Understanding how the human cerebral cortex processes information and integrates activity across distributed regions is a central goal of modern neuroscience. The cortex is organized into specialized areas connected through short- and long-range corticocortical pathways, whose interactions unfold on a sub-second timescale. Two key properties characterize the functional state of these cortical circuits: cortical excitability, referring to the local responsiveness of neuronal populations to external or internal inputs, and effective connectivity, defined as the causal influence that one neural system exerts over another.
Alterations in excitability and effective connectivity have been associated with physiological states such as sleep and wakefulness, as well as with pathological conditions including psychiatric disorders, neurodegenerative diseases, and disorders of consciousness. Therefore, reliable methods to probe these properties in vivo, non-invasively, and with high temporal resolution are crucial both for basic neuroscience research and for clinical applications.
Traditional neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), have substantially improved our understanding of brain function. However, these approaches are largely observational in nature and rely on correlational measures, which limit their ability to directly infer causal interactions within neural networks. In this context, approaches based on controlled perturbations of the brain represent a powerful alternative.
Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique based on the principle of electromagnetic induction. A brief and intense magnetic pulse applied over the scalp induces an electric field in the underlying cortical tissue, leading to the depolarization of neuronal elements, primarily axons. From the stimulated site, neural activity propagates along anatomical connections to distant cortical and subcortical regions.
Figure 1. The three fundamental components that compose the TMS/EEG setup: a cap for high-density EEG recordings (60 channels) connected to a TMS compatible amplifier; a focal figure of eight stimulating coil (TMS), held in place by a specifically designed mechanical arm; the display of the navigated brain stimulation system (NBS).
Because TMS directly perturbs cortical tissue, it provides an intrinsically causal approach to the study of brain function. Depending on stimulation parameters, such as intensity, coil orientation, and stimulation site, TMS can be used to probe different aspects of cortical reactivity and network dynamics. TMS has therefore become a widely used tool to investigate motor control, perception, cognition, and plasticity, as well as to modulate dysfunctional neural activity in clinical contexts.
How does the production of useful data take place? Using the combination of TMS and EEG: an approach defined as a “perturb-and-measure” strategy. While TMS acts as a direct probe to activate a specific cortical area, EEG serves as a high-resolution tool to record the resulting brain activity. Unlike sensory stimulation, TMS-EEG bypasses peripheral pathways (like the eyes or ears), providing a direct measure of cortical reactivity and connectivity that does not require the subject’s active participation. Electroencephalography offers a direct measure of neuronal population activity with millisecond temporal resolution. When combined with TMS, EEG enables the recording of TMS-evoked potentials (TEPs), which reflect the immediate electrical response of the cortex to a controlled perturbation.
Figure 2. Schematic representation of EEG signal acquisition and electrode placement on the scalp.
The integration of TMS and EEG allows researchers to overcome several limitations of purely observational methods. First, TMS-evoked cortical activations are causal, making it possible to assess effective connectivity rather than simple functional correlations. Second, TMS bypasses sensory pathways and peripheral input, enabling direct access to cortical dynamics even in subjects who are unresponsive, paralyzed, or unable to perform behavioral tasks. Third, TMS/EEG does not require active participation or task engagement, making it particularly well suited for the assessment of global brain states. Then, the method can be used to estimate i) cortical excitability, as the amplitude of the early components elicited nearby the TMS target, ii) effective connectivity, as the spread of a focal stimulation across distant cortical areas; iii) complexity, as the spatiotemporal distribution of the deterministic cortical activations following TMS pulses. Measuring these parameters may help identifying specific pathological alterations (e.g. cognitive impairment, psychiatric conditions,…) and can be reliably performed over time to quantitatively monitor the effects of treatment and spontaneous recovery. Finally, TMS/EEG provides, at the same time, a direct stimulation of virtually any cortical area and a quantitative neurophysiological output, regardless of any sensory or motor impairment: therefore, this tool is particularly useful for studying brain-injured patients, in whom the integrity of sensory-motor pathways might prevent the recording of standard event-related potentials.
Despite its advantages, the combination of TMS and EEG poses significant technical challenges, which can be solved by employing dedicated hardware solutions and by applying specific data analysis procedures, signal processing, and modeling of electric field distribution.
To address some issues related to the development of advanced clinical tools using TMS, a collaboration has been initiated between a group coordinated by Prof. S. Casarotto at the Department of Biomedical and Clinical Sciences, University of Milan, and Department of Environmental Science and Policy, together with several Master’s students from the Department of Mathematics at the University of Milan. This collaboration is leading to the development of appropriate methodologies and scientific software in two main areas: the classification of EEG recordings in TMS stimulation experiments/protocol; source reconstruction.
For classification, an initial preprocessing of the EEG signal was performed using the Wavelet Scattering Transform (WST), which provides a stable and invariant representation of signals, particularly focusing on its ability to preserve informative content while remaining insensitive to translations and small deformations (diffeomorphisms). In her Master’s thesis, Alessandra Caloni developed a form of EEG tomography that allows clinicians to observe the variability of the response to TMS stimulation across different brain regions and across relevant frequency bands. Subsequently, suitable classifiers are being investigated, which must preserve a clear interpretability of the results.
Figure 3. An EEG tomography for relevant frequency class obtained by using the Scattering Wavelet Transform in the master thesis work by Alessandra Caloni.
However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity.
In this setting, Tania Allegri developed and analyzed models of the cerebral cortex from the perspective of neuronal signal propagation, as well as the inverse problem aimed at identifying the sources responsible for the signals measured during experiments following TMS stimulation. As part of this work, statistical indices were also developed for the analysis of these sources. An initial validation test was the analysis of data obtained from stimulations comparing sighted and blind populations.
Figure 4. Statistical analysis of sources under different stimulations (different rows) and by population: sighted subjects, column (a), and blind subjects, column (b). Signal processing and inverse problem solution carried out in the master’s thesis by Tania Allegri.
All The contributions of this collaboration aim to support the potential clinical application of TMS stimulation and the development of appropriate protocols: in certain fields, it could represent one of the few available resources for diagnostic purposes.
Part of this collaboration was carried out within the framework Multi-Scale Brain Function (MSBFIINE), India-Italy Network of Excellence Project https://www.amrita.edu/project/multi-scale-brain-functionindia-italy-network-of-excellence/).
Contact for additional information: giovanni.naldi@unimi.it