During the first years of the project, the corona crisis had slowed down most aspects of our work. As a result, the project duration was extended by 12 months. Additional delays were due to unforeseen technical challenges to engineer the device and to produce documentation to conform to European medical device regulations (MDR). A significant additional delay of more than a year was due to getting regulatory approval in compliance with MDR for the already installed device to be used for human studies at EKUT. A further delay in both EKUT and UdA was due to local approvals: while the mTMS systems were delivered to EKUT in October 2022 and to UdA in September/October 2023, human studies were approved in EKUT only in 2025 and in UdA, the final permission has still not been given (although expected to be received in Q2/2026). Due to these delays, the planned experiments in EKUT and UdA could not be done. Therefore, the project was again extended. This will allow experimentation at these two sites, although the original technical plan can not be realized. Due to technical challenges to go to more complicated coils, the realization that already the 5-coil system are a breakthrough and that the cost of building large coil sets is exorbitantly expensive, we were forced to conclude that we should concentrate the technical effort to perfecting the present technology rather than trying to build the next level of coil sets before sufficient feedback from the users at EKUT and UdA.
At AALTO, we experimented with the mTMS prototype, developed its embedded software, user interface and algorithms. We continued the work to develop closed-loop algorithms for the mTMS. The electronics development proved to be more challenging than initially anticipated, consuming therefore more time and more personnel than planned. Nevertheless, the TMS electronics project was very successful. We now have a very reliable and professional stimulator electronics, which also conforms to the strict regulatory requirements of MDR. However, the multi-coil set, although working as expected, proved to be less reliable. We now know that this problem is due to the huge forces (several tens of kilonewtons) between the coils in the different layers of the coil set. We have spent a lot of time and resources for modifying the design, fixing broken coils, and making spare ones. The operating software was also a very challenging undertaking, consuming a great amount of time by the software team. In 2025, we were able to make it easy to operate although also the software is considered a prototype. We have used the mTMS in several experiments and published our results in conferences and scientific papers.
Thanks to our technical progress and the perceived need of mTMS technology in the market, we were able to launch in 2025 a startup company called Cortisys Oy in Finland, with pre-seed funding from the New Jersey VC investor SOSV/HAX. Four members of the Aalto University TMS team left the university to work full time at the company. Cortisys Oy will deliver the first commercial instrument to the University of Jyväskylä in 2026. Because it is still a device only for research use, the company is working towards a technology approved for clinical use as well.
We have developed solutions for TMS-compatible EEG and algorithms to suppress EEG signal artifacts and noise. Our Bayesian algorithm has been extended to change stimulation parameters based on EEG responses, automatically finding optimal stimulation parameters such as those that maximize a given EEG deflection.
At EKUT, we have completed proposed experiments on brain-state-dependent TMS in simple 2-node motor networks in healthy subjects. We confirmed our hypothesis that effective connectivity between the the two nodes is most strongly expressed if probed when the local µ-rhythm in the two nodes is in-phase rather than out-of-phase. We have completed experiments to test the Communication Through Coherence theory, one of the central neurobiological hypotheses of the ConnectToBrain project, by repetitively stimulating the two nodes at specific phase relations of the ongoing µ-rhythm for plasticity induction. The findings overall were non-significant. . In addition, we have completed several studies that elaborate on principles of signal-to-noise ratio for real-time estimation of the phase in EEG-recorded oscillations, and on causal decoding of EEG data for estimation of cortical excitability at the individual level. We have extended the application of real-time analysis of EEG for brain-state-dependent TMS to the alpha- and theta-rhythms of prefrontal cortex. We have completed data acquisition in a proof-of-principle experiment of closed-loop TMS in a 2-node motor network in healthy subjects with conventional TMS. The experiments were preceded by extensive simulation studies to demonstrate feasibility of deep reinforced learning algorithms. The findings demonstrate the capability of the deep machine-learning algorithm to fully automatically identify the phase of the µ-rhythm associated with the highest or lowest excitability of the 2-node motor network in a given individual, which is breakthrough in the C2B project. We will validate these findings using the EKUT 5-channel mTMS device.
At UDA, we have worked towards establishing algorithms to assess whole-brain connectivity in real time from EEG data. We have demonstrated that a reliable connectivity can be estimate in real-time with a data analysis window of about 5 cycles. Using data acquired at EKUT, we found a relationship between mu rhythm functional connectivity in the motor network and Motor Evoked Potential (MEP). In a larger cohort, we identified, using Hidden Markov Models, fast large-scale brain states lasting few hundreds of milliseconds with a specific spatial, temporal and spectral fingerprint and variable relation to MEP amplitude. Using motor cortex stimulation data from the AALTO 2-coil mTMS system, we showed that connectivity between the left Supplementary Motor Area (SMA) and left and right M1 is modulated by the stimulation orientation over the left SMA. Finally, we ran an experiment using the 5-coil mTMS system in AALTO, exploiting the possibility to stimulate different locations. In parallel, we developed a novel algorithm for cross-frequency coupling based on multidimensional data. We also collected a large multimodal dataset to exploit the strength of machine learning techniques with the goal to identify fast-dynamic large-scale brain states that predict corticospinal excitability. Overall, the results of these studies are crucial for assessing the role of connectivity in brain-state-dependent stimulation at multiple sites by mTMS.
At AALTO, we organized the 10th and 11th Science Factory TMS–EEG Summer Schools and Workshops in May 2024 and May 2025, respectively, demonstrating to the participants also the new mTMS technology and presenting some key ideas of it also during the course. In addition, all 3 sites have presented in conferences their ConnectToBrain results, for instance, in the Brain Stimulation Conference (Kobe, Japan, February 2025), which is the main brain stimulation conference series in the world.
