TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction

OBJECTIVE: During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. METHODS: We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. RESULTS: Our results demonstrated that (1) the DA affected the EEG signal in the spatiotemporal domain; (2) ADA was able to completely remove the DA without affecting the TEP waveforms; (3). ICA corrections produced significant changes in peak-to-peak TEP amplitude. CONCLUSIONS: ADA is a reliable solution for the DA correction, especially considering that (1) it does not affect physiological responses; (2) it is completely data-driven and (3) its effectiveness does not depend on the characteristics of the artefact and on the number of recording electrodes. SIGNIFICANCE: We proposed a new reliable algorithm of correction for long-lasting TMS-EEG artifacts

Electroencephalographic functional connectivity in extreme prematurity: a pilot study based on graph theory

Background: Connectivity studies based on functional magnetic resonance imaging (MRI) provided new insights in neonatal brain development but cannot be performed at bedside in the clinical setting. The electroencephalogram (EEG) connectivity has been less studied, particularly using the new approach based on graph theory. This study aimed to explore the functional EEG connectivity using graph theory analysis at an early post-conception age in extremely premature and late-preterm babies free of medical complications and overt brain damage. Methods: Sixteen neonates (8 extremely low gestational age (ELGA) and 8 late-preterm infants), both groups having performed multichannel EEG recordings at 35 weeks’ post-conception, were recruited in a single tertiary-level neonatal intensive care unit and well-baby nursery, respectively. Global (i.e., small-worldness) and local (i.e., clustering and strength) connectivity measures were calculated on a single-subject connectivity matrix of EEG data. Results: Both ELGA and late-preterm infants showed small-worldness organization at 35 weeks’ post-conception. The ELGA group had the strength parameter of the theta frequency band lower in the right than in the left hemisphere. This asymmetry did not emerge in the late-preterm group. Moreover, the mean strength parameter was significantly greater in the right hemisphere in the late preterms than in the ELGA group. Conclusion: EEG connectivity measures could represent an index of left-to-right maturation and developmental disadvantage in extremely preterm infants

Transcranial Magnetic Stimulation and Neuroimaging Coregistration

The development of neuroimaging techniques is one of the most impressive advancements in neuroscience. The main reason for the widespread use of these instruments lies in their capacity to provide an accurate description of neural activity during a cognitive process or during rest. This important advancement is related to the possibility to selectively detect changes of neuronal activity in space and time by means of different biological markers. Specifically, functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and nearinfrared spectroscopy (NIRS) use metabolic markers of ongoing neuronal activity to provide an accurate description of the activation of specific brain areas with high spatial resolution. Similarly, electroencephalography (EEG) is able to detect electric markers of neuronal activity, providing an accurate description of brain activation with high temporal resolution. The application of these techniques during a cognitive task allows important inferences regarding the relation between the detected neural activity, the cognitive process involved in an ongoing task, and behaviour: this is known as a \u201ccorrelational approach\u201d

Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach

We aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations with clinical parameters. We conducted a prospective observational study in children with CHD who underwent cardiac surgery within five years of age. At least 18\ua0months after cardiac surgery, we performed an extensive neuropsychological (intelligence, language, attention, executive function, memory, social skills) and psychopathological assessment, implementing a machine-learning approach for clustering and influencing variable classification. We examined 74 children (37 with CHD and 37 age-matched controls). Group comparisons have shown differences in many domains: intelligence, language, executive skills, and memory. From CHD questionnaires, we identified two clinical subtypes of psychopathological profiles: a small subgroup with high symptoms of psychopathology and a wider subgroup of patients with ADHD-like profiles. No associations with the considered clinical parameters were found. CHD patients are prone to high interindividual variability in neuropsychological and psychological outcomes, depending on many factors that are difficult to control and study. Unfortunately, these dysfunctions are under-recognized by clinicians. Given that brain maturation continues through childhood, providing a significant window for recovery, there is a need for a lifespan approach to optimize the outcome trajectory for patients with CHD

Theta and alpha oscillations as signatures of internal and external attention to delayed intentions: A magnetoencephalography (MEG) study

Background: Remembering to execute delayed intentions (i.e., prospective memory, PM) entails the allocation of internal and external attention. These processes are crucial for rehearsing PM intentions in memory and for monitoring the presence of the PM cue in the environment, respectively. Aim: The study took advantage of the excellent spatial and temporal resolution of magnetoencephalography (MEG) to delineate the neural mechanisms of the memory and monitoring processes underlying PM. Method: The spatio-temporal dynamic of theta and alpha oscillations were explored in 21 participants in two PM tasks compared to a baseline condition (i.e., a lexical decision task with no PM instruction). The PM tasks varied for the load of internally-directed attention (Retrospective-load task) vs externally-directed attention (Monitoring-load task). Results: Increase in theta activity was observed in the Retrospective-load task, and was particularly expressed in the regions of the Default Mode Network, such as in medial temporal regions, precuneus, posterior cingulate cortex and medial prefrontal cortex. Alpha decrease was the most relevant feature of the Monitoring-load task, and it was expressed over bilateral occipital, occipito-parietal and fronto-temporal regions, as well as over left dorsal fronto-parietal regions. Conclusions: Theta and alpha oscillations are strictly associated with the direction of attention during the PM tasks. In particular, theta increase is linked to internal attention necessary for maintaining the intention active in working memory, whereas alpha decrease supports the external attention for detecting the PM cue in the environment

Early-type stars in the young open cluster NGC 2244 and in the Mon OB2 association I. The multiplicity of O-type stars

Aims. We present the results obtained from a long-term spectroscopic campaign devoted to the multiplicity of O-type stars in the young open cluster NGC2244 and in the Mon OB2 association. Methods. Our spectroscopic monitoring was performed over several years, allowing us to probe different time-scales. For each star, several spectral diagnostic tools are applied, in order to search for line shifts and profile variations. We also measure the projected rotational velocity and revisit the spectral classification. Results. In our sample, several stars were previously considered as spectroscopic binaries, though only a few scattered observations were available. Our results now reveal a more complex situation. Our study identifies two new spectroscopic binaries (HD46149 in NGC2244 and HD46573 in MonOB2). The first object is a long-period double-lined spectroscopic binary, though the exact value of its period remains uncertain and the second object is classified as an SB1 system with a period of about 10.67 days but the time series of our observations do not enable us to derive a unique orbital solution for this system. We also classify another star as variable in radial velocity (HD46150) and we detect line profile variations in two rapid rotators (HD46056 and HD46485). Conclusions. This spectroscopic investigation places a firm lower limit (17%) on the binary fraction of O-stars in NGC2244 and reveals the lack of short-period O+OB systems in this cluster. In addition, a comparison of these new results with two other well-studied clusters (NGC6231 and IC1805) puts forward possible hints of a relation between stellar density and binarity, which could provide constraints on the theories about the formation and early evolution of hot stars.Comment: 14 pages, 10 figures, 9 table