# 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

# (Re)connecting people at Jürgen Kurths’ 70th anniversary

Gorka Zamora-López

This is going to be a rather personal post but I thought it could be a good opener for a new section of the blog titled “Life in Academia.” I would like to write  (and I would like to invite others to write) about the daily experience of being a scientist and surviving (or struggling) through the academic system.

During the week of March 13 – 19, 2023, I returned to Berlin and Potsdam (where I lived for ten years and did my Ph.D.) for a visit with the occasion of the NDA23 conference to cellebrate Prof. Jürgen Kurths’ 70th birthday. The week turned into an emotional rollercoster for me. It was very touching to meet so many old colleagues and friends. And we certainly missed others who couldn’t be there.

I am aware Jürgen and his way of managing a large research group faced several detractors over the years. And surely he has been a tough person to negotiate with. Thankfully I rarely had to. But in the occasions he would reach to you and say “this or that needs to be done” you knew there was little margin. Those needs would range from basic things such as attending a talk or having a discussion with someone who was visiting the group, to help organising conferences.  As a student, I rarely felt those duties as an annoyance. Despite they would sometimes interrupt my flow, I could see the bigger picture behind. How that would help me become a better scientist and a better professional. Because, yes, science is not a hobby as many people tend to say. It is a profession and you need to learn to behave professionally. So, all in all, I am also convinced that many in academia simply didn’t grasp the human legacy that Jürgen was leaving and he still nurtures.
Continue reading (Re)connecting people at Jürgen Kurths’ 70th anniversary

# 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