Neuroscience, like physics, astronomy and genomics, is being transformed by the advent of Big Data. This avalanche of information promises to revolutionize our understanding of the brain, but it requires that the scientific community be prepared to deal with the challenges it poses.
One of the main obstacles to be overcome is the integration of data from different scales and analysis techniques (Sejnowski, Churchland & Movshon, 2014). Neuroscience currently collects data at an impressive range of scales, from the molecular level to the level of whole brain systems, using techniques ranging from patch clamping to fMRI. However, the integration of these data is hampered by the lack of standardization and the diversity of animal models used.
The need to integrate data collected across different animal species, such as flies, worms, fish, mice, rats, monkeys, and humans, adds another layer of complexity (Sejnowski, Churchland, & Movshon, 2014). Each animal model has its advantages and disadvantages, and extrapolating results across species requires a deep understanding of comparative and evolutionary neurobiology.
Furthermore, functional data integration is often performed in isolation in individual laboratories, which limits the ability of the scientific community to share information and take full advantage of the potential of Big Data (Sejnowski, Churchland & Movshon, 2014). This “vertical” approach to integration, focused on specific problems, needs to be complemented by a “horizontal” approach that integrates data across a variety of problems, such as learning, decision-making, perception, emotion, and motor control.
To overcome these challenges, neuroscience needs to adopt a culture of data sharing and invest in advanced computational tools for data analysis in high-dimensional spaces (Sejnowski, Churchland & Movshon, 2014). The BRAIN initiative, which aims to record and manipulate large numbers of neurons during complex behavioral experiments, is an example of how the scientific community is moving in this direction.
The development of theories that explain the patterns emerging from the analysis of large data sets is also crucial for the advancement of neuroscience in the era of Big Data (Sejnowski, Churchland & Movshon, 2014). The scientific community needs to cultivate a new generation of researchers with computational training, capable of integrating data from multiple sources and developing innovative theoretical models.
In short, Big Data offers neuroscience an unprecedented opportunity to unlock the mysteries of the brain. However, to make the most of this opportunity, the scientific community needs to adapt to the challenges of the information age by adopting a culture of data sharing, investing in advanced computational tools, and cultivating a new generation of theorists.
Reference:
SEJNOWSKI, T.J.; CHURCHLAND, PS; MOVSHON, JA Putting big data to good use in neuroscience. Nature Neuroscience, vol. 17, no. 11, p. 1440-1441, 2014.
