Computational Neuroscience’s Database Networked Future

Designed to Cure

Basic schematic of computationally derived networks within the brain. By Soon-Beom et. al. [Licensed under CC BY-SA 3.0], via Wikimedia Commons.
It’s not often in biology that a field of inquiry becomes distinct enough to branch off from its progenitors and form a new subfield, but that’s exactly what the creators of the freshly founded Network Neuroscience journal aim to do.1. “The Future Of Network Neuroscience.” April 2017, http://www.mitpressjournals.org/doi/full/10.1162/NETN_e_00005.] Published via the prestigious MIT Press Journals,1  the creators of the new journal argue that the future of neuroscience data “are relational in nature– they involve the large-scale mapping and recording of anatomical and functional interactions in neuronal systems, often across multiple scales.”

While providing a central aggregation point for all relational studies within neuroscience, Network Neuroscience promises that “the journal will mainly publish articles that report on primary research, articles that describe significantly new methods, algorithms and software tools, and network datasets.” Given the special emphasis on software and relational databases that Network Neuroscience is sure to have, researchers interested in implementing the journal’s findings would do well to have a software platform capable of keeping up with the latest methodologies and data sets.

Defining A Field

Before Network Neuroscience was a journal, it was a field in formation. Network neuroscience as a concept tracked the rise of the “big data” movement, and researchers quickly saw the potential for network science’s contributions to understanding neural pathways, temporal activation patterns, and functional interactions.2 As recently as 2015, researchers sought to unite the concepts of neuronal pathways and network science to explain higher order phenomena like cognition.3 Network neuroscience is a field that’s developing quickly, and now that it has a journal to call home, it’ll be easier for researchers to keep track of the latest developments and integrate them into their own practices.

Though neuroscientists will understand exactly what “network” neuroscience entails, most other biologists will only have a vague idea of the concept, so it bears a brief explanation. Within neuroscience, all biologists are familiar with the major taxonomic divisions of inquiry:

  • Neurophysiology
  • Cognitive neuroscience
  • Neurogenetics
  • Neurochemistry
  • Computational neuroscience
  • Molecular neuroscience
  • Cellular neuroscience

Each of these divisions seek to understand different phenomena at a given level of organization. Where these fields overlap, biologists readily recognize the consequent subfields:

  • Connectomics
  • Clinical neuroscience and neurology
  • Psychiatry
  • Systems neuroscience

So, where does network neuroscience fit? Is network neuroscience a high order discipline, creating abstractions of underlying phenomena in order to generate insights for downstream inquiry? Or is network neuroscience a methodology for understanding new phenomena with thin slices of information that span across multiple divergent disciplines? As it turns out, the articles within the new journal make good arguments for both possibilities and more. By combining computational methods and the data accumulated from physiological, cellular, molecular, and genetic information, researchers make a network that can adaptively respond to stimuli and conduct housekeeping activity– just like a real brain.

Networked Information Is Dense Information

In Network Neuroscience’s first issue, a paper published on the timings of neuronal pathway firing cuts across systems neuroscience, cellular neuroscience, and even neurophysics to show off the value of network science.4 While that paper wasn’t the first to investigate time as a property of neuronal networks, it’s important to note that tracking time in addition to cellular, chemical, and high level brain activation adds a massive data burden to the experiment.5 All of this data has to go somewhere that makes it easy to share, analyze, and publish.

Working with neural networks involves tracking nodes– nominally, neurons– and storing information about them. The larger scale the network, the more useful it is to examine experimentally– and the more data it produces. But introducing time as a variable to track for each node in the network effectively increases the amount of data to track exponentially. After all, while there are many studies that could benefit from careful examination of a static neural network, the real juicy breakthroughs lie in sussing out patterns and counter-patterns.

Hence the emphasis on the new journal’s inclusion of network data analytics and algorithms– it’s simply impossible to approach network neuroscience without having powerful software on your side. Software goes hand in hand with network neuroscience because researchers won’t even be able to plan an experiment without first having a model of the network they’re investigating. Luckily, the researchers who publish in the new journal are likely to have plenty of ideas for algorithmic analysis and experiment planning, but they probably don’t have any single unifying opinion on the basic data platform to perform all their laboratory’s research from.

Databases A Necessity

Most neuroscience laboratories will have sets of software tools which they can use to analyze networks on the scale of their research. For a neurophysiology lab, this might mean software capable of tracking a few dozen nodes in a neural network– impressive by the standards of 2005, but no longer good enough. The modern network neuroscience environment requires a software platform that can provide the tools to effectively analyze a wide and complex variety of scientific data and efficiently share results.  

If neuroscientists want to take advantage of the new insights published in Network Neuroscience, and indeed, reap the benefits of inquiry into the entire field, they’ll need to equip themselves with software that’s powerful enough to keep up with the vast and deep data required to form useful networks. Thankfully, there is such a software package that can bring neuroscience laboratories into the age of network neuroscience.

Designed to Cure is the network neuroscience analysis platform that your laboratory will need to dive into the world of big data network neuroscience. Using Designed to Cure, you’ll be able to upload neural network data, extract dense variables from simulations and experiments, and distribute your findings to your collaborators and lab personnel. Contact us today to find out how we can help your lab connect to the growing network of neuroscience as a connected data discipline.

  1.  http://www.mitpressjournals.org/
  2. “Contributions And Challenges For Network Models In Cognitive Neuroscience.” March 2014, https://www.nature.com/neuro/journal/v17/n5/full/nn.3690.html?foxtrotcallback=true.
  3. “Cognitive Network Neuroscience.” June 2015, http://www.mitpressjournals.org/doi/abs/10.1162/jocn_a_00810?journalCode=jocn.
  4.  “Spontaneous Brain Network Activity: Analysis Of Its Temporal Complexity.” June 2017, http://www.mitpressjournals.org/doi/abs/10.1162/NETN_a_00006.
  5.  “Time-Frequency Dynamics Of Resting-State Brain Connectivity Measured With fMRI.” March 2010, http://www.sciencedirect.com/science/article/pii/S1053811909012981?via%3Dihub.