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Unraveling Hidden Patterns of Brain Activity: A Journey Through Hemodynamic Deconvolution in Functional MRI

:::{note} Welcome to my PhD dissertation!

Thank you for your interest in my work. This website is a summary of my PhD dissertation and is the perfect place to learn about hemodynamic deconvolution in functional MRI: a technique that allows you to blindly estimate the neuronal-related activity without any knowledge of the timings of the neuronal events. :::

The thesis defense presentation

:::{iframe} https://player.vimeo.com/video/935334650?h=9bd47d083a The video of my thesis defense presentation is available on Vimeo. Click here to watch it. :::

The different chapters

If you are interested in learning about the current background of the analysis of resting-state functional MRI data, please refer to .

If you are interested in learning about hemodynamic deconvolution and how it works, please refer to .

If you are interested in learning how you can make your estimates more robust without having to select a fixed value for the regularization parameter $\lambda$, please refer to and .

If you want to learn how you can exploit spatial information to improve your estimates of the activity-inducing signal, please refer to .

If you are interested in employing hemodynamic deconvolution to study the shared and individual responses of a group of subjects in a naturalistic paradigm, please refer to .

Finally, if you are interested in learning how you can reduce the bias of global components like the global signal or respiration-related artifacts from your estimates of neuronal-related activity, please refer to .

How to use the Python packages

If you are interested in using the Python packages I developed during my PhD, please refer to the Paradigm Free Mapping organization on GitHub or click on the Packages button on the top right corner of this page.

To install the individual packages, you can use the following commands:

::::{tab-set} :::{tab-item} pySPFM

pip install pySPFM

::: :::{tab-item} splora

pip install splora

::: :::{tab-item} msPFM

msPFM will be available soon. Please stay tuned and follow eurunuela on X for updates. ::: ::::

How to cite our work if you used one of our Python packages

Select a tab below to see the citation information for the package you used.

::::{tab-set} :::{tab-item} pySPFM

Please cite the following if you use the package on single-echo data:

If you use the package on multi-echo data, please cite the following:

If you use stability selection, please cite the following as well:

::: :::{tab-item} splora

Please cite the following if you use the package:

If you use the package without using the low-rank model, please add the following citation as well:

If you do use the sparse & low-rank model, pleasee add the following citation:

::: :::{tab-item} msPFM

msPFM will be available soon. Please stay tuned and follow eurunuela on X for updates.

::: ::::

Acknowledgements

I would like to thank the Signal Processing in Neuroimaging (SPiN) lab at the Basque Center on Cognition, Brain and Language (BCBL) for their support and guidance throughout my PhD and I would like to especially thank my supervisors, Dr. César Caballero-Gaudes and Dr. Miguel Ángel Veganzones, for their support and guidance.

Feedback

If you have any feedback or questions, please feel free to reach out to me at eurunuela on X or by opening an issue on the GitHub repository of the package you used.