Efficient and robust identification of cortical targets in concurrent TMS-fMRI experiments

Jeffrey M. Yau, Jun Hua, Diana A. Liao, John E. Desmond

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Transcranial magnetic stimulation (TMS) can be delivered during fMRI scans to evoke BOLD responses in distributed brain networks. While concurrent TMS-fMRI offers a potentially powerful tool for non-invasively investigating functional human neuroanatomy, the technique is currently limited by the lack of methods to rapidly and precisely localize targeted brain regions - a reliable procedure is necessary for validly relating stimulation targets to BOLD activation patterns, especially for cortical targets outside of motor and visual regions. Here we describe a convenient and practical method for visualizing coil position (in the scanner) and identifying the cortical location of TMS targets without requiring any calibration or any particular coil-mounting device. We quantified the precision and reliability of the target position estimates by testing the marker processing procedure on data from 9 scan sessions: Rigorous testing of the localization procedure revealed minimal variability in coil and target position estimates. We validated the marker processing procedure in concurrent TMS-fMRI experiments characterizing motor network connectivity. Together, these results indicate that our efficient method accurately and reliably identifies TMS targets in the MR scanner, which can be useful during scan sessions for optimizing coil placement and also for post-scan outlier identification. Notably, this method can be used generally to identify the position and orientation of MR-compatible hardware placed near the head in the MR scanner.

Original languageEnglish (US)
Pages (from-to)134-144
Number of pages11
JournalNeuroImage
Volume76
DOIs
StatePublished - Aug 1 2013

Keywords

  • Concurrent TMS-fMRI
  • Connectivity
  • Interleaved TMS-fMRI
  • Stereotaxy

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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