Preliminary investigation of BOLD dependence on EEG–TMS using a closed-loop TMS–EEG–fMRI
Poster No:
54
Submission Type:
Abstract Submission
Authors:
Joonas Laurinoja1, Umair Hassan2, Christoph Zrenner3, Matilda Makkonen4, Mikko Nyrhinen4, Pantelis Lioumis4, Fa-Hsuan Lin3, Risto Ilmoniemi4, Dogu Baran Aydogan1
Institutions:
1University of Eastern Finland, Kuopio, Finland, 2Stanford University, Stanford, United States, 3University of Toronto, Toronto, Canada, 4Aalto University, Espoo, Finland
First Author:
Co-Author(s):
Introduction:
Transcranial magnetic stimulation (TMS) is a powerful tool for non-invasive brain stimulation, with versatile applications in research, diagnostics, and the treatment of psychiatric and neurological disorders. Currently, TMS is administered the same way to all individuals, resulting in inconsistent treatment outcomes. To increase its effectiveness, one approach involves personalizing the treatment by incorporating concurrent neuroimaging methods to guide the procedure. By integrating TMS with real-time neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), our objective is to tailor TMS treatment by optimizing stimulation timing, targeting, and dosing on an individualized basis. This abstract outlines our experimental configuration for closed-loop TMS–EEG–fMRI, and shares our preliminary findings on investigating brain state-dependent TMS–EEG responses and their correlation with concurrently measured BOLD activity in fMRI.
Methods:
The experimental setup consists of MRI-compatible EEG amplifiers (NeurOne Tesla, Bittium Ltd.), TMS stimulator (MagPro R30, MagVenture Inc.) and TMS coil (MagVenture MRi-B91). The EEG amplifiers and TMS coil are positioned within the MRI scanner (3T Siemens Skyra) bore. The TMS coil is attached to a custom-made coil holder arm [2] and is integrated with a slightly curved custom-made 8-channel MRI surface head coil array [3]. The subject is equipped with a 64-channel EEG cap (Easycap GmbH) that features seven custom-made carbon-wire-loops (CWLs) leveraged in EEG artifact suppression [6]. EEG is streamed to a real-time processing unit [4] operating on a modified firmware capable of suppressing MRI-induced artifacts and timing TMS to a predefined oscillatory brain state of EEG [5].
150 single TMS pulses were delivered to the left primary motor cortex (M1) of a right-handed healthy volunteer while the TMS-elicited network activity was monitored with interleaved fMRI acquisition. Functional images were acquired with a gradient-echo echo-planar-imaging (GE-EPI) sequence (TR = 2000 ms; TE = 26.0 ms; voxel resolution 2×2×2 mm3; flip angle 90°). Each TMS–fMRI run consisted of five 30-second "TMS" and "REST" blocks. Each TR included a silent 1-second gap during which a TMS pulse was delivered. 64-channel EEG was recorded at 20 kHz throughout the experiment.
The spatially filtered (surface Laplacian) phase of the signal in the C3 electrode at each TMS onset was extracted and divided into four parts: peak, trough, falling, and rising. fMRI images were slice-time and motion corrected, spatially smoothed, coregistrated with anatomical T1 images, and analyzed with SPM12 [7]. The design matrix included four regressors, each corresponding to a different EEG phase condition. Additionally, realignment parameters were included in the model as nuisance regressors.
150 single TMS pulses were delivered to the left primary motor cortex (M1) of a right-handed healthy volunteer while the TMS-elicited network activity was monitored with interleaved fMRI acquisition. Functional images were acquired with a gradient-echo echo-planar-imaging (GE-EPI) sequence (TR = 2000 ms; TE = 26.0 ms; voxel resolution 2×2×2 mm3; flip angle 90°). Each TMS–fMRI run consisted of five 30-second "TMS" and "REST" blocks. Each TR included a silent 1-second gap during which a TMS pulse was delivered. 64-channel EEG was recorded at 20 kHz throughout the experiment.
The spatially filtered (surface Laplacian) phase of the signal in the C3 electrode at each TMS onset was extracted and divided into four parts: peak, trough, falling, and rising. fMRI images were slice-time and motion corrected, spatially smoothed, coregistrated with anatomical T1 images, and analyzed with SPM12 [7]. The design matrix included four regressors, each corresponding to a different EEG phase condition. Additionally, realignment parameters were included in the model as nuisance regressors.
·An experimental setup for concurrent TMS–EEG–fMRI.
Results:
During TMS, a significantly increased (p < 0.05 FWE) fMRI signal was detected in M1 and the supplementary motor area (SMA) under the trough, falling, and rising phase conditions. However, the activation varied in strength and morphology across the conditions: TMS applied on the trough of EEG signal elicited broader network activity in SMA and M1 compared to other conditions. TMS applied during the EEG peak condition did not elicit significant activation (p < 0.05 FWE) in M1 nor SMA, which has been reported as a low-excitability state in earlier TMS–EEG studies with motor-evoked potentials (MEPs) [4]. The activated regions of M1 and SMA matched the activated sensorimotor network during a voluntary finger-tapping experiment.
·During TMS, a significantly increased (p < 0.05 FWE) fMRI signal was detected in M1 and supplementary motor area (SMA) under the trough, falling, and rising phase conditions.
Conclusions:
Here we present the preliminary results from our multimodal TMS–EEG–fMRI system. In the future, we aim for full real-time integration including acquisition and data processing of fMRI and EEG as well as TMS delivery. This technical endeavor could open new avenues of subject-specific research and development of novel diagnostic methods and treatment strategies for a variety of brain disorders.
Brain Stimulation:
Non-invasive Magnetic/TMS 1
TMS 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Methods Development
Keywords:
Electroencephaolography (EEG)
FUNCTIONAL MRI
Transcranial Magnetic Stimulation (TMS)
1|2Indicates the priority used for review
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[2] Lee, H. J. et al. (2023). “Design of coil holder for the improved maneuvering in concurrent TMS-MRI”. Brain Stimulation.
[3] Wu P.-Y. et al. (2015). “A 10-channel TMS-compatible planar RF coil array for human brain MRI at 3T”. Intl. Soc. Mag. Reson. Med., 625.
[4] https://sync2brain.com/
[5] Zrenner, C. et al. (2018). “Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex”. Brain Stimulation, 11(2), 374–389.
[6] Laurinoja, J. et al, (2023). “Accuracy of EEG-phase estimation for closed-loop brain stimulation inside the MRI device”. Brain Stimulation, 16(1), 302–303.
[7] http://fil.ion.ucl.ac.uk/spm/