BiomedicinesNeurosciencePeer-reviewed journal article
Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study
Laura MarzettiiD, Alessio BastiiD, Roberto GuidottiiD, Antonello BaldassarreiD, Johanna MetsomaaiD, Christoph ZrenneriD, Antea D’AndreaiD, Saeed MakkinayeriiD, Giulia PieramicoiD, Risto Juhani IlmoniemiiD, Ulf ZiemanniD, Gian Luca RomaniiD, Vittorio PizzellaiD
Abstract
State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focused on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the motor network (MN) and how MN state affects responses to transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to left M1 in 8 subjects at rest, with simultaneous EEG. Motor evoked potentials (MEPs) were measured from the right hand. Source-space functional connectivity of left M1 to the whole-brain was assessed using the imaginary part of the Phase Locking Value at the frequency of the sensorimotor {micro}-rhythm in a 1-second window before the pulse. Group-level connectivity revealed functional links between left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At single-subject level, this relation is more expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity based NIBS. HighlightsO_LIEEG pre-stimulus connectivity of left M1 largely corresponds to the motor network C_LIO_LIStronger motor network (MN) connectivity corresponds to greater MEP amplitudes C_LIO_LILinear regression models based on MN connectivity predicts MEP amplitude C_LI
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
- Citations
- 16
- FWCI
- 2.34
- Field percentile
- top 11%
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