Taxonomy of deep-water tetillid sponges (Porifera, Demospongiae, Spirophorina) from Brazil, with description of three new species and new characters.

Since the 19th century the deep-sea sponges from Brazil have been studied and many of them are still being discovered. This study describes five species of tetillid sponges from deep waters of the Brazilian economic exclusive zone; three are new, one is a new occurrence, and another is a known species which is here analyzed. The new species found are: Cinachyrella clavaeformis sp. nov. from the Columbia Seamount (85 m depth), Cinachyrella strongylophora sp. nov. from the Almirante Saldanha Seamount (270 m depth) and Craniella curviclada sp. nov. from slope of the Espírito Santo Basin (500 m depth). The two new species of Cinachyrella possess microacanthoxeas like those found in Cinachyrella kuekenthali (Uliczka 1929); this last species occurs in Caribbean region (4-100 m depth) and in N, NE and SE Brazil (0.2-100 m depth). The type material of Craniella corticata (Boury-Esnault 1973); from NE Brazil (75 m depth), has been found to be a synonym of Cinachyrella kuekenthali. Craniella crustocorticata van Soest 2017; from the Guyana shelf and slope (618-500 m depth), is here reported from the slope of NE and SW Brazil (400-700 m depth). A disorganized choanosomal skeleton (in Cinachyrella clavaeformis sp. nov.), strongyles (in Cinachyrella strongylophora sp. nov.) and a single-layered cortex of tangential oxeas (in Craniella crustocorticata) are new diagnostic characters that have led us to propose slight amendments in the definitions of Cinachyrella Wilson 1925 and Craniella Schmidt 1870. We discuss these and other morphological characters as well as their usefulness in Tetillidae. The diversity, distribution and bathymetry of tetillid sponges from Brazil are discussed and our knowledge of the composition of deep-sea sponges (deeper than 100 m) off Brazil is updated.

An algebraic topological method for multimodal brain networks comparisons.

Understanding brain connectivity is one of the most important issues in neuroscience. Nonetheless, connectivity data can reflect either functional relationships of brain activities or anatomical connections between brain areas. Although both representations should be related, this relationship is not straightforward. We have devised a powerful method that allows different operations between networks that share the same set of nodes, by embedding them in a common metric space, enforcing transitivity to the graph topology. Here, we apply this method to construct an aggregated network from a set of functional graphs, each one from a different subject. Once this aggregated functional network is constructed, we use again our method to compare it with the structural connectivity to identify particular brain regions that differ in both modalities (anatomical and functional). Remarkably, these brain regions include functional areas that form part of the classical resting state networks. We conclude that our method -based on the comparison of the aggregated functional network- reveals some emerging features that could not be observed when the comparison is performed with the classical averaged functional network.