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36 results for “Causal inference”
Code and dataset for neural dynamics of causal inference in the macaque frontoparietal circuit
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Genetic overlap and causal inferences between kidney function and cerebrovascular disease
<p><u>Objective:</u> Leveraging large-scale genetic data, we aimed to identify shared pathogenic mechanisms and causal relationships between impaired kidney function and cerebrovascular disease phenotypes.</p> <p><u>Methods:</u> We used summary statistics from genome-wide association studies (GWAS) of kidney function traits (<a name="_Hlk536087805">chronic kidney disease (CKD) diagnosis, estimated glomerular filtration rate (eGFR), and Urinary Albumin-to-Creatinine Ratio (UACR)</a>), and of cerebrovascular disease phenotypes: ischemic stroke and its subtypes, intracerebral hemorrhage (ICH), white matter hyperintensities (WMH) on brain MRI. We (i) tested the genetic overlap between them with polygenic risk scores (PRS), (ii) searched for common pleiotropic loci with pairwise GWAS analyses, and (iii) explored causal associations by employing two-sample Mendelian Randomization (MR).</p> <p><u>Results:</u> A PRS for lower eGFR was associated with higher large-artery stroke (LAS) risk (p=1x10<sup>-4</sup>). Multiple pleiotropic loci were identified between kidney function traits and cerebrovascular disease phenotypes, with 12q24 associated with eGFR and both LAS and small-vessel stroke (SVS), and 2q33 associated with UACR and both SVS and WMH. MR revealed associations of both lower eGFR (OR per 1-log decrement=2.10, 95%CI=1.38-3.21) and higher UACR (OR per 1-log increment=2.35, 95%CI=1.12-4.94) with a higher risk of LAS, as well as between higher UACR and higher risk of ICH.</p> <p><u>Conclusions:</u> Impaired kidney function, as assessed by decreased eGFR and increased UACR, may be causally involved in the pathogenesis of LAS. Increased UACR, previously proposed as a marker of systemic small vessel disease, is involved in ICH risk and shares a genetic risk factor at 2q33 with manifestations of cerebral small vessel disease.</p>
Data from: Modeling the perception of audiovisual distance: Bayesian causal inference and other models
Studies of audiovisual perception of distance are rare. Here, visual and auditory cue interactions in distance are tested against several multisensory models, including a modified causal inference model. In this causal inference model predictions of estimate distributions are included. In our study, the audiovisual perception of distance was overall better explained by Bayesian causal inference than by other traditional models, such as sensory dominance and mandatory integration, and no interaction. Causal inference resolved with probability matching yielded the best fit to the data. Finally, we propose that sensory weights can also be estimated from causal inference. The analysis of the sensory weights allows us to obtain windows within which there is an interaction between the audiovisual stimuli. We find that the visual stimulus always contributes by more than 80% to the perception of visual distance. The visual stimulus also contributes by more than 50% to the perception of auditory distance, but only within a mobile window of interaction, which ranges from 1 to 4 m.
Data from: A new method of Bayesian causal inference in non-stationary environments
Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method such as exponential moving average (EMA) with a discounting rate is used to improve the ability to respond to a sudden change; it is also necessary to increase the discounting rate. That is, a trade-off is established in which the followability is improved by increasing the discounting rate, but the accuracy is reduced. Here, we propose an extended Bayesian inference (EBI), wherein human-like causal inference is incorporated. We show that both the learning and forgetting effects are introduced into Bayesian inference by incorporating the causal inference. We evaluate the estimation performance of the EBI through the learning task of a dynamically changing Gaussian mixture model. In the evaluation, the EBI performance is compared with those of the EMA and a sequential discounting expectation-maximization algorithm. The EBI was shown to modify the trade-off observed in the EMA.
Genetic overlap and causal inferences between kidney function and cerebrovascular disease
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Data from: Bayesian causal inference explains movement coordination to auditory beats
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Data from: A new method of Bayesian causal inference in non-stationary environments
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Data from: Modeling the perception of audiovisual distance: Bayesian causal inference and other models
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Patient Centered Adaptive Treatment Strategies Using Bayesian Causal Inference
ClinicalTrials.gov study NCT02524340. IPD Sharing: YES. Countries: 1. Publications: 0.
The Causal Role of Ketone Bodies in Obesity-associated Disease Prevention - Combining Genetic Epidemiology With a Randomised Trial to Infer Causality
ClinicalTrials.gov study NCT06668168. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Causal network inference from gene transcriptional time-series response to glucocorticoids [A549_OE]
GEO Series GSE144660. Homo sapiens. 163 samples. Type: Expression profiling by high throughput sequencing.
Causal network inference from gene transcriptional time-series response to glucocorticoids [A549_dedex]
GEO Series GSE144662. Homo sapiens. 36 samples. Type: Expression profiling by high throughput sequencing.
Causal network inference from gene transcriptional time-series response to glucocorticoids
GEO Series GSE144663. Homo sapiens. 199 samples. Type: Expression profiling by high throughput sequencing.
Automated causal inference in application to randomized controlled clinical trials
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Hyponatraemia and Mortality in Schizophrenic and Bipolar Patients: Protocol for a Bayesian Causal Inference Study
ClinicalTrials.gov study NCT04409626. IPD Sharing: NO. Countries: 0. Publications: 0.
Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome
GEO Series GSE54852. Schizosaccharomyces pombe; Saccharomyces cerevisiae. 43 samples. Type: Expression profiling by array.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.