Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.     

Non alcoholic fatty liver disease and risk of incident diabetes in subjects who are not obese

Background and aims

It is not known whether non alcoholic fatty liver disease (NAFLD) is a risk factor for diabetes in non obese, non centrally-obese subjects. Our aim was to investigate relationships between fatty liver, insulin resistance and a biomarker score for liver fibrosis with incident diabetes at follow up, in subjects who were neither obese nor centrally-obese.

Genetic toxicity assessment using liver cell models: past,present, and future

ABSTRACT

Genotoxic compounds may be detoxified to non-genotoxic metabolites while many pro-carcinogens require metabolic activation to exert their genotoxicity in vivo. Standard genotoxicity assays were developed and utilized for risk assessment for over 40 years. Most of these assays are conducted in metabolically incompetent rodent or human cell lines. Deficient in normal metabolism and relying on exogenous metabolic activation systems, the current in vitro genotoxicity assays often have yielded high false positive rates, which trigger unnecessary and costly in vivo studies. Metabolically active cells such as hepatocytes have been recognized as a promising cell model in predicting genotoxicity of carcinogens in vivo. In recent years, significant advances in tissue culture and biological technologies provided new opportunities for using hepatocytes in genetic toxicology. This review encompasses published studies (both in vitro and in vivo) using hepatocytes for genotoxicity assessment. Findings from both standard and newly developed genotoxicity assays are summarized. Various liver cell models used for genotoxicity assessment are described, including the potential application of advanced liver cell models such as 3D spheroids, organoids, and engineered hepatocytes. An integrated strategy, that includes the use of human-based cells with enhanced biological relevance and throughput, and applying the quantitative analysis of data, may provide an approach for future genotoxicity risk assessment.