Biology subjects

Belardinelli, P.

Publications and source records attributed to Belardinelli, P..

11 recordsLinked to original sources

bioRxiv2026

Aperiodic neural activity predicts cortical excitability in TMS-EEG

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.

neuroscience↗

bioRxiv2026

Real-time brain-state-coupled cortico-cortical paired associative stimulation of cognitive networks

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. SignificanceThe 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 individuals 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, Alzheimers disease, and major depressive disorder, potentially maximizing efficacy in patients unresponsive to existing treatments.

neuroscience↗

bioRxiv2026

Not Just Noise: Aperiodic Brain Activity Reflects Corticospinal Excitability

BackgroundElectroencephalography (EEG) can be combined with transcranial magnetic stimulation (TMS) to perform brain-state-dependent stimulation. EEG-TMS studies have shown that corticospinal excitability, as measured via motor evoked potentials (MEPs), is modulated by pre-stimulus periodic EEG features, such as sensorimotor mu-rhythm phase and power. However, the influence of aperiodic brain activity on corticospinal excitability is largely unexplored. ObjectivesWe evaluated the relationship between aperiodic and periodic mu-power, aperiodic exponent, and mu-phase on MEP amplitudes using EEG-TMS. MethodsWe applied 800 single TMS pulses to the left primary motor cortex in 78 healthy adults. We calculated aperiodic/periodic mu-power, aperiodic exponent, and mu-phase for each trial from the pre-stimulus C3-Hjorth transformed EEG. MEP amplitudes were extracted from the right first dorsal interosseous muscle. A linear mixed-effects model assessed relationships between MEP amplitudes and EEG features, with interactions between mu-phase and all other EEG features. ResultsAperiodic and periodic mu-power, aperiodic exponent, and mu-phase significantly modulated MEP amplitudes. Higher aperiodic/periodic mu-power was associated with larger MEP amplitudes, while higher aperiodic exponent was associated with smaller MEP amplitudes. We found a significant interaction effect of aperiodic exponent and mu-phase on MEP amplitude. Aperiodic exponent was negatively associated with MEPs for trough, rising, falling phases, but positively associated with MEPs for peak phase. ConclusionsAperiodic and periodic features of brain activity are reflective of dissociable corticospinal excitability states. Future brain-state-dependent TMS interventions may include aperiodic EEG features, such as aperiodic mu-power and exponent, in addition to the well-established periodic features.

neuroscience↗

bioRxiv2025

Plasticity of interhemispheric motor cortex connectivity induced by brain state-dependent cortico-cortical paired-associative stimulation

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 {micro}-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 {micro}-rhythm) but not other states resulted in long-term potentiation-like MEP increase. Here, we tested to what extent the phase of the ongoing {micro}-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 {micro}-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.

neuroscience↗

bioRxiv2024

Fully Hyperbolic Neural Networks: A Novel Approach to Studying Aging Trajectories

Characterizing age-related alterations in brain networks is crucial for understanding aging trajectories and identifying deviations indicative of neurodegenerative disorders, such as Alzheimers disease. In this study, we developed a Fully Hyperbolic Neural Network (FHNN) to embed functional brain connectivity graphs derived from magnetoencephalography (MEG) data into low dimensions on a Lorentz model of hyperbolic space. Using this model, we computed hyperbolic embeddings of the MEG brain networks of 587 individuals from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Notably, we leveraged a unique metric--the radius of the node embeddings--which effectively captures the hierarchical organization of the brain, to characterize subtle hierarchical organizational changes in various brain subnetworks attributed to the aging process. Our findings revealed that a considerable number of subnetworks exhibited a reduction in hierarchy during aging, with some showing gradual changes and others undergoing rapid transformations in the elderly. Moreover, we demonstrated that hyperbolic features outperform traditional graph-theoretic measures in capturing age-related information in brain networks. Overall, our study represents the first evaluation of hyperbolic embeddings in MEG brain networks for studying aging trajectories, shedding light on critical regions undergoing significant age-related alterations in the large cohort of the Cam-CAN dataset.

bioinformatics↗

bioRxiv2024

Decoding Motor Excitability in TMS using EEG-Features:An Exploratory Machine Learning Approach

BackgroundWith the burgeoning interest in personalized treatments for brain network disorders, closed-loop transcranial magnetic stimulation (TMS) represents a promising frontier. Relying on the real-time adjustment of stimulation parameters based on brain signal decoding, the success of this approach depends on the identification of precise biomarkers for timing the stimulation optimally. ObjectiveWe aimed to develop and validate a supervised machine learning framework for the individualized prediction of motor excitability states, leveraging a broad spectrum of sensor and source space EEG features. MethodsOur approach integrates multi-scale EEG feature extraction and selection within a nested cross-validation scheme, tested on a cohort of 20 healthy participants. We assessed the frameworks performance across different classifiers, feature sets, and experimental protocols to ensure robustness and generalizability. ResultsPersonalized classifiers demonstrated a statistically significant mean predictive accuracy of 72 {+/-} 11%. Consistent performance across various testing conditions highlighted the sufficiency of sensor-derived features for accurate excitability state predictions. Subtype analysis revealed distinct clusters linked to specific brain regions and oscillatory features as well as the need for a more extensive feature set for effective biomarker identification than conventionally considered. ConclusionsOur machine learning framework effectively identifies predictive biomarkers for motor excitability, holding potential to enhance the efficacy of personalized closed-loop TMS interventions. While the clinical applicability of our findings remains to be validated, the consistent performance across diverse testing conditions and the efficacy of sensor-only features suggest promising avenues for clinical research and wider applications in brain signal classification.

neuroscience↗

bioRxiv2023

Region-specific impact of aging on cortical myelination and thickness

Healthy aging affects both grey and white matter. However, the trajectories of regional specific degeneration are not fully understood. Here we investigate the effects of aging on cortical thickness and myelin concentration in a large cohort of healthy participants (N = 610) aged between 18 and 89 years old who underwent single-site T1-weighted, T2-weighted and MTI sequences in the context of the Cam-CAN project. Participants were subdivided in three age groups representative of young, middle and late adulthood. The large size of the dataset allowed us to minimize the impact of sample variance without relying on multi-site acquisition protocols. We assessed linear changes in cortical thickness and cortical myelin concentration; the latter was assessed using both T1w/T2w ratio and MTR proxies, to evaluate which is the most stable metrics. Our results do not fit with either the anterior-posterior gradient or the last-in/first-out hypothesis. We demonstrate that aging patterns are more complex than just depending on a spatial gradient or the temporally reversed order of regional development. Moreover, we show a dissociation in aging patterns between somatosensory and motor regions both in terms of cortical thickness and myelin concentration. Finally, comparing T1w/T2w and MTR results of cortical myelination, we found the latter being a more stable and reliable proxy. HighlightsO_LICortical thickness and myelo-architecture changes must be jointly considered in investigating brain aging trajectories. C_LIO_LIWe assessed linear changes in cortical thickness and myelination in a large, homogeneous and single site MRI dataset. C_LIO_LIMotor and sensory regions show a dissociation in their aging trajectories both in terms of cortical thickness and myelin concentration. C_LIO_LISensory processing regions show similar aging trajectories in both cortical thickness and myelin concentration. C_LIO_LIMTR is a more reliable proxy for myelin concentration compared to T1w/T2w ratio. C_LI

neuroscience↗

bioRxiv2021

Artifacts in EEG-based BCI therapies: friend or foe?

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain-states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data was collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain-states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

neuroscience↗

bioRxiv2019

TMS-EEG signatures of glutamatergic neurotransmission in human cortex

Neuronal activity in the brain is regulated by an excitation-inhibition balance. Glutamate is the main excitatory neurotransmitter. Transcranial magnetic stimulation (TMS) evoked electroencephalographic (EEG) potentials (TEPs) represent a novel way to quantify pharmacological effects on neuronal activity in the human cortex. Here we tested TEPs under the influence of a single oral dose of two anti-glutamatergic drugs, perampanel, an AMPA-receptor antagonist, and dextromethorphan, an NMDA-receptor antagonist, and nimodipine, an L-type voltage-gated calcium channel blocker in 16 healthy adults in a pseudorandomized, double-blinded, placebo-controlled, crossover design. Single-pulse TMS was delivered to the left motor cortex and TEPs were obtained pre-and post-drug intake. Dextromethorphan specifically increased the amplitude of the N45, a negative potential around 45 ms after the TMS pulse, while perampanel reduced the P70 amplitude in the non-stimulated hemisphere. Nimodipine and placebo had no effect on TEPs. These data extend previous pharmaco-TMS-EEG studies by demonstrating that the N45 is regulated by a balance of GABAAergic inhibition and NMDA-receptor-mediated glutamatergic excitation. In contrast, AMPA-receptor-mediated glutamatergic neurotransmission contributes to interhemispherically propagated activity reflected in the P70. These data are important to understand the physiology of TEPs as markers of excitability and propagated activity in the human cortex in health and disease.

neuroscience↗

bioRxiv2018

TMS-evoked EEG potentials from prefrontal and parietal cortex: reliability, site specificity, and effects of dextromethorphan

Measuring the brains response to transcranial magnetic stimulation (TMS) with electroencephalography (EEG) offers a unique insight into the local cortical circuits and networks activated following stimulation, particularly in non-motor regions where less is known about TMS physiology. However, the mechanisms underlying TMS-evoked EEG potentials (TEPs) remain largely unknown. We assessed TEP reliability, site-specificity, and sensitivity to changes in excitatory neurotransmission mediated by n-methyl-d-aspartate (NMDA) receptors following stimulation of non-motor regions. In fourteen male volunteers, resting EEG and TEPs from prefrontal (PFC) and parietal (PAR) cortex were measured before and after administration of either dextromethorphan (an NMDA receptor antagonist) or placebo across two sessions separated by at least a week in a double-blinded pseudo-randomised crossover design. At baseline, TEPs showed lower within-than between-subject variability for both stimulation sites across sessions, demonstrating the reliability of non-motor TEPs within individuals. There were differences in amplitude between PFC and PAR TEPs across a wide time range (15-250 ms), however the signals were correlated after ~80 ms, suggesting that early peaks reflect site-specific activity, whereas late peaks reflect activity patterns less dependent on the stimulated sites. TEPs were not altered following dextromethorphan compared to placebo, however low frequency resting oscillations were reduced in power. Our findings suggest that TEPs from PFC and PAR: 1) are reliable within and variable between individuals; 2) reflect stimulation site specific activity across early time periods (<80 ms); and 3) are not sensitive to changes in NMDA receptor-mediated neurotransmission.

neuroscience↗

bioRxiv2018

μ-rhythm extracted with personalized EEG filters correlates with corticospinal excitability in real-time phase-triggered EEG-TMS

Ongoing brain activity has been implicated in the modulation of cortical excitability. The combination of electroencephalography (EEG) and transcranial magnetic stimulation (TMS) in a real-time triggered setup is a novel method for testing hypotheses about the relationship between spontaneous neuronal oscillations, cortical excitability, and synaptic plasticity. For this method, a reliable real-time extraction of the neuronal signal of interest from scalp EEG with high signal-to-noise ratio (SNR) is of crucial importance. Here we compare individually tailored spatial filters as computed by spatial-spectral decomposition (SSD), which maximizes SNR in a frequency band of interest, against established local C3-centered Laplacian filters for the extraction of the sensorimotor -rhythm. Single-pulse TMS over the left primary motor cortex was synchronized with the surface positive or negative peak of the respective extracted signal, and motor evoked potentials (MEP) were recorded with electromyography (EMG) of a contralateral hand muscle. Both extraction methods led to a comparable degree of MEP amplitude modulation by phase of the sensorimotor -rhythm at the time of stimulation. This could be relevant for targeting other brain regions with no working benchmark such as the local C3-centered Laplacian filter, as sufficient SNR is an important prerequisite for reliable real-time single-trial detection of EEG features.

neuroscience↗