A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample.

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Machine Learning for Health: Algorithm Auditing & Quality Control.

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

Neural dynamics of sentiment processing during naturalistic sentence reading.

When we read, our eyes move through the text in a series of fixations and high-velocity saccades to extract visual information. This process allows the brain to obtain meaning, e.g., about sentiment, or the emotional valence, expressed in the written text. How exactly the brain extracts the sentiment of single words during naturalistic reading is largely unknown. This is due to the challenges of naturalistic imaging, which has previously led researchers to employ highly controlled, timed word-by-word presentations of custom reading materials that lack ecological validity. Here, we aimed to assess the electrical neural correlates of word sentiment processing during naturalistic reading of English sentences. We used a publicly available dataset of simultaneous electroencephalography (EEG), eye-tracking recordings, and word-level semantic annotations from 7129 words in 400 sentences (Zurich Cognitive Language Processing Corpus; Hollenstein et al., 2018). We computed fixation-related potentials (FRPs), which are evoked electrical responses time-locked to the onset of fixations. A general linear mixed model analysis of FRPs cleaned from visual- and motor-evoked activity showed a topographical difference between the positive and negative sentiment condition in the 224-304 â€‹ms interval after fixation onset in left-central and right-posterior electrode clusters. An additional analysis that included word-, phrase-, and sentence-level sentiment predictors showed the same FRP differences for the word-level sentiment, but no additional FRP differences for phrase- and sentence-level sentiment. Furthermore, decoding analysis that classified word sentiment (positive or negative) from sentiment-matched 40-trial average FRPs showed a 0.60 average accuracy (95% confidence interval: [0.58, 0.61]). Control analyses ruled out that these results were based on differences in eye movements or linguistic features other than word sentiment. Our results extend previous research by showing that the emotional valence of lexico-semantic stimuli evoke a fast electrical neural response upon word fixation during naturalistic reading. These results provide an important step to identify the neural processes of lexico-semantic processing in ecologically valid conditions and can serve to improve computer algorithms for natural language processing.

Automagic: Standardized preprocessing of big EEG data.

Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

Comorbidity of reading disabilities and ADHD: Structural and functional brain characteristics.

Reading disabilities (RD) and attention-deficit/hyperactivity disorder (ADHD) are two of the most common developmental disorders. RD and ADHD frequently co-occur, which raises questions about how the disorders interact and to what extent they can be differentiated. To date, the underlying neural mechanisms leading to RD-ADHD comorbidity (COM) are not understood. In this study, structural and functional magnetic resonance imaging (fMRI) were combined with comprehensive behavioral testing in order to characterize the behavior, brain structure, and neural correlates of executive function, phonological processing and reading fluency in 60 children with clinical diagnoses of RD, ADHD, or COM, and controls. Whole-brain analyses of variance were performed on cortical thickness values and on the data of the three fMRI tasks to investigate overall group differences. To validate these findings, a region of interest analysis was performed in regions that have previously been shown to exhibit group differences in children with RD or ADHD using the same paradigms. The neuroimaging results demonstrated structural and functional atypicalities for COM in regions that are frequently associated with deficits in children with isolated ADHD or RD. A combination of shared and distinctive brain alterations between the clinical groups was identified, supporting the multiple deficit model for ADHD, RD, and its comorbidity.

White Matter Alterations in Infants at Risk for Developmental Dyslexia.

Developmental dyslexia (DD) is a heritable condition characterized by persistent difficulties in learning to read. White matter alterations in left-lateralized language areas, particularly in the arcuate fasciculus (AF), have been observed in DD, and diffusion properties within the AF correlate with (pre-)reading skills as early as kindergarten. However, it is unclear how early these alterations can be observed. We investigated white matter structure in 14 infants with (FHD+; ages 6.6-17.6 months) and 18 without (FHD-; ages 5.1-17.6 months) familial risk for DD. Diffusion scans were acquired during natural sleep, and early language skills were assessed. Tractography for bilateral AF was reconstructed using manual and automated methods, allowing for independent validation of results. Fractional anisotropy (FA) was calculated at multiple nodes along the tracts for more precise localization of group differences. The analyses revealed significantly lower FA in the left AF for FHD+ compared with FHD- infants, particularly in the central portion of the tract. Moreover, expressive language positively correlated with FA across groups. Our results demonstrate that atypical brain development associated with DD is already present within the first 18 months of life, suggesting that the deficits associated with DD may result from altered structural connectivity in left-hemispheric regions.

The neural correlates of reading fluency deficits in children.

Multiple studies have shown that individuals with a reading disability (RD) demonstrate deficits in posterior left-hemispheric brain regions during reading-related tasks. These studies mainly focused on reading sub-skills, and it remains debated whether such dysfunction is apparent during more ecologically valid reading skills, such as reading fluency. In this fMRI study, reading fluency was systematically varied to characterize neural correlates of reading fluency in 30 children with (RD) and without (typical developing children, TYP) a RD. Sentences were presented at constrained, comfortable, and accelerated speeds, which were determined based on individual reading speed. Behaviorally, RD children displayed decreased performance in several reading-related tasks. Using fMRI, we demonstrated that both TYP and RD children display increased activation in several components of the reading network during fluent reading. When required to read at an accelerated speed, RD children exhibited less activation in the fusiform gyrus (FG) compared with the TYP children. A region of interest analysis substantiated differences in the FG and demonstrated a relationship to behavioral reading performance. These results suggest that the FG plays a key role in fluent reading and that it can be modulated by speed. These results and their implications for remediation strategies should be considered in educational practice.

Disentangling tinnitus distress and tinnitus presence by means of EEG power analysis.

The present study investigated 24 individuals suffering from chronic tinnitus (TI) and 24 nonaffected controls (CO). We recorded resting-state EEG and collected psychometric data to obtain information about how chronic tinnitus experience affects the cognitive and emotional state of TI. The study was meant to disentangle TI with high distress from those who suffer less from persistent tinnitus based on both neurophysiological and behavioral data. A principal component analysis of psychometric data uncovers two distinct independent dimensions characterizing the individual tinnitus experience. These independent states are distress and presence, the latter is described as the perceived intensity of sound experience that increases with tinnitus duration devoid of any considerable emotional burden. Neuroplastic changes correlate with the two independent components. TI with high distress display increased EEG activity in the oscillatory range around 25 Hz (upper ß-band) that agglomerates over frontal recording sites. TI with high presence show enhanced EEG signal strength in the δ-, α-, and lower γ-bands (30-40 Hz) over bilateral temporal and left perisylvian electrodes. Based on these differential patterns we suggest that the two dimensions, namely, distress and presence, should be considered as independent dimensions of chronic subjective tinnitus.

Decomposing neurophysiological underpinnings of age-related decline in visual working memory.

Exploring the neural basis of age-related decline in working memory is vital in our aging society. Previous electroencephalographic studies suggested that the contralateral delay activity (CDA) may be insensitive to age-related decline in lateralized visual working memory (VWM) performance. Instead, recent evidence indicated that task-induced alpha power lateralization decreases in older age. However, the relationship between alpha power lateralization and age-related decline of VWM performance remains unknown, and recent studies have questioned the validity of these findings due to confounding factors of the aperiodic signal. Using a sample of 134 participants, we replicated the age-related decrease of alpha power lateralization after adjusting for the aperiodic signal. Critically, the link between task performance and alpha power lateralization was found only when correcting for aperiodic signal biases. Functionally, these findings suggest that age-related declines in VWM performance may be related to the decreased ability to prioritize relevant over irrelevant information. Conversely, CDA amplitudes were stable across age groups, suggesting a distinct neural mechanism possibly related to preserved VWM encoding or early maintenance.

Trajectories of amyloid beta accumulation - Unveiling the relationship with APOE genotype and cognitive decline.

Amyloid beta (Aß) follows a sigmoidal time function with varying accumulation rates. We studied how the position on this function, reflected by different Aß accumulation phases, influences APOE ɛ4's association with Aß and cognitive decline in 503 participants without dementia using Aß-PET imaging over 5.3-years. First, Aß load and accumulation were analyzed irrespective of phases using linear mixed regression. Generally, ɛ4 carriers displayed a higher Aß load. Moreover, Aß normal (Aß-) ɛ4 carriers demonstrated higher accumulation. Next, we categorized accumulation phases as "decrease", "stable", or "increase" based on trajectory shapes. After excluding the Aß-/decrease participants from the initial regression, the difference in accumulation attributable to genotype among Aß- individuals was no longer significant. Further analysis revealed that in increase phases, Aß accumulation was higher among noncarriers, indicating a genotype-related timeline shift. Finally, cognitive decline was analyzed across phases and was already evident in the Aß-/increase phase. Our results encourage early interventions for ɛ4 carriers and imply that monitoring accumulating Aß- individuals might help identify those at risk for cognitive decline.

Investigating the relationship between resting-state EEG microstates and executive functions: A null finding.

Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = -.18, 95% CI [-.33, -.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics - as measured by resting-state EEG microstate metrics - are a reliable signature of executive functioning remains unsupported.

Effects of working memory training in young and old adults.

Many cognitive abilities, including working memory and reasoning ability, decline with progressing age. In this study, we investigated whether four weeks of intensive working memory training would enhance working memory and reasoning performance in an age-comparative setting. Groups of 34 young (19-36 years) and 27 older (62-77 years) adults practiced tasks representing the three functional categories in the facet model of working memory capacity: storage and processing, relational integration, and supervision. The data were compared to those of a young and an old active control group who practiced tasks with low working memory demands. A cognitive test battery measuring near and far transfer was administered before and after training. Both age groups showed increased working memory performance in the trained tasks and in one structurally similar, but nontrained, task. Young adults also improved in a task measuring word-position binding in working memory. However, we found no far transfer to reasoning in either age group. The results provide evidence that working memory performance can be improved throughout the life span. However, in contrast to a previous study in which each facet of working memory capacity was trained separately, the present study showed that training multiple functional categories simultaneously induces less transfer.

Neurophysiological markers of successful learning in healthy aging.

The capacity to learn and memorize is a key determinant for the quality of life but is known to decline to varying degrees with age. However, neural correlates of memory formation and the critical features that determine the extent to which aging affects learning are still not well understood. By employing a visual sequence learning task, we were able to track the behavioral and neurophysiological markers of gradual learning over several repetitions, which is not possible in traditional approaches that utilize a remember vs. forgotten comparison. On a neurophysiological level, we focused on two learning-related centro-parietal event-related potential (ERP) components: the expectancy-driven P300 and memory-related broader positivity (BP). Our results revealed that although both age groups showed significant learning progress, young individuals learned faster and remembered more stimuli than older participants. Successful learning was directly linked to a decrease of P300 and BP amplitudes. However, young participants showed larger P300 amplitudes with a sharper decrease during the learning, even after correcting for an observed age-related longer P300 latency and increased P300 peak variability. Additionally, the P300 amplitude predicted learning success in both age groups and showed good test-retest reliability. On the other hand, the memory formation processes, reflected by the BP amplitude, revealed a similar level of engagement in both age groups. However, this engagement did not translate into the same learning progress in the older participants. We suggest that the slower and more variable timing of the stimulus identification process reflected in the P300 means that despite the older participants engaging the memory formation process, there is less time for it to translate the categorical stimulus location information into a solidified memory trace. The results highlight the important role of the P300 and BP as a neurophysiological marker of learning and may enable the development of preventive measures for cognitive decline.

Decomposing age effects in EEG alpha power.

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.

Test-retest reliability of resting-state EEG in young and older adults.

The quantification of resting-state electroencephalography (EEG) is associated with a variety of measures. These include power estimates at different frequencies, microstate analysis, and frequency-resolved source power and connectivity analyses. Resting-state EEG metrics have been widely used to delineate the manifestation of cognition and to identify psychophysiological indicators of age-related cognitive decline. The reliability of the utilized metrics is a prerequisite for establishing robust brain-behavior relationships and clinically relevant indicators of cognitive decline. To date, however, test-retest reliability examination of measures derived from resting human EEG, comparing different resting-state measures between young and older participants, within the same adequately powered dataset, is lacking. The present registered report examined test-retest reliability in a sample of 95 young (age range: 20-35 years) and 93 older (age range: 60-80 years) participants. A good-to-excellent test-retest reliability was confirmed in both age groups for power estimates on both scalp and source levels as well as for the individual alpha peak power and frequency. Partial confirmation was observed for hypotheses stating good-to-excellent reliability of microstates measures and connectivity. Equal levels of reliability between the age groups were confirmed for scalp-level power estimates and partially so for source-level power and connectivity. In total, five out of the nine postulated hypotheses were empirically supported and confirmed good-to-excellent reliability of the most commonly reported resting-state EEG metrics.

Electroencephalographic topography measures of experienced utility.

Economic theory distinguishes two concepts of utility: decision utility, objectively quantifiable by choices, and experienced utility, referring to the satisfaction by an obtainment. To date, experienced utility is typically measured with subjective ratings. This study intended to quantify experienced utility by global levels of neuronal activity. Neuronal activity was measured by means of electroencephalographic (EEG) responses to gain and omission of graded monetary rewards at the level of the EEG topography in human subjects. A novel analysis approach allowed approximating psychophysiological value functions for the experienced utility of monetary rewards. In addition, we identified the time windows of the event-related potentials (ERP) and the respective intracortical sources, in which variations in neuronal activity were significantly related to the value or valence of outcomes. Results indicate that value functions of experienced utility and regret disproportionally increase with monetary value, and thus contradict the compressing value functions of decision utility. The temporal pattern of outcome evaluation suggests an initial (∼250 ms) coarse evaluation regarding the valence, concurrent with a finer-grained evaluation of the value of gained rewards, whereas the evaluation of the value of omitted rewards emerges later. We hypothesize that this temporal double dissociation is explained by reward prediction errors. Finally, a late, yet unreported, reward-sensitive ERP topography (∼500 ms) was identified. The sources of these topographical covariations are estimated in the ventromedial prefrontal cortex, the medial frontal gyrus, the anterior and posterior cingulate cortex and the hippocampus/amygdala. The results provide important new evidence regarding "how," "when," and "where" the brain evaluates outcomes with different hedonic impact.

Diminished whole-brain but enhanced peri-sylvian connectivity in absolute pitch musicians.

Several anatomical studies have identified specific anatomical features within the peri-sylvian brain system of absolute pitch (AP) musicians. In this study we used graph theoretical analysis of cortical thickness covariations (as indirect indicator of connectivity) to examine whether AP musicians differ from relative pitch musicians and nonmusicians in small-world network characteristics. We measured "local connectedness" (local clustering = γ), "global efficiency of information transfer" (path length = λ), "small-worldness" (σ = γ/λ), and "degree" centrality as measures of connectivity. Although all groups demonstrated typical small-world features, AP musicians showed significant small-world alterations. "Degree" as a measure of interconnectedness was globally significantly decreased in AP musicians. These differences let us suggest that AP musicians demonstrate diminished neural integration (less connections) among distant brain regions. In addition, AP musicians demonstrated significantly increased local connectivity in peri-sylvian language areas of which the planum temporale, planum polare, Heschl's gyrus, lateral aspect of the superior temporal gyrus, STS, pars triangularis, and pars opercularis were hub regions. All of these brain areas are known to be involved in higher-order auditory processing, working or semantic memory processes. Taken together, whereas AP musicians demonstrate decreased global interconnectedness, the local connectedness in peri-sylvian brain areas is significantly higher than for relative pitch musicians and nonmusicians.

Functional brain network efficiency predicts intelligence.

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.

Perspective and agency during video gaming influences spatial presence experience and brain activation patterns.

BACKGROUND: The experience of spatial presence (SP), i.e., the sense of being present in a virtual environment, emerges if an individual perceives himself as 1) if he were actually located (self-location) and 2) able to act in the virtual environment (possible actions). In this study, two main media factors (perspective and agency) were investigated while participants played a commercially available video game. METHODS: The differences in SP experience and associated brain activation were compared between the conditions of game play in first person perspective (1PP) and third person perspective (3PP) as well as between agency, i.e., active navigation of the video game character (active), and non-agency, i.e., mere passive observation (passive). SP was assessed using standard questionnaires, and brain activation was measured using electroencephalography (EEG) and sLORETA source localisation (standard low-resolution brain electromagnetic tomography). RESULTS: Higher SP ratings were obtained in the 1PP compared with the 3PP condition and in the active compared with the passive condition. On a neural level, we observed in the 1PP compared with the 3PP condition significantly less alpha band power in the parietal, the occipital and the limbic cortex. In the active compared with the passive condition, we uncovered significantly more theta band power in frontal brain regions. CONCLUSION: We propose that manipulating the factors perspective and agency influences SP formation by either directly or indirectly modulating the ego-centric visual processing in a fronto-parietal network. The neuroscientific results are discussed in terms of the theoretical concepts of SP.