@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} } @inbook {bCasamitjana, title = {3D Convolutional Neural Networks for Brain Tumor Segmentation: a comparison of multi-resolution architectures}, booktitle = {Lecture Notes in Computer Vision}, volume = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries}, year = {2017}, pages = {150-161}, publisher = {Springer}, organization = {Springer}, issn = {978-3-319-55524-9}, author = {Adri{\`a} Casamitjana and Santi Puch and Asier Aduriz and Ver{\'o}nica Vilaplana} } @conference {cCasamitjana16, title = {3D Convolutional Neural Networks for Brain Tumor Segmentation}, booktitle = {MICCAI 2016 - Brain Lesion Workshop (BrainLes), Multimodal Brain Tumor Segmentation Challenge (BRATS)}, year = {2016}, month = {11/2016}, author = {Adri{\`a} Casamitjana and Santi Puch and Asier Aduriz and Elisa Sayrol and Ver{\'o}nica Vilaplana} } @mastersthesis {xAduriz16, title = {Analysis of the dynamics of gray matter reduction in Alzheimer{\textquoteright}s Disease}, year = {2016}, abstract = {

Advisor: Ver{\'o}nica Vilaplana

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

This project attempts to study the cerebral atrophy patterns in gray matter across the
di erent stages of the Alzheimer{\textquoteright}s Disease (AD), or more speci cally, along the entire AD
continuum, in a voxelwise approach. To this end, we propose and implement an extensible
toolbox that allows to t di erent models to the data, hence de ning a curve for each voxel
that shows the evolution of the gray matter volume in the respective region as compared
to the progression of the disease. The toolbox also includes several evaluation methods
to estimate how closely the proposed model ts the data for each particular voxel. The
resulting values, namely tting-scores, serve as a base to achieve two di erent goals: a)
to identify the regions within the brain that are (most) likely to follow the curve-shape
speci ed in a given model, and b) to depict the model that best describes the behavior of
the gray matter volume in each voxel from a xed set of models.

}, author = {Asier Aduriz}, editor = {Ver{\'o}nica Vilaplana} } @conference {cPuch, title = {Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling}, booktitle = {NIPS 2016 Workshop on Machine Learning for Health}, year = {2016}, month = {12/2016}, author = {Santi Puch and Asier Aduriz and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Juan D. Gispert} }