Recently I attended a conference dedicated to 'Big Data' with focus on neuroimaging. There were a lot of biostatisticians who shared their work and discussed various methods and approaches when encountering data from neuroimaging (MRI, CT, EEG, PET) - usually that being the prototype for 'Big Data' concept and a great source for generating some unified conslusions which one would think may yield scientificaly or clinicaly valuable or new findings.
However, what I was struck by is that a majority (with the exception of a few individuals) seemed to lack even the BASIC understanding of how the human central nervous system operates in real life, i.e. basic concepts of neurobiology. It sure does lower the bias of the observer and it's not on them to provide diagnoses or similar biological conclusions, but that's a whole lot of resources (Brain Project, Brain Initiative etc), that end up being spent for methodological and statistical masturbation (pardon my expression, but I'm still under the fresh impression of the lacking utilitarian logic behind their real efforts). There was a considerable number of method approaches described and most of these were applied to neuroimaging data obtained by a substantial amount of actual patients or animals, but with no general real-life conlusions, rather some sort of statistical method that works in that case and that case only. To give a plastic example, a rat was a subject in a classical T-maze test and neuron activity and spiking in prelimbic area was invasively measured. After considerable amount of analysis the conclusion was that certain neurons act as a central hub and communicate with much more cells that other, but it wasn't put into any context of the actual choice the rat made in the T-maze test - so from a stance of neruobiology it was just statistical 'playing' (something that was also regularly mentioned 'playing with data'). I don't have anything against playing, but at least constructive one (if it costs that much resources in the first place). There were a fair amount of limitations in their methodology in the first place which the speakers admitted themselves.
What worries me is that this represents sort of a corelate with what happened in financial sector with all those derivates (pardon me not being an economics expert, consider this in context, e.g. movie 'Big Short' showed the irresponsibility which caused major consequences) - eventually there isn't anything tangible to deal with. And human brain is very well a 3D structure. What I think is that biostatistics should be a service and an integrative part when dealing with 'Big Data' in neuroscience and letting biostatisticans alone to gather something meaningful for the neuro-community or wider, might backfire in a dead.alley with yielding a model after a model . which could finally make the public/government reluctant to give funding so eagerly in the future. Potential damage could also come in terms of wasted resources and generally holding back the true path in which the discovery of the brain's functioning is meant to be on.
I do apologize if I stepped over the line with some statements, but please do comment and constribute with your personal experience on the perspectives of 'Big Data' in neuroscience and how to make the most of it. I see tremendous potential, but coordination and communication in this interdisciplinary approach is something that I think is currently lacking. Only a couple of non-statisticians were present at the conference, whereas the topics presented were potentialy very clinicaly and scientificaly relevant. Who knows what is being done under the areas of 'neuroeconomics, neuroesthetics, neuroethics etc.' Could this do more harm than good to our research in neuroscience?
Neuro might not have a "true" meaning but your description of "silos" in the scientific field is a reality. There is a lot of talk about transactional research but more collaborative work in-between fields is needed. There is an ocean between neurophysiological and rehabilitation research.
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