@conference {968, title = {Long-term grey matter structural changes in the transition from chronic migraine to episodic migraine}, booktitle = {8th Congress of the European Academy of Neurology}, year = {2022}, month = {2022}, abstract = {

Background and aims: The objective was to assess grey matter longitudinal changes in patients with chronic migraine (CM) who reverse to episodic migraine (EM) compared to those who do not reverse.

Methods: High-resolution 3D brain T1-weighted Magnetic Resonance Imaging data were obtained twice from migraine patients. The first acquisition was performed immediately after the first visit to the Headache Unit, before taking preventive treatments. The second timepoint was at least three years after the first acquisition. From the longitudinal pipeline of FreeSurfer (v6.0), the mean values of cortical thickness, surface area and grey matter volume of 68 cortical, 14 subcortical regions and the cerebellum were extracted. Longitudinal changes between patients with CM and those who reversed to EM were assessed with linear
mixed-effects models, setting p\<0.05 (false discovery rate corrected) as threshold for statistical significance.

Results: 22 patients were included, and 10 of them (45.5\%) reversed to EM. No statistically significant differences of age (42.0+-9.0 years) and sex (21 women, 95.5\%) were found between patient groups. Higher statistically significant values of the three parameters in patients who reversed to EM were found in the pericalcarine, parietal, orbitofrontal cortex, and amygdala (Table 1, Figure 1). In contrast, lower values were detected in the cingulum, caudal middle frontal cortex, cerebellum, caudate nucleus and pallidum (Figure 2). In the insula, higher thickness but lower area was appreciated in patients who reversed.

Conclusion: Patients with CM who reverse to EM present distinct patterns of increased and decreased morphometric parameters propagated in the orbital frontal cortex and the cingulum, respectively.

Disclosure: Nothing to disclose.

}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Marchante-Re{\'\i}llo, Ginebra and Sierra, {\'A}lvaro and Garc{\'\i}a-Azor{\'\i}n, David and Mart{\'\i}n-Mart{\'\i}n, Carmen and de Luis-Garc{\'\i}a, Rodrigo and Aja-Fern{\'a}ndez, Santiago and Moro, Ra{\'u}l and Rodr{\'\i}guez, Margarita and Gonz{\'a}lez-Osorio, Y{\'e}sica and {\'A}ngel L. Guerrero} } @article {534, title = {A local fuzzy thresholding methodology for multiregion image segmentation}, journal = {Knowledge-Based Systems}, volume = {83}, year = {2015}, month = {07/2015}, pages = {1-12}, abstract = {

Abstract Thresholding is a direct and simple approach to extract different regions from an image. In its basic formulation, thresholding searches for a global value that maximizes the separation between output classes. The use of a single hard threshold value is precisely the source of important segmentation errors in many scenarios like noisy images or uneven illumination. If no connectivity or closed objects are considered, the method is prone to produce isolated pixels. In this paper a new multiregion thresholding methodology is presented to overcome the common drawbacks of thresholding methods when images are corrupted with artifacts and noise. It is based on relating each pixel in the image to different output centroids via a fuzzy membership function, avoiding any initial hard decision. The starting point of the technique is the definition of the output centroids using a clustering method compatible with most thresholding techniques in the literature. The method makes use of the spatial information through a local aggregation step where the membership degree of each pixel is modified by local information that takes into account the memberships of the surrounding pixels. This makes the method robust to noise and artifacts. The general formulation of the proposed methodology allows the design of spatial aggregations for multiple applications, including the possibility of including heuristic information via a fuzzy inference rule base.

}, issn = {0950-7051}, doi = {http://dx.doi.org/10.1016/j.knosys.2015.02.029}, url = {http://www.sciencedirect.com/science/article/pii/S095070511500129X}, author = {Santiago Aja-Fern{\'a}ndez and Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {tristan2012least, title = {Least squares for diffusion tensor estimation revisited: Propagation of uncertainty with Rician and non-Rician signals}, journal = {NeuroImage}, volume = {59}, number = {4}, year = {2012}, pages = {4032{\textendash}4043}, publisher = {Academic Press}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @conference {tristan2008local, title = {Local similarity measures for demons-like registration algorithms}, booktitle = {Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on}, year = {2008}, pages = {1087{\textendash}1090}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} }