Neuronal Models for EEG–fMRI Integration

TY - CHAP

T1 - Neuronal Models for EEG–fMRI Integration

AU - Hermes, Dora

AU - Siero, Jeroen C.W.

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2022.

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Human brain activity can be measured in many ways, providing different views into its functions. Common measurements of human brain function, such as functional MRI (fMRI) and electroencephalography (EEG), are often integrated to get the best spatial and temporal resolution, but these measurements frequently show differences. This chapter therefore considers how the pooling of signals across populations of neurons affects these measurements. We consider a modeling framework in which fMRI and field potential signals integrate across transmembrane currents in a population of neurons, because synaptic activity is thought to be the largest drive of the fMRI signal and transmembrane potentials are the biggest source of field potentials. We formulate computations that provide simplified abstractions that inform how levels of synaptic activity or synchrony across neurons contribute to fMRI or electrophysiological signals and how to interpret deviations or similarities between measurements. The modeling framework and computations highlight that the level of activity in a neuronal population influences each measurement, but synchrony only has a large effect on field potentials and not on the fMRI signal. An application to data from human visual cortex explains why certain signals correlate and others do not. Advancing the fundamental understanding of how different measurements integrate neuronal activity will be important to combine fMRI and EEG measurements to better understand human brain function.

AB - Human brain activity can be measured in many ways, providing different views into its functions. Common measurements of human brain function, such as functional MRI (fMRI) and electroencephalography (EEG), are often integrated to get the best spatial and temporal resolution, but these measurements frequently show differences. This chapter therefore considers how the pooling of signals across populations of neurons affects these measurements. We consider a modeling framework in which fMRI and field potential signals integrate across transmembrane currents in a population of neurons, because synaptic activity is thought to be the largest drive of the fMRI signal and transmembrane potentials are the biggest source of field potentials. We formulate computations that provide simplified abstractions that inform how levels of synaptic activity or synchrony across neurons contribute to fMRI or electrophysiological signals and how to interpret deviations or similarities between measurements. The modeling framework and computations highlight that the level of activity in a neuronal population influences each measurement, but synchrony only has a large effect on field potentials and not on the fMRI signal. An application to data from human visual cortex explains why certain signals correlate and others do not. Advancing the fundamental understanding of how different measurements integrate neuronal activity will be important to combine fMRI and EEG measurements to better understand human brain function.

KW - ECoG

KW - EEG

KW - Field potential

KW - fMRI

KW - iEEG

KW - Modeling

KW - Multimodal integration

KW - Neurovascular coupling

KW - Oscillations

KW - Power spectrum

KW - Synchrony

UR - http://www.scopus.com/inward/record.url?scp=85159020737&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-07121-8_25

DO - 10.1007/978-3-031-07121-8_25

M3 - Chapter

AN - SCOPUS:85159020737

SN - 9783031071201

SP - 625

EP - 638

BT - EEG-fMRI

PB - Springer International Publishing AG

ER -