Bayesian Dynamic DAG Learning: Application in Discovering Dynamic Effective Connectome of Brain

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization \textbf{(BDyMA)} method to address the challenges in discovering DEC. The presented dynamic causal model enables us to discover bidirected edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and baseline methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth

Estimating A Reference Standard Segmentation with Spatially Varying Performance Parameters: Local MAP STAPLE

International audienceWe present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new Maximum A Posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-ofthe- art algorithms

A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation

Objectives:. To determine whether machine learning algorithms can better predict PICU mortality than the Pediatric Logistic Organ Dysfunction-2 score. Design:. Retrospective study. Setting:. Quaternary care medical-surgical PICU. Patients:. All patients admitted to the PICU from 2013 to 2019. Interventions:. None. Measurements and Main Results:. We investigated the performance of various machine learning algorithms using the same variables used to calculate the Pediatric Logistic Organ Dysfunction-2 score to predict PICU mortality. We used 10,194 patient records from 2013 to 2017 for training and 4,043 patient records from 2018 to 2019 as a holdout validation cohort. Mortality rate was 3.0% in the training cohort and 3.4% in the validation cohort. The best performing algorithm was a random forest model (area under the receiver operating characteristic curve, 0.867 [95% CI, 0.863–0.895]; area under the precision-recall curve, 0.327 [95% CI, 0.246–0.414]; F1, 0.396 [95% CI, 0.321–0.468]) and significantly outperformed the Pediatric Logistic Organ Dysfunction-2 score (area under the receiver operating characteristic curve, 0.761 [95% CI, 0.713–0.810]; area under the precision-recall curve (0.239 [95% CI, 0.165–0.316]; F1, 0.284 [95% CI, 0.209–0.360]), although this difference was reduced after retraining the Pediatric Logistic Organ Dysfunction-2 logistic regression model at the study institution. The random forest model also showed better calibration than the Pediatric Logistic Organ Dysfunction-2 score, and calibration of the random forest model remained superior to the retrained Pediatric Logistic Organ Dysfunction-2 model. Conclusions:. A machine learning model achieved better performance than a logistic regression-based score for predicting ICU mortality. Better estimation of mortality risk can improve our ability to adjust for severity of illness in future studies, although external validation is required before this method can be widely deployed

More details and discussions on our methods and results.

More details and discussions on our methods and results.</p

Results of applying Bayesian methods on mean PFDR values for ECs with 200 edges for test and retest data.

Results of applying Bayesian methods on mean PFDR values for ECs with 200 edges for test and retest data.</p

The region names are provided in this appendix.

Neuroscientific studies aim to find an accurate and reliable brain Effective Connectome (EC). Although current EC discovery methods have contributed to our understanding of brain organization, their performances are severely constrained by the short sample size and poor temporal resolution of fMRI data, and high dimensionality of the brain connectome. By leveraging the DTI data as prior knowledge, we introduce two Bayesian causal discovery frameworks -the Bayesian GOLEM (BGOLEM) and Bayesian FGES (BFGES) methods- that offer significantly more accurate and reliable ECs and address the shortcomings of the existing causal discovery methods in discovering ECs based on only fMRI data. Moreover, to numerically assess the improvement in the accuracy of ECs with our method on empirical data, we introduce the Pseudo False Discovery Rate (PFDR) as a new computational accuracy metric for causal discovery in the brain. Through a series of simulation studies on synthetic and hybrid data (combining DTI from the Human Connectome Project (HCP) subjects and synthetic fMRI), we demonstrate the effectiveness of our proposed methods and the reliability of the introduced metric in discovering ECs. By employing the PFDR metric, we show that our Bayesian methods lead to significantly more accurate results compared to the traditional methods when applied to the Human Connectome Project (HCP) data. Additionally, we measure the reproducibility of discovered ECs using the Rogers-Tanimoto index for test-retest data and show that our Bayesian methods provide significantly more reliable ECs than traditional methods. Overall, our study’s numerical and visual results highlight the potential for these frameworks to significantly advance our understanding of brain functionality.</div