When exposing subjects to a continuous segment of an audiovisual movie, a large expanse of human cortex, especially in the posterior half of the cerebral cortex, shows stimulus-driven activity. However, embedded within this widespread activity, there are cortical regions whose activity is dissociated from the external stimulation. These regions are intercorrelated among themselves, forming a functional network, which largely overlaps with cortical areas previously shown to be deactivated by task-oriented paradigms. Moreover, the network of areas whose neuronal dynamics are associated with external inputs and the network of areas that appears to be intrinsically driven complement each other, providing coverage of most of the posterior cortex. Thus, we propose that naturalistic stimuli reveal a fundamental neuroanatomical partition of the human posterior cortex into 2 global networks: an "extrinsic" system, comprising areas associated with the processing of external inputs, and an "intrinsic" system, largely overlapping with the task-negative, default-mode network, comprising areas associated with--as yet not fully understood--intrinsically oriented functions.

The role of sex in biomedical studies has often been overlooked, despite evidence of sexually dimorphic effects in some biological studies. Here, we used high-throughput phenotype data from 14,250 wildtype and 40,192 mutant mice (representing 2,186 knockout lines), analysed for up to 234 traits, and found a large proportion of mammalian traits both in wildtype and mutants are influenced by sex. This result has implications for interpreting disease phenotypes in animal models and humans.

We are concerned with the detection of associations between random vectors of any dimension. Few tests of independence exist that are consistent against all dependent alternatives. We propose a powerful test that is applicable in all dimensions and is consistent against all alternatives. The test has a simple form and is easy to implement. We demonstrate its good power properties in simulations and on examples.

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We consider the problem of testing for partial conjunction of hypothesis, which argues that at least u out of n tested hypotheses are false. It offers an in-between approach to the testing of the conjunction of null hypotheses against the alternative that at least one is not, and the testing of the disjunction of null hypotheses against the alternative that all hypotheses are not null. We suggest powerful test statistics for testing such a partial conjunction hypothesis that are valid under dependence between the test statistics as well as under independence. We then address the problem of testing many partial conjunction hypotheses simultaneously using the false discovery rate (FDR) approach. We prove that if the FDR controlling procedure in Benjamini and Hochberg (1995, Journal of the Royal Statistical Society, Series B 57, 289-300) is used for this purpose the FDR is controlled under various dependency structures. Moreover, we can screen at all levels simultaneously in order to display the findings on a superimposed map and still control an appropriate FDR measure. We apply the method to examples from microarray analysis and functional magnetic resonance imaging (fMRI), two application areas where the need for partial conjunction analysis has been identified.