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We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity(More)
Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that(More)
We argue that robust statistics has multiple goals, which are not always aligned. Robust thinking grew out of data analysis and the realisation that empirical evidence is at times supported merely by one or a few observations. The paper examines the outgrowth from this criticism of the statistical method over the last few decades. 1 A brief history(More)
Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase the power of detecting true between-group effects. We present an adaptive strategy that exploits the(More)
Correlations among suicide rate, unemployment rate, and enterprise failure were examined from 1976 to 1994 by using a Loess smoothing method. Significant positive correlations were found between suicide rate in men and unemployment and between suicide rate in both sexes and enterprise failure (p < .001).
Multistage carcinogenesis models describe the evolution of the cells in an individual's organ from a normal stage to a pre-neoplastic stage to a neoplastic stage. The triggers for the passage from one stage to the next one are presumed to be genetic alterations, which are not only governed by purely random events but also by individual environmental and(More)
Understanding brain structure and function can benefit from studying functional connectivity. A common methodology to measure functional connectivity between two brain regions is to estimate the correlation between their corresponding average time courses. Usually, these correlations are computed either via the Pearson estimator or the non-parametric(More)
Carcinogenesis is commonly described as a multistage process, in which stem cells are transformed into cancer cells via a series of mutations. In this article, we consider extensions of the multistage carcinogenesis model by mixture modeling. This approach allows us to describe population heterogeneity in a biologically meaningful way. We focus on finite(More)
Resting-state functional MRI (rs-fMRI) opens a window on large-scale organization of brain function. However, establishing relationships between resting-state brain activity and cognitive or clinical scores is still a difficult task, in particular in terms of prediction as would be meaningful for clinical applications such as early diagnosis of Alzheimer's(More)