A Statistical Method to Distinguish Functional Brain Networks

Abstract

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001).

The Cognitive Information Effect of Televised News

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“…However, the functional brain networks differ among people from the same population, due to personalized characteristics. Nonetheless, the functional networks of humans from diverse populations can be generated using a similar stochastic process (Fujita et al, 2017). Mathematical modeling of neural activity suggests that the internal and external variability is strikingly similar (Dumont et al, 2016).…”

Can Neuroscience Assist Us in Constructing Better Patterns of Economic Decision-Making?

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