A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings
Name
1-s2.0-S105381192300085X-main.pdf
Description
Published version
Size
4.13 MB
Format
Adobe PDF
Checksum (MD5)
dea1236d6ce66cf9a9c81ce391de3d12
Author(s)
Sanchez-Todo, Roser
Bastos, André M
Lopez-Sola, Edmundo
Mercadal, Borja
Santarnecchi, Emiliano
Miller, Earl K
Deco, Gustavo
Ruffini, Giulio
Date Issued
April 2023
Journal
NeuroImage
Publisher
Elsevier BV
Citation
Sanchez-Todo, Roser, Bastos, André M, Lopez-Sola, Edmundo, Mercadal, Borja, Santarnecchi, Emiliano et al. 2023. "A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings." NeuroImage, 270.
Version
Final published version
Abstract
Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.neuroimage.2023.119938
