Effect of group-based vs individualized stimulation site selection on reliability of network-targeted TMS.

Neuroimage

Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address:

Published: December 2022

Background: Transcranial magnetic stimulation (TMS) is a widely used technique for the noninvasive assessment and manipulation of brain activity and behavior. Although extensively used for research and clinical purposes, recent studies have questioned the reliability of TMS findings because of the high inter-individual variability that has been observed.

Objective: In this study, we compared the efficacy and reliability of different targeting scenarios on the TMS-evoked response.

Methods: 24 subjects underwent a single pulse stimulation protocol over two parietal nodes belonging to the Dorsal Attention (DAN) and Default Mode (DMN) Networks respectively. Across visits, the stimulated target for both networks was chosen either based on group-derived networks' maps or personalized network topography based on individual anatomy and functional profile. All stimulation visits were conducted twice, one month apart, during concomitant electroencephalography recording.

Results: At the network level, we did not observe significant differences in the TMS-evoked response between targeting conditions. However, reliable patterns of activity were observed- for both networks tested- following the individualized targeting approach. When the same analyses were carried out at the electrode space level, evidence of reliable patterns was observed following the individualized stimulation of the DAN, but not of the DMN.

Conclusions: Our findings suggest that individualization of stimulation sites might ensure reliability of the evoked TMS-response across visits. Furthermore, individualized stimulation sites appear to be of foremost importance in highly variable, high order task-positive networks, such as the DAN.

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http://dx.doi.org/10.1016/j.neuroimage.2022.119714DOI Listing

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