Paolo Belardinelli

Background: Fluctuating cortical excitability states are inherent to and critical for brain function. They can be probed non-invasively using transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) by measuring early local TMS-evoked potentials (EL-TEPs). Pre-stimulus periodic neural activity can predict fluctuating excitability states. To what extent aperiodic neural activity represents a marker of cortical excitability has not been explored. Objectives: We investigated the relationship between pre-stimulus aperiodic and periodic neural activity and EL-TEP amplitude in both the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC). Methods: Single-pulse TMS was delivered to the left M1 or left DLPFC in 29 and 11 healthy adults, respectively, while EEG was recorded simultaneously. Pre-stimulus EEG was parameterized into aperiodic and periodic components to extract the aperiodic exponent (slope of 1/f-like activity), alpha power (8–13 Hz), and beta power (13–30 Hz). EL-TEP peak-to-peak amplitudes were extracted from approximately 20-65 ms after the TMS pulse. Results: Aperiodic exponent showed a strong negative relationship with EL-TEP amplitude in both M1 and DLPFC, with steeper 1/f-like slopes associated with smaller EL-TEP amplitudes. Inconsistent relationships were found between alpha and beta power with EL-TEP amplitude across the two regions. Conclusions: These findings indicate that pre-stimulus aperiodic neural activity influences cortical excitability in both motor and prefrontal areas. Notably, the aperiodic exponent exhibited stronger associations with cortical excitability compared to periodic alpha or beta power. Brain-state-dependent protocols may benefit from real-time extraction of aperiodic exponent as a target feature to improve the precision and efficacy of TMS. Highlights: Pre-stimulus aperiodic exponent predicts EL-TEP amplitude in M1 and DLPFC. Larger aperiodic exponent is associated with smaller EL-TEP amplitudes. Periodic alpha and beta power do not consistently influence EL-TEP amplitude. Aperiodic exponent may inform brain-state-dependent and closed-loop TMS.

Brain stimulation is progressively evolving from anatomically targeted interventions toward individualized, adaptive, and network-oriented approaches. Recent developments in brain-state-dependent stimulation have demonstrated that neuromodulation can be optimized by synchronizing stimulation with ongoing neural activity, while adaptive cortico-cortical paired associative stimulation (ccPAS) has extended this principle to distributed cortical networks. However, current approaches continue to define brain state primarily through local electrophysiological features, such as oscillatory phase or power, even though ccPAS is specifically designed to modify communication between interconnected cortical regions. In this opinion paper, we argue that the endogenous variable guiding ccPAS should instead describe the instantaneous physiological state of the targeted cortico-cortical pathway. We propose that online source-space functional connectivity provides a biologically meaningful and technically feasible estimate of this pathway state, thereby enabling a new framework of network-state-dependent neuromodulation. As illustrative applications, we consider pathways spanning different levels of experimental characterization, from established sensorimotor interactions to the less-characterized ventral premotor cortex (PMv)-posterior superior temporal sulcus (pSTS) pathway within the action-observation network (AON). This distinction highlights that the proposed framework is pathway-general, while its experimental implementation requires pathway-specific characterization of stimulation parameters and appropriate physiological readouts. In this framework, resting-state EEG is rapidly reconstructed in source space to estimate pathway connectivity, which is subsequently used to personalize stimulation targets and guide state-dependent ccPAS of a functionally defined cortico-cortical circuit. Finally, we discuss the technological prerequisites, methodological challenges, biological rationale, and open scientific questions that must be addressed before pathway-state-guided neuromodulation can be experimentally validated. We argue that redefining brain state as the instantaneous physiological state of the communication pathway targeted by stimulation may represent the next conceptual step in brain stimulation.

Transcranial magnetic stimulation (TMS) enables non-invasive, focal modulation of cortical circuits by inducing electric currents in the brain through electromagnetic induction, thereby influencing neuronal excitability and synaptic plasticity. High inter- and intra-individual variability has led, however, to moderate efficacy and reproducibility of stimulation and treatment protocols, motivating a shift toward brain-state-dependent stimulation. Over the past decade, real-time phase-triggered EEG-TMS has established the oscillatory phase—particularly focusing on the sensorimotor mu rhythm—as a key determinant of cortical excitability and plasticity modulation. The field, however, remains largely confined to univariate, sensor-space analyses of local mu-rhythm phase, missing large-scale network dynamics. Recent advances in online EEG source reconstruction and multivariate machine and deep learning (ML/DL) approaches have begun to move beyond local phase toward whole-brain, network-level state estimation, achieving encouraging preliminary accuracies in predicting trial-by-trial cortical excitability, with promising applications in network-dysregulation conditions such as chronic pain. Integrating source-space reconstruction and individual biological variability, and adaptive ML/DL pipelines into closed-loop frameworks promises to move beyond generic stimulation protocols toward selective, network-targeted neuromodulation tailored to the individual’s dynamic brain state. Against this background, this review provides a critical overview of current achievements and limitations, while highlighting emerging methodological directions toward fully brain-state-adaptive and network-targeted EEG-TMS. We further present an illustrative use case of adaptive EEG-TMS for pain modulation, where treatment responses remain heterogeneous and the relevant dynamics are distributed across networks, and which therefore stands to gain most from individualized, network-targeted protocols.

Brain networks coordinate distributed neuronal assemblies to support cognition. Spike-timing-dependent plasticity (STDP) and neuronal oscillations are key substractes for state-gated learning rules that shape network coupling and cognitive operations; nonetheless, how STDP mechanisms interact with neuronal oscillations is largely unexplored in humans. Corticocortical paired associative stimulation (ccPAS) provides a noninvasive system-level model of associative timing rules by pairing dual-site transcranial magnetic stimulation (TMS) across axonally connected regions with an interstimulus interval matched to pathway conduction. Here we (1) synthesize ccPAS applications and barriers to brain-state-coupled implementation in cognitive networks; (2) provide an actionable roadmap for real-time state estimation, targeting, and dual-site parameter selection; and (3) demonstrate a novel implementation of theta phase-locked frontoparietal (FP) ccPAS with concurrent EEG in adult human participants. We tested whether ccPAS delivered at the positive phase of ongoing theta (POS) induces distinct changes in evoked EEG activity and FP connectivity compared with phase-uncoupled ccPAS (RAND) and phase-locked single-site prefrontal (PREF) controls. At the evoked level, POS produced a frontocentral polarity reversal of the canonical N45 component and a right parietotemporal negativity relative to both controls. At the network level, POS induced frequency-specific reconfigurations in postintervention connectivity beyond either control ingredient alone. Together, these changes in evoked activity and rapid network reconfiguration provide the first empirical evidence consistent with phase-gated STDP in humans—whereby oscillatory phase gates cortical excitability and modulates STDP efficacy—emerging as short-term network-level expression. Future work will assess long-term plasticity by tracking connectivity at later time points and testing for concomitant behavioral effects.

Brain networks coordinate distributed neuronal assemblies to support cognition. Spike-timing-dependent plasticity (STDP) and neuronal oscillations are key substrates for state-gated learning rules that shape network coupling and cognitive operations; nonetheless, how STDP mechanisms interact with neuronal oscillations is largely unexplored in humans. Cortico-cortical paired associative stimulation (ccPAS) provides a non-invasive system-level model of associative timing rules by pairing dual-site transcranial magnetic stimulation (TMS) across axonally connected regions with an inter-stimulus interval matched to pathway conduction. Here we: 1) synthesize ccPAS applications and barriers to brain-state-coupled implementation in cognitive networks; 2) provide an actionable roadmap for real-time state estimation, targeting, and dual-site parameter selection; and 3) demonstrate a novel implementation of theta-phase-locked fronto-parietal (FP) ccPAS with concurrent EEG in adult human participants. We tested whether ccPAS delivered at the positive phase of ongoing theta (POS) induces distinct changes in evoked EEG activity and FP connectivity compared to phase-uncoupled ccPAS (RAND) and phase-locked single-site prefrontal (PREF) controls. At the evoked level, POS produced a fronto-central polarity reversal of the canonical N45 component and a right parieto-temporal negativity relative to both controls. At the network level, POS induced frequency-specific reconfigurations in post-intervention connectivity beyond either control ingredient alone. Together, these changes in evoked activity and rapid network reconfiguration provide the first empirical evidence consistent with phase-gated STDP in humans—whereby oscillatory phase gates cortical excitability and modulates STDP efficacy—emerging as short-term network-level expression. Future work will assess long-term plasticity by tracking connectivity at later time points and testing for concomitant behavioral effects. Significance: The real-time brain state critically shapes how plasticity mechanisms are expressed in response to brain stimulation. This article provides a forward-looking synthesis of the scientific and technical challenges associated with ccPAS—an STDP induction model in the human cortex—and outlines the steps required to advance it toward real-time brain-state-coupled implementation. To our knowledge, this is the first application of brain-state-coupled ccPAS within a cognitive network. By personalizing stimulation to the individual’s ongoing neural state, this approach may reduce variability, limit off-target effects, and enhance plasticity induction. Ultimately—by modulating network-level function in a brain-state-dependent manner—this technique could augment therapeutic outcomes in disorders marked by network dysfunction such as ADHD, Alzheimer’s disease, and major depressive disorder, potentially maximizing efficacy in patients unresponsive to existing treatments.

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) provides a powerful framework to probe and modulate human cortical and corticospinal excitability. In recent years, brain state-dependent EEG–TMS paradigms have gained increasing interest by synchronizing stimulation to ongoing neural activity. However, traditional approaches relying on single oscillatory features or fixed thresholds have yielded heterogeneous and often inconsistent results, motivating the adoption of machine learning (ML) and artificial intelligence (AI) methods to model brain state in a multivariate, data-driven manner. This review synthesizes current ML and deep learning (DL) approaches aimed at predicting cortical and corticospinal excitability from pre-stimulus EEG. We contextualize these methods within brain state-dependent EEG–TMS frameworks based on oscillatory phase, power, and network-level features, and within evolving definitions of brain state that move beyond local biomarkers toward distributed, large-scale, and dynamically evolving neural representations. The reviewed studies span feature-engineered models, data-driven decoding approaches, and emerging adaptive closed-loop frameworks. Finally, we discuss key methodological challenges, translational barriers, and future directions toward personalized, interpretable, and fully closed-loop neuromodulation systems.

Brain oscillations are rapidly fluctuating neural activity carried out by large ensembles of neurons. The responsiveness of the brain to external stimuli and the plasticity induced by external stimulation depend strongly on these oscillatory brain states. Here we detail step-by-step instructions for stimulating the brain by tracking human brain activity via the real-time analysis of brain signals recorded by electroencephalography (EEG) and reconstructed in source space, and timing the application of noninvasive transcranial magnetic stimulation (TMS) in synchronization with oscillatory brain states. Real-time EEG–TMS enables the millisecond-precise timing of TMS pulses relative to target oscillatory brain states, such as the phase of an ongoing oscillation. Initial evidence indicates that oscillatory brain state-dependent EEG–TMS is more effective in inducing long-term plasticity than conventional TMS uncoupled to instantaneous oscillatory brain state. This opens the possibility of personalizing therapeutic brain stimulation by coupling it to specific physiological or pathological brain states. In the procedure, we cover brain MRI and image segmentation for anatomical modeling and accurate source reconstruction of the EEG signals, EEG recording without TMS to validate the origin of the oscillation of interest and to determine its phase targeting accuracy, and the main experiment of oscillatory brain state-dependent real-time EEG–TMS to achieve the desired neuroplastic effect. A moderate level of computer science expertise, standard MRI and TMS neuronavigation equipment and TMS-compatible EEG with an accessible online output copy suffice to perform the protocol. The protocol requires ~10 h to complete.

Transcallosal connectivity between the hand areas of the two primary motor cortices (M1) is important for coordination of unimanual and bimanual hand motor function. Effective connectivity of this M1-M1 pathway can be tested in the form of short-interval interhemispheric inhibition (SIHI) using dual-coil transcranial magnetic stimulation (TMS). Recently, we and others have demonstrated that the phase of the ongoing sensorimotor µ-rhythm has significant impact on corticospinal excitability as measured by motor evoked potential (MEP) amplitude, and repetitive TMS of the high-excitability state (trough of the µ-rhythm) but not other states resulted in long-term potentiation-like MEP increase. Here, we tested to what extent the phase of the ongoing µ-rhythm in the two M1 cortices affects long-term change in SIHI. In healthy subjects we applied cortico-cortical paired associative stimulation (ccPAS) in four different µ-phase conditions in the left conditioning M1 and right test M1 (trough-trough, trough-positive peak, positive peak-trough, random phase). We found long-term strengthening of SIHI but no differential effect of phase conditions. Findings point to a distinct regulation of plasticity of corticospinal vs. M1-M1 connectivity. The observed ccPAS-induced strengthening of effective M1-M1 connectivity (SIHI) may be utilized for therapeutic applications that potentially benefit from modification of interhemispheric excitation/inhibition balance.