Methods for inferring neural circuit interactions and neuromodulation from local field potential and electroencephalogram measures

Abstract

Electrical recordings of neural mass activity, such as local field potentials (LFPs) and electroencephalograms (EEGs), have been instrumental in studying brain function. However, these aggregate signals lack cellular resolution and thus are not easy to be interpreted directly in terms of parameters of neural microcircuits. Developing tools for a reliable estimation of key neural parameters from these signals, such as the interaction between excitation and inhibition or the level of neuromodulation, is important for both neuroscientific and clinical applications. Over the years, we have developed tools based on neural network modeling and computational analysis of empirical data to estimate neural parameters from aggregate neural signals. This review article gives an overview of the main computational tools that we have developed and employed to invert LFPs and EEGs in terms of circuit-level neural phenomena, and outlines future challenges and directions for future research.

Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease

“…For these reasons, current MRS approaches have limited utility for tracking variations of E/I across cortical regions or behavioral states. Recently, a range of novel features derived from electroencephalography (EEG) and magnetoencephalography (MEG) recordings have been described as robust proxy makers for noninvasive real‐time measurements of changes in E/I balance 18–22 . One of the most promising candidate E/I biomarkers is the exponent of the 1/f spectral power law, often referred to in the literature as the ‘1/f slope’.…”

“…Recently, a range of novel features derived from electroencephalography (EEG) and magnetoencephalography (MEG) recordings have been described as robust proxy makers for noninvasive real‐time measurements of changes in E/I balance. 18 , 19 , 20 , 21 , 22 One of the most promising candidate E/I biomarkers is the exponent of the 1/f spectral power law, often referred to in the literature as the ‘1/f slope’. Converging evidence from neuroimaging, pharmacological, chemogenetic, and computational modeling studies has linked changes in this marker to conditions related to altered E/I balance, 23 such as autism, 19 , 24 schizophrenia, 21 epilepsy, 25 , 26 and attention‐deficit/hyperactivity disorder.…”

Combining Aperiodic 1/f Slopes and Brain Simulation: An EEG/MEG Proxy Marker of Excitation/Inhibition Imbalance in Alzheimer’s Disease

“…For these reasons, current MRS approaches have limited utility for tracking variations of E/I across cortical regions or behavioural states. Recently, a range of novel features derived from electroencephalography (EEG) and magnetoencephalography (MEG) recordings have been described as robust proxy makers for noninvasive real-time measurements of changes in E/I balance (Ahmad et al 2022; Manyukhina et al 2022; Pablo Martínez-Cañada, Noei, and Panzeri 2021; Molina et al 2020; Gao, Peterson, and Voytek 2017). One of the most promising candidate E/I biomarkers is the exponent of the 1/f spectral power law, often referred to in the literature as the ‘1/f slope’.…”

Changes in theta and gamma oscillations of the medial prefrontal cortex following hippocampal-focused ultrasound stimulation

“…To overcome this limitation, the development of functional imaging or electrophysiological technology is required [8]. In previous neuromodulation studies using chemical and optogenetic methods, neural activity was more accurately measured using an electroencephalogram than using an extracellular microelectrode [1,9]. Also, electrophysiology in vivo during FUS sonication needs to be challenged due to the vibrations of recording electrodes and mechano-electrical coupling that can occur in the tissue [3][4][5]8].…”

“…The HPC-mPFC circuit has a fundamental role in cognitive functions such as short-term and long-term memory, attention, and decision-making, which are affected in several neurological diseases [10,[14][15][16]. During spontaneous behavior, functional connectivity between the HPC and mPFC can be inferred from electrophysiological recordings of spike activity and LFPs [9,16].…”