Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models

The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias 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 compared 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) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.Additional co-authors: Sofie Van Den Bossche, Xenia Kobeleva, Jon Haitz Legarreta, Samuel Guay, Selim Melvin Atay, Gael P. Varoquaux, Dorien C. Huijser, Malin S. Sandström, Peer Herholz, Samuel A. Nastase, AmanPreet Badhwar, Guillaume Dumas, Simon Schwab, Stefano Moia, Michael Dayan, Yasmine Bassil, Paula P. Brooks, Matteo Mancini, James M. Shine, David O’Connor, Xihe Xie, Davide Poggiali, Patrick Friedrich, Anibal S. Heinsfeld, Lydia Riedl, Roberto Toro, César Caballero-Gaudes, Anders Eklund, Kelly G. Garner, Christopher R. Nolan, Damion V. Demeter, Fernando A. Barrios, Junaid S. Merchant, Elizabeth A. McDevitt, Robert Oostenveld, R. Cameron Craddock, Ariel Rokem, Andrew Doyle, Satrajit S. Ghosh, Aki Nikolaidis, Olivia W. Stanley, Eneko Uruñuela, The Brainhack Communit

Charting brain growth and aging at high spatial precision

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

International audienceBrainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress

Proceedings of the OHBM Brainhack 2021

The global pandemic presented new challenges and op-portunities for organizing conferences, and OHBM 2021was no exception. The OHBM Brainhack is an event thatoccurs just prior to the OHBM meeting, typically in-per-son, where scientists of all levels of expertise and interestgather to work and learn together for a few days in a col-laborative hacking-style environment on projects of com-mon interest (1). Building off the success of the OHBM2020 Hackathon (2), the 2021 Open Science SpecialInterest Group came together online to organize a largecoordinated Brainhack event that would take place overthe course of 4 days. The OHBM 2021 Brainhack eventwas organized along two guiding principles, providinga highly inclusive collaborative environment for inter-action between scientists across disciplines and levelsof expertise to push forward important projects thatneed support, also known as the “Hack-Track” of theBrainhack. The second aim of the OHBM Brainhack is toempower scientists to improve the quality of their sci-entific endeavors by providing high-quality hands-ontraining on best practices in open-science approaches.This is best exemplified by the training events providedby the “Train-Track” at the OHBM 2021 Brainhack. Here,we briefly explain both of these elements of the OHBM2021 Brainhack, before continuing on to the Brainhackproceedings