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

2025

Aslan, S., Hocke, L.M., & Frederick, B.B. (2025). Improving delay and strength maps derived from resting-state fMRI using PCA-based denoising and group data from the HCP dataset. Computers in Biology and Medicine, 192. Doi:10.1016/j.compbiomed.2025.110262

2024

Castro-Macías, F.M., Pérez-Bueno, F., Vega, M., Mateos, J., Molina, R., & Katsaggelos, A.K. (2024). Bayesian Blind Image Deconvolution using an Hyperbolic-Secant prior. Proceedings International Conference on Image Processing Icip, 1500-1506. Doi:10.1109/ICIP51287.2024.10647808
López-Pérez, M., Morquecho, A., Schmidt, A., Pérez-Bueno, F., Martín-Castro, A., Mateos, J., & Molina, R. (2024). The CrowdGleason dataset: Learning the Gleason grade from crowds and experts. Computer Methods and Programs in Biomedicine, 257. Doi:10.1016/j.cmpb.2024.108472
Tabatabaei, Z., Pérez Bueno, F., Colomer, A., Moll, J.O., Molina, R., & Naranjo, V. (2024). Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques. Applied Sciences Switzerland, 14(5). Doi:10.3390/app14052063

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