Content posted to this wiki are contributions made by the IMAG research community.
Any questions or concerns should be directed to the individual authors. Full disclaimer statement found here

Cross-Spectral Factor Analysis

What is being modeled?
We are modeling multi-region electrophysiological recordings (Local Field Potentials)
Description & purpose of resource

The purpose of this resource is a computational framework of a machine learning technique to analyze multi-region electrophysiological recordings and learn electrical connectome networks that are related to outcomes of interest (e.g., mouse model of depression).  The learned networks are visualizable and explainable.

Spatial scales
Temporal scales
10-3 - 1 s
1 - 103 s
This resource is currently
mature and useful in ongoing research
Has this resource been validated?
Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)
Key publications (e.g. describing or using resource)

Talbot, A., Dunson, D., Dzirasa, K., & Carlson, D. (2020). Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. arXiv preprint arXiv:2004.05209.

Gallagher, N., Ulrich, K. R., Talbot, A., Dzirasa, K., Carin, L., & Carlson, D. E. (2017). Cross-spectral factor analysis. In Advances in Neural Information Processing Systems (pp. 6842-6852).

Hultman, R., Ulrich, K., Sachs, B. D., Blount, C., Carlson, D. E., Ndubuizu, N., ... & Dzirasa, K. (2018). Brain-wide electrical spatiotemporal dynamics encode depression vulnerability. Cell173(1), 166-180.

David Carlson
PI contact information
Table sorting checkbox