Estimating cortical column sensory networks in rodents from micro-electrocorticograph (μECoG) recordings

Ricardo Pizarro, Tom Richner, Sarah Brodnick, Sanitta Thongpang, Justin Williams, Barry Van Veen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Micro-electrocorticograph (μECoG) arrays offer the flexibility to record local field potentials (LFPs) from the surface of the cortex, using high density electrodes that are sub-mm in diameter. Research to date has not provided conclusive evidence for the underlying signal generation of μECoG recorded LFPs, or if μECoG arrays can capture network activity from the cortex. We studied the pervading view of the LFP signal by exploring the spatial scale at which the LFP can be considered elemental. We investigated the underlying signal generation and ability to capture functional networks by implanting, μECoG arrays to record sensory-evoked potentials in four rats. The organization of the sensory cortex was studied by analyzing the sensory-evoked potentials with two distinct modeling techniques: (1) The volume conduction model, that models the electrode LFPs with an electrostatic representation, generated by a single cortical generator, and (2) the dynamic causal model (DCM), that models the electrode LFPs with a network model, whose activity is generated by multiple interacting cortical sources. The volume conduction approach modeled activity from electrodes separated < 1000 μm, with reasonable accuracy but a network model like DCM was required to accurately capture activity > 1500 μm. The extrinsic network component in DCM was determined to be essential for accurate modeling of observed potentials. These results all point to the presence of a sensory network, and that μECoG arrays are able to capture network activity in the neocortex. The estimated DCM network models the functional organization of the cortex, as signal generators for the μECoG recorded LFPs, and provides hypothesis-testing tools to explore the brain.

Original languageEnglish (US)
Pages (from-to)342-357
Number of pages16
StatePublished - Dec 2017

Bibliographical note

Funding Information:
This research was performed using the compute resources and assistance of the UW-Madison Center For High Throughput Computing (CHTC) in the Department of Computer Sciences. The CHTC is supported by UW-Madison , the Advanced Computing Initiative , the Wisconsin Alumni Research Foundation , the Wisconsin Institutes for Discovery , and the National Science Foundation , and is an active member of the Open Science Grid, which is supported by the National Science Foundation and the U.S. Department of Energy's Office of Science .

Funding Information:
The corresponding author would like to thank the Computation and Informatics in Biology and Medicine (CIBM) funds, training and support that make it possible to conduct this work. Ricardo Pizarro was supported by the CIBM Training Program , funded by the NLM training grant 5T15LM007359 .

Publisher Copyright:
© 2017 Elsevier Inc.


  • Cortical columns
  • DCM-shotgun
  • Dynamic causal model (DCM)
  • Micro-Electrocorticograph (μECoG)
  • Modeling
  • Sensory cortex
  • Volume conduction model


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