Biology subjects

Fecchio, M.

Publications and source records attributed to Fecchio, M..

14 recordsLinked to original sources

bioRxiv2025

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: a machine learning approach

BackgroundTranscranial Magnetic Stimulation (TMS) with simultaneous Electroencephalogram (TMS-EEG) allows assessing the neurophysiological properties of cortical neurons. However, TMS-evoked EEG potentials (TEPs) can be affected by components unrelated to TMS direct neuronal activation. Accurate, automatic tools are therefore needed to establish the quality of TEPs. ObjectiveTo assess the discriminability of EEG responses to TMS vs. EEG responses to sham stimulations using sequence-to-sequence machine learning (ML). MethodsTwo indipendent TMS-EEG datasets including TMS and several sham stimulation conditions were obtained from the left motor area of healthy volunteers (N=33 across datasets). A Bi-directional Long Short-Term Memory (BiLSTM) ML network was used to label each time point of the EEG signals as pertaining to TMS or sham conditions. Main outcome measures included accuracy at single-trial level and after averaging five to twenty trials. ResultsFor TMS conditions, post-stimulus vs. baseline/pre-stimulus EEG comparisons yielded moderate (60%-75%) single-trial accuracy and high-accuracy (>75%) for 20 trials across datasets, while for sham conditions post- vs. baseline/pre-stimulus EEG comparisons yielded lower accuracy rates than for TMS conditions, except for unmasked auditory stimulation. Furthermore, baseline/pre-stimulus TMS vs. baseline/pre-stimulus sham EEG comparisons showed chance-level accuracy, whereas post-stimulus TMS vs. post-stimulus sham EEG comparisons had moderate (single trial) to high (20 trial) accuracy, except for TMS with and without the click noise masking. Single-subject findings were comparable to group-level results across datasets. ConclusionsTEPs after active TMS are discernible from various sham stimulations even after a handful of trials and at the single-subject level using a BiLSTM ML approach.

neuroscience↗

bioRxiv2025

The specific spatiotemporal evolution of TMS-evoked potentials reflects the engagement of cortical circuits

Transcranial Magnetic Stimulation (TMS) evokes electroencephalographic (EEG) responses that can persist for hundreds of milliseconds. While the first 80 ms after the pulse are widely accepted to reflect genuine cortical responses to TMS, later components have mainly been attributed to the effects of sensory co-stimulations. Here we reappraise this view by investigating the target-specificity of the spatiotemporal evolution of TMS-evoked potentials (TEPs). To this end, we compared TEPs elicited by targeting the premotor and primary motor cortices in 16 healthy subjects, under conditions designed to optimize TMS effectiveness on the cortex while minimizing peripheral confounds. As a counterfactual, we conducted the same comparison on the EEG responses evoked by realistic sham TMS and high-intensity somatosensory scalp stimulation. We found that EEG responses to motor and premotor TMS can exhibit distinct spatiotemporal evolutions, lasting up to 300 ms, both at the group and single-subject levels. These differences were absent or marginally detectable in both realistic sham TMS and high-intensity somatosensory scalp stimulation. Our findings suggest that, when effectiveness is optimized and peripheral confounds are controlled, TMS elicits specific long-lasting genuine EEG responses that reflect the initial engagement of specific cortical targets. These results challenge previous assumptions and highlight how TMS-EEG can be reliably used to assess large-scale properties within corticothalamic networks.

physiology↗

bioRxiv2025

Transcranial magnetic vs intracranial electric stimulation: a direct comparison of their effects via scalp EEG recordings

BackgroundSingle-pulse Transcranial Magnetic Stimulation (TMS) and Intracranial Electrical Stimulation (IES) are widely used to probe cortical excitability and connectivity, but their electrophysiological effects have never been compared. ObjectiveThis study aims to fill this gap by using high-density scalp electroencephalogram (hd-EEG) as a common read-out to compare human brain responses to TMS and IES. MethodsThe dataset includes TMS-evoked potentials (TEPs) acquired from healthy subjects (n=22) and IES-evoked potentials (IEPs) recorded from drug-resistant epileptic patients (n=31) during wakefulness. In a subset of subjects TEPs (n=12) and IEPs (n=13) were also recorded during NREM sleep. Amplitude, spectral, and spatiotemporal features of TMS and IES responses, as well as their estimated electrical fields, were compared. ResultsWe observed marked differences between TMS and IES responses. During wakefulness, IEPs are considerably larger, slower and associated with a suppression of cortical activity, whereas TEPs are characterized by multiple waves of recurrent activation. These differences are attenuated during NREM, during which both TMS and IES elicit large EEG responses associated with a prominent suppression of cortical activity. At the global level, the spatiotemporal complexity of the responses to both TMS and IES decreases consistently following the transition from wakefulness to NREM sleep. ConclusionDespite the limitations due to different subject populations (healthy vs pathological), our findings provide a first reference to parallel non-invasive and invasive brain stimulation and to interpret their differential effects. They also offer important insight on how cortical responsiveness is shaped by inhibition and adaptation mechanisms depending on input parameters and brain states. Highlights- IES evokes higher signal-to-noise EEG responses than TMS. - IES responses are larger, slower, and lead to cortical suppression. - TMS elicits faster recurrent waves, with no suppression during wakefulness. - Both TMS and IES converge to similar patterns during NREM sleep.

neuroscience↗

bioRxiv2025

Visualizing Effective Connectivity in the Human Brain

To enhance mechanistic understanding of effective connectivity in the human brain, we created a tool that links high-temporal resolution transcranial magnetic stimulation electroencephalography (TMS-EEG) with high-spatial resolution diffusion MRI. This tool, Surface to Tractography Real-time EEG Activation Mapping in 4 Dimensions (STREAM-4D), integrates electrophysiologic source estimation models from TMS-evoked potentials (TEPs) with structural connectivity models from diffusion MRI tractography. In a proof-of-principle application, we used STREAM-4D to analyze TMS-EEG and diffusion MRI tractography data in a neurotypical subject across three stimulation sites: premotor, parietal, and occipital cortex. STREAM-4D revealed extensive structural connections associated with the TMS-evoked source models, involving thalamocortical, ipsilateral cortico-cortical, and transcallosal cortico-cortical connections. Activity-weighted structural connectivity differed for the three stimulation sites but shared two extensively connected nodes: the ipsilateral thalamus and putamen. STREAM-4D provides opportunities to elucidate how electrical activity evoked by a TMS pulse is related to underlying white matter architecture.

neuroscience↗

bioRxiv2022

Adaptation shapes local cortical reactivity: from bifurcation diagram and simulations to human physiological and pathological responses

Human studies employing intracerebral and transcranial perturbations suggest that the input-output properties of cortical circuits are dramatically affected during sleep in healthy subjects as well as in awake patients with multifocal and focal brain injury. In all these conditions, cortical circuits react to direct stimulation with an initial activation followed by suppression of activity (Off-period) that disrupts the build-up of sustained causal interactions typically observed in healthy wakefulness. The transition to this stereotypical response is of clinical relevance, being associated with loss of consciousness or loss of function. Here, we provide a mechanistic explanation of these findings by means of mean-field theory and simulations of a cortical-like module endowed with activity-dependent adaptation. First, we show that fundamental aspects of the local responses elicited in humans by direct cortical stimulation can be replicated by systematically varying the relationships between adaptation strength and excitation level in the network. Then, we reveal a region in the adaptation-excitation parameter space of key relevance for both physiological and pathological conditions, where spontaneous activity and responses to perturbation diverge in their ability to reveal Off-periods. Finally, we substantiate through simulations of connected cortical-like modules the role of adaptation mechanisms in preventing cortical neurons from engaging in reciprocal causal interactions, as suggested by empirical studies. These modeling results provide a general theoretical framework and a mechanistic interpretation for a body of neurophysiological measurements that bears key relevance for physiological states as well as for the assessment and rehabilitation of brain-injured patients. Significance StatementSuppression of cortical activity following an initial activation is a defining feature of deep sleep in healthy subjects and wakefulness in patients affected by focal and multifocal brain injuries. Experimental findings suggest that these bimodal responses disrupt the emergence of complex interactions among cortical regions, leading to loss of consciousness or functional impairments. Given their practical implications, it is important to study the mechanisms involved within a general theoretical framework. Using a neuronal network model, we provide evidence for a key role of activity-dependent adaptation mechanisms in shaping the responses to perturbation and in affecting the build-up of complex cortical interactions. Overall, this work provides a mechanistic interpretation relevant for the stratification, follow-up, and rehabilitation of brain-injured patients.

neuroscience↗

bioRxiv2021

Simultaneous stereo-EEG and high-density scalp EEG recordings to study the effects of intracerebral stimulation parameters

BackgroundCortico-cortical evoked potentials (CCEPs) recorded by stereo-electroencephalography (SEEG) are a valuable clinical tool to investigate brain reactivity and effective connectivity. However, these invasive recordings are spatially sparse since they depend on clinical needs. This sparsity hampers systematic comparisons across-subjects, the detection of the whole-brain spatiotemporal properties of CCEPs, as well as their relationships with classic sensory evoked potentials. ObjectiveTo demonstrate that CCEPs recorded by high-density electroencephalography (hd-EEG) are sensitive to changes in stimulation parameters and compensate for the limitations typical of invasive recordings. MethodsSEEG and hd-EEG activities were simultaneously recorded during SPES in drug-resistant epileptic patients (N=36). Changes in stimulation parameters encompassed physical (pulse intensity and width), geometrical (angle and position with respect to white/grey matter) and topological (stimulated cortical area) properties. Differences were assessed by measuring the overall responses and the amplitude of N1 and N2 components of the CCEPs, and by their spectral profiles. ResultsWhile invasive and non-invasive CCEPs were generally correlated, differences in pulse duration, angle and stimulated cortical area were better captured by hd-EEG. Further, hd-EEG responses to SPES reproduced basic features of responses to transcranial magnetic stimulation and showed a much larger amplitude as compared to typical sensory evoked potentials. ConclusionsThe present results show that macroscale hd-EEG recordings are exquisitely sensitive to variations in SPES parameters, including local changes in physical and geometrical stimulus properties, while providing valuable information about whole-brain dynamics. Moreover, the common reference space across subjects represented by hd-EEG may facilitate the construction of a perturbational atlas of effective connectivity. HighlightsO_LICCEPs recorded with hd-EEG and SEEG are correlated. C_LIO_LIhd-EEG recording is highly sensitive to changes in stimulation parameters. C_LIO_LIhd-EEG responses show higher amplitude responses with respect to non-invasive ones. C_LIO_LISimultaneous recordings provide a fixed observation point across subjects. C_LI

neuroscience↗

bioRxiv2021

Local brain-state dependency of effective connectivity: evidence from TMS-EEG

BackgroundSpontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). If not controlled for, they might increase variability in responses and mask meaningful changes in the signals of interest when studying the brain with TMS combined with electroencephalography (TMS-EEG). To address this challenge in future closed-loop stimulation paradigms, we need to understand how spontaneous oscillations affect TMS-evoked responses. ObjectiveTo describe the effect of the pre-stimulus phase of cortical mu (8-13 Hz) and beta (13-30 Hz) oscillations on TMS-induced effective connectivity patterns. MethodsWe applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes. ResultsPhase had significant effects on the GMFA in 11 out of 12 datasets (3 subjects x 2 stimulation sites x 2 frequency bands). Seven of the datasets showed significant differences in the time range 15-50 ms, nine in 50-150 ms, and eight after 150 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. ConclusionsTMS-evoked effective connectivity appears to depend on the phase of local cortical oscillations at the stimulated site. This may be crucial for efficient design of future brain-state-dependent and closed-loop stimulation paradigms.

neuroscience↗

bioRxiv2021

The rt-TEP tool: real-time visualization of TMS-evoked potentials to maximize cortical activation and minimize artifacts

BackgroundThe impact of transcranial magnetic stimulation (TMS) on cortical neurons is currently hard to predict based on a priori biophysical and anatomical knowledge alone. This problem can hamper the reliability and reproducibility of protocols aimed at measuring electroencephalographic (EEG) responses to TMS. New MethodWe introduce and release a novel software tool to facilitate and standardize the acquisition of TMS-evoked potentials (TEPs). The tool, rt-TEP (real-time TEP), interfaces with different EEG amplifiers and offers a series of informative visualization modes to assess in real time the immediate impact of TMS on the underlying neuronal circuits. ResultsWe show that rt-TEP can be used to abolish or minimize magnetic and muscle artifacts contaminating the post-stimulus period thus affording a clear visualization and quantification of the amplitude of the early (<50 ms) EEG response after averaging a limited number of trials. This real-time readout can then be used to adjust TMS parameters (e.g. site, orientation, intensity) and experimental settings (e.g. loudness and/or spectral features of the noise masking) to ultimately maximize direct cortical effects over the undesired sensory effects of the coils discharge. Comparison with Existing MethodsThe ensemble of real-time visualization modes of rt-TEP are not implemented in any current commercial software and provide a key readout to titrate TMS parameters beyond the a priori information provided by anatomical models. ConclusionsReal-time optimization of stimulation parameters with rt-TEP can facilitate the acquisition of reliable TEPs with a high signal-to-noise ratio and improve the standardization and reproducibility of data collection across laboratories. HighlightsO_LIrt-TEP provides an immediate EEG readout to assess the quality of TEPs in real time C_LIO_LIrt-TEP interfaces with most commercial EEG systems C_LIO_LIInformative, real time visualization allows maximising the cortical impact of TMS while minimizing artifacts C_LIO_LIrt-TEP facilitates the acquisition of reliable TEPs with a high signal-to-noise ratio C_LI

neuroscience↗

bioRxiv2021

TAAC - TMS Adaptable Auditory Control: a universal tool to mask TMS click

BackgroundCoupling transcranial magnetic stimulation with electroencephalography (TMS-EEG) allows recording the EEG response to a direct, non-invasive cortical perturbation. However, obtaining a genuine TMS-evoked EEG potential requires controlling for several confounds, among which a main source is represented by the auditory evoked potentials (AEPs) associated to the TMS discharge noise (TMS click). This contaminating factor can be in principle prevented by playing a masking noise through earphones. New methodHere we release TMS Adaptable Auditory Control (TAAC), a highly flexible, open-source, Matlab(R)-based interface that generates in real-time customized masking noises. TAAC creates noises starting from the stimulator-specific TMS click and tailors them to fit the individual, subject-specific click perception by mixing and manipulating the standard noises in both time and frequency domains. ResultsWe showed that TAAC allows us to provide standard as well as customized noises able to effectively and safely mask the TMS click. Comparison with existing methodsHere, we showcased two customized noises by comparing them to two standard noises previously used in the TMS literature (i.e., a white noise and a noise generated from the stimulator-specific TMS click only). For each, we quantified the Sound Pressure Level (SPL; measured by a Head and Torso Simulator - HATS) required to mask the TMS click in a population of 20 healthy subjects. Both customized noises were effective at safe (according to OSHA and NIOSH safety guidelines), lower SPLs with respect to standard noises. ConclusionsAt odds with previous methods, TAAC allows creating effective and safe masking noises specifically tailored on each TMS device and subject. The combination of TAAC with tools for the real-time visualization of TEPs can help control the influence of auditory confounds also in non-compliant patients. Finally, TAAC is a highly flexible and open-source tool, so it can be further extended to meet different experimental requirements.

neuroscience↗

bioRxiv2020

Assessing the Intra- and Inter-Subject Reliability of the Perturbational Complexity Index (PCI) of Consciousness for Three Brain Regions Using TMS-EEG

BackgroundThe perturbational complexity index (PCI) is a useful measure of consciousness that combines transcranial magnetic stimulation (TMS) with electroencephalography (EEG). However, the PCI has not been assessed for reliability between sessions nor is there a clear best stimulation target to acquire a PCI. Objective/HypothesisWe assessed the reliability of within-subject PCIs between 3 sessions with stimulation over the same premotor, motor, and parietal targets between visits, hypothesizing that we could determine a most reliable TMS-EEG target to acquire PCIs in healthy, conscious adults. MethodsPCIs were acquired for 9 participants (5 women) over 3 sessions within a week. A 64 channel EEG system was used with all electrode impedances [≤] 10k{Omega}. Premotor, motor, and parietal stimulation targets were identified using a real-time Matlab graphical user interface (GUI). Neuronavigation using an MRI-template brain ensured that every TMS pulse was delivered within 3.0mm and 5{degrees} of the stimulation target. ResultsPremotor, motor, and parietal PCIs all had significant PCIs (all p < 0.05). However, parietal and motor PCIs had excellent reliability (ICCs = 0.927 and 0.857 respectively) whereas premotor PCIs had good reliability (ICC = 0.737). PCIs were similar between brain sites within each subject in a single visit, but with only a moderate effect (p = 0.024, ICC = 0.480). PCIs on a group level did not differ between brain sites (p = 0.589). ConclusionsThe PCI is a reliable measure over this timeframe within each subject for single brain targets. PCIs for parietal and motor sites are most similar between visits. Due to only moderate similarity between PCIs from three brain sites within each session, PCIs should be acquired over at least two brain sites, with parietal and motor regions as the top candidates.

neuroscience↗

bioRxiv2019

Local sleep-like cortical reactivity in the awake brain after focal injury

The functional consequences of brain injury are known to depend on neuronal alterations extending beyond the area of structural damage. Although a lateralized EEG slowing over the injured hemisphere was known since the early days of clinical neurophysiology, its electrophysiological mechanisms were not systematically investigated. In parallel, basic sleep research has thoroughly characterized the neuronal events underlying EEG slow waves in physiological conditions. These EEG events reflect brief interruptions of neuronal firing (OFF-periods) that can occur locally and have prominent consequences on network and behavioral functions. Notably, the EEG slow waves observed following focal brain injury have been never explicitly connected to local sleep-like neuronal events. In previous works, probing cortical circuits with transcranial magnetic stimulation coupled with EEG (TMS/EEG) proved as an effective way to reveal the tendency of cortical circuits to transiently plunge into silent OFF-periods. Here, using this approach, we show that the intact cortex surrounding focal brain injuries engages locally in pathological sleep-like dynamics. Specifically, we employed TMS/EEG in a cohort of thirty conscious awake patients with chronic focal and multifocal brain injuries of various etiologies. TMS systematically evoked prominent slow waves associated with sleep-like OFF-periods in the area surrounding focal cortico-subcortical lesions. These events were associated with a local disruption of signal complexity and were absent when stimulating the contralateral hemisphere. Perilesional sleep-like OFF-periods may represent a valid read-out of the electrophysiological state of discrete cortical circuits following brain injury as well as a potential target of interventions aimed at fostering functional recovery. One Sentence SummaryFocal cortical injuries are associated with local intrusion of sleep-like dynamics over the perilesional areas which disrupt local signal complexity and coexist with typical wakefulness cortical reactivity patterns within the same brain.

neuroscience↗

bioRxiv2018

A fast and general method to empirically estimate the complexity of distributed causal interactions in the brain

BackgroundThe Perturbational Complexity Index (PCI) was recently introduced to assess the capacity of thalamocortical circuits to engage in complex patterns of causal interactions. While showing high accuracy in detecting consciousness in brain injured patients, PCI depends on elaborate experimental setups and offline processing and has restricted applicability to other types of brain signals beyond transcranial magnetic stimulation and high-density EEG (TMS/hd-EEG) recordings. ObjectiveWe aim to address these limitations by introducing PCIST, a fast method for estimating perturbational complexity of any given brain response signal. MethodsPCIST is based on dimensionality reduction and state transitions (ST) quantification of evoked potentials. The index was validated on a large dataset of TMS/hd-EEG recordings obtained from 108 healthy subjects and 108 brain injured patients, and tested on sparse intracranial recordings (SEEG) of 9 patients undergoing intra-cerebral single-pulse electrical stimulation (SPES). ResultsWhen calculated on TMS/hd-EEG potentials, PCIST performed with the same accuracy as the original PCI, while improving on the previous method by being computed in less than a second and requiring a simpler set-up. In SPES/SEEG signals, the index was able to quantify a systematic reduction of intracerebral complexity during sleep, confirming the occurrence of state-dependent changes in the effective connectivity of thalamocortical circuits, as originally assessed through TMS/hd-EEG. ConclusionsPCIST represents a fundamental advancement towards the implementation of a reliable and fast clinical tool for the bedside assessment of consciousness as well as a general measure to explore the neuronal mechanisms of loss/recovery of brain complexity across scales and models.

neuroscience↗

bioRxiv2018

Sleep-like bistability, loss of causality and complexity in the brain of Unresponsive Wakefulness Syndrome patients

Unresponsiveness Wakefulness Syndrome (UWS) patients may retain intact portions of the thalamocortical system that are spontaneously active and responsive to sensory stimuli. In these patients, Transcranial Magnetic Stimulation combined with electroencephalography (TMS/EEG) also reveals preserved cortical reactivity, but in most cases, the residual thalamocortical circuits fail to engage complex causal interactions, as assessed by the perturbational complexity index (PCI). Another condition during which thalamocortical circuits are intact, active and reactive, yet unable to generate complex responses, is physiological non-rapid eye movement (NREM) sleep. The underlying mechanism is bistability: the tendency of cortical neurons to fall into a silent period (OFF-period) upon receiving an input. Here we tested whether a pathological form of bistability may be responsible for loss of brain complexity in UWS patients. Time-frequency decomposition analysis of TMS/EEG responses in UWS patients revealed the occurrence of OFF-periods (detected as a transient suppression of high-frequency oscillations in the EEG) similar to the ones evoked by TMS in the cortex of healthy sleeping subjects. Pathological OFF-periods were detected in any cortical area, significantly impaired local causal interactions (as measured by PLF) and prevented the build-up of global complexity (as measured by PCI) in the brain of UWS patients. Our results draw a first link between neuronal events (OFF-periods) and global brain dynamics (complexity) in UWS patients. To the extent that sleep-like bistability represents the common functional endpoint of loss of complexity, detecting its presence and tracking its evolution over time, may offer a valuable read-out to devise, guide and titrate therapeutic strategies aimed at restoring consciousness.

neuroscience↗

bioRxiv2017

The Spectral Features Of EEG Responses To Transcranial Magnetic Stimulation Of The Primary Motor Cortex Depend On The Amplitude Of The Motor Evoked Potentials

Transcranial magnetic stimulation (TMS) of the primary motor cortex (M1) can excite both cortico-cortical and cortico-spinal axons resulting in TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs), respectively. Despite this remarkable difference with other cortical areas, the influence of motor output and its amplitude on TEPs is largely unknown. Here we studied TEPs resulting from M1 stimulation and assessed whether their waveform and spectral features depend on the MEP amplitude. To this aim, we performed two separate experiments. In experiment 1, single-pulse TMS was applied at the same supra-threshold intensity on primary motor, prefrontal, premotor and parietal cortices and the corresponding TEPs were compared by means of local mean field power and time-frequency spectral analysis. In experiment 2 we stimulated M1 at resting motor threshold in order to elicit MEPs characterized by a wide range of amplitudes. TEPs computed from high-MEP and low-MEP trials were then compared using the same methods applied in experiment 1. In line with previous studies, TMS of M1 produced larger TEPs compared to other cortical stimulations. Notably, we found that only TEPs produced by M1 stimulation were accompanied by a late ([~]300 ms after TMS) event-related desynchronization (ERD), whose magnitude was strongly dependent on the amplitude of MEPs. Overall, these results suggest that M1 produces peculiar responses to TMS possibly reflecting specific anatomo-functional properties, such as the re-entry of proprioceptive feedback associated with target muscle activation.

neuroscience↗