文献类型： 会议论文

第一作者： Agnes Perez-Millan

作者： Agnes Perez-Millan ¹ ; Jose Contador ² ; Aida Ninerola-Baizan ³ ; Raul Tudela ⁴ ; Xavier Setoain ³ ; Albert Llado ⁵ ; Raquel Sanchez-Valle ⁵ ; Roser Sala-Llonch ⁶ ;

作者机构： 1.Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic of Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Fundacio Clinic per a la Recerca Biomedica, Universitat de Barcelona, Barcelona, 08036, Spain,Institute of Neurosciences. Department of Biomedicine, Faculty of Medicine, University of Barcelona, Barcelona, 08036, Spain

2.Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic of Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Fundacio Clinic per a la Recerca Biomedica, Universitat de Barcelona, Barcelona, 08036, Spain

3.Nuclear Medicine Department, Hospital Clinic Barcelona, Barcelona, Spain,Centro de Investigacion Biomedica en Red de Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain

4.Centro de Investigacion Biomedica en Red de Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain

5.Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic of Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Fundacio Clinic per a la Recerca Biomedica, Universitat de Barcelona, Barcelona, 08036, Spain,Centro de Investigacion Biomedica en Red de Enfermedades Neurodegenerativas. CIBERNED, Spain

6.Institute of Neurosciences. Department of Biomedicine, Faculty of Medicine, University of Barcelona, Barcelona, 08036, Spain,Centro de Investigacion Biomedica en Red de Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain

关键词： Bayesian;Frequentist;Statistical Inference;Alzheimer's Disease;Longitudinal Study;Linear Mixed Effects;Neuroimaging;Magnetic Resonance Images

会议名称： Emerging Topics in Artificial Intelligence Conference

主办单位：

页码： 118040S.1-118040S.10

摘要： Large datasets of longitudinal data, made available for the neuroimage community, offer the possibility to study trajectories of biomarkers throughout the course of the diseases. For such modelling, approaches emerging from both the frequentist and the Bayesian frameworks have been suggested. When datasets are large, homogeneous, and balanced, both approaches seem to perform similarly. However, in compromised datasets, with limited number of samples and unbalanced data, this is not clear. Here we included data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study the behaviour of different statistical approaches using different dataset configurations. We used the hippocampal volume (HV) at different timepoints and we analysed the capability of the methods to find differences across clinical groups. For that, we used a Linear Mixed Effects (LME) modelling under both the frequentist and the Bayesian approaches. We started with a large, homogeneous, and symmetric database, and we created different configurations, by sequentially removing data points, to simulate different real-life situations. Using the frequentist approach to predict conversion on mild cognitively impaired patients, we found that we need a mean of 115 subjects to differentiate converters vs non converters. When classifying between the five ADNI clinical groups we need 147 subjects (mean across datasets) to differentiate between all clinical groups. With the Bayesian approach, we demonstrated that the results were stronger and of higher interpretability, specially at the borderline significant datasets.

分类号： tp2