Statistical power in network neuroscience

Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies

Statistical power in network neuroscience

Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies

Statistical power in network neuroscience

Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies

Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox

We describe a Connectivity Analysis TOolbox (CATO) for the reconstruction of structural and functional brain connectivity based on diffusion weighted imaging and resting-state functional MRI data. CATO is a multimodal software package that enables researchers to run end-to-end reconstructions from MRI data to structural and functional connectome maps, customize their analyses and utilize various software packages to preprocess data. Structural and functional connectome maps can be reconstructed with respect to user-defined (sub)cortical atlases providing aligned connectivity matrices for integrative multimodal analyses. We outline the implementation and usage of the structural and functional processing pipelines in CATO. Performance was calibrated with respect to simulated diffusion weighted imaging from the ITC2015 challenge, test-retest diffusion weighted imaging data and resting-state functional MRI data from the Human Connectome Project. CATO is open-source software distributed under the MIT License and available as a MATLAB toolbox and as a stand-alone application at www.dutchconnectomelab.nl/CATO

Complementing CO2 emission reduction by solar radiation management might strongly enhance future welfare

Solar radiation management (SRM) has been proposed as a means to reduce global warming in spite of high greenhouse-gas concentrations and to lower the chance of warming-induced tipping points. However, SRM may cause economic damages and its feasibility is still uncertain. To investigate the trade-off between these (economic) gains and damages, we incorporate SRM into a stochastic dynamic integrated assessment model and perform the first rigorous cost-benefit analysis of sulfate-based SRM under uncertainty, treating warming-induced climate tipping and SRM failure as stochastic elements. We find that within our model, SRM has the potential to greatly enhance future welfare and merits being taken seriously as a policy option. However, if only SRM and no CO2 abatement is used, global warming is not stabilised and will exceed 2 K. Therefore, even if successful, SRM can not replace but only complement CO2 abatement. The optimal policy combines CO2 abatement and modest SRM and succeeds in keeping global warming below 2 K

Complementing CO2 emission reduction by solar radiation management might strongly enhance future welfare

Solar radiation management (SRM) has been proposed as a means to reduce global warming in spite of high greenhouse-gas concentrations and to lower the chance of warming-induced tipping points. However, SRM may cause economic damages and its feasibility is still uncertain. To investigate the trade-off between these (economic) gains and damages, we incorporate SRM into a stochastic dynamic integrated assessment model and perform the first rigorous cost-benefit analysis of sulfate-based SRM under uncertainty, treating warming-induced climate tipping and SRM failure as stochastic elements. We find that within our model, SRM has the potential to greatly enhance future welfare and merits being taken seriously as a policy option. However, if only SRM and no CO2 abatement is used, global warming is not stabilised and will exceed 2 K. Therefore, even if successful, SRM can not replace but only complement CO2 abatement. The optimal policy combines CO2 abatement and modest SRM and succeeds in keeping global warming below 2 K