@article {aCasamitjanab, title = {NeAT: a nonlinear analysis toolbox for neuroimaging}, journal = {Neuroinformatics}, year = {2020}, month = {03/2020}, abstract = {

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer{\textquoteright}s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.

}, keywords = {Alzheimer{\textquoteright}s disease, APOE, GAM, GLM, inference, neuroimaging, nonlinear., SVR}, doi = {10.1007/s12021-020-09456-w}, url = {https://link.springer.com/article/10.1007/s12021-020-09456-w}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Santi Puch and Asier Aduriz and Carlos Lopez and G. Operto and R. Cacciaglia and C. Falcon and J.L. Molinuevo and Juan D. Gispert} } @conference {cPareto, title = {Prediction of a second clinical event in CIS patients by combining lesion and brain features}, booktitle = {Congress of the European Comitee for Treatment and Research in Multiple Sclerosis (ECTRIMS 2019)}, year = {2019}, month = {09/2019}, abstract = {

Prediction of a second clinical event in clinically isolated syndrome (CIS) patients, leading to clinically definite multiple sclerosis (CDMS), is still a matter of investigation. The aim of the current study was to predict conversion to CDMS after a first attack, by means of a machine-learning approach and MRI-derived brain features. For that purpose, lesion-based features (volume and distribution within brain lobes) as well as cortical thickness (CT) and deep grey matter volumes (DGMV) were used. The final goal was to determine which features were more frequently found in the classification between converters and non-converters.

}, author = {Deborah Pareto and Pau Vidal and M. Alberich and Carlos Lopez and C. Auger and M. Tintor{\'e} and X. Montalban and J. Sastre-Garriga and Ver{\'o}nica Vilaplana and Alex Rovira} }