Image credit : Pixabay
In few decades, cutting-edge technologies and methodologies have been speeding up advances in all neuroscientific disciplines. This amount of data exponentially increased, leading to new important evidences. Albeit, on one side, this stockpile of data embodies the pillars of science progress, on the other side, it conceals an underrated downside.
As long as this knowledge development draws apart among different disciplines (and sometime also within the same discipline), the perspective to infer regularities across data and thus an effective explanation of brain operation, lessens.
During the Cell satellite event of Society for Neuroscience 2016 in San Diego, I had the opportunity to listen to an inspiring symposium by Jeff Lichtman entitled “Does connectomics make sense?”. In his talk, the scientist presented an extraordinary pioneering technique that, starting from thin slices of tissue, allows to reconstruct the mouse brain cells shapes and their network of connections (known as “connectome”) in an accurate 3D image. What came out in this research is that in just one mm3, one might find 1.000.000.000 connections! Now, how much will we never be able to explain about brain function from that? How is it possible to get a sense of such surprisingly huge amount of complex information?
In the same occasion, during the following days, I eventually ended up in a discussion between a professor, with a background on physics and working on theoretical neurosciences and a researcher, with a background in physiology, more interested in cell microscopic scale mechanisms. The discussion was encouraged by the informal context of a bar and the two positions quickly got drifted apart. On one side the physicist argued that neurosciences need a deep reflexion on what was discovered so far, in order to find regularities in the data that allow an integrated explanation of the brain mechanisms. On the other side, the physiologist was postulating that the brain is structurally too complex to admit a unified perspective of it. Indeed, many years of research and papers in neuroscience have taught us that the brain is a very complex biochemical machine with an uncountable number of cells (even different type of cells!) making a huge number of connections among them (1.000.000.000 in each 1mm3!). In my opinion, beyond the fact that the second point of view is harshly pessimist, it is also representative of one of the biases that neurosciences suffer from. It is a sort of “intellectual apartheid” where who is working in a certain field, too often and too easily overestimates the potentiality of his/her methods of investigation compared to the others preventing cross-disciplinary communication and integration. Since most of the talks at the SfN were very specific and technical, only those with a certain expertise in the topic were able to understand the concepts.
With scientific progress and technical advances, specialization turns out to be a natural consequence. In this context, a U-turn in terms of openness toward other fields is needed anytime soon. During events in which there is the possibility to exchange information and expertise with people working in different fields, easy communication and flexibility should be a must. Integration is more and more important in order to yield originality in new information but above all to make a sense of the great amount of data that we already have and to find a coherent explanation of brain functions. Cross-disciplinary linkage uniformly lights up an object previously spotted by single beam light and to fully understand its beauty, future actions should combine all the frames caught so far.