On the variability of dynamic functional connectivity assessment methods

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Abstract

Backgr ound: Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker . However , various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this w ork, w e aimed to study the results variability of commonly used dFC methods. Methods: We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify over all, tempor al, spatial, and intersubject similarity. Results: Our results showed a range of weak to strong similarity between the results of differ ent methods, indicating considera b le ov erall v aria bility. Somewhat surprisingl y, the observ ed v aria bility in dFC estimates w as found to be compara b le to the expected functional connecti vity v ariation ov er time, emphasizing the impact of methodological choices on the final results. Our findings r ev ealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and adv anta ges. Conclusions: Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for mul- tianal ysis appr oaches and car eful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-dri v en dFC v ariations fr om ph ysiological confounds and de v eloping v alidation fr amew orks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC , which facilitates m ultianal ysis dFC assessment, with the goal of enhancing the r elia bility and interpr eta bility of dFC studies.

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Torabi, M., Mitsis, G. D., & Poline, J. B. (2024). On the variability of dynamic functional connectivity assessment methods. GigaScience, 13. https://doi.org/10.1093/gigascience/giae009

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