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Circular and unified analysis in network neuroscience

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  • Rubinov, Mikail

Abstract

Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.

Suggested Citation

  • Rubinov, Mikail, 2023. "Circular and unified analysis in network neuroscience," OSF Preprints mdqak_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:mdqak_v1
    DOI: 10.31219/osf.io/mdqak_v1
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    1. Dean Prichard & James Theiler, 1994. "Generating Surrogate Data for Time Series with Several Simultaneously Measured Variables," Working Papers 94-04-023, Santa Fe Institute.
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