scholarly journals Hierarchical Bayesian Regression for Multi-site Normative Modeling of Neuroimaging Data

2020 ◽  
pp. 699-709
Author(s):  
Seyed Mostafa Kia ◽  
Hester Huijsdens ◽  
Richard Dinga ◽  
Thomas Wolfers ◽  
Maarten Mennes ◽  
...  
Keyword(s):  
Bayesian Regression ◽  
Hierarchical Bayesian ◽  
Neuroimaging Data

scholarly journals Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression

2021 ◽  
Author(s):  
Seyed Mostafa Kia ◽  
Hester Huijsdens ◽  
Saige Rutherford ◽  
Richard Dinga ◽  
Thomas Wolfers ◽  
...  
Keyword(s):  
Data Privacy ◽  
Small Sample ◽  
Bayesian Regression ◽  
Data Availability ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Current Standard ◽  
Statistical Tool ◽  
Neuroimaging Data ◽  
Small Sample Sizes

Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) for multi-site normative modeling. The proposed method completes the life-cycle of normative modeling by providing the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed federated framework closes the technical loop for applying normative modeling across multiple sites in a decentralized manner. This will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.

Driving behavior at a roundabout: A hierarchical Bayesian regression analysis

2014 ◽  
Vol 26 ◽  
pp. 20-26 ◽  
Author(s):  
Abhisek Mudgal ◽  
Shauna Hallmark ◽  
Alicia Carriquiry ◽  
Konstantina Gkritza
Keyword(s):  
Regression Analysis ◽  
Driving Behavior ◽  
Bayesian Regression ◽  
Hierarchical Bayesian

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 Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach

10.2196/15028 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e15028 ◽  
Author(s):  
Jonas Busk ◽  
Maria Faurholt-Jepsen ◽  
Mads Frost ◽  
Jakob E Bardram ◽  
Lars Vedel Kessing ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Bipolar Disorder ◽  
Time Series ◽  
Randomized Clinical Trial ◽  
Cross Validation ◽  
Bayesian Regression ◽  
Forecast Errors ◽  
Hierarchical Bayesian ◽  
Validation Experiment ◽  
Self Assessments

Background Bipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days. Objective This study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial. Methods We applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood for up to seven days based on 15,975 smartphone self-assessments from 84 patients with bipolar disorder participating in a randomized clinical trial. We reported the results of two time-series cross-validation 1-day forecast experiments corresponding to two different real-world scenarios and compared the outcomes with commonly used baseline methods. We then applied the best model to evaluate a 7-day forecast. Results The best performing model used a history of 4 days of self-assessment to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a 1-day forecast time-series cross-validation experiment and achieved the predicted metrics, R2=0.51 and root mean squared error of 0.32, for mood scores on a scale of −3 to 3. When increasing the forecast horizon, forecast errors also increased and the forecast regressed toward the mean of data distribution. Conclusions Our proposed method can forecast mood for several days with low error compared with common baseline methods. The applicability of a mood forecast in the clinical treatment of bipolar disorder has also been discussed.

scholarly journals Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

2019 ◽  
Author(s):  
Zhengke Pan ◽  
Pan Liu ◽  
Shida Gao ◽  
Jun Xia ◽  
Jie Chen ◽  
...  
Keyword(s):  
Spatial Coherence ◽  
Model Performance ◽  
Climatic Conditions ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Time Varying ◽  
Hierarchical Bayesian ◽  
Hydrological Models ◽  
Varying Parameters ◽  
Time Varying Parameters

Abstract. Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce the performance degradation. Many existing literatures model the time-varying parameters as functions of physically-based covariates; however, a major challenge remains finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes, and one scenario with a stationary scheme for model parameters, were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with stationary setting of model parameters; (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance; and (3) model parameters calibrated over dry periods were not suitable for predicting runoff over wet periods because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.

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