@conference {938,
title = {White matter microstructural alterations in patients with persistent headache after COVID-19 infection: an exploratory study},
booktitle = {International Headache Congress 2021},
year = {2021},
month = {2021},
publisher = {International Headache Society \& European Headache Federation},
organization = {International Headache Society \& European Headache Federation},
address = {Virtual Congress},
abstract = {
Objective: To evaluate white matter alterations in patients with persistent headache after COVID-19 resolution.
Methods: Exploratory case-control study. Highresolution brain diffusion Magnetic Resonance Imaging data were acquired in patients with persistent headache after COVID-19 infection and healthy controls (HC). Tract-Based Spatial Statistics was used to compare fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD) and the return-to-axial (RTAP), return-to-origin (RTOP) and return-to-plane probability (RTPP) between the groups. RTAP, RTOP and RTPP were obtained with a new approach called AMURA (https://www.lpi.tel.uva.es/AMURA). Significant results were considered with p \< 0.05 (Family-Wise Error corrected) and region size larger than 30 mm3.
Results: Ten patients with persistent headache after COVID-19 (mean age: 53.8 +- 7.8 years; nine women) and 10 HC balanced for age and sex (mean age: 53.1 +- 7.0 years; nine women) were included in the study. Significant higher AD and lower RTPP values were found in patients with persistent headache compared to HC in five regions from the corona radiata, and the external and internal capsule. In the patients, significant lower RTPP values were identified in six additional areas from the same tracts and the superior longitudinal fasciculus. No additional changes were found.
Conclusions: White matter axonal alterations are present in patients with persistent headache after COVID-19 infection.
},
url = {https://journals.sagepub.com/doi/pdf/10.1177/03331024211034005},
author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Moro, Ra{\'u}l and Rodrigo de Luis-Garc{\'\i}a}
}
@article {841,
title = {A Web-Based Educational Magnetic Resonance Simulator: Design, Implementation and Testing},
journal = {Journal of Medical Systems},
volume = {44},
year = {2020},
month = {2020},
pages = {9},
author = {Trece{\~n}o-Fern{\'a}ndez, Daniel and Calabia-del-Campo, Juan and Bote-Lorenzo, Miguel L and S{\'a}nchez, Eduardo G{\'o}mez and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos}
}
@article {866,
title = {Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields},
journal = {Plants},
volume = {9},
year = {2020},
pages = {559},
abstract = {Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74\% and 87.96\% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02\% and 90.7\%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62\% and 85.59\% for the classification of the right and left channel data, respectively, and 85.84\% and 84.07\% for the arithmetic and geometric mean values, respectively.},
doi = {https://doi.org/10.3390/plants9050559},
url = {https://www.mdpi.com/2223-7747/9/5/559},
author = {Dadashzadeh, Mojtaba and Yousef Abbaspour-Gilandeh and Mesri-Gundoshmian, Tarahom and Sabzi, Sajad and Hern{\'a}ndez-Hern{\'a}ndez, Jose Luis and Hern{\'a}ndez-Hern{\'a}ndez, Mario and J I Arribas}
}
@article {837,
title = {White matter changes in chronic and episodic migraine: a diffusion tensor imaging study},
journal = {The Journal of Headache and Pain},
volume = {21},
year = {2020},
pages = {1},
chapter = {1},
abstract = {
BACKGROUND:
White matter alterations have been observed in patients with migraine. However, no microstructural white matter alterations have been found particularly in episodic or chronic migraine patients, and there is limited research focused on the comparison between these two groups of migraine patients.
METHODS:
Fifty-one healthy controls, 55 episodic migraine patients and 57 chronic migraine patients were recruited and underwent brain T1-weighted and diffusion-weighted MRI acquisition. Using Tract-Based Spatial Statistics (TBSS), fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity were compared between the different groups. On the one hand, all migraine patients were compared against healthy controls. On the other hand, patients from each migraine group were compared between them and also against healthy controls. Correlation analysis between clinical features (duration of migraine in years, time from onset of chronic migraine in months, where applicable, and headache and migraine frequency, where applicable) and Diffusion Tensor Imaging measures was performed.
RESULTS:
Fifty healthy controls, 54 episodic migraine and 56 chronic migraine patients were finally included in the analysis. Significant decreased axial diffusivity (p \< .05 false discovery rate and by number of contrasts corrected) was found in chronic migraine compared to episodic migraine in 38 white matter regions from the Johns Hopkins University ICBM-DTI-81 White-Matter Atlas. Significant positive correlation was found between time from onset of chronic migraine and mean fractional anisotropy in the bilateral external capsule, and negative correlation between time from onset of chronic migraine and mean radial diffusivity in the bilateral external capsule.
CONCLUSIONS:
These findings suggest global white matter structural differences between episodic migraine and chronic migraine. Patients with chronic migraine could present axonal integrity impairment in the first months of chronic migraine with respect to episodic migraine patients. White matter changes after the onset of chronic migraine might reflect a set of maladaptive plastic changes.
},
keywords = {Diffusion tensor imaging, Magnetic resonance imaging (MRI), Migraine, Tract-based spatial statistics, chronic migraine},
doi = {10.1186/s10194-019-1071-3},
url = {https://thejournalofheadacheandpain.biomedcentral.com/articles/10.1186/s10194-019-1071-3},
author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Rodrigo de Luis-Garc{\'\i}a}
}
@conference {821,
title = {White Matter Alterations in Chronic Migraine: A Diffusion Tensor Imaging and Structural Connectivity Study},
booktitle = {19th International Headache Congress International Headache Society},
year = {2019},
month = {2019},
publisher = {Cephalalgia},
organization = {Cephalalgia},
address = {Dublin, Ireland},
abstract = {Objective: White matter alterations have been observed in patients with migraine. However, no microstructural white matter alterations have been found particularly in Episodic Migraine (EM) with respect to Chronic Migraine (CM) patients. In this study, we investigated whether there are significant differences between EM and CM, and between these groups and healthy controls, using diffusion Magnetic Resonance Imaging (dMRI) data.
Methods: We acquired high-resolution 3D brain T1-weighted and dMRI from 51 Healthy Controls (HC), 55 EM patients and 57 CM patients. Using Tract-Based Spatial Statistics, we compared Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (AD) between the different groups. We also obtained structural connectome matrices for each subject employing both dMRI and T1-weighted acquisitions. Number of streamlines, mean FA and mean AD for each white matter connection were compared between the three groups.
Results: Significant decreased AD (p \<.05 Family Wise Error corrected and volume \>30 mm3) were found in CM compared to EM in 38 white matter regions. Significant differences in the number of streamlines were found in 18 connections from the connectome when comparing migraine patients with healthy controls (p \<.05 False Discovery Rate corrected); significant differences were also found between CM and EM in one of these connections. Furthermore, significant differences in FA and AD were found in three and four connections from the connectome respectively (p \<.05 False Discovery Rate corrected); significant differences were also found between CM and EM in two of AD connections.
Conclusion: Our findings suggest global white matter structural differences between EM and CM, and structural connectivity alterations in migraine patients with respect to healthy controls, and in CM compared to EM.
Disclosure of Interest: None Declared.
},
doi = {https://doi.org/10.1177/0333102419859835},
url = {https://journals.sagepub.com/doi/full/10.1177/0333102419859835},
author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Rodrigo de Luis-Garc{\'\i}a}
}
@conference {690,
title = {Whole-heart single breath-hold cardiac cine: A robust motion-compensated compressed sensing reconstruction method},
booktitle = {International Workshop on Reconstruction and Analysis of Moving Body Organs (RAMBO/MICCAI) },
year = {2016},
month = {2016},
address = {Athens, Greece},
author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and Marcos Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Claudia Prieto and Carlos Alberola-Lopez}
}
@article {de2014white,
title = {White matter abnormalities in chronic migraine patients are located in anterior corpus callosum: study using a new automatic tractography selection method},
journal = {EUROPEAN JOURNAL OF NEUROLOGY},
volume = {21},
year = {2014},
pages = {51{\textendash}51},
publisher = {WILEY-BLACKWELL 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
author = {De la Cruz, C and {\'A}ngel L. Guerrero and Penas, ML and Daniel Argibay-Qui{\~n}ones and Jose M Sierra and Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a}
}
@article {casaseca2006weaning,
title = {Weaning from mechanical ventilation: a retrospective analysis leading to a multimodal perspective},
journal = {Biomedical Engineering, IEEE Transactions on},
volume = {53},
number = {7},
year = {2006},
pages = {1330{\textendash}1345},
publisher = {IEEE},
author = {Pablo Casaseca-de-la-Higuera and Marcos Martin-Fernandez and Carlos Alberola-Lopez}
}
@conference {429,
title = {Weaning from mechanical ventilation: feature extraction from a statistical signal processing viewpoint},
booktitle = {Proc. 13th Signal Processing Conf., EUSIPCO},
year = {2005},
author = {Pablo Casaseca-de-la-Higuera and Rodrigo de Luis-Garc{\'\i}a and Federico Simmross-Wattenberg and Carlos Alberola-Lopez}
}