A unified framework for group independent component analysis for multi-subject fMRI data

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT [Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–151.] and tensor PICA [Beckmann, C.F., Smith, S.M., 2005. Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25, 294–311.], make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation–Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation.     

激活似然估计法分析运动相关专业人员脑灰质可塑性

目的 采用激活似然估计（ALE）荟萃分析方法观察长期专业技能训练诱发运动相关专业人员大脑灰质结构的可塑性。方法 于CNKI、PubMed、Web of Science数据库及Google scholar检索2005年1月1日-2020年6月12日基于体素形态学分析（VBM）运动相关专业人员大脑灰质变化的文献,以Ginger ALE软件包对检索结果进行Meta分析。结果 共纳入21项研究,包括396名专业运动员（试验组）和538名健康对照者（对照组）。与对照组相比,试验组左侧脑岛、右侧顶下小叶、右侧额中回、右侧前扣带回、左侧楔前叶及右侧颞上回灰质体积增大;右侧小脑前叶、右侧小脑顶、左侧海马旁回、右侧丘脑、右侧小脑后叶、右侧额上回及左侧楔前叶灰质密度增加。结论 运动相关专业人员多个脑区灰质结构可塑性可能与其所受长期专业技能训练相关。     

The absence of `minor' risk factors for recurrent venous thromboembolism: a systematic review of negative predictive values and negative likelihood ratios

Summary.  Background : The decision to stop anticoagulation after an episode of venous thromboembolism (VTE) depends on the risk of recurrence. A number of recently discussed risk factors that are not routinely used in clinical practice might be useful for the identification of patients with a low risk of recurrence. Objectives : The aim of this study was to determine the negative predictive value and likelihood ratio of the absence of each of these factors in patients with VTE. Patients/methods : We performed a systematic review of the literature, calculated negative predictive values and likelihood ratios, and summarized the available evidence. Results : A negative D-dimer result, non-elevated factor VIII level and non-elevated thrombin generation after discontinuation of anticoagulant treatment, female sex and distal location might be useful in determining a low risk of recurrence in patients with VTE. Each of these factors individually is associated with a negative predictive value for recurrence-free survival in the next 2–3 years of about 90% (with an a priori risk of about 85%), with negative likelihood ratios between 0.8 and 0.5. Some of the risk factors for first VTE event (FV Leiden and prothrombin mutation) are not useful at all for prediction of non-recurrence. Conclusions : None of the risk factors that we identified is strong enough on its own to guide treatment. Combinations of risk factors might be more useful, but additional data on the independence of these factors are required before a prediction rule can be designed.