Putting big data to good use in neuroscience

Abstract

Big data has transformed fields such as physics and genomics. Neuroscience is set to collect its own big data sets, but to exploit its full potential, there need to be ways to standardize, integrate and synthesize diverse types of data from different levels of analysis and across species. This will require a cultural shift in sharing data across labs, as well as to a central role for theorists in neuroscience research.Big data, the buzz phrase of our time, has arrived on the neuroscientific scene, as it has already in physics, astronomy and genomics. It offers enlightenment and new depths of understanding, but it can also be a bane if it obscures, obstructs and overwhelms. The arrival of big data also marks a cultural transition in neuroscience, from many isolated 'vertical' efforts applying single techniques to single problems in single species to more 'horizontal' efforts that integrate data collected using a wide range of techniques, problems and species. We face five main issues in making big data work for us.First, data in neuroscience exist at an astonishing range of scales of both space and time. Neuroscientific data are obtained from a wide range of techniques, from patch clamping to optogenetics to fMRI (Fig. 1). Most of these techniques are used one at a time. One lab will record spikes from an array of neurons, but not be able to determine which types of neurons they are or how they are connected to other neurons. Another lab will reconstruct the wiring diagram of the same circuit, but without recording data to identify the properties of the reconstructed neurons. In some heroic cases, functional data have been laboriously combined with anatomical reconstructions 1 , but rarely if ever in a broad behavioral context.

Stimulus-dependent spiking relationships with the EEG

“…Typically, EEG is useful for implicating activity in parts of the brain on the order of a cubic centimeter (Sejnowski et al 2014). The computational machinations of the neurons within that volume that give rise to the particular overlying EEG signal are too minute to be probed by EEG.…”

Cerebral cartography and connectomics

“…Almost certainly, the future of both fields will bring a flood of data ('big data' as it is fashionably called) that will require a whole new set of analytic and visualization tools. But one hopes that, in addition to more data, there will also be progress in creating a theoretical framework for understanding the brain [151]-a framework that serves to organize our rapidly increasing knowledge and reveals fundamental principles of operation. If current trends prevail, network science will be a cornerstone of this framework, paving the way towards a more complete understanding of the brain as a complex networked system.…”

Ethical Challenges of Simulation-Driven Big Neuroscience

“…However, neuroscientists face numerous problems when trying to integrate diverse data sets into a coherent understanding of brain functioning, which will require a cultural shift in not only sharing data across laboratories, but also making theorists central players in its analysis and interpretation (Sejnowski et al 2014). One example concerns the re-use of so-called "long-tail" data, small data sets whose reuse is often stymied by a lack of community data-sharing standards (Ferguson et al 2014 In summary, models, simulations and large-scale ICT infrastructures acquire new functions within neuroscience as instruments to integrate systemic biological knowledge, gained on all levels of neuronal organization.…”