文献类型： 会议论文
第一作者： Adrià Casamitjana
作者： Adrià Casamitjana 1 ; Juan Eugenio Iglesias 2 ; Raúl Tudela 3 ; Aida Niñerola-Baizán 4 ; Roser Sala-Llonch 5 ;
作者机构： 1.Universitat Politècnica de Catalunya|Institut de Neurociències Department of Biomedicine, University of Barcelona
2.Martinos Center for Biomedical Imaging, MGH and Harvard Medical School|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology|Centre for Medical Image Computing, University College London
3.Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, ISCIII
4.Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, ISCIII|Nuclear Medicine Department, Hospital Clínic
5.Institut de Neurociències Department of Biomedicine, University of Barcelona|Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, ISCIII|Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
关键词： Neuroimaging;Codes;Pipelines;Functional magnetic resonance imaging;Biomarkers;Periodic structures;Diseases
会议名称： IEEE International Symposium on Biomedical Imaging
主办单位：
页码： 1-5
摘要： We present a pipeline for unbiased and robust multimodal registration of neuroimaging modalities with minimal pre-processing. While typical multimodal studies need to use multiple independent processing pipelines, with diverse options and hyperparameters, we propose a single and structured framework to jointly process different image modalities. The use of state-of-the-art learning-based techniques enables fast inferences, which makes the presented method suitable for large-scale and/or multi-cohort datasets with a diverse number of modalities per session. The pipeline currently works with structural MRI, resting state fMRI and amyloid PET images. We show the predictive power of the derived biomarkers using in a case-control study and study the cross-modal relationship between different image modalities. The code can be found in https: //github.com/acasamitjana/JUMP.
分类号： tp391.4
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Statistical modelling of compromised longitudinal neuroimaging datasets: An application to Alzheimer's Disease
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关键词：Bayesian;Frequentist;Statistical Inference;Alzheimer's Disease;Longitudinal Study;Linear Mixed Effects;Neuroimaging;Magnetic Resonance Images
