More than fulfilled expectations: An electrophysiological investigation of varying cause-effect relationships and schizotypal personality traits as related to the sense of agency.

The sense of agency (SoA) is central to human experience. The comparator model, contrasting sensory prediction and action feedback, is influential but limited in explaining SoA. We investigated mechanisms beyond the comparator model, focusing on the processing of unpredictable stimuli, perimotor components of SoA, and their relation to schizotypy. ERPs were recorded from 18 healthy participants engaged in button-pressing tasks while perceiving tones with varying causal relationships with their actions. We investigated the processing of non-causally related tones, contrasted this to causally related tones, and examined perimotor correlates of subjective expectancy and experience of agency. We confirmed N100 attenuation for self-generated stimuli but found similar effects for expectancy-dependent processing of random tones. SoA also correlated with perimotor ERP components, modulated by schizotypy. Thus, neural processes preceding actions contribute to the formation of SoA and are associated with schizotypy. Unpredictable events also undergo sensory attenuation, implying additional mechanisms contributing to SoA.

Current State of EEG/ERP Microstate Research.

The analysis of EEG microstates for investigating rapid whole-brain network dynamics during rest and tasks has become a standard practice in the EEG research community, leading to a substantial increase in publications across various affective, cognitive, social and clinical neuroscience domains. Recognizing the growing significance of this analytical method, the authors aim to provide the microstate research community with a comprehensive discussion on methodological standards, unresolved questions, and the functional relevance of EEG microstates. In August 2022, a conference was hosted in Bern, Switzerland, which brought together many researchers from 19 countries. During the conference, researchers gave scientific presentations and engaged in roundtable discussions aiming at establishing steps toward standardizing EEG microstate analysis methods. Encouraged by the conference's success, a special issue was launched in Brain Topography to compile the current state-of-the-art in EEG microstate research, encompassing methodological advancements, experimental findings, and clinical applications. The call for submissions for the special issue garnered 48 contributions from researchers worldwide, spanning reviews, meta-analyses, tutorials, and experimental studies. Following a rigorous peer-review process, 33 papers were accepted whose findings we will comprehensively discuss in this Editorial.

On the Reliability of the EEG Microstate Approach.

EEG microstates represent functional brain networks observable in resting EEG recordings that remain stable for 40-120ms before rapidly switching into another network. It is assumed that microstate characteristics (i.e., durations, occurrences, percentage coverage, and transitions) may serve as neural markers of mental and neurological disorders and psychosocial traits. However, robust data on their retest-reliability are needed to provide the basis for this assumption. Furthermore, researchers currently use different methodological approaches that need to be compared regarding their consistency and suitability to produce reliable results. Based on an extensive dataset largely representative of western societies (2 days with two resting EEG measures each; day one: n = 583; day two: n = 542) we found good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages (average ICCs = 0.874-0.920). There was good overall long-term retest-reliability of these microstate characteristics (average ICCs = 0.671-0.852), even when the interval between measures was longer than half a year, supporting the longstanding notion that microstate durations, occurrences, and coverages represent stable neural traits. Findings were robust across different EEG systems (64 vs. 30 electrodes), recording lengths (3 vs. 2 min), and cognitive states (before vs. after experiment). However, we found poor retest-reliability of transitions. There was good to excellent consistency of microstate characteristics across clustering procedures (except for transitions), and both procedures produced reliable results. Grand-mean fitting yielded more reliable results compared to individual fitting. Overall, these findings provide robust evidence for the reliability of the microstate approach.

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

The Functional Aspects of Resting EEG Microstates: A Systematic Review.

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

Normative Intercorrelations Between EEG Microstate Characteristics.

EEG microstates are brief, recurring periods of stable brain activity that reflect the activation of large-scale neural networks. The temporal characteristics of these microstates, including their average duration, number of occurrences, and percentage contribution have been shown to serve as biomarkers of mental and neurological disorders. However, little is known about how microstate characteristics of prototypical network types relate to each other. Normative intercorrelations among these parameters are necessary to help researchers better understand the functions and interactions of underlying networks, interpret and relate results, and generate new hypotheses. Here, we present a systematic analysis of intercorrelations between EEG microstate characteristics in a large sample representative of western working populations (n = 583). Notably, we find that microstate duration is a general characteristic that varies across microstate types. Further, microstate A and B show mutual reinforcement, indicating a relationship between auditory and visual sensory processing at rest. Microstate C appears to play a special role, as it is associated with longer durations of all other microstate types and increased global field power, suggesting a relationship of these parameters with the anterior default mode network. All findings could be confirmed using independent EEG recordings from a retest-session (n = 542).

Electrocorticographic Activation Patterns of Electroencephalographic Microstates.

Electroencephalography (EEG) microstates are short successive periods of stable scalp field potentials representing spontaneous activation of brain resting-state networks. EEG microstates are assumed to mediate local activity patterns. To test this hypothesis, we correlated momentary global EEG microstate dynamics with the local temporo-spectral evolution of electrocorticography (ECoG) and stereotactic EEG (SEEG) depth electrode recordings. We hypothesized that these correlations involve the gamma band. We also hypothesized that the anatomical locations of these correlations would converge with those of previous studies using either combined functional magnetic resonance imaging (fMRI)-EEG or EEG source localization. We analyzed resting-state data (5 min) of simultaneous noninvasive scalp EEG and invasive ECoG and SEEG recordings of two participants. Data were recorded during the presurgical evaluation of pharmacoresistant epilepsy using subdural and intracranial electrodes. After standard preprocessing, we fitted a set of normative microstate template maps to the scalp EEG data. Using covariance mapping with EEG microstate timelines and ECoG/SEEG temporo-spectral evolutions as inputs, we identified systematic changes in the activation of ECoG/SEEG local field potentials in different frequency bands (theta, alpha, beta, and high-gamma) based on the presence of particular microstate classes. We found significant covariation of ECoG/SEEG spectral amplitudes with microstate timelines in all four frequency bands (p = 0.001, permutation test). The covariance patterns of the ECoG/SEEG electrodes during the different microstates of both participants were similar. To our knowledge, this is the first study to demonstrate distinct activation/deactivation patterns of frequency-domain ECoG local field potentials associated with simultaneous EEG microstates.

EEG microstate D as psychosis-specific correlate in adolescents and young adults with clinical high risk for psychosis and first-episode psychosis.

Resting-state electroencephalography (EEG) microstates are brief periods (60-120 ms) of quasi-stable scalp field potentials, indicating simultaneous activity of large-scale networks. Microstates are assumed to reflect basic neuronal information processing. A common finding in psychosis spectrum disorders is that microstates classes C and D are altered. Whereas evidence in adults with schizophrenia is substantial, little is known about effects in underage patients, particularly in those at clinical high risk for psychosis (CHR) and first-episode psychosis (FEP). The present study used 74-channel EEG to investigate microstate effects in a large sample of patients with CHR (n = 100) and FEP (n = 33), clinical controls (CC, n = 18), as well as age-matched healthy controls (HC, n = 68). Subjects span an age range from 9 to 35 years, thus, covering underage patients as well as the most vulnerable period for the emergence of psychosis and its prodrome. Four EEG microstates classes were analyzed (A-D). In class D, CHR and FEP patients showed a decrease compared to HC, and CHR patients also to CC. An increase in class C was found in CHR and FEP compared to HC but not to CC. Results were independent of age and no differences were found between the psychosis spectrum groups. The findings suggest an age-independent decrease of microstate class D to be specific to the psychosis spectrum, whereas the increase in class C seems to reflect unspecific psychopathology. Overall, present data strengthens the role of microstate D as potential biomarker for psychosis, as early as in adolescence and already in CHR status.

EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis.

Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.

MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis.

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.

Real-time 3D reconstruction of guidewires and stents using two update X-ray projections in a rotating imaging setup.

BACKGROUND: Vascular diseases are often treated minimally invasively. The interventional material (stents, guidewires, etc.) used during such percutaneous interventions are visualized by some form of image guidance. Today, this image guidance is usually provided by 2D X-ray fluoroscopy, that is, a live 2D image. 3D X-ray fluoroscopy, that is, a live 3D image, could accelerate existing and enable new interventions. However, existing algorithms for the 3D reconstruction of interventional material require either too many X-ray projections and therefore dose, or are only capable of reconstructing single, curvilinear structures. PURPOSE: Using only two new X-ray projections per 3D reconstruction, we aim to reconstruct more complex arrangements of interventional material than was previously possible. METHODS: This is achieved by improving a previously presented deep learning-based reconstruction pipeline, which assumes that the X-ray images are acquired by a continuously rotating biplane system, in two ways: (a) separation of the reconstruction of different object types, (b) motion compensation using spatial transformer networks. RESULTS: Our pipeline achieves submillimeter accuracy on measured data of a stent and two guidewires inside an anthropomorphic phantom with respiratory motion. In an ablation study, we find that the aforementioned algorithmic changes improve our two figures of merit by 75 % (1.76 mm → 0.44 mm) and 59 % (1.15 mm → 0.47 mm) respectively. A comparison of our measured dose area product (DAP) rate to DAP rates of 2D fluoroscopy indicates a roughly similar dose burden. CONCLUSIONS: This dose efficiency combined with the ability to reconstruct complex arrangements of interventional material makes the presented algorithm a promising candidate to enable 3D fluoroscopy.

A multivariate approach to investigate the associations of electrophysiological indices with schizophrenia clinical and functional outcome.

BACKGROUND: Different electrophysiological (EEG) indices have been investigated as possible biomarkers of schizophrenia. However, these indices have a very limited use in clinical practice, as their associations with clinical and functional outcomes remain unclear. This study aimed to investigate the associations of multiple EEG markers with clinical variables and functional outcomes in subjects with schizophrenia (SCZs). METHODS: Resting-state EEGs (frequency bands and microstates) and auditory event-related potentials (MMN-P3a and N100-P3b) were recorded in 113 SCZs and 57 healthy controls (HCs) at baseline. Illness- and functioning-related variables were assessed both at baseline and at 4-year follow-up in 61 SCZs. We generated a machine-learning classifier for each EEG parameter (frequency bands, microstates, N100-P300 task, and MMN-P3a task) to identify potential markers discriminating SCZs from HCs, and a global classifier. Associations of the classifiers' decision scores with illness- and functioning-related variables at baseline and follow-up were then investigated. RESULTS: The global classifier discriminated SCZs from HCs with an accuracy of 75.4% and its decision scores significantly correlated with negative symptoms, depression, neurocognition, and real-life functioning at 4-year follow-up. CONCLUSIONS: These results suggest that a combination of multiple EEG alterations is associated with poor functional outcomes and its clinical and cognitive determinants in SCZs. These findings need replication, possibly looking at different illness stages in order to implement EEG as a possible tool for the prediction of poor functional outcome.

Processing of semantic incongruency at the onset of sleep: An auditory N400 evoked potential study.

The ability to organize self-generated thought into coherent, meaningful semantic representations is a central aspect of human cognition and undergoes regular alterations throughout the day. To investigate whether changes in semantic processing might explain the loss of coherence, logic, and voluntary control over thinking typically accompanying the transition to sleep, we recorded N400 evoked potentials from 44 healthy subjects. Auditory word pairs with varying semantic distance were presented while they were allowed to fall asleep. Using semantic distance and wakefulness level as regressors, we found that semantic distance reliably elicited an N400, and lower wakefulness levels were associated with increased frontal negativity within a similar time range. Additionally, and contrary to our initial hypothesis, the results showed an interaction of semantic distance and wakefulness that is best interpreted as an increased N400 effect with decreasing wakefulness. While these results do not rule out a possible role of semantic processes in the generation of diminished logic and thought control during the transition to sleep, we discuss the possibility of additional brain mechanisms that usually constrain the inner stream of consciousness during wakefulness.

Feasibility, efficacy, and functional relevance of automated auditory closed-loop suppression of slow-wave sleep in humans.

Slow-wave sleep (SWS) is a fundamental physiological process, and its modulation is of interest for basic science and clinical applications. However, automatised protocols for the suppression of SWS are lacking. We describe the development of a novel protocol for the automated detection (based on the whole head topography of frontal slow waves) and suppression of SWS (through closed-loop modulated randomised pulsed noise), and assessed the feasibility, efficacy and functional relevance compared to sham stimulation in 15 healthy young adults in a repeated-measure sleep laboratory study. Auditory compared to sham stimulation resulted in a highly significant reduction of SWS by 30% without affecting total sleep time. The reduction of SWS was associated with an increase in lighter non-rapid eye movement sleep and a shift of slow-wave activity towards the end of the night, indicative of a homeostatic response and functional relevance. Still, cumulative slow-wave activity across the night was significantly reduced by 23%. Undisturbed sleep led to an evening to morning reduction of wake electroencephalographic theta activity, thought to reflect synaptic downscaling during SWS, while suppression of SWS inhibited this dissipation. We provide evidence for the feasibility, efficacy, and functional relevance of a novel fully automated protocol for SWS suppression based on auditory closed-loop stimulation. Future work is needed to further test for functional relevance and potential clinical applications.

The path of dishonesty: identification of mental processes with electrical neuroimaging.

Much research finds that lying takes longer than truth-telling. Yet, the source of this response time difference remains elusive. Here, we assessed the spatiotemporal evolution of electrical brain activity during honesty and dishonesty in 150 participants using a sophisticated electrical neuroimaging approach-the microstate approach. This uniquely positioned us to identify and contrast the entire chain of mental processes involved during honesty and dishonesty. Specifically, we find that the response time difference is the result of an additional late-occurring mental process, unique to dishonest decisions, interrupting the antecedent mental processing. We suggest that this process inhibits the activation of the truth, thus permitting the execution of the lie. These results advance our understanding of dishonesty and clarify existing theories about the role of increased cognitive load. More broadly, we demonstrate the vast potential of our approach to illuminate the temporal organization of mental processes involved in decision-making.

AAPM Task Group Report 238: 3D C-arms with volumetric imaging capability.

This report reviews the image acquisition and reconstruction characteristics of C-arm Cone Beam Computed Tomography (C-arm CBCT) systems and provides guidance on quality control of C-arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image-Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C-arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C-arm fluoroscopy quality control (QC) in relation to 3D C-arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C-arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C-arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free-in-air and in-phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6.

Effect of Acoustic fMRI-Scanner Noise on the Human Resting State.

Our knowledge about the human resting state is predominantly based on either electroencephalographic (EEG) or functional magnetic resonance imaging (fMRI) methods. While EEG recordings can be performed in seated posture in quiet conditions, the fMRI environment presents a substantial contrast with supine and restricted posture in a narrow tube that is filled with acoustic scanner noise (ASN) at a chainsaw-like volume level. However, the influence of these diverging conditions on resting-state brain activation is neither well studied nor broadly discussed. In order to promote data as a source of sharper hypotheses for future studies, we investigated alterations in EEG-frequency-band power (delta, theta, alpha, beta, gamma) and spatial power distribution as well as cortical vigilance measures in different postures and ASN surroundings over the course of time. Participants (N = 18) underwent three consecutive resting-state EEG recordings with a fixed posture and ASN setting sequence; seated, supine, and supine with ASN (supnoise) using an MRI simulator. The results showed that compared to seated, supnoise, the last instance within the posture sequence, was characterized by lower power and altered spatial power distribution in all assessed frequency bands. This might also have been an effect of time alone. In delta, theta, alpha, and beta, the power of supnoise was also reduced compared to supine, as well as the corresponding distribution maps. The vigilance analysis revealed that in supine and supnoise, the highest and lowest vigilance stages were more dominant compared to the seated and earliest posture condition within the sequence. Hence, our results demonstrate that the differences in recording settings and progress of time are related to changes in cortical arousal and vigilance regulation, findings that should be taken into account more profoundly for hypothesis generation as well as analytic strategies in future resting-state studies.

Acoustic stimulation during sleep predicts long-lasting increases in memory performance and beneficial amyloid response in older adults.

BACKGROUND: Sleep and neurodegeneration are assumed to be locked in a bi-directional vicious cycle. Improving sleep could break this cycle and help to prevent neurodegeneration. We tested multi-night phase-locked acoustic stimulation (PLAS) during slow wave sleep (SWS) as a non-invasive method to improve SWS, memory performance and plasma amyloid levels. METHODS: 32 healthy older adults (agemean: 68.9) completed a between-subject sham-controlled three-night intervention, preceded by a sham-PLAS baseline night. RESULTS: PLAS induced increases in sleep-associated spectral-power bands as well as a 24% increase in slow wave-coupled spindles, known to support memory consolidation. There was no significant group-difference in memory performance or amyloid-beta between the intervention and control group. However, the magnitude of PLAS-induced physiological responses were associated with memory performance up to 3 months post intervention and beneficial changes in plasma amyloid. Results were exclusive to the intervention group. DISCUSSION: Multi-night PLAS is associated with long-lasting benefits in memory and metabolite clearance in older adults, rendering PLAS a promising tool to build upon and develop long-term protocols for the prevention of cognitive decline.

Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals.

Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.