주최: 서울대학교 뇌영상센터(SNUBIC)

과제명: 인프라 고도화: 고경사 확산 자기공명 영상장치 도입을 통한 뇌 구조와 기능에 대한 난제 해결(RS-2024-00435727)

후원: 국가연구시설장비진흥센터(NFEC), 교육부

Brain disorders are increasingly understood not as focal lesions, but as disruptions in coordinated communication across distributed neural circuits. This shift raises a fundamental question: what variables should we control to restore circuit function? Converging evidence suggests that neural rhythms, specifically their frequency, phase, and synchrony, play a central role in organizing information flow within and across brain networks. This talk argues that these variables are not merely correlates of cognition and behavior, but causal control parameters.

I present a series of studies using noninvasive brain stimulation (HD-tACS) to directly manipulate frequency- and phase-specific dynamics within and between brain regions. By selectively altering these parameters while holding all other aspects of stimulation constant, we observe systematic, bidirectional changes in behavior. These effects are evident across multiple cognitive domains, including cognitive control, working memory, long-term memory, and impulsivity, and extend to reductions in compulsive tendencies in individuals with elevated subsyndromal obsessive-compulsive traits. Notably, these changes can persist beyond the stimulation period, in some cases lasting up to three months. Together, these findings support a framework in which brain function can be modulated by tuning the temporal structure of circuit interactions, rather than targeting regions alone.

I then discuss key technological constraints, including limited precision in targeting and restricted access to deep brain structures, which continue to shape what can be causally controlled in human circuits.

Finally, I present recent work on noninvasive deep brain targeting using temporal interference (TI) stimulation. I introduce a modeling framework for precision TI targeting, along with empirical and computational results that provide mechanistic insight into how deep circuit modulation shapes learning. Together, these findings extend rhythmic control beyond surface networks and toward deeper circuit engagement.