Alternative Labeling Tool: a minimal algorithm for denoising single-subject resting-state fMRI data with ICA-MELODIC

Subject-level independent component analysis (ICA) is a well-established and widely used approach in denoising of resting-state functional magnetic resonance imaging (fMRI) data. However, approaches such as ICA-FIX and ICA-AROMA require advanced setups and/or are computationally intensive. Here, we aim to introduce a user-friendly, computationally lightweight toolbox for labeling independent signal and noise components, termed Alternative Labeling Tool (ALT). ALT uses two features that require manual tuning: proportion of an independent component’s spatial map located inside gray matter and positive skew of the power spectrum. ALT is tightly integrated with the commonly used FMRIB’s statistical library (FSL). Using the Open Access Series of Imaging Studies (OASIS-3) ageing dataset (n=30), we found that ALT shows a high degree of inter-rater agreement with manual labeling (over 86% of true positives for both signal and noise components on average). Crucially, denoising using ALT-generated labels significantly reduced mean framewise displacement (p<0.001). In conclusion, ALT can be extended to small and large-scale datasets when the use of more complex tools such as ICA-FIX is not possible. ALT will thus allow for more widespread adoption of ICA-based denoising of resting-state fMRI data.

60 years of ”Statistical News”

The article aims at presenting a synthetic overview of the ”Statistical News” genesis, its scientific editorial board and the profile of topics covered over its 60-year existence. It provides also the picture of the future scenario of the journal, focusing on the way of its dissemination. In the contextual analysis of the journal, apart from its content, the annotated bibliographic records published in a multivolume series Bibliografia wydawnictw GUS within 1968—2016 were used along with the resources of the Stefan Szulc Central Statistical Library and biographical compilations of the Polish statisticians.

Statistical methods for body mass index: A selective review

Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index, is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. To guide effective public health action, we need to understand the complex system of intercorrelated influences on body mass index. This paper, based on all eligible articles searched from Global health, Medline and Web of Science databases, reviews both classical and modern statistical methods for body mass index analysis. We give a description of each of these methods, exploring the classification, links and differences between them and the reasons for choosing one over the others in different settings. We aim to provide a key resource and statistical library for researchers in public health and medicine to deal with obesity and body mass index data analysis.

A Mixture of Two Burr Type III Distributions: Identifiability and Estimation under Type II Censoring

The mixture of two Burr Type III distributions (MTBIIID) is investigated. First, the identifiability property of the MTBIIID is proved. Then, two different methods of estimation are used. Next, the estimates of the unknown five parameters and reliability function of the MTBIIID under Type II censoring are obtained. To study the performance of the estimation technique in the paper, a Monte Carlo simulation is presented. In addition, the numerical illustration requires solving nonlinear equations; therefore, the software international mathematical statistical library (IMSL) is used to assess these effects numerically. Finally, a real data set is applied to illustrate the methods proposed here.