scholarly journals A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets

NeuroImage ◽  
2021 ◽  
pp. 118854
Author(s):  
Fabio S. Ferreira ◽  
Agoston Mihalik ◽  
Rick A. Adams ◽  
John Ashburner ◽  
Janaina Mourao-Miranda
Keyword(s):  
Incomplete Data ◽  
Bayesian Model ◽  
Data Sets ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model

scholarly journals Accommodating site variation in neuroimaging data using hierarchical and Bayesian models

2021 ◽  
Author(s):  
Johanna M. M. Bayer ◽  
Richard Dinga ◽  
Seyed Mostafa Kia ◽  
Akhil R. Kottaram ◽  
Thomas Wolfers ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Site Effects ◽  
Data Exchange ◽  
Data Sets ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Data Set ◽  
Neuroimaging Data ◽  
Normative Models ◽  
Age And Sex

AbstractThe potential of normative modeling to make individualized predictions has led to structural neu-roimaging results that go beyond the case-control approach. However, site effects, often con-founded with variables of interest in a complex manner, induce a bias in estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compare the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange, http://preprocessed-connectomes-project.org/abide/) data set in our experiments. We compare the proposed method to several harmonization techniques commonly used to deal with additive and multiplicative site effects, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance related to age and sex as biological variation of interest. In addition, we make predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method shows the best performance according to multiple metrics. Performance is particularly bad for the regression model and the ComBat model when age and sex are not explicitly modeled. In addition, the predictions of those models are noticeably poorly calibrated, suffering from a loss of more than 90 % of the original variance. From these results we conclude that harmonization techniques like regressing out site and ComBat do not sufficiently accommodate for multi-site effects in pooled neuroimaging data sets. Our results show that the complex interaction between site and variables of interest is likely to be underestimated by those tools. One consequence is that harmonization techniques removed too much variance, which is undesirable and may have unpredictable consequences for subsequent analysis. Our results also show that this can be mostly avoided by explicitly modeling site as part of a hierarchical Bayesian Model. We discuss the potential of z-scores derived from normative models to be used as site corrected variables and of our method as site correction tool.

scholarly journals Towards a Hierarchical Bayesian Model of Multi-View Anomaly Detection

2020 ◽  
Author(s):  
Zhen Wang ◽  
Chao Lan
Keyword(s):  
Latent Variables ◽  
Bayesian Model ◽  
State Of The Art ◽  
Bayesian Estimator ◽  
Data Sets ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Single View ◽  
Public Data ◽  
Anomaly Detector

Traditional anomaly detectors examine a single view of instances and cannot discover multi-view anomalies, i.e., instances that exhibit inconsistent behaviors across different views. To tackle the problem, several multi-view anomaly detectors have been developed recently, but they are all transductive and unsupervised thus may suffer some challenges. In this paper, we propose a novel inductive semi-supervised Bayesian multi-view anomaly detector. Specifically, we first present a generative model for normal data. Then, we build a hierarchical Bayesian model, by first assigning priors to all parameters and latent variables, and then assigning priors over the priors. Finally, we employ variational inference to approximate the posterior of the model and evaluate anomalous scores of multi-view instances. In the experiment, we show the proposed Bayesian detector consistently outperforms state-of-the-art counterparts across several public data sets and three well-known types of multi-view anomalies. In theory, we prove the inferred Bayesian estimator is consistent and derive a proximate sample complexity for the proposed anomaly detector.

Robust resource demand estimation using hierarchical Bayesian model in a distributed service system

2020 ◽  
Author(s):  
Sumanta Mukherjee ◽  
Krishnasuri Narayanam ◽  
Nupur Aggarwal ◽  
Digbalay Bose ◽  
Amith Singhee
Keyword(s):  
Bayesian Model ◽  
Service System ◽  
Demand Estimation ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Resource Demand ◽  
Distributed Service

scholarly journals Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels

Genetics ◽  
2021 ◽  
Vol 217 (2) ◽  
Author(s):  
L E Puhl ◽  
J Crossa ◽  
S Munilla ◽  
P Pérez-Rodríguez ◽  
R J C Cantet
Keyword(s):  
Hexaploid Wheat ◽  
Variance Components ◽  
Bayesian Model ◽  
Synthetic Hexaploid Wheat ◽  
Data Sets ◽  
Hierarchical Bayesian Model ◽  
Additive Variance ◽  
Ploidy Levels ◽  
Breeding Values ◽  
Synthetic Hexaploid

Abstract Cultivated bread wheat (Triticum aestivum L.) is an allohexaploid species resulting from the natural hybridization and chromosome doubling of allotetraploid durum wheat (T. turgidum) and a diploid goatgrass Aegilops tauschii Coss (Ae. tauschii). Synthetic hexaploid wheat (SHW) was developed through the interspecific hybridization of Ae. tauschii and T. turgidum, and then crossed to T. aestivum to produce synthetic hexaploid wheat derivatives (SHWDs). Owing to this founding variability, one may infer that the genetic variances of native wild populations vs improved wheat may vary due to their differential origin and evolutionary history. In this study, we partitioned the additive variance of SHW and SHWD with respect to their breed origin by fitting a hierarchical Bayesian model with heterogeneous covariance structure for breeding values to estimate variance components for each breed category, and segregation variance. Two data sets were used to test the proposed hierarchical Bayesian model, one from a multi-year multi-location field trial of SHWD and the other comprising the two species of SHW. For the SHWD, the Bayesian estimates of additive variances of grain yield from each breed category were similar for T. turgidum and Ae. tauschii, but smaller for T. aestivum. Segregation variances between Ae. tauschii—T. aestivum and T. turgidum—T. aestivum populations explained a sizable proportion of the phenotypic variance. Bayesian additive variance components and the Best Linear Unbiased Predictors (BLUPs) estimated by two well-known software programs were similar for multi-breed origin and for the sum of the breeding values by origin for both data sets. Our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.

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