Spatial specificity of cortical reactivity: a TMS-EMG-EEG mapping of hand, arm, and leg motor areas
Poster No:
71
Submission Type:
Abstract Submission
Authors:
Maria Nazarova1, Macey Higdon1, Vadim Nikulin2, Tuomas Mutanen1, Elena Ukharova1, Joel Rouste1, Risto Ilmoniemi1, Pantelis Lioumis1, Hanna Renvall1
Institutions:
1Aalto University, Espoo, Finland, 2Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
First Author:
Co-Author(s):
Introduction:
There is no clear consensus on how to choose intensities for transcranial magnetic stimulation (TMS). A typical reference is the hand area's resting motor threshold (RMT) - the intensity needed to evoke motor-evoked potentials (MEPs) during rest in one of the distal upper limb muscles (Rossi et al., 2021). MEP, a corticospinal output, is unique for motor cortices, while TMS-evoked EEG potentials (TEPs) - corticocortical output - are available for investigation for the whole brain convexity (Hernandez-Pavon et al., 2023; Julkunen et al., 2022). MEPs and TEPs have very different neuronal sources, still in the majority of TMS-EEG studies, the intensity used is based on the hand RMT. Yet, even for the motor cortex itself, we do not understand how MEP and TEP thresholds correspond and how spatially specific they are.
It is known that the hand muscles' RMT is lower than that of the arm muscles, with the leg muscles' RMT being even higher. However, information about TEP thresholds along the motor cortex is lacking. Moreover, while movement execution is a routine way to increase the probability of obtaining MEPs, e.g., in clinical TMS investigation (Nazarova et al., 2021; Stinear et al., 2017), it is unknown how spatially specific this approach is for TMS thresholding.
This work aims to compare the TMS corticocortical and corticospinal outputs, including an approach to determine TEP thresholds in relation to RMT for hand, arm, and leg muscles during rest and isometric contractions.
It is known that the hand muscles' RMT is lower than that of the arm muscles, with the leg muscles' RMT being even higher. However, information about TEP thresholds along the motor cortex is lacking. Moreover, while movement execution is a routine way to increase the probability of obtaining MEPs, e.g., in clinical TMS investigation (Nazarova et al., 2021; Stinear et al., 2017), it is unknown how spatially specific this approach is for TMS thresholding.
This work aims to compare the TMS corticocortical and corticospinal outputs, including an approach to determine TEP thresholds in relation to RMT for hand, arm, and leg muscles during rest and isometric contractions.
Methods:
Ten healthy subjects (5 females, 23–33 years old.) participated in the study. We used individual MRI-navigated TMS and recorded EMG/EEG with a TMS-compatible system: 62 EEG channels with a mastoid reference and four bipolar EMG channels recording from abductor pollicis brevis, abductor digiti minimi, biceps brachii, and tibialis anterior muscles.
First, we performed a rough mapping of the motor cortex and found tentative EMG-based hotspots for the investigated muscles. Second, we tested the tentative EMG-based hotspots to evaluate TEPs online (Figure 1), checking two main factors: (1) TMS-related artifact, including scalp muscle activation, and (2) peak-to-peak amplitude of the early part of the TEP (N15–P30), to define an optimal TMS–EMG–EEG hotspot. We used individually thresholded noise masking to avoid the auditory component in the TEPs. Third, we looked for corticospinal (RMT) and corticocortical (TEP) thresholds in these TMS–EMG–EEG hotspots. The RMT was defined using an adaptive threshold hunting tool (by P. Julkunen, see Awiszus, 2003), and the TMS–EEG threshold was defined for the early TEP peak (N15–P30 peak-to-peak amplitude ~8 μV in at least one of the channels close to the stimulation site) (Figure 1). We iteratively recorded 15–20 TEPs to probe the early TEP amplitude while changing the stimulation intensity.
First, we performed a rough mapping of the motor cortex and found tentative EMG-based hotspots for the investigated muscles. Second, we tested the tentative EMG-based hotspots to evaluate TEPs online (Figure 1), checking two main factors: (1) TMS-related artifact, including scalp muscle activation, and (2) peak-to-peak amplitude of the early part of the TEP (N15–P30), to define an optimal TMS–EMG–EEG hotspot. We used individually thresholded noise masking to avoid the auditory component in the TEPs. Third, we looked for corticospinal (RMT) and corticocortical (TEP) thresholds in these TMS–EMG–EEG hotspots. The RMT was defined using an adaptive threshold hunting tool (by P. Julkunen, see Awiszus, 2003), and the TMS–EEG threshold was defined for the early TEP peak (N15–P30 peak-to-peak amplitude ~8 μV in at least one of the channels close to the stimulation site) (Figure 1). We iteratively recorded 15–20 TEPs to probe the early TEP amplitude while changing the stimulation intensity.
Results:
Our preliminary results demonstrate that in 17% of all cases, the thresholds for the early TEP peak were higher than the RMTs for that cortical spot. Both RMT and TEP thresholds increased from the hand to leg areas but to a variable extent among subjects.
Conclusions:
Besides providing us with the correspondence between the TEP and MEP thresholds, this study will help us better understand the motor cortex functional processes, such as surround inhibition.
Brain Stimulation:
TMS 1
Modeling and Analysis Methods:
Methods Development
Motor Behavior:
Motor Behavior Other
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Novel Imaging Acquisition Methods:
EEG 2
Keywords:
Cortex
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
Motor
MRI
Transcranial Magnetic Stimulation (TMS)
Other - TMS-EEG, motor mapping, thresholds
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hernandez-Pavon, J. C. (2023). TMS combined with EEG: Recommendations and open issues for data collection and analysis. Brain Stimulation, 16(2), 567–593.
Julkunen, P. (2022). Bridging the gap: TMS-EEG from lab to clinic. Journal of Neuroscience Methods, 369, 109482.
Nazarova, M. (2021). Multimodal Assessment of the Motor System in Patients With Chronic Ischemic Stroke. Stroke, 52(1), 241–249.
Rossi, S. (2021). Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert Guidelines. Clinical Neurophysiology, 132(1), 269–306.
Stinear, C. M. (2017). PREP2: A biomarker‐based algorithm for predicting upper limb function after stroke. Annals of Clinical and Translational Neurology, 4(11), 811–820.