Signal Processing in Neuroimaging

Signal Processing in Neuroimaging

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SPIN-Group. Our group broadly focuses on investigating advanced methods for the acquisition and analysis of neuroimaging and biomedical data in order to advance our understanding of human brain function, structure and physiology.

With a focus on cognitive and clinical neuroscience, our current projects mainly concern the development of signal processing algorithms for functional magnetic resonance imaging and functional near-infrared spectroscopy, including signal denoising and deconvolution, physiological and neurovascular processes, functional connectivity analyses, decoding and encoding brain activity, and multimodal imaging. We aim to apply these methods to examine the functional organization of large-scale brain networks and how they shape cognition in single individuals and across subjects in healthy and diseased conditions across their lifespan.

Publications

2024

Uruñuela, E., Gonzalez-Castillo, J., Zheng, C., Bandettini, P., & Caballero-Gaudes, C. (2024). Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection. Medical Image Analysis, 91. Doi:10.1016/j.media.2023.103010

2023

Delgado-Alvarado, M., Ferrer-Gallardo, V.J., Paz-Alonso, P.M., Caballero-Gaudes, C., & Rodríguez-Oroz, M.C. (2023). Interactions between functional networks in Parkinson's disease mild cognitive impairment. Scientific Reports, 13(1). Doi:10.1038/s41598-023-46991-3
Zvolanek, K.M., Moia, S., Dean, J.N., Stickland, R.C., Caballero-Gaudes, C., & Bright, M.G. (2023). Comparing end-tidal CO<inf>2</inf>, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI. NeuroImage, 272. Doi:10.1016/j.neuroimage.2023.120038

2022

Basaia, S., Agosta, F., Diez, I., Bueichekú, E., d'Oleire Uquillas, F., Delgado-Alvarado, M., Caballero-Gaudes, C., Rodriguez-Oroz, M.C., Stojkovic, T., Kostic, V.S., Filippi, M., & Sepulcre, J. (2022). Neurogenetic traits outline vulnerability to cortical disruption in Parkinson's disease. NeuroImage: Clinical, 33. Doi:10.1016/j.nicl.2022.102941

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