Biology subjects

Pellegrino, G.

Publications and source records attributed to Pellegrino, G..

11 recordsLinked to original sources

bioRxiv2026

Where is ADHD in the brain? Evidence for a neurodevelopmental continuum of brain dynamics

BackgroundAttention-deficit/hyperactivity disorder (ADHD) has traditionally been conceptualized categorically, with efforts to identify disorder-specific neurobiological endophenotypes. However, dimensional models suggest that brain-behavior organization may follow developmental axes that cut across diagnostic boundaries. We tested whether neural dynamics and cortical excitability differentiate those with ADHD diagnoses from typically developing (TD) peers, and whether brain-behavior covariance aligns with diagnostic or developmental dimensions. MethodsWe studied 84 participants aged 8-17 years (51 ADHD, 33 TD). High-density electrophysiological (hdEEG) measures included task-free source-resolved data used to derive mean global brain fluidity (variance of dynamic functional connectivity) and region-specific cortical excitability. Behavioural measures included self- and parent-report questionnaires, cognitive control (CC) tasks, and neuropsychological tests. Partial least squares (PLS) assessed multivariate brain-behavior associations including age, followed by clustering based on latent component scores. ResultsGroup differences emerged in parent-report questionnaires and CC tasks, but not in neuropsychological measures. ADHD individuals showed higher mean global brain fluidity and increased cortical excitability. The excitability-fluidity relationship was network-dependent: higher excitability predicted higher fluidity in task-positive networks and lower fluidity in default-mode and salience networks, with no group effects. PLS identified a latent dimension linking neural metrics with age, verbal fluency, inhibitory control, and positive affect, but it did not distinguish ADHD from TD. Clustering revealed two neurodevelopmental profiles spanning both groups. ConclusionsWhile ADHD is associated with mean-level differences in neural dynamics, brain-behaviour organization follows a developmental neurocognitive-affective axis that transcends the diagnostic boundary. These findings support a dimensional framework for understanding neurobiological variation in neurodevelopmental conditions.

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bioRxiv2026

Assessing whole-cortex excitability from electromagnetic brain signals

Cortical excitability, the propensity of neural circuits to respond to internal or external perturbations, is a fundamental property of brain functioning, shaped by local microcircuitry, large-scale networks, and neurochemical architecture. In humans, excitability is inferred from multiple indirect metrics derived from spontaneous and stimulus-driven electromagnetic activity, yet it remains unclear whether these measures reflect a common underlying construct or distinct physiological processes. Here, we systematically compared ten previously validated excitability metrics using whole-head magnetoencephalography (MEG) recorded at rest and during 40 Hz auditory stimulation in a large sample of healthy adults. The measures spanned stimulus-driven synchronization, spectral power, 1/f activity (hereafter defined as aperiodic), signal complexity, long-range temporal correlations, and phase-gamma synchronization. Hierarchical clustering revealed six separable excitability dimensions with limited redundancy, demonstrating that cortical excitability is inherently multidimensional. Aperiodic and alpha-band measures showed the strongest mutual coupling and the furthest spatial correspondence to stimulus-related responses, whereas temporal, complexity, and synchronization-based metrics were largely independent. The similarity between measures varied across cortical regions and functional networks, with maximal cluster separability in integrative and internally driven regions. These regions included orbitofrontal, temporal, anterior cingulate, and default mode areas, as well as visual and somatomotor networks. Finally, excitability dimensions showed distinct relationships with cortical morphology and neurotransmitter receptor density, implicating heterogeneous neurobiological substrates. Together, these findings provide a unified framework for interpreting excitability metrics and highlight the need for multimodal approaches when probing cortical excitability in health and disease.

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bioRxiv2024

Excitation/Inhibition balance relates to cognitive function and gene expression in Temporal Lobe Epilepsy: an hdEEG assessment with aperiodic exponent

Patients with epilepsy are characterized by a dysregulation of excitation-inhibition balance (E/I). The assessment of E/I may inform clinicians during the diagnosis and therapy management, even though it is rarely performed. An accessible measure of the E/I of the brain represents a clinically relevant feature. Here we exploited the exponent of the aperiodic component of the power spectrum of EEG signal as a noninvasive and cost-effective proxy of the E/I balance. We recorded resting-state activity with high-density EEG from 65 patients with temporal lobe epilepsy (TLE) and 35 controls. We extracted the exponent of the aperiodic fit of the power spectrum from source-reconstructed EEG and tested differences between TLE and controls. Spearmans correlation was performed between the exponent and clinical variables (age of onset, epilepsy duration and neuropsychology) and cortical expression of epilepsy-related genes derived from Human Allen Brain Atlas. Patients with TLE showed a significantly larger exponent, corresponding to an inhibition directed E/I balance, in bilateral frontal and temporal regions. Lower E/I in the left entorhinal, and bilateral dorsolateral prefrontal cortices corresponded to a lower performance of short term verbal memory. Limited to TLE, we detected a significant correlation between the exponent and the cortical expression of GABRA1, GRIN2A, GABRD, GABRG2, KCNA2and PDYN. EEG aperiodic exponent maps the E/I balance non-invasively in patients with epilepsy and reveals a tight relationship between altered E/I patterns, cognition and genetics.

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bioRxiv2023

Functional displacement of cortical neuromagnetic somatosensory responses: enhancing embodiment in the rubber hand illusion

The integration of an artificial limb as part of ones body involves complex neuroplastic changes resulting from various sensory inputs to the brain. While sensory feedback is known to be crucial for embodiment, current evidence points merely to the attenuation of somatosensory processing, while the positive contributions of somatosensory areas to embodiment remain unknown. This study investigated the relationship between embodiment and adaptive neuroplasticity of early-latency somatosensory evoked fields (SEFs) in the primary somatosensory cortex (S1) following the Rubber Hand Illusion (RHI), known to induce short-term artificial limb embodiment. Nineteen healthy adults underwent neuromagnetic recordings during electrical stimulation of the little finger and thumb, before and after the RHI. We found a displacement of early SEF sources. In particular, we observed a correlation between the extent of rubber hand embodiment and specific changes to the m20 component (magnetic equivalent to the N20) in Brodmann Area 3b: a larger displacement and a greater reduction in m20 magnitude predicted the amount of embodiment, highlighting an important functional contribution of this first cortical input. Furthermore, we observed a posteriorly directed m35 displacement towards Area 1, known to be important for visual integration during touch perception (Rosenthal et al., 2023). Our finding that the larger displacement for the m35 did not correlate with the extent of embodiment implies a functional distinction between neuroplastic changes across these two components and areas in their contributions to successful artificial limb embodiment: (i) the earlier neuroplastic changes to m20 may shape the extent of artificial limb ownership, and (ii) the posteriorward shift of the m35 into Area 1 is suggestive of a mechanistic contribution to early visual-tactile integration that initially establishes the embodiment. Taken together, these findings suggest that multiple distinct changes occur during early-latency SEFs and their displacement in S1 last beyond the duration of the illusion and are important for the successful integration of an artificial limb within the body representation.

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bioRxiv2023

Assessing cortical excitability with electroencephalography: a pilot study with EEG-iTBS.

Cortical excitability measures neural reactivity to stimuli, usually delivered via Transcranial Magnetic Stimulation (TMS). Excitation/inhibition balance (E/I) is the ongoing equilibrium between excitatory and inhibitory activity of neural circuits. According to some studies, E/I could be estimated in-vivo and non-invasively through the modeling of electroencephalography (EEG) signals. Several measures have been proposed (phase consistency in the gamma band, sample entropy, exponent of the power spectral density 1/f curve, E/I index extracted from detrend fluctuation analysis, and alpha power). It remains to be investigated to what extent they scale with excitability and how they relate to each other. Intermittent theta burst stimulation (iTBS) of the primary motor cortex (M1) is a non-invasive neuromodulation technique allowing controlled and focal enhancement of cortical excitability and E/I of the stimulated hemisphere. M1 excitability and several E/I estimates extracted from resting state EEG recordings were assessed before and after iTBS in a cohort of healthy subjects. Enhancement of M1 excitability, as measured through motor-evoked potentials (MEPs), and phase consistency of the cortex in high gamma band correlated with each other. Other measures of E/I showed some expected results, but no correlation with TMS excitability measures or consistency with each other. EEG E/I estimates offer an intriguing opportunity to map cortical excitability non-invasively, with high spatio-temporal resolution and with a stimulus independent approach. While different EEG E/I estimates may reflect the activity of diverse excitatory-inhibitory circuits, spatial phase synchrony in the gamma band is the measure that best captures excitability changes in the primary motor cortex.

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bioRxiv2023

Altered spread of large scale brain dynamics is influenced by cortical thickness organization in temporal lobe epilepsy: a MRI and hdEEG study

Temporal lobe epilepsy (TLE) is a brain network disorder characterized by alterations at both the structural and the functional level. It remains unclear how structure and function are related and whether this has any clinical relevance. In the present work, we adopted a novel methodological approach investigating how network structural features influence the large-scale dynamics. The functional network was defined by the spatio-temporal spreading of aperiodic bursts of activations (neuronal avalanches), as observed utilizing high-density electroencephalography (hdEEG) in TLE patients. The structural network was modeled as the region-based thickness covariance. Loosely speaking, we quantified the similarity of the cortical thickness of any two brain regions, both across groups, and at the individual level, the latter utilizing a novel approach to define the personalized covariance network (pCN). In order to compare the structural and functional networks (at the nodal level), we studied the correlation between the probability that a wave of activity would propagate from a source to a target region, and the similarity of the source region thickness as compared to other target brain regions. Building on the recent evidence that large-waves of activities pathologically spread through the epileptogenic network in TLE, also during resting state, we hypothesize that the structural cortical organization might influence such altered spatio-temporal dynamics. We observed a stable cluster of structure-function correlation in the bilateral limbic areas across subjects, highlighting group specific features for left, right and bilateral TLE. The involvement of contralateral areas was observed in unilateral TLE. We showed that in temporal lobe epilepsy alterations of structural and functional networks pair in the regions where seizures propagate and are linked to disease severity. In this study we leveraged on a well-defined model of neurological disease and pushed forward personalization approaches potentially useful in clinical practice. Finally, the methods developed here could be exploited to investigate the relationship between structure-function networks at subject level in other neurological conditions.

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bioRxiv2022

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

BackgroundMagnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. MethodWe validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (IEEG) atlas (https://mni-open-ieegatlas.research.mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space, by applying a forward model to the MEG reconstructed source maps and estimated virtual IEEG (VIEEG) potentials on each IEEG channel location and quantitatively compared those with actual IEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. ResultsThe MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the VIEEG than in the IEEG were more accurately recovered. In the deep regions, MEG estimated amplitudes were largely underestimated and the spectra were poorly recovered. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of IEEG but detected by MEG. Importantly, we found that MEG estimated spectra were more comparable to spectra from the IEEG atlas after the aperiodic components were removed. ConclusionThis study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies. HighlightsO_LIValidation of MEG source imaging with intracranial EEE atlas C_LIO_LIAssessment of resting state human brain oscillations from healthy brain C_LIO_LIAdapted source imaging method, wMEM, to localize resting state oscillations C_LIO_LIIdentified brain regions with oscillations accurately estimated by MEG C_LIO_LIMEG estimated spectra dominated by oscillations in the alpha band C_LI

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bioRxiv2022

Task matters: individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magnetoencephalography (MEG) recordings. However, to what extent MEG signatures constitute a marker of human identifiability when engaged in task-related behavior remains an open question. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

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bioRxiv2021

Hierarchical Bayesian Modeling of the Relationship between Task Related Hemodynamic Responses and Neuronal Excitability: a Simultaneous fNIRS/TMS Study

BackgroundInvestigating the relationship between task-related hemodynamic responses and cortical excitability is challenging because it requires simultaneous measurement of hemodynamic responses while applying non-invasive brain stimulation. Moreover, cortical excitability and task-related hemodynamic responses are both associated with inter-/intra-subject variability. To reliably assess such a relationship, we applied hierarchical Bayesian modeling. MethodsThis study involved 16 healthy subjects who underwent simultaneous Paired Associative Stimulation (PAS10, PAS25, Sham) while monitoring brain activity using functional Near-Infrared Spectroscopy (fNIRS), targeting the primary motor cortex (M1). Cortical excitability was measured by Motor Evoked Potentials (MEPs), and the motor task-related hemodynamic responses were measured using fNIRS 3D reconstructions. We constructed three models to investigate: 1) PAS effects on the M1 excitability; 2) PAS effects on fNIRS hemodynamic responses to a finger tapping task, and 3) the correlation between PAS effects on M1 excitability and PAS effects on task-related hemodynamic responses. ResultsSignificant increase in cortical excitability was found following PAS25, whereas a small reduction of the cortical excitability was shown after PAS10 and a subtle increase occurred after sham. Both HbO and HbR absolute amplitudes increased after PAS25 and decreased after PAS10. The probability of the positive correlation between modulation of cortical excitability and hemodynamic activity was 0.77 for HbO and 0.79 for HbR. ConclusionWe demonstrated that PAS stimulation modulates task-related cortical hemodynamic responses in addition to M1 excitability. Moreover, the positive correlation between PAS modulations of excitability and hemodynamics brought insight into understanding the fundamental properties of cortical function and cortical excitability.

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bioRxiv2021

Hemodynamic Correlates of Fluctuations in Neuronal Excitability: A Simultaneous Paired Associative Stimulation (PAS) and functional Near Infra-Red Spectroscopy (fNIRS) Study

BackgroundThe relationship between task-related hemodynamic activity and brain excitability is poorly understood in humans as it is technically challenging to combine simultaneously non-invasive brain stimulation and neuroimaging modalities. Cortical excitability corresponds to the readiness to become active and as such it may be linked to metabolic demand. HypothesesCortical excitability and hemodynamic activity are positively linked so that increases in hemodynamic activity correspond to increases in excitability and vice-versa. MethodsFluctuations of excitability and hemodynamic activity were investigated via simultaneous Transcranial Magnetic Stimulation (TMS) and functional Near Infrared Spectroscopy (fNIRS). Sixteen healthy subjects participated in a sham-controlled, pseudorandomized, counterbalanced study with PAS (PAS10/PAS25/Sham) on the right primary motor cortex (M1). The relationship between M1 excitability (Motor Evoked Potentials, MEP) and hemodynamic responses to finger tapping reconstructed via personalized fNIRS was assessed. ResultsHemodynamic activity exhibited a significant correlation with cortical excitability: increased HbO and HbR (absolute amplitude) corresponded to increased excitability and vice-versa (r=0.25; p=0.03 and r=0.16; p=0.17, respectively). The effect of PAS on excitability and hemodynamic activity showed a trend of positive correlation: correlation of MEP ratios (post-PAS/pre-PAS) with HbO and HbR ratios (r=0.19, p=0.29; r=0.18, p=0.30, respectively). ConclusionsTMS-fNIRS is a suitable technique for simultaneous investigation of excitability and hemodynamic responses and indicates a relationship between these two cortical properties. PAS effect is not limited to cortical excitability but also impacts hemodynamic processes. These findings have an impact on the application of neuromodulatory interventions in patients with neuropsychiatric disorders.

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bioRxiv2021

Diffuse optical reconstructions of NIRS data using Maximum Entropy on the Mean

Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists of reconstructing the optical density changes measured from scalp channels to the oxy-/deoxy-hemoglobin (i.e., HbO/HbR) concentration changes within the cortical regions. In the present study, we adapted a nonlinear source localization method developed and validated in the context of Electro- and Magneto-Encephalography (EEG/MEG): the Maximum Entropy on the Mean (MEM), to solve the inverse problem of DOT reconstruction. We first introduced depth weighting strategy within the MEM framework for DOT reconstruction to avoid biasing the reconstruction results of DOT towards superficial regions. We also proposed a new initialization of the MEM model improving the temporal accuracy of the original MEM framework. To evaluate MEM performance and compare with widely used depth weighted Minimum Norm Estimate (MNE) inverse solution, we applied a realistic simulation scheme which contained 4000 simulations generated by 250 different seeds at different locations and 4 spatial extents ranging from 3 to 40cm2 along the cortical surface. Our results showed that overall MEM provided more accurate DOT reconstructions than MNE. Moreover, we found that MEM was remained particularly robust in low signal-to-noise ratio (SNR) conditions. The proposed method was further illustrated by comparing to functional Magnetic Resonance Imaging (fMRI) activation maps, on real data involving finger tapping tasks with two different montages. The results showed that MEM provided more accurate HbO and HbR reconstructions in spatial agreement with the main fMRI cluster, when compared to MNE. HighlightsO_LIWe introduced a new fNIRS reconstruction method - Maximum Entropy on the Mean. C_LIO_LIWe implemented depth weighting strategy within the MEM framework. C_LIO_LIWe improved the temporal accuracy of the original MEM reconstruction. C_LIO_LIPerformances of MEM and MNE were evaluated with realistic simulations and real data. C_LIO_LIMEM provided more accurate and robust reconstructions than MNE. C_LI

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