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Peer-reviewedCC BY-NC-NDiScience

The dual interpretation of edge time series: Time-varying connectivity versus statistical interaction

  • Haily Merritt1,2,3
  • Amanda F. Mejia4,3
  • Richard F. Betzel1,5,6,7,8
8 affiliations
  1. 1Cognitive Science Program, Indiana University, Bloomington, IN, USA
  2. 2School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
  3. 3Indiana University
  4. 4Department of Statistics, Indiana University, Bloomington, IN, USA
  5. 5Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
  6. 6Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
  7. 7Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
  8. 8University of Minnesota
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Abstract

Brain activity and connectivity have been linked to ongoing behavior and mentation but usually in isolation and almost never in the same model. Here, we show that "edge time series"—a recently proposed method for tracking moment-to-moment connectivity changes—are equivalent to an interaction term in a linear model. By including terms for activations in the same model, it provides an elegant framework for assessing the relative explanatory power of edges and activations. In our work, we use this modeling framework to study time-varying behavior in zebrafish, worms, and humans. We find that connectivity contains unique explanatory power above and beyond activity.

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Cite

BibTeXRIS
BibTeX
@article{merritt2026dual,
  title = {{The dual interpretation of edge time series: Time-varying connectivity versus statistical interaction}},
  author = {Haily Merritt and Amanda F. Mejia and Richard F. Betzel},
  journal = {iScience},
  year = {2026},
  month = may,
  doi = {10.1016/j.isci.2026.115949},
  url = {https://doi.org/10.1016/j.isci.2026.115949},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - The dual interpretation of edge time series: Time-varying connectivity versus statistical interaction
AU  - Haily Merritt
AU  - Amanda F. Mejia
AU  - Richard F. Betzel
PY  - 2026
DA  - 2026/05/22
JO  - iScience
DO  - 10.1016/j.isci.2026.115949
UR  - https://doi.org/10.1016/j.isci.2026.115949
AB  - Brain activity and connectivity have been linked to ongoing behavior and mentation but usually in isolation and almost never in the same model. Here, we show that "edge time series"—a recently proposed method for tracking moment-to-moment connectivity changes—are equivalent to an interaction term in a linear model. By including terms for activations in the same model, it provides an elegant framework for assessing the relative explanatory power of edges and activations. In our work, we use this modeling framework to study time-varying behavior in zebrafish, worms, and humans. We find that connectivity contains unique explanatory power above and beyond activity.
SN  - 2589-0042
ER  -