Biology subjects

Casali, A. G.

Publications and source records attributed to Casali, A. G..

10 recordsLinked to original sources

bioRxiv2026

The Rhythm of Normality: A Comprehensive Normative Database for TMS-EEG Metrics with Reliability Characterization

BackgroundTranscranial Magnetic Stimulation combined with electroencephalography (TMS-EEG) offers unique insights into cortical excitability and connectivity, yet current analyses are primarily limited to group-level inferences with little validation of individual reliability and feature redundancy. ObjectiveTo construct a comprehensive, open-access, and reliable normative dataset of TMS-EEG features that enables individual-level comparison MethodsWe aggregated TMS-EEG data recorded over the primary motor cortex (M1) from 164 healthy adults (30.8 {+/-} 9.8 years; 88 female) across nine studies using harmonized acquisition and preprocessing pipelines. Reliability analysis was conducted on a test-retest subset (N=57) for 968 extracted features, evaluating systematic bias, absolute error, and relative reliability (Intraclass Correlation Coefficient categorized by the lower bound of the 95% confidence interval). Additionally, feature clustering was performed to quantify redundancy and correlations across the high-dimensional feature space. We then established normative distributions and developed an online benchmarking platform. ResultsReliability analyses (N=57) of the high-dimensional feature set revealed that 525 out of 968 features (54.3%) met at least moderate reliability standards (ICC lower bound > 0.5). Cluster analysis indicated substantial redundancy among metrics, with three distinct clusters having a moderate-to-high internal correlation (|r| = 0.64, 0.48, 0.39, respectively). Finally, normative data from the database identified abnormal results in a test patient, supporting the feasibility of individual-level classification in an open-science framework. ConclusionsHarmonization of data acquisition and analysis pipelines led to the development of a reliable normative M1 TMS-EEG reference. This publicly available resource provides a validated tool for future individual-level classifications and an open platform for ongoing community contributions.

neuroscience↗

bioRxiv2025

REDUCED ALPHA-BAND PHASE COHERENCE AND CORTICAL COMPLEXITY IN FIBROMYALGIA: A TMS-EEG EXPLORATORY STUDY

ObjectivesCortico-spinal excitability of the primary motor cortex (M1) is reduced in fibromyalgia, and repetitive transcranial magnetic stimulation (TMS) targeting M1 normalizes these changes and relieves symptoms. TMS combined with electroencephalography (TMS-EEG) allows the measurement of M1 excitability and its connectivity to other regions, which may help clarify neurophysiological effects in fibromyalgia. We assessed cortical excitability, oscillatory activity, and complexity in individuals with fibromyalgia compared to pain-free healthy controls. MethodsGlobal and local mean field power, peak-to-peak amplitude, event-related spectral perturbation, intertrial coherence (ITC), natural frequency, and perturbational complexity index (PCIst) of the EEG response after left-M1 TMS were compared between groups (n=18 fibromyalgia; n=15 controls). Pain intensity, interference, relief of current therapy, mood, and quality of life were assessed in individuals with fibromyalgia. ResultsCompared with controls, individuals with fibromyalgia showed a reduction in the alpha-band ITC in middle and right parieto-occipital areas (P<0.05). Middle-parieto-occipital ITC negatively correlated with reported pain relief (rho=-0.552, p=0.019). The PCIst was lower in fibromyalgia compared with controls (P<0.01) and correlated with higher pain interference in general activity (rho=-0.486, p=0.042). ConclusionIndividuals with fibromyalgia showed abnormal cortical connectivity compared with asymptomatic controls. SignificanceTMS-EEG measurements may provide insights on brain connectivity relevant for therapy.

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↗

bioRxiv2023

Acute pain drives different effects on local and global cortical excitability in motor and prefrontal areas: Insights into interregional and interpersonal differences in pain processing

Pain-related depression of motor cortico-spinal excitability has been explored using transcranial magnetic stimulation (TMS)-based motor evoked potentials. Recently, TMS combined with concomitant high-density electroencephalography (TMS-EEG) enabled cortical excitability (CE) assessments in non-motor areas, offering novel insights into CE changes during pain states. Here, pain-related CE changes were explored in the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC). CE was recorded in 24 healthy participants before (Baseline), during painful heat (Acute Pain), and non-painful warm (Non-noxious warm) stimulation for eight minutes at the right forearm in a randomized sequence, followed by a pain-free stimulation measurement. Local CE was measured as peak-to-peak amplitude of the early latencies of the TMS-evoked potential (<120 ms) on each target. Furthermore, global-mean field power (GMFP) was used to measure global excitability. Relative to the Baseline, Acute Pain induced a decrease of -9.9{+/-}8.8% in the peak-to-peak amplitude in M1 and -10.2{+/-}7.4% in DFPFC, while no significant differences were found for Non-noxious warm (+0.6{+/-}8.0% in M1 and +3.4{+/-}7.2% in DLPFC; both P<0.05). A reduced GMFP of - 9.1{+/-}9.0% was only found in M1 during Acute Pain compared with Non-noxious warm (P=0.003). Participants with the largest reduction in local CE under Acute Pain showed a negative correlation between DLPFC and M1 local CE (r=-0.769; P=0.006). Acute experimental pain drove differential pain-related effects on local and global CE changes in motor and non-motor areas at a group level while also revealing different interindividual patterns of CE changes, which can be explored when designing personalized treatment plans. SUMMARYCortical motor and prefrontal areas present reduced excitability during acute pain, but they occur in different patterns across individuals and present distinct impacts on global connectivity.

neuroscience↗

bioRxiv2020

General anaesthesia disrupts complex cortical dynamics in response to intracranial electrical stimulation in rats

The capacity of the human brain to sustain complex dynamics consistently drops when consciousness fades. Several recent studies in humans found a remarkable reduction of the complexity of cortical responses to local stimulation during dreamless sleep, general anaesthesia, and coma. So far, this perturbational complexity has never been estimated in non-human animals in vivo. Here, we quantify the complexity of electroencephalographic responses to intracranial electrical stimulation in rats, comparing wakefulness to propofol, sevoflurane, and ketamine anaesthesia. We confirm the changes previously observed in humans: from highly complex evoked activity during wakefulness, to simpler responses, suppression of high frequencies, and reduced phase-locking with propofol and sevoflurane. We then deepen our mechanistic understanding by analyzing functional connectivity, and by showing how these parameters dissociate with ketamine, and depend on intensity and site of stimulation. This approach opens the way for further direct investigations of the mechanisms underlying brain complexity and consciousness.

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

TMS orientation and pulse waveform manipulation activates different neural populations: direct evidence from TMS-EEG

Monophasic and biphasic TMS pulses and coil orientations produce different responses in terms of motor output and sensory perception. Those differences have been attributed to the activation of specific neural populations. However, up to date, direct evidence supporting this hypothesis is still missing since studies were mostly based on indirect measures of cortical activation, i.e., motor evoked potentials or phosphenes. Here, we investigated for the first time the impact of different coil orientations and waveforms on a non-primary cortical area, namely the premotor cortex, by measuring TMS evoked EEG potentials (TEPs). We aimed at determining whether TEPs produced by differently oriented biphasic and monophasic TMS pulses diverge and whether these differences are underpinned by the activation of specific neural populations. To do so, we applied TMS over the right premotor cortex with monophasic or biphasic waveforms oriented perpendicularly (in the anterior-posterior direction and vice-versa) or parallel (latero-medial or medio-laterally) to the target gyrus. EEG was concurrently recorded from 60 electrodes. We analyzed TEPs at the level of EEG sensors and cortical sources both in time and time-frequency domain. Biphasic pulses evoked larger early TEP components, which reflect cortical excitability properties of the underlying cortex, in both parallel directions when compared to the perpendicular conditions. Conversely, monophasic pulses, when oriented perpendicularly to the stimulated gyrus, elicited a greater N100, which is a reliable TEP component linked to GABAb-mediated inhibitory processes, than when parallel to the gyrus. Our results provide direct evidence supporting the hypothesis that TMS pulse waveform and TMS coil orientations affect which neural population is engaged.

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↗