Biology subjects

Parmigiani, S.

Publications and source records attributed to Parmigiani, S..

18 recordsLinked to original sources

bioRxiv2026

Cognitive load modulates cortical excitability in the human prefrontal cortex

The prefrontal cortex dynamically adjusts its excitability to meet changing cognitive demands, yet our understanding of how these rapid state changes unfold in real time remains limited. Existing evidence relies predominantly on correlational neuroimaging and invasive electrophysiology in clinical populations, neither of which affords causal measurement in healthy humans. Here, using concurrent single-pulse TMS-EEG during a parametric cognitive multi-source interference task (MSIT) in 27 healthy participants, we causally probed left prefrontal excitability across conditions spanning a range of cognitive interference levels. Prefrontal excitability scaled systematically with cognitive load, peaking at intermediate incongruency levels where trial-type uncertainty was greatest, and differentiating high-from low-conflict trials at the single-subject level. Critically, prefrontal excitability and midfrontal theta power showed parallel state dependency patterns, linking causal perturbation of prefrontal circuit responsiveness to an established oscillatory marker of cognitive control. The observed excitability modulation was selective to the left prefrontal cortex: stimulation of right dlPFC, pre-SMA, and vertex in a separate control cohort (N=5) produced no load-dependent effects. These results position prefrontal excitability as a real-time, regionally selective physiological readout of how the human prefrontal cortex adapts to cognitive demand. HighlightsO_LIWe evaluated the links between cognitive interference, prefrontal excitability, and intrinsic theta activity during a multi-source interference task C_LIO_LICognitive load, estimated based on behavioral performance, was greatest at at intermediate interference levels (33% congruent), where trial-type uncertainty was maximal C_LIO_LIPrefrontal excitability, measured via single-pulse TMS-evoked potentials, scaled with cognitive load C_LIO_LIEffects were observed at the single subject level and were specific to prefrontal stimulation C_LIO_LIPrefrontal excitability changes and midfrontal theta power showed parallel scaling with cognitive load, linking causal probing to established oscillatory markers C_LIO_LIThis noninvasive technique provides causal measurement of prefrontal engagement during cognitive processing C_LIO_LIThe methodology demonstrates potential for tracking brain state dynamics in both healthy cognition and neuropsychiatric disorders C_LI

neuroscience↗

bioRxiv2025

Noninvasive profiling of input-output excitability curves in human prefrontal cortex

Abstract.O_ST_ABSBackgroundC_ST_ABSThe prefrontal cortex plays a critical role in cognitive control and behavior, and its dysfunction has been linked to numerous psychiatric and neurological disorders. However, noninvasive measurement of prefrontal activity remains challenging, limiting our understanding of how to optimize prefrontal treatments. Input-output relationships reveal how neural circuits respond to different inputs and are essential for determining optimal treatment parameters and understanding individual variability in treatment response, yet systematic investigation of prefrontal input-output relationships has been lacking. ObjectiveTo characterize human prefrontal excitability with input-output (I/O) curves. MethodsWe employed transcranial magnetic stimulation (TMS) with electroencephalography in a randomized mixed-block design with 28 healthy participants receiving single-pulse TMS to left dorsolateral prefrontal cortex (dlPFC) across 12 stimulation intensities (60-140% of resting motor threshold). We quantified prefrontal excitability using early local TMS-evoked potentials (EL-TEPs), cortical responses measured locally 20-60 ms post-stimulus. ResultsWe observed a strong effect of TMS intensity on prefrontal EL-TEP amplitudes. Sigmoidal EL-TEP I/O curves were observed in 57% of participants, with the sigmoidality partially explained by EL-TEP signal quality. Correlations were observed between EL-TEP and motor-evoked potential curve parameters, but intensity parameterization approaches did not significantly differ in explaining inter-individual EL-TEP response variability. Reliable EL-TEPs could be obtained using fewer TMS pulses at high intensities, and test- retest assessments revealed robust I/O curve profiles. ConclusionsThese findings provide a systematic noninvasive characterization of prefrontal input-output physiology, establishing a framework for estimating prefrontal excitability. The comparison of various intensity parameterizations motivates the need for enhanced models and individualized measurement of stimulation responses. HighlightsO_LIWe present noninvasive input-output curves for prefrontal TMS. C_LIO_LIEL-TEPs exhibit robust dose-dependent responses to TMS intensity. C_LIO_LISigmoidal I/O curves observed in 57% of participants, with a strong dependence on signal quality. C_LIO_LICorrelations observed between MEP and prefrontal EL-TEP curve parameters. C_LIO_LIHigh test-retest reliability and rapid protocols at [≥]110% rMT C_LI

neuroscience↗

bioRxiv2025

Sensory Entrained TMS (seTMS) enhances motor cortex plasticity

Neural excitability fluctuates with sensory events, creating windows of opportunity to enhance brain stimulation. Repetitive transcranial magnetic stimulation (TMS), including intermittent theta burst stimulation (iTBS), is a promising treatment for neurological and psychiatric disorders, but does not account for fluctuations in neural excitability, likely contributing to variable outcomes. Sensory Entrained TMS (seTMS) leverages sensorimotor oscillations to enhance corticospinal responses, but the sustained effects as a repetitive protocol are unknown. We extended seTMS to iTBS measuring motor-evoked potentials (MEPs) as a physiological readout in a randomized crossover study comparing standard iTBS with sensory entrained iTBS (se-iTBS, n=20). se-iTBS more than doubled the MEP effect (55% vs. 26% MEP enhancement) and persisted for at least 30 minutes. Notably, more than 80% of participants showed larger responses with se-iTBS at all time points. se-iTBS may provide a robust and practical framework for optimizing TMS that bridges electrophysiological mechanisms and clinical applications.

neuroscience↗

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

Extracting Reproducible Components from Electroencephalographic Responses to Transcranial Magnetic Stimulation with Group Task-Related Component Analysis

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is a powerful technique for investigating human cortical circuits. However, characterizing TMS-evoked potentials (TEPs) at the group level typically relies on grand averaging across stimulus repetitions (trials) and subjects - an approach that assumes a level of spatial and temporal consistency that is often lacking in TEPs. Here, we introduce an adaptation of Group Task-Related Component Analysis (gTRCA), a novel multivariate signal decomposition method, to automatically extract TEP components that are maximally reproducible across both trials and subjects. Following the validation of a new permutation-based statistical test for gTRCA using simulated data, the method was applied to two independent TMS-EEG datasets, in which stimulation was targeted to the primary motor cortex (M1) in cohorts of 16 and 22 healthy participants. We found that gTRCA reliably identified TEP components that were reproducible at the group level. Notably, the main gTRCA component captured the key spatial, temporal, and spectral features of motor TEPs, remained robust despite reduced number of stimuli and participants, and was consistent across different recordings. These findings demonstrate that gTRCA affords a more reliable characterization of TEPs at the group level, thereby facilitating the translation of TMS-EEG research into clinical practice.

neuroscience↗

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

Scalp EEG predicts intracranial brain activity in humans

Inferring deep brain activity from noninvasive scalp recordings remains a fundamental challenge in neuroscience. Here, we analyzed concurrent scalp and intracranial recordings from 1918 electrode contacts across 20 patients affected by drug-resistant epilepsy undergoing intracranial depth electrode monitoring for pre-surgical evaluation to establish predictive relationships between surface and deep brain signals. Using regularized and cross-validated linear regression within subjects, we demonstrate that scalp recordings can predict spontaneous intracranial activity, with accuracy varying by region, depth, and frequency. Low-frequency signals (<12 Hz) were most predictable, with our models explaining approximately 10% of intracranial signal variance across contacts. Prediction accuracy decreased with contact depth, particularly for high-frequency signals. Using Bayesian modeling with leave-one-patient-out cross-validation, we observed generalizable prediction of activity in mesial temporal, prefrontal, and orbitofrontal cortices, explaining 10-12% of low-frequency signal variance. This scalp-to-intracranial mapping derived from spontaneous activity was further validated by its correlation with scalp responses evoked by direct electrical stimulation. These findings support the development of improved inverse models of brain activity and potentially more accurate scalp-based markers of disease and treatment response.

neuroscience↗

bioRxiv2024

NaviNIBS: a comprehensive and open-source software toolbox for neuronavigated noninvasive brain stimulation.

Image-guided positioning, or neuronavigation, is critical for precise targeting of transcranial magnetic stimulation (TMS) and other noninvasive brain stimulation. However, existing commercial systems have limitations in flexibility and extensibility for research applications. We present new open-source software for neuronavigated non-invasive brain stimulation (NaviNIBS) that provides comprehensive functionality for TMS experiments. NaviNIBS supports imaging data import, target planning, head registration, real-time tool tracking, and integration with robotic positioning and electrophysiology systems. Key features include flexible target specification, support for multiple tracking hardware options, refined head registration techniques, and an extensible addon system. We describe the software architecture, core functionality, characterization of tracking performance, and example applications of NaviNIBS. This software aims to facilitate methodological improvements and novel experimental paradigms in noninvasive brain stimulation research.

neuroscience↗

bioRxiv2024

Sensory Entrained TMS (seTMS) enhances motor cortex excitability

Transcranial magnetic stimulation (TMS) applied to the motor cortex has revolutionized the study of motor physiology in humans. Despite this, TMS-evoked electrophysiological responses show significant variability, due in part to inconsistencies between TMS pulse timing and ongoing brain oscillations. Variable responses to TMS limit mechanistic insights and clinical efficacy, necessitating the development of methods to precisely coordinate the timing of TMS pulses to the phase of relevant oscillatory activity. We introduce Sensory Entrained TMS (seTMS), a novel approach that uses musical rhythms to synchronize brain oscillations and time TMS pulses to enhance cortical excitability. Focusing on the sensorimotor alpha rhythm, a neural oscillation associated with motor cortical inhibition, we examine whether rhythm-evoked sensorimotor alpha phase alignment affects primary motor cortical (M1) excitability in healthy young adults (n=33). We first confirmed using electroencephalography (EEG) that passive listening to musical rhythms desynchronizes inhibitory sensorimotor brain rhythms (mu oscillations) around 200 ms before auditory rhythmic events (27 participants). We then targeted this optimal time window by delivering single TMS pulses over M1 200 ms before rhythmic auditory events while recording motor-evoked potentials (MEPs; 19 participants), which resulted in significantly larger MEPs compared to standard single pulse TMS and an auditory control condition. Neither EEG measures during passive listening nor seTMS-induced MEP enhancement showed dependence on musical experience or training. These findings demonstrate that seTMS effectively enhances corticomotor excitability and establishes a practical, cost-effective method for optimizing non-invasive brain stimulation outcomes.

neuroscience↗

bioRxiv2024

Real-time optimization to enhance noninvasive cortical excitability assessment in the human dorsolateral prefrontal cortex

ObjectiveWe currently lack a robust noninvasive method to measure prefrontal excitability in humans. Concurrent TMS and EEG in the prefrontal cortex is usually confounded by artifacts. Here we asked if real-time optimization could reduce artifacts and enhance a TMS-EEG measure of left prefrontal excitability. MethodsThis closed-loop optimization procedure adjusts left dlPFC TMS coil location, angle, and intensity in real-time based on the EEG response to TMS. Our outcome measure was the left prefrontal early (20-60 ms) and local TMS-evoked potential (EL-TEP). ResultsIn 18 healthy participants, this optimization of coil angle and brain target significantly reduced artifacts by 63% and, when combined with an increase in intensity, increased EL-TEP magnitude by 75% compared to a non-optimized approach. ConclusionsReal-time optimization of TMS parameters during dlPFC stimulation can enhance the EL-TEP. SignificanceEnhancing our ability to measure prefrontal excitability is important for monitoring pathological states and treatment response. HighlightsO_LIWe developed a real-time closed-loop optimization procedure to obtain high amplitude early local TEPs (EL-TEPs) from dlPFC TMS. C_LIO_LISequential optimization of coil angle and brain target reduced artifacts by 63%. C_LIO_LISequential optimization of coil angle, brain target, and intensity increased EL-TEP amplitude by 75%. C_LI

neuroscience↗

bioRxiv2024

Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks

The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the causal significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp EEG) recordings in patients during presurgical intracerebral electrical stimulation (iES). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence of recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show a more functionally integrated processing style than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure.

neuroscience↗

bioRxiv2023

Reliability of the TMS-evoked potential in dorsolateral prefrontal cortex

BackgroundWe currently lack a robust and reliable method to probe cortical excitability noninvasively from the human dorsolateral prefrontal cortex (dlPFC), a region heavily implicated in psychiatric disorders. We recently found that the strength of early and local dlPFC single pulse transcranial magnetic stimulation (TMS)-evoked potentials (EL-TEPs) varied widely depending on the anatomical subregion probed, with more medial regions eliciting stronger responses than anterolateral sites. Despite these differences in amplitude of response, the reliability at each target is not known. ObjectiveTo evaluate the reliability of EL-TEPs across the dlPFC. MethodsIn 15 healthy subjects, we quantified within-session reliability of dlPFC EL-TEPs after single pulse TMS to six dlPFC subregions. We evaluated the concordance correlation coefficient (CCC) across targets and analytical parameters including time window, quantification method, region of interest, sensor-vs. source-space, and number of trials. ResultsAt least one target in the anterior and posterior dlPFC produced reliable EL-TEPs (CCC>0.7). The medial target was most reliable (CCC = 0.78) and the most anterior target was least reliable (CCC = 0.24). ROI size and type (sensor vs. source space) did not affect reliability. Longer (20-60 ms, CCC = 0.62) and later (30-60 ms, CCC = 0.61) time windows resulted in higher reliability compared to earlier and shorter (20-40 ms, CCC 0.43; 20-50 ms, CCC = 0.55) time windows. Peak-to-peak quantification resulted in higher reliability than the mean of the absolute amplitude. Reliable EL-TEPs (CCC up to 0.86) were observed using only 25 TMS trials for a medial dlPFC target. ConclusionsMedial TMS location, wider time window (20-60ms), and peak-to-peak quantification improved reliability. Highly reliable EL-TEPs can be extracted from dlPFC after only a small number of trials. HighlightsO_LIMedial dlPFC target improved EL-TEP reliability compared to anterior targets. C_LIO_LIAfter optimizing analytical parameters, at least one anterior and one posterior target was reliable (CCC>0.7). C_LIO_LILonger (20-60 ms) and later (30-60 ms) time windows were more reliable than earlier and shorter (20-40 ms or 20-50 ms) latencies. C_LIO_LIPeak-to-peak quantification resulted in higher reliability compared to the mean of the absolute amplitude. C_LIO_LIAs low as 25 trials can yield reliable EL-TEPs from the dlPFC. C_LI

neuroscience↗

bioRxiv2023

Mapping cortical excitability in the human dorsolateral prefrontal cortex

ObjectiveTo characterize early TEPs anatomically and temporally (20-50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and that EL-TEP amplitude is inversely related to muscle artifact. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach. MethodsIn 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses. ResultsStimulation location significantly influenced EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%. SignificanceEarly local TMS-evoked potentials (EL-TEPs) can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols. HighlightsO_LIEarly local TMS-evoked potentials (EL-TEPs) varied significantly across the dlPFC as a function of TMS target. C_LIO_LITMS targets with less muscle artifact had significantly larger EL-TEPs. C_LIO_LISelection of a postero-medial target increased EL-TEPs by 102% compared to anterior targets. C_LI

neuroscience↗

bioRxiv2022

Connectivity by the Frontal Aslant Tract (FAT) explains local functional specialization of the superior and inferior frontal gyri in humans while choosing predictive over reactive strategies: a tractography-guided TMS study

Predictive and reactive behaviors represent two mutually exclusive strategies for successfully completing a sensorimotor task. It is thought that predictive actions are based on the medial premotor system, in the superior frontal gyrus (SFG) and reactive stimulus-response behaviors rely on a lateral premotor system, in the inferior frontal gyrus (IFG). The frontal aslant tract (FAT), a white matter tract connecting SFG and IFG, is a possible neural substrate of the predictive/reactive interactions. We used diffusion-weighted imaging (DWI) of 17 male and female healthy human volunteers, to dissect 3 sub-bundles of fibers belonging to the left FAT (bundles 1, 2 and 3), arising ventrally from 1) the ventral precentral gyrus, 2) midway between the PCG and pars opercularis (POp) and 3) the POp and terminating dorsally in 3 different parts of the SFG, in a caudal-rostral order. We applied online transcranial magnetic stimulation (TMS) to 6 spots, corresponding to the medial and lateral terminations of bundles 1-3 during the fixed-duration set period of a delayed reaction task, that can be solved using a predictive (anticipatory) strategy or with a reactive strategy. Results showed that TMS changed the frequency of predictive/reactive strategies only when applied over 2 spots, the SFG and IFG terminations of bundle 2. Importantly, the effects of TMS were opposite when applied to the SFG or to the IFG. Our data show that the SFG and the IFG have opposite roles in producing predictive or reactive behavior and that reciprocal integration or competition is probably mediated by the FAT. Significance StatementAs is well-known by athletes at starting blocks, interaction with the world can occur with a predictive strategy (anticipating a GO-signal) or a reactive strategy (waiting for the GO-signal to be manifest) and they are mutually exclusive. Here we showed, by using non-invasive brain stimulation (TMS), that two specific cortical regions in the superior frontal gyrus (SFG) and the inferior frontal gyrus (IFG) have opposite roles in facilitating a predictive or a reactive strategy. Importantly these two very distant regions but with highly interconnected functions are specifically connected by a small white matter bundle, which probably mediates the competition between predictive and reactive strategies. More generally, we show that the implementing anatomical connectivity in TMS studies strongly reduces spatial noise.

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

A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation

Deidentifying MRIs constitutes an imperative challenge, as it aims at precluding the possibility of re-identification of a research subject or patient, but at the same time it should preserve as much geometrical information as possible, in order to maximize data reusability and to facilitate interoperability. Although several deidentification methods exist, no comprehensive and comparative evaluation of deidentification performance has been carried out across them. Moreover, the possible ways these methods can compromise subsequent analysis has not been exhaustively tested. To tackle these issues, we developed AnonyMI, a novel MRI deidentification method, implemented as a user-friendly 3D Slicer plugin-in, which aims at providing a balance between identity protection and geometrical preservation. To test these features, we performed two series of analyses on which we compared AnonyMI to other two state-of-the-art methods, to evaluate, at the same time, how efficient they are at deidentifying MRIs and how much they affect subsequent analyses, with particular emphasis on source localization procedures. Our results show that all three methods significantly reduce the re-identification risk but AnonyMI provides the best geometrical conservation. Notably, it also offers several technical advantages such as a user-friendly interface, multiple input-output capabilities, the possibility of being tailored to specific needs, batch processing and efficient visualization for quality assurance.

neuroscience↗

bioRxiv2020

Simultaneous human intracerebral stimulation and HD-EEG: ground-truth for source localization methods

Precisely localizing the sources of brain activity as recorded by EEG is a fundamental procedure and a major challenge for both research and clinical practice. Even though many methods and algorithms have been proposed, their relative advantages and limitations are still not well established. Moreover, these methods involve tuning multiple parameters, for which no principled way of selection exists yet. These uncertainties are emphasized due to the lack of ground-truth for their validation and testing. Here we provide the first open dataset that comprises EEG recorded electrical activity originating from precisely known locations inside the brain of living humans. High-density EEG was recorded as single-pulse biphasic currents were delivered at intensities ranging from 0.1 to 5 mA through stereotactically implanted electrodes in diverse brain regions during pre-surgical evaluation of patients with drug-resistant epilepsy. The uses of this dataset range from the estimation of in vivo tissue conductivity to the development, validation and testing of forward and inverse solution methods.

neuroscience↗