Investigating the impact of the regularization parameter on EEG resting-state source reconstruction and functional connectivity using real and simulated data.

Accurate EEG source localization is crucial for mapping resting-state network dynamics and it plays a key role in estimating source-level functional connectivity. However, EEG source estimation techniques encounter numerous methodological challenges, with a key one being the selection of the regularization parameter in minimum norm estimation. This choice is particularly intricate because the optimal amount of regularization for EEG source estimation may not align with the requirements of EEG connectivity analysis, highlighting a nuanced trade-off.

The impact of ROI extraction method for MEG connectivity estimation: practical recommendations for the study of resting state data.

Magnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis requires the extraction of measures from regions of interest (ROI). M/EEG ROI-derived source activity can be treated in different ways. It is possible, for instance, to average each ROI's time series prior to calculating connectivity measures.

Involvement of muscarinic acetylcholine receptor-mediated cholinergic neurotransmission in TMS-EEG responses.

The combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is emerging as a valuable tool for investigating brain functions in health and disease. However, the detailed neural mechanisms underlying TMS-EEG responses, including TMS-evoked EEG potentials (TEPs) and TMS-induced EEG oscillations (TIOs), remain largely unknown. Combining TMS-EEG with pharmacological interventions provides a unique opportunity to elucidate the roles of specific receptor-mediated neurotransmissions in these responses.

Towards the definition of a standard in TMS-EEG data preprocessing.

Combining Non-Invasive Brain Stimulation (NIBS) techniques with the recording of brain electrophysiological activity is an increasingly widespread approach in neuroscience. Particularly successful has been the simultaneous combination of Transcranial Magnetic Stimulation (TMS) and Electroencephalography (EEG). Unfortunately, the strong magnetic pulse required to effectively interact with brain activity inevitably induces artifacts in the concurrent EEG acquisition.

Brain state-dependent repetitive transcranial magnetic stimulation for motor stroke rehabilitation: a proof of concept randomized controlled trial.

Background: In healthy subjects, repetitive transcranial magnetic stimulation (rTMS) targeting the primary motor cortex (M1) demonstrated plasticity effects contingent on electroencephalography (EEG)-derived excitability states, defined by the phase of the ongoing sensorimotor μ-oscillation. The therapeutic potential of brain state-dependent rTMS in the rehabilitation of upper limb motor impairment post-stroke remains unexplored.

Objective: Proof-of-concept trial to assess the efficacy of rTMS, synchronized to the sensorimotor μ-oscillation, in improving motor impairment and reducing upper-limb spasticity in stroke patients.

Blindly separated spontaneous network-level oscillations predict corticospinal excitability.

The corticospinal responses of the motor network to transcranial magnetic stimulation (TMS) are highly variable. While often regarded as noise, this variability provides a way of probing dynamic brain states related to excitability. We aimed to uncover spontaneously occurring cortical states that alter corticospinal excitability.

Investigating cortical excitability and inhibition in patients with schizophrenia: A TMS-EEG study.

Background: Transcranial magnetic stimulation (TMS) combined with electromyography (EMG) has widely been used as a non-invasive brain stimulation tool to assess excitation/inhibition (E/I) balance. E/I imbalance is a putative mechanism underlying symptoms in patients with schizophrenia. Combined TMS-electroencephalography (TMS-EEG) provides a detailed examination of cortical excitability to assess the pathophysiology of schizophrenia.

Measuring the accuracy of ICA-based artifact removal from TMS-evoked potentials.

Background: The analysis and interpretation of transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) relies on successful cleaning of the artifacts, which typically mask the early (0-30 ms) TEPs. Independent component analysis (ICA) is possibly the single most utilized methodology to clean these signals.

Objective: ICA-based cleaning is reliable provided that the input data are composed of independent components.

Targeting motor cortex high-excitability states defined by functional connectivity with real-time EEG-TMS.

We tested previous post-hoc findings indicating a relationship between functional connectivity (FC) in the motor network and corticospinal excitability (CsE), in a real-time EEG-TMS experiment in healthy participants. We hypothesized that high FC between left and right motor cortex predicts high CsE. FC was quantified in real-time by single-trial phase-locking value (stPLV), and TMS single pulses were delivered based on the current FC.

Evoked EEG Responses to TMS Targeting Regions Outside the Primary Motor Cortex and Their Test-Retest Reliability.

Transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) potentials (TEPs) provide unique insights into cortical excitability and connectivity. However, confounding EEG signals from auditory and somatosensory co-stimulation complicate TEP interpretation. Our optimized sham procedure established with TMS of primary motor cortex (Gordon in JAMA 245:118708, 2021) differentiates direct cortical EEG responses to TMS from those caused by peripheral sensory inputs.

Untangling TMS-EEG responses caused by TMS versus sensory input using optimized sham control and GABAergic challenge.

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) elegantly probes the excitability and connectivity of the human brain. However, TMS-EEG signals inevitably also contain sensory-evoked responses caused by TMS-associated auditory and somatosensory inputs, constituting a substantial confounding factor. Here we applied our recently established optimized SHAM protocol (Gordon et al.

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.

Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior.

Piecewise integrable neural network: An interpretable chaos identification framework.

Artificial neural networks (ANNs) are an effective data-driven approach to model chaotic dynamics. Although ANNs are universal approximators that easily incorporate mathematical structure, physical information, and constraints, they are scarcely interpretable. Here, we develop a neural network framework in which the chaotic dynamics is reframed into piecewise models.

TMS combined with EEG: Recommendations and open issues for data collection and analysis.

Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS-EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution.

Cortical reactivity to transcranial magnetic stimulation predicts risk of post-stroke delirium.

Objective: Post-stroke delirium (PSD) is a frequent and with regard to outcome unfavorable complication in acute stroke. The neurobiological mechanisms predisposing to PSD remain poorly understood, and biomarkers predicting its risk have not been established. We tested the hypothesis that hypoexcitable or disconnected brain networks predispose to PSD by measuring brain reactivity to transcranial magnetic stimulation with electroencephalography (TMS-EEG).

Corticospinal excitability is highest at the early rising phase of sensorimotor µ-rhythm.

Alpha oscillations are thought to reflect alternating cortical states of excitation and inhibition. Studies of perceptual thresholds and evoked potentials have shown the scalp EEG negative phase of the oscillation to correspond to a short-lasting low-threshold and high-excitability state of underlying visual, somatosensory, and primary motor cortex. The negative peak of the oscillation is assumed to correspond to the state of highest excitability based on biophysical considerations and considerable effort has been made to improve the extraction of a predictive signal by individually optimizing EEG montages.

Motor cortex excitability is reduced during freezing of upper limb movement in Parkinson's disease.

Whilst involvement of the motor cortex in the phenomenon of freezing in Parkinson's disease has been previously suggested, few empiric studies have been conducted to date. We investigated motor cortex (M1) excitability in eleven right-handed Parkinson's disease patients (aged 69.7 ± 9.

Sensitivity of viscoelastic characterization in multi-harmonic atomic force microscopy.

Quantifying the nanomechanical properties of soft-matter using multi-frequency atomic force microscopy (AFM) is crucial for studying the performance of polymers, ultra-thin coatings, and biological systems. Such characterization processes often make use of cantilever's spectral components to discern nanomechanical properties within a multi-parameter optimization problem. This could inadvertently lead to an over-determined parameter estimation with no clear relation between the identified parameters and their influence on the experimental data.

µ-rhythm phase from somatosensory but not motor cortex correlates with corticospinal excitability in EEG-triggered TMS.

Background: Sensorimotor µ-rhythm phase is correlated with corticospinal excitability. Transcranial magnetic stimulation (TMS) of motor cortex results in larger motor evoked potentials (MEPs) during the negative peak of the EEG oscillation as extracted with a surface Laplacian. However, the anatomical source of the relevant oscillation is not clear and demonstration of the relationship is sensitive to the choice of EEG montage.

Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy.

Dynamic atomic force microscopy (AFM) is a key platform that enables topological and nanomechanical characterization of novel materials. This is achieved by linking the nanoscale forces that exist between the AFM tip and the sample to specific mathematical functions through modeling. However, the main challenge in dynamic AFM is to quantify these nanoscale forces without the use of complex models that are routinely used to explain the physics of tip-sample interaction.

Longitudinal associations between different measures of socioeconomic status and health behavior among adolescents. Data from a wealthy Italian region.

We investigate the association between socio-economic status and unhealthy behaviors among adolescents. By using different measures of socio-economic status, we capture both subjective aspects, as operationalized by perceived family affluence, and objective aspects, such as parents' educational levels and family affluence scale. We use data from a sample of 11,623 adolescents who participated in the Health Behavior in School-aged Children (HBSC) study in 2007, 2010, and 2014 in the Lombardy region of Italy.

Functional Connectivity States of Alpha Rhythm Sources in the Human Cortex at Rest: Implications for Real-Time Brain State Dependent EEG-TMS.

Alpha is the predominant rhythm of the human electroencephalogram, but its function, multiple generators and functional coupling patterns are still relatively unknown. In this regard, alpha connectivity patterns can change between different cortical generators depending on the status of the brain. Therefore, in the light of the communication through coherence framework, an alpha functional network depends on the functional coupling patterns in a determined state.