Timo Roine

This study identifies distinct neurophysiological and structural connectivity profiles in the human SMA complex using TMS-EEG and diffusion-weighted connectomics.

The paper develops a method to predict TMS-evoked responses using pre-stimulus EEG features and finds that alpha, beta, and gamma oscillations correlate with TEP and MEP amplitudes.

This paper develops an automated algorithm for determining motor hotspots and thresholds in TMS, achieving faster and more accurate results than previous methods.

This paper develops a digital interface for precise, brain-state-dependent TMS timing, achieving sub-millisecond accuracy in EEG-TMS synchronization.

NeuroSimo provides open-source software for real-time closed-loop EEG-TMS stimulation, enabling flexible and precise control with low timing error.

This paper applies probabilistic deep learning to forecast resting-state EEG signals, achieving more accurate long-term predictions than autoregressive models.

The paper presents novel data using dMRI tractography-guided TMS-EEG on a single healthy young adult to better understand brain circuitry engagement during neuromodulation.

This study examines the reliability of dynamic functional connectivity in MEG data related to language processing and identifies properties associated with reliable connections.