Individualized time windows enhance TMS-EEG signal characterization and improve assessment of cortical function in schizophrenia

Abstract

Transcranial magnetic stimulation and electroencephalography (TMS-EEG) recordings are crucial to directly assess cortical excitability and inhibition in a non-invasive and task-free manner. TMS-EEG signals are characterized by TMS-evoked potentials (TEPs), which are employed to evaluate cortical function. Nonetheless, different time windows (TW) have been used to compute them over the years. Moreover, these TWs tend to be the same for all participants omitting the intersubject variability. Therefore, the objective of this study is to assess the effect of using different TWs to compute the TEPs, moving from a common fixed TW to more adaptive individualized TWs. Twenty-nine healthy (HC) controls and twenty schizophrenia patients (SCZ) underwent single-pulse (SP) TMS-EEG protocol. Firstly, only the HC were considered to evaluate the TEPs for three different TWs in terms of amplitude and topographical distribution. Secondly, the SCZ patients were included to determine which TW is better to characterize the brain alterations of SCZ. The results indicate that a more individualized TW provides a better characterization of the SP TMS-EEG signals, although all of them show the same tendency. Regarding the comparison between groups, the individualized TW is the one that provides a better differentiation between populations. They also provide further support to the possible imbalance of cortical excitability/inhibition in the SCZ population due to its reduced activity in the N45 TEP and greater amplitude values in the N100. Results also suggest that the SCZ brain has a baseline hyperactive state since the TEPs of the SCZ appear earlier than those of the HC.

This study is partially supported by the “Ministerio de Ciencia e Innovación (MICINN)” (grant ID PID2020-117751RB-I00), by the “Fundació La Marató de TV3” (grant ID 202219-30-31), by the “Gerencia Regional de Salud de Castilla y León” (grant ID GRS 2487/A/22), by the “Instituto de Salud Carlos III” (grant ID PI22/00465) and by the project HybridNeuro, a European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 101079392. GMM is in receipt of a FI-2022 grant from “Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR)”, AB is a Serra Húnter Fellow, and IFL is in receipt of predoctoral grants from the “Consejería de Educación, Junta de Castilla y León” (Spain) and the European Social Fund (grant IDs VA-183-18).

Peer Reviewed

Postprint (author's final draft)