Chin Nathaniel A. , Nair Veena A. , Prabhakaran Vivek , Okonkwo Ozioma C. , Li Shi‐Jiang , Bendlin Barbara B. 
Published: December 2022

AbstractBackgroundThere has been increasing recognition for the decline of motor function in Alzheimer’s disease (AD) progression and its substantial role in disease morbidity. However, less is known about regional pathology burden—including amyloid‐β and tau protein deposition and cortical trophy—and its relationship to motor performance. Here investigate this relationship using standardized motor function assessment tools.MethodParticipants along a control‐to‐impaired continuum were enrolled in the Alzheimer’s Disease Connectome Project and underwent T1‐weighted MRI (N=206), with a subset undergoing [C‐11]PiB (N=61) and [F‐18 ]Flortaucipir PET (N=58) (Table 1) imaging for amyloid and tau pathology, respectively. T1‐weighted scans were processed using Freesurfer to estimate regional cortical thickness. [C‐11]PiB distribution volume ratios (DVR; cerebellar GM reference region) and [F‐18] Flortaucipir standardized uptake value ratios (SUVr; inferior cerebellar GM reference region) were assessed. Regions of interest (ROI) for analysis included the parahippocampal, entorhinal, motor, and frontal regions. Motor function was assessed utilizing NIH toolbox tests including the 2‐minute walking, 9‐hole pegboard dexterity, and dynamometer grip strength assessments. Motor performance was analyzed using participants uncorrected standardized scores. Scores were collected from participant‐reported dominant hand assessments. Multiple linear regression models tested relationships between AD biomarker burden and motor performance while controlling for age and sex of participants.ResultIncreased amyloid‐β (β‐0.42, p‐value: <0.05) and tau (β: ‐0.53, p‐value: <0.05) burden in primary motor cortices were associated with worse dexterity scores independent of age and sex. Grip strength did not show a significant correlation to cortical amyloid‐β (β: ‐0.14, p‐value 0.33) or tau burden (β: 0.23, p‐value: 0.36). Higher cortical thickness in primary motor regions were associated with higher dexterity (β: 0.21, p‐value <0.0001) and 2‐minute walking scores (β: 0.24, p‐value <0.001). When controlling for age and sex, grip strength predicted decreasing cortical thickness (β: 0.01, p‐value: <0.001) (Table 2).ConclusionLower dexterity performance may be driven by elevated AD pathological burden and cortical thinning in cortical motor regions. Further research into motor performance and clinical disease manifestation is needed to determine the diagnostic utility of fine motor assessment as a predictor of motor decline in individuals with Alzheimer’s disease pathology.
Multi‐modal inadvertent signals enabling the capture of the learning process in real‐time. A new opportunity for preclinical intervention?
Authors: Penney Dana L , Sarkar Sarbari , Davis Randall 
Published: December 2022

AbstractBackgroundGold standard episodic memory tests used to detect AD‐related cognitive decline are insensitive to subjective cognitive decline evident in preclinical AD. We believe a breakdown in the healthy learning process may be a cornerstone in preclinical AD cognitive impairment. We demonstrate the ability to capture the learning process in real‐time using strategic test design, inadvertent eye‐movements and digital ink behavior. We show early evidence of previously unseen learning strategies using data from three participants.MethodWe used the Digital Symbol Digit Learning Test (dSDLT) that we designed to explore learning. One condition presents a key that pairs elementary geometric symbols with numbers and a section of symbols alone, and is administered twice. The participant writes the appropriate digit beneath each symbol using a digitizing pen (providing time‐stamped digital ink coordinates) while wearing a lightweight PupilLabs eye‐tracker (permitting unconstrained head movement, reporting gaze position 240 times/sec to ±2 mm). Time‐synchronized signals measure gaze time in the key. Heat maps show fixation points in the key and response boxes.ResultFigure 1 shows gaze time in the key for each administration (by item, in sequence for one subject), revealing no time in key for some items and less total time in the key for the second administration. We interpret no time in key as paired‐learning. Fixation heat maps showed an expected gaze pattern of looking from the stimulus symbol up to and scanning the key. Unexpected gaze patterns also emerged: Targeted gazes from the stimulus symbol directly to the corresponding symbol in the key, Figure 2, (suggesting spatial placement learning); Within‐key symbol comparisons, indicated by looking back‐and‐forth between items, suggesting figure discrimination learning (Figure3); and Referring back to a recently completed response box containing the same symbol, suggesting working memory (Figure 4).ConclusionMultimodal behavior capture combining inadvertent eye‐tracking signals with digital ink enables the capture of real‐time learning and provides insights into what participants are thinking in addition to what they are doing. The identification of unexpectedly rich, previously unseen learning behaviors provides a glimpse of the complex healthy learning process, provides opportunity to identify strategies heralding preclinical AD and potential preclinical intervention targets.
PCBrainAge: A Brain‐Specific AD‐Associated DNA Methylation Clock
Authors: Thrush Kyra , Markov Yaroslav , Higgins‐Chen Albert , Morgan Levine 
Published: December 2022

AbstractBackgroundOur group previously developed PCBrainAge, a novel predictor of brain tissue measured DNA methylation (DNAm) aging. We find that this predictor is correlated with a variety of components of Alzheimer’s disease (AD), including neuropathological criterion, clinical diagnoses, and APOE ε4 carrier status.MethodWe collected publicly available paired blood and brain tissue DNAm from cohorts annotated with AD diagnostic criterion. This includes the gene expression omnibus (GEO) dataset GSE59685 (n = 48). Where possible, we elected to compare blood measures to cortical measures due to the training parameters of PCBrainAge. PCBrainAge scores were calculated: In brief, PCBrainAge uses our method of principal component projection as defined by the original training datasets, followed by pretrained weighted regression to provide a single valued age prediction for the sample of interest. PCBrainAge was calculated and its acceleration, as an age residual, was determined separately for blood and brain samples.ResultWhile PCBrainAge Acceleration retains our previously discussed significant difference between AD and control samples in brain, it does not demonstrate any such distinction in blood tissue. We find that the principal components selected for regression in the PCBrainAge model training most highly weight CpGs that tend to be associated with brain specific pathways and development.ConclusionTherefore, despite recent evidence supporting systemic changes or even pathogenesis of AD, PCBrainAge capture of neuropathological changes and elevated AD risk associated with APOE ε4 carrier status appears reliant upon brain‐specific DNAm. This indicates that PCBrainAge has the most potential to be used as an investigative tool to track stages of AD development and interrogate underlying molecular changes. However, more work must be done to generate a harmonized brain and blood clock that captures neuropathology and disease to PCBrainAge’s current extent.