Exploring target-specificity of immediate TMS effects using interleaved TMS-fMRI
Poster No:
66
Submission Type:
Abstract Submission
Authors:
Sarah Grosshagauer1, David Linhardt1, Anna-Lisa Schuler2, Maria Vasileiadi3, Manish Saggar4, Michael Woletz1, Martin Tik5
Institutions:
1Medical University of Vienna, Vienna, Austria, 2Max Planck Institute for Human Cognitive and Brain Sciences, Leipzip, Germany, 3Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, 4Stanford University, palo alto, CA, 5Medical University of Vienna, Wien, Vienna
First Author:
Co-Author(s):
Maria Vasileiadi
Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, University of Toronto
Toronto, Ontario
Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, University of Toronto
Toronto, Ontario
Introduction:
Transcranial magnetic stimulation (TMS) is a non-invasive technique used to modulate brain activity and has become a valuable psychiatric treatment option. The integration with functional magnetic resonance imaging (fMRI), allows for monitoring the immediate effects on brain functional connectivity (FC). The Dorsolateral Prefrontal Cortex (DLPFC) is a key region involved in a variety of networks (Braga & Buckner, 2017) and a highly important clinical target for TMS treatments. It has been shown repeatedly that targeting a specific portion of the DLPFC which is highly anticorrelated to anterior cingulate cortex (ACC) increases clinical efficacy (e.g. Fox et al., 2012; Weigand et al., 2018). While recent clinical trials focus on a single baseline connectivity-defined target (Batail et al., 2023; Ge et al., 2022), it remains unclear whether the reported TMS-induced changes in FC are network-specific. Therefore, we leveraged a recently published dataset to investigate the effects of single-pulse TMS targeting two DLPFC positions associated with different resting state (RS) networks.
Methods:
We analyzed a subsample of a publicly available dataset (OpenNeuro ds005498; Glick et al., 2024). More precisely, we included data from healthy participants (N=36) where baseline RS data, as well as single pulse TMS/fMRI (spTMSfMRI), delivered to two distinct targets in the DLPFC, were available. The two DLPFC targets comprised of a node of the salience network (SAL, MNI: -32, 42, 34) and a node of the default mode network (DMN, MNI: -38, 22, 48). These networks show different connectivity profiles, including directionality of ACC connections (Figure 1A). During interleaved TMS-fMRI measurements, 68 single pulses were applied in a jittered fashion (min. ISI: 2 s) during a total scan duration of 6 min 41 s.
Preprocessing was performed using fMRIPrep 24.0.0 (fmriprep.org; Esteban et al., 2019). Subsequent analysis was conducted in Python. Confound regression included the regression of 6 motion parameters, mean WM, CSF and global signal as well as bandpass filtering. Further, we applied a 6 mm FWHM Gaussian smoothing kernel. We calculated seed-based connectivity (MNI coordinates of each target, r=10 mm) for each subject during rest as well as during the spTMSfMRI runs. Resulting seedmaps went into a one-sample t-tests across subjects to identify the group networks and paired t-tests to assess changes between rest and TMS. In addition, we repeated similar analysis on a whole-brain FC level, by constructing 100x100 connectivity matrices for rest and TMS using Schaefer et al. (2018) brain parcellation. Finally, we compared whole-brain connectivity changes of the two stimulation targets to identify target-specific effects.
Preprocessing was performed using fMRIPrep 24.0.0 (fmriprep.org; Esteban et al., 2019). Subsequent analysis was conducted in Python. Confound regression included the regression of 6 motion parameters, mean WM, CSF and global signal as well as bandpass filtering. Further, we applied a 6 mm FWHM Gaussian smoothing kernel. We calculated seed-based connectivity (MNI coordinates of each target, r=10 mm) for each subject during rest as well as during the spTMSfMRI runs. Resulting seedmaps went into a one-sample t-tests across subjects to identify the group networks and paired t-tests to assess changes between rest and TMS. In addition, we repeated similar analysis on a whole-brain FC level, by constructing 100x100 connectivity matrices for rest and TMS using Schaefer et al. (2018) brain parcellation. Finally, we compared whole-brain connectivity changes of the two stimulation targets to identify target-specific effects.
Results:
Seed-connectivity of both DLPFC targets changed on a group level during TMS, most significant clusters of decreased target-connectivity being located in the ACC. Importantly, these areas were within the respective positively correlated networks of the seeds but are not specific to the stimulated target (Figure 1b). Concerning whole brain connectivity, connections decreased for both stimulation targets as compared to rest. A larger number of affected connections could be identified for the SAL target (X vs Y). Target specific connection changes, i.e. differences between TMS to SAL vs TMS to DMN, are outlined in figure 2b.
·Figure 1: Baseline connectivity of the two selected targets and their changes in seed-connectivity during spTMS/fMRI.
·Figure 2: Whole-brain connectivity for baseline and TMS/fMRI to the SAL and DMN target.
Conclusions:
Although changes in seed connectivity did not show target-specific changes with TMS, we observed trends towards decreased connectivity in the stimulated network in direct comparisons of TMS runs. Importantly, ACC connectivity seems to be highly sensitive to TMS, independent of the DLPFC target. Notably, changes in FC of this region have been shown to be a biomarker for rTMS depression treatment response (Mitra et al. 2023). This highlights the need for further studies to better understand the nuanced effects of TMS on target-specific connectivity dynamics and their potential clinical implications.
Brain Stimulation:
TMS 1
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Data analysis
FUNCTIONAL MRI
MRI
Other - TMS-fMRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Braga, R. M., & Buckner, R. L. (2017). Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity. Neuron, 95(2), 457-471.e5.
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116.
Fox, M. D., Buckner, R. L., White, M. P., Greicius, M. D., & Pascual-Leone, A. (2012). Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biological Psychiatry, 72(7), 595–603.
Ge, R., Humaira, A., Gregory, E., Alamian, G., MacMillan, E. L., Barlow, L., Todd, R., Nestor, S., Frangou, S., & Vila-Rodriguez, F. (2022). Predictive Value of Acute Neuroplastic Response to rTMS in Treatment Outcome in Depression: A Concurrent TMS-fMRI Trial. The American Journal of Psychiatry, 179(7), 500–508.
Glick, C., Gajawelli, N., Sun, Y., Badami, F., Saggar, M., & Etkin, A. (2024). Concurrent single-pulse (sp) TMS/fMRI to reveal the causal connectome in healthy and patient populations (p. 2024.09.25.614833). bioRxiv.
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