van Wezel, R. J. A. | bioRxiv Science

bioRxiv2026

Phase-lag-dependent modulation of interhemispheric β-band functional connectivity by dual-site tACS

Dual-site transcranial alternating current stimulation (ds-tACS) is a promising tool for causally manipulating interhemispheric communication; nevertheless, how different stimulation phase-lags selectively modulate functional connectivity (FC) remains unclear. We recruited 29 healthy participants and included 23 in the final analyses. Participants received 20 Hz ds-tACS over the bilateral primary motor cortices (M1s) using four phase-lags (0, pi/2, pi, 3pi/2) and sham stimulation, combined with recording of high-density EEG before and after stimulation. We quantified the interhemispheric FC using the debiased weighted phase-lag index. No significant differences were found in the average FC changes between overall active stimulation and sham. However, the phase-lag associated with maximum absolute FC changes varied substantially across participants. Accordingly, the assessment of phase-lag-dependency of FC using the Kullback-Leibler distance (DKL) revealed a significant phase-lag-dependent modulation of FC that was transient (limited to the first 5 s after stimulation), restricted to frequencies around the stimulation frequency (18-22 Hz), and occurred without corresponding phase-lag-dependent modulation of the spectral power. In an exploratory analysis, the modulation magnitude showed a quadratic relationship with the individual {beta}-peak FC frequency, where participants with {beta}-peak frequencies farther from the 20 Hz stimulation frequency exhibited stronger effects. These findings indicate that ds-tACS can induce time- and frequency-specific modulation of interhemispheric FC across phase-lags. Given the observed association with individual {beta}-peak connectivity frequency and the variability in the phase-lag associated with maximal FC change, our findings motivate further investigation of personalised neuromodulation protocols.

neuroscience↗

bioRxiv2020

End-to-end optimization of prosthetic vision

Neural prosthetics may provide a promising solution to restore visual perception in some forms of blindness. The restored prosthetic percept is rudimentary compared to normal vision and can be optimized with a variety of image preprocessing techniques to maximize relevant information transfer. Extracting the most useful features from a visual scene is a non-trivial task and optimal preprocessing choices strongly depend on the context. Despite rapid advancements in deep learning, research currently faces a difficult challenge in finding a general and automated preprocessing strategy that can be tailored to specific tasks or user requirements. In this paper we present a novel deep learning approach that explicitly addresses this issue by optimizing the entire process of phosphene generation in an end-to-end fashion. The proposed model is based on a deep auto-encoder architecture and includes a highly adjustable simulation module of prosthetic vision. In computational validation experiments we show that such an approach is able to automatically find a task-specific stimulation protocol. The presented approach is highly modular and could be extended to dynamically optimize prosthetic vision for everyday tasks and requirements of the end-user.

bioengineering↗