A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

Abstract

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM), which represent the mean activity of large numbers of neurons. In order to properly reproduce experimental data, these models require the addition of further elements. Here we provide a framework integrating conduction physics that can be used to simulate cortical electrophysiology measurements, in particular those obtained from multicontact laminar electrodes. This is achieved by endowing NMMs with basic physical properties, such as the average laminar location of the apical and basal dendrites of pyramidal cell populations. We call this framework laminar NMM, or LaNMM for short. We then 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, based on the literature of columnar connectivity, a minimal neural mass model capable of generating amplitude and phase coupled slow (alpha, 4–22 Hz) and fast (gamma, 30–250 Hz) oscillations. The synapse layer locations of the two pyramidal cell populations are treated as optimization parameters, together with two more LaNMM-specific parameters, to compare the models with the multicontact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity of the model and data, where the 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 while 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.

A ubiquitous spectrolaminar motif of local field potential power across the primate cortex

“…Superficial to deep layer volume conduction has been proposed to explain why alpha/beta power appears relatively more powerful in superficial layers when calculated on CSD signals 13,34 – the opposite pattern that we observe in the unipolar LFP referenced to the top of cortex. Recent biophysical modeling, however, suggests an alternative interpretation: an alpha/beta power peak in deep layers in the local field potential (referenced to the top of cortex, as we have done in the present study) together with a superficial CSD power peak (as observed by 13,34 ) can both be modeled by considering the elongated cell bodies of deep layer pyramidal neurons which receive synaptic inputs in both their apical dendrites in superficial layers as well as near the cell body in deep layers 49,50 . This modeling work suggests that the alpha/beta-generating circuitry includes both superficial and deep layers and is more spatially extended (extends into deeper layers) than the gamma-generating circuitry.…”

AIT Foundations of Structured Experience

“…In KT, this is the neurobiological realization of the Comparator, which is reflected in the canonical microcircuit. In the concept of cortical canonical microcircuits, the cortical column is a motif replicated with minor variations in the cortex that contains the elements necessary to perform computations [Bastos et al, 2018;Sanchez-Todo et al, 2022], and where feedback and feed-forward information streams merge for comparison of data and models via the summation of excitation and inhibition signals.…”

Signal processing and computational modeling for interpretation of SEEG-recorded interictal epileptiform discharges in epileptogenic and non-epileptogenic zones

“…The integration of these aspects is crucial for solving the SEEG-forward problem and hence simulating realistic SEEG signals. The present model includes a laminar architecture that represents the cortical layers of the human brain, following the framework in [30,31] and also used in [32] for the analysis of seizure data. A laminar representation of a human cortical column of six layers with a physiological thickness [33] and uniform conductivity σ = 0.3 × 10 −3 S mm −1 [34] was considered.…”