Abstract
Transcranial magnetic stimulation (TMS) has the potential to yield insights into cortical functions and improve the treatment of neurological and psychiatric conditions. However, its reliability is hindered by a low reproducibility of results. Among other factors, such low reproducibility is due to structural and functional variability between individual brains. Informing stimulation protocols with individual neuroimaging data could mitigate this issue, ensuring accurate targeting of structural brain areas and functional brain states in a subject-by-subject fashion. However, this process poses a set of theoretical and technical challenges. We focus on the problem of online functional targeting, which requires collecting electroencephalography (EEG) data, extracting brain states, and using them to trigger TMS in real time. This stream of operations introduces hardware and software delays in the real time set-up, such that brain states of interest may vanish before TMS delivery. To compensate for delays, it is necessary to process the EEG signal in real time, forecast it, and instruct TMS devices to target forecasted - rather than measured - brain states. Recently, this approach has been adopted successfully in a number of studies, opening interesting opportunities for personalised brain stimulation treatments. However, little has been done to explore and overcome the limitations of current forecasting methods. After reviewing the state of the art in brain state-dependent stimulation, we will discuss two broad classes of forecasting methods and their suitability for application to EEG time series. Subsequently, we will review the evidence in favour of data-driven forecasting and discuss its potential contributions to TMS methodology and the scientific understanding of brain dynamics, highlighting the transformative potential of big open datasets.
Published Version
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