Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems

Letter Binding and Invariant Recognition of Masked Words: Behavioral and neuroimaging evidence

d together in a specific order, because different words can be written with the same letters. The present research had two aims: first, to clarify the cerebral stages of processing that lead to invariant word recognition, and second, to examine whether those stages can proceed in the absence of consciousness. In literate adults, an extended strip of the left fusiform gyrus activates whenever visual words are presented (Cohen et al., 2000; Cohen et al., 2002; Nobre, Allison, & McCarthy, 1994). This region, which has been termed the Visual Word Form Area (VWFA), is responsive only to written words, not to spoken words (Dehaene, Le Clec'H, Poline, Le Bihan, & Cohen, 2002). Its lesioning results in a severe word identification impairment, pure alexia, which is restricted to the visual modality (Leff et al., 2001). Thus, it is a plausible candidate for the neural basis of invariant visual word recognition. To further specify the nature of word coding in the VWFA, we used the priming meth

Brainhack: a collaborative workshop for the open neuroscience community

Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science