Circular and unified analysis in network neuroscience
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...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2023-11-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/79559 |
| _version_ | 1797242527649103872 |
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| author | Mika Rubinov |
| author_facet | Mika Rubinov |
| author_sort | Mika Rubinov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T09:44:21Z |
| format | Article |
| id | doaj.art-1d14d1dcef134a46ae9d3e85af3404f4 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-24T18:40:38Z |
| publishDate | 2023-11-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-1d14d1dcef134a46ae9d3e85af3404f42024-03-27T12:57:54ZengeLife Sciences Publications LtdeLife2050-084X2023-11-011210.7554/eLife.79559Circular and unified analysis in network neuroscienceMika Rubinov0https://orcid.org/0000-0002-4787-7075Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt University, Nashville, United States; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United StatesGenuinely 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.https://elifesciences.org/articles/79559network neurosciencesystems neurosciencecomputational neurosciencestatistical modelsexplanatory modelsbenchmark models |
| spellingShingle | Mika Rubinov Circular and unified analysis in network neuroscience eLife network neuroscience systems neuroscience computational neuroscience statistical models explanatory models benchmark models |
| title | Circular and unified analysis in network neuroscience |
| title_full | Circular and unified analysis in network neuroscience |
| title_fullStr | Circular and unified analysis in network neuroscience |
| title_full_unstemmed | Circular and unified analysis in network neuroscience |
| title_short | Circular and unified analysis in network neuroscience |
| title_sort | circular and unified analysis in network neuroscience |
| topic | network neuroscience systems neuroscience computational neuroscience statistical models explanatory models benchmark models |
| url | https://elifesciences.org/articles/79559 |
| work_keys_str_mv | AT mikarubinov circularandunifiedanalysisinnetworkneuroscience |