The Problem with NaN Values

Oct 13, 2025 by magicalceylontours.com 7 views

Introduction

When working with functional magnetic resonance imaging (fMRI) data, it's not uncommon to encounter missing or NaN values in the confounds outputs from fMRIPrep. These NaN values can cause issues when using Tedana, a popular tool for analyzing fMRI data. In this article, we'll discuss the problem of NaN values in fMRIPrep confounds outputs and propose a solution to replace them with zeros or other values before z-scoring.

The Problem with NaN Values

NaN values in fMRIPrep confounds outputs can cause problems when using Tedana. Specifically, when the full model is fitted using the least squares method (lstsq), the presence of NaN values can lead to a crash without a clear error message. This is because the NaN values are not handled properly during the z-scoring process, resulting in a model that is not well-defined.

The Need for a Solution

To make fMRIPost-tedana more robust, it would be beneficial to replace the NaN values in the fMRIPrep confounds outputs with zeros or other values before z-scoring. This would ensure that the model is well-defined and can be fitted without issues. One possible solution is to introduce a nan_fill_value parameter in the external_regressor_config section of the Tedana configuration file.

Proposed Solution

The proposed solution involves introducing a nan_fill_value parameter in the external_regressor_config section of the Tedana configuration file. This parameter would specify the value to be used to replace NaN values in the fMRIPrep confounds outputs. For example, if the nan_fill_value parameter is set to 0, all NaN values would be replaced with zeros.

Here is an example of how the external_regressor_config section might look with the nan_fill_value parameter:

external_regressor_config:
  nan_fill_value: 0
  external_regressors:
    - framewise_displacement
    - ...

Benefits of the Proposed Solution

The proposed solution has several benefits. Firstly, it makes fMRIPost-tedana more robust by ensuring that the model is well-defined and can be fitted without issues. Secondly, it allows users to specify the value to be used to replace NaN values, giving them more control over the analysis process.

Implementation

Implementing the proposed solution would involve modifying the Tedana code to include the nan_fill_value parameter in the external_regressor_config section. This would require changes to the configuration file and the code that handles the fMRIPrep confounds outputs.

Next Steps

If the proposed solution is agreed upon, the next step would be to implement the changes to the Tedana code. This would involve creating a pull request (PR) to the Tedana repository, which would be reviewed and merged by the development team.

Conclusion

In conclusion, the presence of NaN values in fMRIPrep confounds outputs can cause issues when using Tedana. The proposed solution involves introducing a nan_fill_value parameter in the external_regressor_config section of the Tedana configuration file, which would allow users to specify the value to be used to replace NaN values. This would make fMRIPost-tedana more robust and give users more control over the analysis process.

References

fMRIPrep: https://fmriprep.org/
Tedana: https://tedana.readthedocs.io/

Acknowledgments

Introduction

In our previous article, we discussed the problem of NaN values in fMRIPrep confounds outputs and proposed a solution to replace them with zeros or other values before z-scoring. In this article, we'll answer some frequently asked questions (FAQs) about the proposed solution.

Q: What is the problem with NaN values in fMRIPrep confounds outputs?

A: NaN values in fMRIPrep confounds outputs can cause issues when using Tedana. Specifically, when the full model is fitted using the least squares method (lstsq), the presence of NaN values can lead to a crash without a clear error message.

Q: Why is it necessary to replace NaN values with zeros or other values?

A: Replacing NaN values with zeros or other values ensures that the model is well-defined and can be fitted without issues. This is particularly important when using Tedana, as it relies on the least squares method to fit the model.

Q: How does the proposed solution work?

A: The proposed solution involves introducing a nan_fill_value parameter in the external_regressor_config section of the Tedana configuration file. This parameter specifies the value to be used to replace NaN values in the fMRIPrep confounds outputs.

Q: What values can be used to replace NaN values?

A: Any value can be used to replace NaN values, but it's recommended to use a value that is close to zero, such as 0 or a small positive value. This ensures that the model is not biased by the replacement value.

Q: How does the `nan_fill_value` parameter affect the analysis?

A: The nan_fill_value parameter affects the analysis by replacing NaN values with the specified value. This ensures that the model is well-defined and can be fitted without issues.

Q: Can I use a different value to replace NaN values?

A: Yes, you can use a different value to replace NaN values. Simply specify the desired value in the nan_fill_value parameter.

Q: How do I implement the proposed solution?

A: To implement the proposed solution, you'll need to modify the Tedana code to include the nan_fill_value parameter in the external_regressor_config section. This will require changes to the configuration file and the code that handles the fMRIPrep confounds outputs.

Q: What are the benefits of the proposed solution?

A: The proposed solution has several benefits, including:

Making fMRIPost-tedana more robust by ensuring that the model is well-defined and can be fitted without issues.
Allowing users to specify the value to be used to replace NaN values, giving them more control over the analysis process.

Q: Can I use the proposed solution with other analysis tools?

A: Yes, the proposed solution can be used with other analysis tools that rely on the least squares method to fit the model.

Q: How do I report issues or provide feedback on the proposed solution?

A: If you encounter any issues or have feedback on the proposed solution, please report them to the Tedana development team. You can do this by creating a new issue on the Tedana GitHub repository or by contacting the development team directly.

Conclusion

In conclusion, the proposed solution to replace NaN values in fMRIPrep confounds outputs with zeros or other values before z-scoring is a useful tool for making fMRIPost-tedana more robust. By answering some frequently asked questions, we hope to have provided a better understanding of the proposed solution and its benefits.