Improving Coil Setup and Data Processing Strategies for Concurrent MRI and Brain-Stimulation Studies
Poster No:
56
Submission Type:
Abstract Submission
Authors:
Michael Burke1, Yiwu Xiong1, Lorena de Melo1, Kuri Takahashi1, Maximilian Lueckel2, Emilio Chiappini1, Til Ole Bergmann3, Erhan Genc1
Institutions:
1Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany, 2Neuroimaging Center (NIC), Focus Program Translational Neuroscience, Johannes Gutenberg University, Mainz, Germany, 3Johannes-Gutenberg University Medical Center, Mainz, Rhineland-Palatinate
First Author:
Co-Author(s):
Maximilian Lueckel
Neuroimaging Center (NIC), Focus Program Translational Neuroscience, Johannes Gutenberg University
Mainz, Germany
Neuroimaging Center (NIC), Focus Program Translational Neuroscience, Johannes Gutenberg University
Mainz, Germany
Emilio Chiappini
Leibniz Research Centre for Working Environment and Human Factors
Dortmund, Germany
Leibniz Research Centre for Working Environment and Human Factors
Dortmund, Germany
Introduction:
Transcranial magnetic stimulation (TMS) is an established non-invasive method for stimulating the human brain. However, its neurophysiological and behavioral effects remain poorly understood. The concurrent application of TMS and fmri provides a robust research approach that merges TMS's causal capabilities with fMRl's high spatial resolution. Here, we conduct a comparative assessment of two different TMS-fMRI setups and preprocessing methods, all of which will be compared to data acquired using a standard 64ch head coil without stimulation. Our primary objective is to elucidate the constraints inherent in current procedures and thereby define optimal strategies for probing the impact of brain stimulation on both behavior and neural activity. This investigation holds significant promise for advancing future research employing this sophisticated technique.
Methods:
1. Acquisition
a) Testing 3 MRI head coils: 64-Channel coil (Fig1), Custom-made coil setup with two 18ch body array coils wrapped around the head using a home-made holder for maximum accessibility and space for TMS stimulation equipment , commercially available MRI-TMS coil (Navarro et al. MRM 74:1492-1501) consisting of two 7ch coils, one of the two 7ch coils has a MR compatible TMS coil attached, for MR acquisition.
b) MRI sessions included resting-state fMRI (rsfMRI) and anatomical MRI at our 3T Prisma scanner. For rsfMRI (10 min), participants were asked to keep their eyes closed. Multiband-multiecho (MBME) EPI sequence: TR=1250 ms, multiband factor=3, and 3 TE times (13, 35, 56 ms).
2. Preprocessing
a) Homogenization: Spatial signal intensity homogenization was done by applying a signal intensity correction profile obtained from two images acquired with the respective receive coil and with the scanners integrated body coil.
b) Multi echo fMRI data were used to calculate T2*-maps by fitting an exponential decay curve. S0 maps were calculated for each EPI image and time courses with increased SNR were extracted from these S0 maps for further analysis.
3. Postprocessing
a) Independent component analysis (ICA) was performed to obtain brain networks from rsfMRI data using FSL's MELODIC.
b) Motor and visual ICA networks were identified based on cross-correlation analyses with a set of major brain networks as described by Smith et al. PNAS 106, 13040-13045 (2009)
c) Spatial similarities of visual and motor ICAs were compared using the Dice coefficient (0: no similarity, 1: identical spatial overlap of components).
a) Testing 3 MRI head coils: 64-Channel coil (Fig1), Custom-made coil setup with two 18ch body array coils wrapped around the head using a home-made holder for maximum accessibility and space for TMS stimulation equipment , commercially available MRI-TMS coil (Navarro et al. MRM 74:1492-1501) consisting of two 7ch coils, one of the two 7ch coils has a MR compatible TMS coil attached, for MR acquisition.
b) MRI sessions included resting-state fMRI (rsfMRI) and anatomical MRI at our 3T Prisma scanner. For rsfMRI (10 min), participants were asked to keep their eyes closed. Multiband-multiecho (MBME) EPI sequence: TR=1250 ms, multiband factor=3, and 3 TE times (13, 35, 56 ms).
2. Preprocessing
a) Homogenization: Spatial signal intensity homogenization was done by applying a signal intensity correction profile obtained from two images acquired with the respective receive coil and with the scanners integrated body coil.
b) Multi echo fMRI data were used to calculate T2*-maps by fitting an exponential decay curve. S0 maps were calculated for each EPI image and time courses with increased SNR were extracted from these S0 maps for further analysis.
3. Postprocessing
a) Independent component analysis (ICA) was performed to obtain brain networks from rsfMRI data using FSL's MELODIC.
b) Motor and visual ICA networks were identified based on cross-correlation analyses with a set of major brain networks as described by Smith et al. PNAS 106, 13040-13045 (2009)
c) Spatial similarities of visual and motor ICAs were compared using the Dice coefficient (0: no similarity, 1: identical spatial overlap of components).
Results:
Correlation of ICA components with visual component as identified by Smith et al. increased with data preprocessing (from r=0.48 to r=0.59, 64ch,r=0.59 to r=0.61 for body array and r=0.33 to r=0.42 MRI-TMS-coil) and slightly increased for motor components (r=0.33 to r=0.37 for 64ch, r=0.26 to r=0.32 for body array, and remained unchanged for the MRI-TMS coil).
Dice coefficient showed higher similarity of components obtained with 64-channel coil and body array coil setup. However, the spatial similarity of components obtained with MRI-TMS coil vs. 64-channel coil or body array coil was lower for visual and motor networks identified by ICA.
Dice coefficient showed higher similarity of components obtained with 64-channel coil and body array coil setup. However, the spatial similarity of components obtained with MRI-TMS coil vs. 64-channel coil or body array coil was lower for visual and motor networks identified by ICA.
Conclusions:
Brain networks identified from rs-fMRI spatially varied depending on the coil setup and data preprocessing strategy used. Results obtained from wrapped around body array coil setup closer resembled the findings obtained with the 64-channel coil. The optimal coil configuration differs depending on whether brain networks or only local cortical activity in proximity to the coil are under investigation. On the other hand, the MRI-TMS coil provides best accessibility for brain stimulation whereas no brain TMS brain stimulation can be performed using the 64ch coil, the best compromise with respect to access and with improved data preprocessing strategies is the body array setup for deep brain and brains network studies. With the enhanced coil setup we will be able to improve concurrent brain networks studies during brain stimulation.
Brain Stimulation:
Non-invasive Magnetic/TMS 1
Modeling and Analysis Methods:
Methods Development 2
Task-Independent and Resting-State Analysis
Keywords:
Data analysis
FUNCTIONAL MRI
MRI
STRUCTURAL MRI
Transcranial Magnetic Stimulation (TMS)
1|2Indicates the priority used for review
Provide references using author date format
(2) Ahmed, Z. et al. ME-ICA/tedana: 23.0.1. (2023) doi:10.5281/ZENODO.1250561
(3) Smith, S. M. et al. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. U.S.A. 106, 13040-13045 (2009).