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

The thesis defense presentation¶

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 Chapter 1: Functional Magnetic Resonance Imaging and Blood Oxygen Level Dependent Signal.

If you are interested in learning about hemodynamic deconvolution and how it works, please refer to Chapter 2: Synthesis-Based and Analysis-Based Hemodynamic Deconvolution for fMRI.

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 λ, please refer to Chapter 3: Stability-Based Sparse Paradigm Free Mapping and Chapter 4: Whole-Brain Multivariate Deconvolution for Multi-Echo Functional MRI.

If you want to learn how you can exploit spatial information to improve your estimates of the activity-inducing signal, please refer to Chapter 4: Whole-Brain Multivariate Deconvolution for Multi-Echo Functional MRI.

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 Chapter 5: Multi-Subject Paradigm Free Mapping.

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 Chapter 6: Sparse and Low-Rank Paradigm Free Mapping.

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:

pip install pySPFM

pip install splora

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.

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.