# Improving behavior with TMS: A concurrent TMS-fMRI approach > **NIH NIH R21** · GEORGIA INSTITUTE OF TECHNOLOGY · 2021 · $197,250 ## Abstract Project Summary. Transcranial magnetic stimulation (TMS) is a promising tool for the treatment of a large number of neuropsychiatric disorders including depression, obsessive-compulsive disorder, Alzheimer’s disease, and addiction. In fact, TMS has already been approved by the Food and Drug Administration (FDA) for depres- sion treatment, testifying to its effectiveness. However, the effects of TMS are known to vary substantially be- tween subjects, thus reducing its therapeutic efficacy. Traditionally, the large variability in TMS responsiveness has been seen as an inevitable limitation of the technique. On the other hand, computational “state-based” the- ories postulate that the effects of TMS critically depend on a combination of the pre-TMS state of the targeted network and the strength of stimulation. State-based theories imply that it is possible to reduce the variability of TMS and enhance its therapeutic effectiveness by taking into account the state of the brain right before stimula- tion. However, the notion that the pre-TMS state can qualitatively alter the behavioral effects of TMS has not been examined directly by actual recordings of pre-TMS brain activity. The current proposal will perform the first direct test of these theories by an innovative combination of TMS and concurrent functional magnetic resonance imaging (fMRI). Delivering precisely-targeted TMS inside the MRI environment and obtaining artifact-free fMRI data is difficult but the Georgia Tech research group has already built a setup and collected pilot data from three different protocols demonstrating ability to conduct such experiments and obtain high-quality data. Existing pilot data comes from experiments characterizing the effects of TMS on the area under the coil by applying TMS at rest. To directly test state-based theories, the current proposal will employ concurrent TMS-fMRI with two distinct tasks in a large sample of healthy young adults. Aim 1 will examine how the pre-TMS activity in the targeted area influences the effect of TMS on behavior and whether the level of this activity interacts with the intensity of TMS. Based on state-based theories, it is expected that low pre-TMS activity coupled with low-intensity TMS will lead to performance improvement, whereas high pre-TMS activity coupled with high-intensity TMS will lead to perfor- mance impairment. However, the effectiveness of TMS is likely determined not just by the activity of the area under the TMS coil but also by the state of large brain networks. Therefore, Aim 2 will additionally test how the pattern of connectivity in large brain networks affects the behavioral effects of TMS. Further, it will be examined whether the global brain state is important in determining the effect of TMS and whether particular networks can be identified that influence TMS effectiveness. The proposed research will thus test a long-standing hypothesis regarding the state-dependency of TMS effects by an innovative use of ... ## Key facts - **NIH application ID:** 10119331 - **Project number:** 5R21MH122825-02 - **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY - **Principal Investigator:** Dobromir Rahnev - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $197,250 - **Award type:** 5 - **Project period:** 2020-03-04 → 2023-02-28 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10119331 ## Citation > US National Institutes of Health, RePORTER application 10119331, Improving behavior with TMS: A concurrent TMS-fMRI approach (5R21MH122825-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10119331. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*