Network Neuroscience: A hands-on guide
By Alexander Albury
The brain is a densely connected system made up of many different regions that coordinate to produce our thoughts and behavior. Neuroscientists face a challenging goal when trying to make sense of the many structures, and functions of the brain. Modern analytical techniques emphasizing connections between brain areas are changing how neuroscientists study the brain.
With modern neuroimaging techniques, we can collect more and more detailed information about the brain. Different imaging methodologies such as MRI, EEG, MEG, and fNIRS all provide valuable information for neuroscientists. The problem is, they provide a lot of it. Whether it’s ultra high-field MRI or sub-millisecond level EEG, the decision of what to do with this information has always been a challenge for neuroscientists. We can’t look at every neuron of the brain individually. Doing so would be extremely time intensive, and more importantly, neurons don’t work in isolation. Instead, it can be easier to visualize the brain as a network; several interconnected parts communicating in synchrony towards common goals.
In fact, many neuroscientists do think of the brain as a network, and not just figuratively. Network neuroscience has become popular in recent years as a perspective to study the brain in its entirety; not as distinct modules but as a cohesive network. Network neuroscience uses analytical techniques to integrate data from different brain areas and measures how these brain areas interact with each other. However, these analysis techniques are cutting-edge and ever-evolving, and can appear intimidating from the outside. One group of researchers set out to make network neuroscience more accessible, in the hope that more neuroscientists can take advantage of these powerful tools.
Eduarda Centeno and colleagues created a tutorial for understanding and applying network neuroscience techniques. In a paper published in Brain Structure and Function, they present two theories of network analysis—graph theory and topological data analysis (TDA)—along with examples of common metrics used to study brain networks in each. Each metric is accompanied by a description of what it represents, and how it’s calculated.
But the researchers take this a step further than just reviewing these techniques. The paper is intended to be a hands-on tutorial, with relevant Python libraries and code included for all examples. They even facilitate more interactive learning by sharing all of the code in an online Jupyter Notebook. This commitment towards accessibility and reproducibility earned the paper the Best Neuroimaging Paper award from Brain Structure and Function at the 2023 OHBM Annual Meeting.
The authors recognize that the technical aspects of network neuroscience can act as barriers for researchers who might be interested in using these techniques. They hope that through this tutorial they can encourage more neuroscientists to incorporate network analysis into their research. Although they use fMRI data in their tutorial, the authors point out that network analysis can be applied to other types of data as well and is not limited to data from neuroimaging.
Additionally, the complexity of network analysis involves a lot of decision making on the researcher’s part, which can lead to different researchers analyzing their data in very different ways. The authors hope that by creating this tutorial they will make network neuroscience more standardized and reproducible, making it easier to compare findings across studies.
Original Research: Centeno, E. G. Z., Moreni, G., Vriend, C., Douw, L., & Santos, F. A. N. (2022). A hands-on tutorial on network and topological neuroscience. Brain Structure & Function, 227(3), 741–762. https://doi.org/10.1007/s00429-021-02435-0
