The Disco-(nnected) Brain

Welcome to The Disco-(nnected) Brain, a blog to dance around diverse topics in circles and probably arrive nowhere.

I do research in the fields of complex networks, brain connectivity and other related topics such as graph theory or dynamical systems. As a scientist I write and publish my share of academic papers. However, I frequently have the impression that the classical pathway to debate through papers in academic journals is incomplete; slow and too stiff at times. Academic papers are – and should be – trustworthy informed reports, yes. But truth is, academic papers are also opinionated monologues and I usually find it hard to see actual debates flowing out. To me, the journal-paper-based scientific debate often feels more like a multilogue between deaf speakers.

I believe that as scientists we also need other – more informal – playgrounds for debate since the resolution of many issues requires a flexible and a dynamic exchange of views. Specially whenever divergent opinions meet on concepts, theories or methodological procedures. There is nothing wrong about being wrong. There is nothing wrong about being incomplete and opinionated. As long as this happens in an open and honest manner within the proper environment, and as part of a much needed exchange. And more importantly, if that exchange is a chance to reach well-informed conclusions about confusing matters and also to – why not – stablishing standard procedures and methodologies for issues that seem to perpetuate in the literature, floating around forever. Continue reading The Disco-(nnected) Brain

Network analysis abuses of null-models

Gorka Zamora-López

Analysing and interpreting data can be a complicated procedure, a maze made of interlinked steps and traps. There are no official procedures for how one should analyse a network. As it happens in many scientific fields the “standard” approach consists of a set of habits that have been popularised in the literature – repeated over-and-over again – without always being clear why we analyse networks the way we do.

Imagine we wanted to study an anatomical brain connectivity made of \(N = 214\) cortical regions (nodes) interconnected by \(L = 4,593\) white matter fibers (a density of \(\rho = 0.201\)). Following the typical workflow in the literature we would start the analysis by measuring a few basic graph metrics such as the degree of each node \(k_i\) and their distribution \(P(k_i)\), the custering coefficient \(C\) and the average pathlength \(l\) of the network. Imagine we obtain the empirical values \(C_{emp} = 0.497\) for the clustering and \(l_{emp} = 1.918\) for the average pathlength.
Continue reading Network analysis abuses of null-models