5 results on '"Vilaplana, Verónica"'
Search Results
2. Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain MRI
- Author
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Petrone, Paula M., Casamitjana, Adrià, Falcon, Carles, Artigues, Miquel, Operto, Grégory, Cacciaglia, Raffaele, Molinuevo, José Luis, Vilaplana, Verónica, Gispert, Juan Domingo, and for the Alzheimer’s Disease Neuroimaging Initiative
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- 2019
- Full Text
- View/download PDF
3. NeAT: a nonlinear analysis toolbox for neuroimaging
- Author
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Casamitjana, Adrià, Vilaplana, Verónica, Puch, Santi, Aduriz, Asier, López, Carlos, Operto, Grégory, Cacciaglia, Raffaele, Falcón, Carles, Molinuevo, José Luis, Domingo Gispert, Juan, Alzheimer’s Disease Neuroimaging Initiative, Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo, and Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció
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Male ,Aging ,Computer science ,Apolipoprotein E4 ,Inference ,computer.software_genre ,Machine Learning ,0302 clinical medicine ,Ciències de la salut::Medicina::Neurologia [Àrees temàtiques de la UPC] ,Graphical user interface ,0303 health sciences ,General Neuroscience ,Linear model ,Brain ,Alzheimer's disease ,Magnetic Resonance Imaging ,GAM ,Neurology ,Curve fitting ,Female ,Original Article ,Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC] ,APOE ,Information Systems ,SVR ,Genotype ,Neuroimaging ,Nonlinear ,Machine learning ,03 medical and health sciences ,Neurologia ,Alzheimer Disease ,Image Interpretation, Computer-Assisted ,Aprenentatge automàtic ,Humans ,030304 developmental biology ,Aged ,business.industry ,Brain morphometry ,Visualization ,Nonlinear system ,Artificial intelligence ,GLM ,business ,computer ,030217 neurology & neurosurgery ,Software - 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’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-e4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/. This work has been partially supported by the project MALEGRA TEC2016-75976-R financed by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). Adrià Casamitjana is supported by the Spanish “Ministerio de Educación, Cultura y Deporte” FPU Research Fellowship. Juan D. Gispert holds a “‘Ramón y Cajal’” fellowship (RYC-2013-13054). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how to apply/ADNI Acknowledgement List.pdf.
- Published
- 2020
4. MRI-Based Screening of Preclinical Alzheimer's Disease for Prevention Clinical Trials.
- Author
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Casamitjana, Adrià, Petrone, Paula, Tucholka, Alan, Falcon, Carles, Skouras, Stavros, Molinuevo, José Luis, Vilaplana, Verónica, Gispert, Juan Domingo, and Alzheimer’s Disease Neuroimaging Initiative
- Subjects
ALZHEIMER'S disease prevention ,ALZHEIMER'S patients ,CLINICAL trials ,MAGNETIC resonance angiography ,MACHINE learning - Abstract
The identification of healthy individuals harboring amyloid pathology represents one important challenge for secondary prevention clinical trials in Alzheimer's disease (AD). Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. In this manuscript, we apply machine learning to structural MRI (T1 and DTI) of 96 cognitively normal subjects to identify amyloid-positive ones. Models were trained on public ADNI data and validated on an independent local cohort. Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60% of unnecessary CSF/PET tests and to reduce 47% of the cost of recruitment. This recruitment strategy capitalizes on available MR scans to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for preclinical AD screening. This protocol could foster the development of secondary prevention strategies for AD. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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- View/download PDF
5. SurvLIMEpy: A Python package implementing SurvLIME.
- Author
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Pachón-García, Cristian, Hernández-Pérez, Carlos, Delicado, Pedro, and Vilaplana, Verónica
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PYTHON programming language , *PROPORTIONAL hazards models , *MACHINE learning , *DEEP learning , *SURVIVAL analysis (Biometry) - Abstract
In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learning algorithms designed for modelling Survival Analysis data. The presented implementation uses a matrix-wise formulation, which allows to speed up the execution time. Additionally, SurvLIMEpy assists the user with visualisation tools to better understand the result of the algorithm. The package supports a wide variety of survival models, from the Cox Proportional Hazards Model to deep learning models such as DeepHit or DeepSurv. Two types of experiments are presented in this paper. First, by means of simulated data, we study the ability of the algorithm to capture the importance of the features. Second, we use three open source survival datasets together with a set of survival algorithms in order to demonstrate how SurvLIMEpy behaves when applied to different models. • Python package implementing SurvLIME algorithm for computing feature importance. • It supports a wide variety of survival models, including deep learning models. • Fast and efficient implementation due to matrix-wise optimisation problems. • An open-source implementation available on GitHub. • Stable release provided to PyPI. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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