Repetitive transcranial magnetic stimulation (rTMS) is a leading noninvasive therapy for treatment resistant depression (TRD), but researchers still seek to understand exactly how it changes brain function. A new 2025 study by Hengda He and colleagues combines three technologies TMS, functional MRI (fMRI), and EEG to explore how brain network modulation during stimulation may explain why some patients respond better than others.
This integrative approach highlights how the brain’s state at the moment of stimulation. Particularly the rhythm of alpha brain waves in the prefrontal cortex. Can shape how effectively TMS engages mood related neural circuits. The study’s findings open new possibilities for tailoring TMS sessions to each individual’s brain dynamics.
Mapping TMS Effects Across Brain Networks
By combining TMS with simultaneous fMRI and EEG, the researchers were able to visualize how stimulation of the left dorsolateral prefrontal cortex (L-DLPFC) is a common target in depression. Treatment instantly changes brain connectivity.
They discovered that TMS influences two major brain systems: the cognitive control network (which supports attention and goal directed behavior) and the default mode network (linked to self-reflection and rumination). These rapid network shifts suggest that TMS does more than just activate neurons locally. It rebalances communication across larger brain circuits involved in mood regulation.
Patients whose brain activity showed stronger initial responses in these networks tended to experience greater symptom relief over the course of EEG-synchronized rTMS therapy. This means that a patient’s baseline network reactivity could help predict how well they’ll respond to treatment.
The Role of Brain-State Synchronization
A standout feature of this study was its focus on state-dependent TMS, meaning stimulation was timed to match specific phases of the brain’s natural oscillations. The team found that aligning TMS pulses with certain alpha wave phases in the prefrontal cortex led to more consistent and effective modulation of key mood circuits, particularly between the L-DLPFC and the posterior subgenual anterior cingulate cortex a region deeply involved in emotional regulation.
This brain-state–synchronized approach may enhance treatment precision, improving the ability of TMS to engage targeted neural pathways at optimal moments. Future protocols could use real-time EEG feedback to personalize TMS delivery, offering a more adaptive and responsive therapy model.
Toward Personalized Neuromodulation
The implications of this work go beyond depression treatment. By combining EEG, fMRI, and TMS, researchers can now directly observe how stimulation reshapes functional connectivity across the brain. This “closed-loop” feedback model could guide future neuromodulation therapies for various psychiatric conditions, from anxiety to obsessive compulsive disorder.
Personalized timing matching each pulse to an individual’s brain rhythm could represent the next frontier in interventional psychiatry. Rather than applying a uniform TMS schedule, clinicians may one day adjust protocols based on each patient’s neural signature, maximizing therapeutic benefit and minimizing side effects.
Conclusion
The study led by He and colleagues provides new insight into how TMS modulation of brain networks can predict and enhance antidepressant response. By synchronizing stimulation with the brain’s natural rhythms, future treatments could become more targeted, efficient, and individualized. This research underscores the growing potential of precision neuromodulation in modern psychiatry.
Citations:
- He H, Sun X, Doose J, et al. TMS-induced modulation of brain networks and its associations to rTMS treatment for depression: a concurrent fMRI-EEG-TMS study. Brain Stimulation. 2025;18(6):1955–1965. https://doi.org/10.1016/j.brs.2025.10.013
- George MS, Post RM. Daily left prefrontal repetitive transcranial magnetic stimulation for acute treatment of medication-resistant depression. Am J Psychiatry. 2011;168(4):356–364. https://doi.org/10.1176/appi.ajp.2010.10060864