Interoceptive signals shape the earliest markers and neural pathway to awareness at the visual threshold.

Variations in interoceptive signals from the baroreceptors (BRs) across the cardiac and respiratory cycle can modulate cortical excitability and so affect awareness. It remains debated at what stages of processing they affect awareness-related event-related potentials (ERPs) in different sensory modalities. We investigated the influence of the cardiac (systole/diastole) and the respiratory (inhalation/exhalation) phase on awareness-related ERPs. Subjects discriminated visual threshold stimuli while their electroencephalogram, electrocardiogram, and respiration were simultaneously recorded. We compared ERPs and their intracranial generators for stimuli classified correctly with and without awareness as a function of the cardiac and respiratory phase. Cyclic variations of interoceptive signals from the BRs modulated both the earliest electrophysiological markers and the trajectory of brain activity when subjects became aware of the stimuli: an early sensory component (P1) was the earliest marker of awareness for low (diastole/inhalation) and a perceptual component (visual awareness negativity) for high (systole/exhalation) BR activity, indicating that BR signals interfere with the sensory processing of the visual input. Likewise, activity spread from the primary visceral cortex (posterior insula) to posterior parietal cortices during high and from associative interoceptive centers (anterior insula) to the prefrontal cortex during low BR activity. Consciousness is thereby resolved in cognitive/associative regions when BR is low and in perceptual centers when it is high. Our results suggest that cyclic fluctuations of BR signaling affect both the earliest markers of awareness and the brain processes underlying conscious awareness.

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.

Microsynt: Exploring the syntax of EEG microstates.

Microstates represent electroencephalographic (EEG) activity as a sequence of switching, transient, metastable states. Growing evidence suggests the useful information on brain states is to be found in the higher-order temporal structure of these sequences. Instead of focusing on transition probabilities, here we propose "Microsynt", a method designed to highlight higher-order interactions that form a preliminary step towards understanding the syntax of microstate sequences of any length and complexity. Microsynt extracts an optimal vocabulary of "words" based on the length and complexity of the full sequence of microstates. Words are then sorted into classes of entropy and their representativeness within each class is statistically compared with surrogate and theoretical vocabularies. We applied the method on EEG data previously collected from healthy subjects undergoing propofol anesthesia, and compared their "fully awake" (BASE) and "fully unconscious" (DEEP) conditions. Results show that microstate sequences, even at rest, are not random but tend to behave in a more predictable way, favoring simpler sub-sequences, or "words". Contrary to high-entropy words, lowest-entropy binary microstate loops are prominent and favored on average 10 times more than what is theoretically expected. Progressing from BASE to DEEP, the representation of low-entropy words increases while that of high-entropy words decreases. During the awake state, sequences of microstates tend to be attracted towards "A - B - C" microstate hubs, and most prominently A - B binary loops. Conversely, with full unconsciousness, sequences of microstates are attracted towards "C - D - E" hubs, and most prominently C - E binary loops, confirming the putative relation of microstates A and B to externally-oriented cognitive processes and microstate C and E to internally-generated mental activity. Microsynt can form a syntactic signature of microstate sequences that can be used to reliably differentiate two or more conditions.

EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness.

Evidence suggests that the stream of consciousness is parsed into transient brain states manifesting themselves as discrete spatiotemporal patterns of global neuronal activity. Electroencephalographical (EEG) microstates are proposed as the neurophysiological correlates of these transiently stable brain states that last for fractions of seconds. To further understand the link between EEG microstate dynamics and consciousness, we continuously recorded high-density EEG in 23 surgical patients from their awake state to unconsciousness, induced by step-wise increasing concentrations of the intravenous anesthetic propofol. Besides the conventional parameters of microstate dynamics, we introduce a new implementation of a method to estimate the complexity of microstate sequences. The brain activity under the surgical anesthesia showed a decreased sequence complexity of the stereotypical microstates, which became sparser and longer-lasting. However, we observed an initial increase in microstates' temporal dynamics and complexity with increasing depth of sedation leading to a distinctive "U-shape" that may be linked to the paradoxical excitation induced by moderate levels of propofol. Our results support the idea that the brain is in a metastable state under normal conditions, balancing between order and chaos in order to flexibly switch from one state to another. The temporal dynamics of EEG microstates indicate changes of this critical balance between stability and transition that lead to altered states of consciousness.

EEG microstate sequences in healthy humans at rest reveal scale-free dynamics.

Recent findings identified electroencephalography (EEG) microstates as the electrophysiological correlates of fMRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks. Surprisingly, these rapidly changing EEG microstates correlate significantly with activity in fMRI resting-state networks after convolution with the hemodynamic response function that constitutes a strong temporal smoothing filter. We postulate here that microstate sequences should reveal scale-free, self-similar dynamics to explain this remarkable effect and thus that microstate time series show dependencies over long time ranges. To that aim, we deploy wavelet-based fractal analysis that allows determining scale-free behavior. We find strong statistical evidence that microstate sequences are scale free over six dyadic scales covering the 256-ms to 16-s range. The degree of long-range dependency is maintained when shuffling the local microstate labels but becomes indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. These results advance the understanding of temporal dynamics of brain-scale neuronal network models such as the global workspace model. Whereas microstates can be considered the "atoms of thoughts," the shortest constituting elements of cognition, they carry a dynamic signature that is reminiscent at characteristic timescales up to multiple seconds. The scale-free dynamics of the microstates might be the basis for the rapid reorganization and adaptation of the functional networks of the brain.

Rhythm evokes action: early processing of metric deviances in expressive music by experts and laymen revealed by ERP source imaging.

To examine how musical expertise tunes the brain to subtle metric anomalies in an ecological musical context, we presented piano compositions ending on standard and deviant cadences (endings) to expert pianists and musical laymen, while high-density EEG was recorded. Temporal expectancies were manipulated by substituting standard "masculine" cadences at metrically strong positions with deviant, metrically unaccented, "feminine" cadences. Experts detected metrically deviant cadences better than laymen. Analyses of event-related potentials demonstrated that an early P3a-like component (~150-300 ms), elicited by musical closure, was significantly enhanced at frontal and parietal electrodes in response to deviant endings in experts, whereas a reduced response to deviance occurred in laymen. Putative neuronal sources contributing to the modulation of this component were localized in a network of brain regions including bilateral supplementary motor areas, middle and posterior cingulate cortex, precuneus, associative visual areas, as well as in the right amygdala and insula. In all these regions, experts showed enhanced responses to metric deviance. Later effects demonstrated enhanced activations within the same brain network, as well as higher processing speed for experts. These results suggest that early brain responses to metric deviance in experts may rely on motor representations mediated by the supplementary motor area and motor cingulate regions, in addition to areas involved in self-referential imagery and relevance detection. Such motor representations could play a role in temporal sensory prediction evolved from musical training and suggests that rhythm evokes action more strongly in highly trained instrumentalists.

Hearing Someone Laugh and Seeing Someone Yawn: Modality-Specific Contagion of Laughter and Yawning in the Absence of Others.

Laughter and yawning can both occur spontaneously and are highly contagious forms of social behavior. When occurring contagiously, laughter and yawning are usually confounded with a social situation and it is difficult to determine to which degree the social situation or stimulus itself contribute to its contagion. While contagious yawning can be reliably elicited in lab when no other individuals are present, such studies are more sparse for laughter. Moreover, laughter and yawning are multimodal stimuli with both an auditory and a visual component: laughter is primarily characterized as a stereotyped vocalization whereas yawning is a predominantly visual signal and it is not known to which degree the visual and auditory modalities affect the contagion of laughter and yawning. We investigated how these two sensory modalities contribute to the contagion of laughter and yawning under controlled laboratory conditions in the absence of a social situation that might confound their contagion. Subjects were presented with naturally produced laughter and yawning in three sensory modalities (audio, visual, audio-visual), and we recorded their reaction to these stimuli. Contagious responses differed for laughter and yawning: overall, laughter elicited more contagious responses than yawning, albeit mostly smiling rather than overt laughter. While the audio-visual condition elicited most contagious responses overall, laughter was more contagious in the auditory modality, and yawning was more contagious in the visual modality. Furthermore, laughter became decreasingly contagious over time, while yawning remained steadily contagious. We discuss these results based on the ontogenetic and phylogenetic trajectories of laughter and yawning.

Right parietal brain activity precedes perceptual alternation during binocular rivalry.

We investigated perceptual reversals for intermittently presented stimuli during binocular rivalry and physical alternation while the ongoing EEG was recorded from 64 channels. EEG topographies immediately preceding stimulus-onset were analyzed and two topographies doubly dissociated perceptual reversals from non-reversals. The estimated intracranial generators associated with these topographies were stronger in right inferior parietal cortex and weaker bilaterally in the ventral stream before perceptual reversals. No such differences were found for physical alternation of the same stimuli. These results replicate and extend findings from a previous study with the Necker cube and suggest common neural mechanisms associated with perceptual reversals during binocular rivalry and ambiguous figure perception. For both types of multi-stable stimuli, the dorsal stream is more active preceding perceptual reversals. Activity in the ventral stream, however, differed for binocular rivalry compared to ambiguous figures. The results from the two studies suggest a causal role for the right inferior parietal cortex in generating perceptual reversals regardless of the type of multi-stable stimulus, while activity in the ventral stream appears to depend on the particular type of stimulus.

Noisy network attractor models for transitions between EEG microstates.

The brain is intrinsically organized into large-scale networks that constantly re-organize on multiple timescales, even when the brain is at rest. The timing of these dynamics is crucial for sensation, perception, cognition, and ultimately consciousness, but the underlying dynamics governing the constant reorganization and switching between networks are not yet well understood. Electroencephalogram (EEG) microstates are brief periods of stable scalp topography that have been identified as the electrophysiological correlate of functional magnetic resonance imaging defined resting-state networks. Spatiotemporal microstate sequences maintain high temporal resolution and have been shown to be scale-free with long-range temporal correlations. Previous attempts to model EEG microstate sequences have failed to capture this crucial property and so cannot fully capture the dynamics; this paper answers the call for more sophisticated modeling approaches. We present a dynamical model that exhibits a noisy network attractor between nodes that represent the microstates. Using an excitable network between four nodes, we can reproduce the transition probabilities between microstates but not the heavy tailed residence time distributions. We present two extensions to this model: first, an additional hidden node at each state; second, an additional layer that controls the switching frequency in the original network. Introducing either extension to the network gives the flexibility to capture these heavy tails. We compare the model generated sequences to microstate sequences from EEG data collected from healthy subjects at rest. For the first extension, we show that the hidden nodes 'trap' the trajectories allowing the control of residence times at each node. For the second extension, we show that two nodes in the controlling layer are sufficient to model the long residence times. Finally, we show that in addition to capturing the residence time distributions and transition probabilities of the sequences, these two models capture additional properties of the sequences including having interspersed long and short residence times and long range temporal correlations in line with the data as measured by the Hurst exponent.

Errors can be related to pre-stimulus differences in ERP topography and their concomitant sources.

Much of the variation in both neuronal and behavioral responses to stimuli can be explained by pre-stimulus fluctuations in brain activity. We hypothesized that also errors are the result of stochastic fluctuations in pre-stimulus activity and investigated the temporal dynamics of the scalp topography and their concomitant intracranial generators of stimulus- and response-locked high-density event-related potentials (ERPs) to errors and correct trials in a Stroop task. We found significant differences in ERP map topography and intracranial sources before the onset of the stimulus and after the initiation of the response but not as a function of stimulus-induced conflict. Before the stimulus, topographic differences were accompanied by differential activity in lateral frontal, parietal and temporal areas known to be involved in voluntary reorientation of attention and cognitive control. Differential post-response activity propagated both medially and laterally on a rostral-caudal axis of a network typically involved in performance monitoring. Analysis of the statistical properties of error occurrences revealed their stochasticity.

BOLD correlates of EEG topography reveal rapid resting-state network dynamics.

Resting-state functional connectivity studies with fMRI showed that the brain is intrinsically organized into large-scale functional networks for which the hemodynamic signature is stable for about 10s. Spatial analyses of the topography of the spontaneous EEG also show discrete epochs of stable global brain states (so-called microstates), but they remain quasi-stationary for only about 100 ms. In order to test the relationship between the rapidly fluctuating EEG-defined microstates and the slowly oscillating fMRI-defined resting states, we recorded 64-channel EEG in the scanner while subjects were at rest with their eyes closed. Conventional EEG-microstate analysis determined the typical four EEG topographies that dominated across all subjects. The convolution of the time course of these maps with the hemodynamic response function allowed to fit a linear model to the fMRI BOLD responses and revealed four distinct distributed networks. These networks were spatially correlated with four of the resting-state networks (RSNs) that were found by the conventional fMRI group-level independent component analysis (ICA). These RSNs have previously been attributed to phonological processing, visual imagery, attention reorientation, and subjective interoceptive-autonomic processing. We found no EEG-correlate of the default mode network. Thus, the four typical microstates of the spontaneous EEG seem to represent the neurophysiological correlate of four of the RSNs and show that they are fluctuating much more rapidly than fMRI alone suggests.

Right parietal brain activity precedes perceptual alternation of bistable stimuli.

Momentary fluctuations of baseline activity have been shown to influence responses to sensory stimulation both behaviorally and neurophysiologically. This suggests that perceptual awareness does not solely arise from physical stimulus properties. Here we studied whether the momentary state of the brain immediately before stimulus presentation indicates how a physically unique but perceptually ambiguous stimulus will be perceived. A complex Necker cube was intermittently presented and subjects indicated whether their perception changed with respect to the preceding presentation. EEG was recorded from 256 channels. The prestimulus brain-state was defined as the spatial configuration of the scalp potential map within the 50 ms before stimulus arrival, representing the sum of all momentary ongoing brain processes. Two maps were found that doubly dissociated perceptual reversals from perceptual stability. For EEG sweeps classified as either map, distributed inverse solutions were computed and statistically compared. This yielded activity confined to a region in right inferior parietal cortex that was significantly more active before a perceptual reversal. In contrast, no significant topographic differences of the evoked potentials elicited by stable vs. reversed Necker cubes were found. This indicates that prestimulus activity in right inferior parietal cortex is associated with the perceptual change.

An electrophysiological study of conscious visual perception using progressively degraded stimuli.

Previous event-related potential (ERP) studies have provided mixed results regarding the earliest manifestations of conscious visual report. One possible explanation for the results could be that conscious visual perception emerges progressively rather than appearing as a binary transition. In the present study, we used electrical neuroimaging to identify the stages of processing that lead to the successful conscious identification of a briefly presented degraded stimulus. Grayscale images of faces and butterflies were presented for 16 ms and their visibility was manipulated by means of random image structure evolution (RISE). Three levels of RISE image distortions were used for each image. First, we determined an individual detection threshold of 50% for each subject. We then added two control conditions, namely fully degraded stimuli and stimuli that yielded 80% detection. Topographic ERP analyses revealed distinct effects for identified and unidentified stimuli at the threshold of detection. Four stages were observed that distinguished successful from unsuccessful stimulus identification. This shows that the events associated with conscious perception occurs at several distinct stages in time starting as early as 220 ms after stimulus presentation, rather than translating as a single temporal event and includes marked top-down activations when identification becomes difficult.

Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect.

Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.

Early neuronal responses in right limbic structures mediate harmony incongruity processing in musical experts.

In western tonal music, musical phrases end with an explicit harmonic consequent which is highly expected. As such expectation is a consequence of musical background, cerebral processing of incongruities of musical grammar might be a function of expertise. We hypothesized that a subtle incongruity of standard closure should evoke a profound and rapid reaction in an expert's brain. If such a reaction is due to neuroplasticity as a consequence of musical training, it should be correlated with distinctive activations in sensory, motor and/or cognitive function related brain areas in response to the incongruent closure. Using event related potential (ERP) source imaging, we determined the temporal dynamics of neuronal activity in highly trained pianists and musical laymen in response to syntactic harmonic incongruities in expressive music, which were easily detected by the experts but not by the laymen. Our results revealed that closure incongruity evokes a selective early response in musical experts, characterized by a strong, right lateralized negative ERP component. Statistical source analysis could demonstrate putative contribution to the generation of this component in right temporal-limbic areas, encompassing hippocampal complex and amygdala, and in right insula. Its early onset (approximately 200 ms) preceded responses in frontal areas that may reflect more conscious processing. These results go beyond previous work demonstrating that musical training can change activity of sensory and motor areas during musical or audio-motor tasks, and suggest that functional plasticity in right medial-temporal structures and insula also modulates processing of subtle harmonic incongruities.

Dorsolateral Prefrontal Transcranial Direct Current Stimulation Modulates Language Processing but Does Not Facilitate Overt Second Language Word Production.

Word retrieval in bilingual speakers partly depends on executive control systems in the left prefrontal cortex - including dorsolateral prefrontal cortex (DLPFC). We tested the hypothesis that DLPFC modulates word production of words specifically in a second language (L2) by measuring the effects of anodal transcranial direct current stimulation (anodal-tDCS) over the DLPFC on picture naming and word translation and on event-related potentials (ERPs) and their sources. Twenty-six bilingual participants with "unbalanced" proficiency in two languages were given 20 min of 1.5 mA anodal or sham tDCS (double-blind stimulation design, counterbalanced stimulation order, 1-week intersession delay). The participants then performed the following tasks: verbal and non-verbal fluency during anodal-tDCS stimulation and first and second language (L1 and L2) picture naming and translation [forward (L1 → L2) and backward (L2 → L1)] immediately after stimulation. The electroencephalogram (EEG) was recorded during picture naming and translation. On the behavioral level, anodal-tDCS had an influence on non-verbal fluency but neither on verbal fluency, nor on picture naming and translation. EEG measures revealed significant interactions between Language and Stimulation on picture naming around 380 ms post-stimulus onset and Translation direction and Stimulation on translation around 530 ms post-stimulus onset. These effects suggest that L2 phonological retrieval and phoneme encoding are spatially and temporally segregated in the brain. We conclude that anodal-tDCS stimulation has an effect at a neural level on phonological processes and, critically, that DLPFC-mediated activation is a constraint on language production specifically in L2.

Whole-scalp EEG mapping of somatosensory evoked potentials in macaque monkeys.

High-density scalp EEG recordings are widely used to study whole-brain neuronal networks in humans non-invasively. Here, we validate EEG mapping of somatosensory evoked potentials (SSEPs) in macaque monkeys (Macaca fascicularis) for the long-term investigation of large-scale neuronal networks and their reorganisation after lesions requiring a craniotomy. SSEPs were acquired from 33 scalp electrodes in five adult anaesthetized animals after electrical median or tibial nerve stimulation. SSEP scalp potential maps were identified by cluster analysis and identified in individual recordings. A distributed, linear inverse solution was used to estimate the intracortical sources of the scalp potentials. SSEPs were characterised by a sequence of components with unique scalp topographies. Source analysis confirmed that median nerve SSEP component maps were in accordance with the somatotopic organisation of the sensorimotor cortex. Most importantly, SSEP recordings were stable both intra- and interindividually. We aim to apply this method to the study of recovery and reorganisation of large-scale neuronal networks following a focal cortical lesion requiring a craniotomy. As a prerequisite, the present study demonstrated that a 300-mm(2) unilateral craniotomy over the sensorimotor cortex necessary to induce a cortical lesion, followed by bone flap repositioning, suture and gap plugging with calcium phosphate cement, did not induce major distortions of the SSEPs. In conclusion, SSEPs can be successfully and reproducibly recorded from high-density EEG caps in macaque monkeys before and after a craniotomy, opening new possibilities for the long-term follow-up of the cortical reorganisation of large-scale networks in macaque monkeys after a cortical lesion.

EEG-microstate dependent emergence of perceptual awareness.

We investigated whether the differences in perceptual awareness for stimuli at the threshold of awareness can arise from different global brain states before stimulus onset indexed by the EEG microstate. We used a metacontrast backward masking paradigm in which subjects had to discriminate between two weak stimuli and obtained measures of accuracy and awareness while their EEG was recorded from 256 channels. Comparing targets that were correctly identified with and without awareness allowed us to contrast differences in awareness while keeping performance constant for identical physical stimuli. Two distinct pre-stimulus scalp potential fields (microstate maps) dissociated correct identification with and without awareness, and their estimated intracranial generators were stronger in primary visual cortex before correct identification without awareness. This difference in activity cannot be explained by differences in alpha power or phase which were less reliably linked with differential pre-stimulus activation of primary visual cortex. Our results shed a new light on the function of pre-stimulus activity in early visual cortex in visual awareness and emphasize the importance of trial-by-trials analysis of the spatial configuration of the scalp potential field identified with multichannel EEG.

Intrinsic coupling modes in source-reconstructed electroencephalography.

Intrinsic coupling of neuronal assemblies constitutes a key feature of ongoing brain activity, yielding the rich spatiotemporal patterns observed in neuroimaging data and putatively supporting cognitive processes. Intrinsic coupling has been investigated in electrophysiological recordings using two types of functional connectivity measures: amplitude and phase coupling. These two coupling modes differ in their likely causes and functions, and have been proposed to provide complementary insights into intrinsic neuronal interactions. Here, we investigate the relationship between amplitude and phase coupling in source-reconstructed electroencephalography (EEG). Volume conduction is a key obstacle for connectivity analysis in EEG-we therefore also test the envelope correlation of orthogonalized signals and the phase lag index. Functional connectivity between six seed source regions (bilateral visual, sensorimotor, and auditory cortices) and all other cortical voxels was computed. For all four measures, coupling between homologous sensory areas in both hemispheres was significantly higher than with other voxels at the same physical distance. The frequency of significant coupling differed between sensory areas: 10 Hz for visual, 30 Hz for auditory, and 40 Hz for sensorimotor cortices. By contrasting envelope correlations and phase locking values, we observed two distinct clusters of voxels showing a different relationship between amplitude and phase coupling. Large clusters contiguous to the seed regions showed an identity (1:1) relationship between amplitude and phase coupling, whereas a cluster located around the contralateral homologous regions showed higher phase than amplitude coupling. These results show a relationship between intrinsic coupling modes that is distinct from the effect of volume conduction.

Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: A vulnerability marker of schizophrenia?

Previous studies have repeatedly found altered temporal characteristics of EEG microstates in schizophrenia. The aim of the present study was to investigate whether adolescents affected by the 22q11.2 deletion syndrome (22q11DS), known to have a 30 fold increased risk to develop schizophrenia, already show deviant EEG microstates. If this is the case, temporal alterations of EEG microstates in 22q11DS individuals could be considered as potential biomarkers for schizophrenia. We used high-density (204 channel) EEG to explore between-group microstate differences in 30 adolescents with 22q11DS and 28 age-matched controls. We found an increased presence of one microstate class (class C) in the 22q11DS adolescents with respect to controls that was associated with positive prodromal symptoms (hallucinations). A previous across-age study showed that the class C microstate was more present during adolescence and a combined EEG-fMRI study associated the class C microstate with the salience resting state network, a network known to be dysfunctional in schizophrenia. Therefore, the increased class C microstates could be indexing the increased risk of 22q11DS individuals to develop schizophrenia if confirmed by our ongoing longitudinal study comparing both the adult 22q11DS individuals with and without schizophrenia, as well as schizophrenic individuals with and without 22q11DS.