Conference Agenda

Transcranial electrical stimulation (tES) techniques, such as direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), cause neurophysiological and behavioral modifications as responses to the electric field induced in the brain. I will report on a recent review on the current tDCS- and tACS-induced electric fields estimations as they are recorded in humans and non-human primates using intracerebral electrodes, applying a variety of experimental protocols. The stimulation parameters (e.g., intensity, frequency and phase), the electrodes' positions and personal anatomy determine whether the intensities might be high enough to affect both neuronal and non-neuronal cell activity, also deep brain structures. Notably, I will report findings suggesting on-line and off-line effects on the gene and protein expression, opening a critical advancement in the understanding of neuroprotective role of tES.

The development of technologies for invasive and transcranial electrical stimulation (ES) therapy, with increasing levels of safety, efficacy and efficiency, opens up important clinical prospects. Therefore, the design of new technologies must dialogue with state-of-the-art neuroscientific knowledge. In turn, neuroscience is adopting a new conceptual framework for brain architecture, in which time and thus temporal patterns play a central role in the neuronal representation of sampled data from the world. I will present how the dynamics of provided stimuli impact neuromodulation strategies. We therefore present a low-frequency, averaged (i.e. low-energy), scale-free, time-randomised ES scheme for the treatment of experimental epilepsy, devised by our group and named NPS (Non-periodic Stimulation). This approach has been shown to have robust anticonvulsant effects in several animal models of acute and chronic seizures (showing dysfunctional hyperexcitable tissue), while preserving neural function. The hypothesis behind the dynamic strategy is to re-stabilise a system that is passing under the control of a single attractor. We conclude by discussing future avenues of investigation and their potentially disruptive impact on neurotechnologies, with a particular interest in the implications of NPS in neural plasticity, motor rehabilitation and their potential for clinical translation.

It is well established that neuronal ongoing electrical activity in the cortex encodes information in the neuronal circuitry whether it would be a cortico-cortical or cortico-spinal neuronal integration for initiation and coordination of motor commands, as well as sensory feedback. However, there is little evidence that these systems could be decoded or at least a fraction of information can be extracted during resting states. Thus, we investigated the intracranial stereotactic electroencephalographic (sEEG) recordings of Montreal Neurological Institute (MNI), in 55 subjects, for precentral, postcentral and superior temporal gyri, during resting wakefulness and three sleep stages. We aimed to grasp whether the signature of ongoing neuronal electrical activity of the three cortical parcels could be determined with a statistically reliable method and be a characteristic feature of the areas even at rest. To deploy this analysis, we studied the power spectral density (PSD) of the sEEG signals and investigated its power-law behaviour (1/f^b) vs frequency. PSD revealed a characteristic signature, with different prevalent oscillations in specific frequency ranges, for the three regions during wakefulness, however these differences vanish during sleep. Instead, the evidence of a power-law trend suggests that the neural electrical signals might contain a scale-free component that is a baseline for the neuronal activity that does not change across the wake/sleep states. The impact of this result stands in the possibility to determine the signature of a cortical area useful for future cortical parcelling and to adjust neuromodulation.

Brain plasticity refers to the brain's ability to change and adapt throughout an individual's lifetime, which involves modifying neural connections and reorganizing neural networks in response to various events. Non-invasive brain stimulation (NIBS) techniques, like transcranial magnetic (TMS) and electrical (tDCS/tACS) stimulations, are interventions aimed at improving brain plasticity. The latter can be measured by different methods, such as functional connectivity and graph theory, with changes in functional connectivity indicating alterations in the organization and communication of neural networks. At the same time, graph theory measures can quantify the role of different networks and their importance for brain functioning and reveal changes in the efficiency of neural networks. These methods provide insights into brain plasticity and could lead to the development of personalized interventions and therapies for various brain disorders. In support of this, the present study highlights the possibility to identify, in functional connectivity and graph analysis, some measures for the recognition of network’s alterations and the prediction of outcomes of functional recovery after stroke. For this purpose, 127 stroke patients and 90 healthy subjects were enrolled and for each one electroencephalography (EEG) signals were recorded. Total Coherence (TotCoh) and Small World (SW) were computed to evaluate the functional connectivity from EEG data in the affected and unaffected hemispheres separately. Furthermore, artificial intelligence (AI) approach was employed to automatically: distinguish stroke from healthy subjects, identify the lesion’s side and predict the functional recovery. In stroke patients, network alterations were detected only in the affected hemisphere. We also found significant correlations between SW and improvement in some clinical scales, giving to this parameter the role of prognosis’s biomarker. AI confirmed this fact, showing more than 90% of accuracy. The results of this study highlight the potential of functional connectivity, graph theory and AI as innovative methods and technologies in neuroscience.