Temporal dynamics of offline transcranial ultrasound stimulation

• Published in: Current research in neurobiology Vol. 8; p. 100148
• Main Authors: Atkinson-Clement, Cyril, Howett, David, Alkhawashki, Mohammad, Ross, James, Slater, Ben, Gatica, Marilyn, Balezeau, Fabien, Zhang, Chencheng, Sallet, Jerome, Petkov, Chris, Kaiser, Marcus
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2025; Elsevier
• Subjects: Cognitive science; fALFF; Neuroscience; ReHo; Seed-based connectivity; Ultrasound neuromodulation; Ultrasound neuromodulation; ReHo; fALFF; Seed-based connectivity
• ISSN: 2665-945X; 2665-945X
• DOI: 10.1016/j.crneur.2025.100148

Transcranial ultrasound stimulation (TUS) is a promising non-invasive neuromodulation modality, characterized by deep-brain accuracy and the capability to induce longer-lasting effects. However, most TUS datasets are underpowered, hampering efforts to identify TUS longevity and temporal dynamics. This primate case was studied awake with over 50 fMRI datasets, with and without left anterior hippocampus TUS. We therefore amassed the highest-powered TUS dataset to date required to reveal TUS longevity and dynamics. Most of the effects were found in the TUS region itself and alongside the default mode and sensorimotor networks. Seed-based functional connectivity exhibited a time-constrained alteration which dissipated ∼60 min post-TUS. Intrinsic activity measure and regional homogeneity displayed extended diffusivity and longer durations. This high-powered dataset allowed predicting TUS using pre-stimulation features that can now extend to modeling of individuals scanned less extensively. This case report reveals the diversity of TUS temporal dynamics to help to advance long-lasting human applications. [Display omitted] •TUS “offline” effects on the primate brain accumulate and last many minutes.•TUS changed functional connectivity withing a window of 60 min.•TUS changed intrinsic activity and regional homogeneity for at least 100 min.•TUS effects could be predicted based on the high-powered control dataset.