Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifying long multivariate time series, optimal prediction models and feature extraction methods for EEG classification remain elusive. Our study addressed the problem of EEG classification under the framework of brain age prediction, applying a deep learning model on EEG time series. We hypothesized that decomposing EEG signals into oscillatory modes would yield more accurate age predictions than using raw or canonically frequency-filtered EEG. Specifically, we employed multivariate intrinsic mode functions (MIMFs), an empirical mode decomposition (EMD) variant based on multivariate iterative filtering (MIF), with a convolutional neural network (CNN) model. Testing a large dataset of routine clinical EEG scans (n = 6540) from patients aged 1 to 103 years, we found that an ad-hoc CNN model without fine-tuning could reasonably predict brain age from EEGs. Crucially, MIMF decomposition significantly improved performance compared to canonical brain rhythms (from delta to lower gamma oscillations). Our approach achieved a mean absolute error (MAE) of 13.76 ± 0.33 and a correlation coefficient of 0.64 ± 0.01 in brain age prediction over the entire lifespan. Our findings indicate that CNN models applied to EEGs, preserving their original temporal structure, remains a promising framework for EEG classification, wherein the adaptive signal decompositions such as the MIF can enhance CNN models' performance in this task.

Comparing neuronal oscillations during visual spatial attention orienting between normobaric and hypobaric hypoxia.

Normobaric hypoxia (NH) and hypobaric hypoxia (HH) are both used to train aircraft pilots to recognize symptoms of hypoxia. NH (low oxygen concentration) training is often preferred because it is more cost effective, simpler, and safer than HH. It is unclear, however, whether NH is neurophysiologically equivalent to HH (high altitude). Previous studies have shown that neural oscillations, particularly those in the alpha band (8-12 Hz), are impacted by hypoxia. Attention tasks have been shown to reliably modulate alpha oscillations, although the neurophysiological impacts of hypoxia during cognitive processing remains poorly understood. To address this we investigated induced and evoked power alongside physiological data while participants performed an attention task during control (normobaric normoxia or NN), NH (fraction of inspired oxygen = 12.8%, partial pressure of inspired oxygen = 87.2 mmHg), and HH (3962 m, partial pressure of inspired oxygen = 87.2 mmHg) conditions inside a hypobaric chamber. No significant differences between NH and HH were found in oxygen saturation, end tidal gases, breathing rate, middle cerebral artery velocity and blood pressure. Induced alpha power was significantly decreased in NH and HH when compared to NN. Participants in the HH condition showed significantly increased induced lower-beta power and evoked higher-beta power, compared with the NH and NN conditions, indicating that NH and HH differ in their impact on neurophysiological activity supporting cognition. NH and HH were found not to be neurophysiologically equivalent as electroencephalography was able to differentiate NH from HH.

Atypical Associations between Functional Connectivity during Pragmatic and Semantic Language Processing and Cognitive Abilities in Children with Autism.

Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain-behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks.

Byte-Pair Encoding for classifying routine clinical electroencephalograms in adults over the lifespan.

Routine clinical EEG is a standard test used for the neurological evaluation of patients. A trained specialist interprets EEG recordings and classifies them into clinical categories. Given time demands and high inter-reader variability, there is an opportunity to facilitate the evaluation process by providing decision support tools that can classify EEG recordings automatically. Classifying clinical EEG is associated with several challenges: classification models are expected to be interpretable; EEGs vary in duration and EEGs are recorded by multiple technicians operating various devices. Our study aimed to test and validate a framework for EEG classification which satisfies these requirements by transforming EEG into unstructured text. We considered a highly heterogeneous and extensive sample of routine clinical EEGs (n = 5785), with a wide range of participants aged between 15 and 99 years. EEG scans were recorded at a public hospital, according to 10/20 electrode positioning with 20 electrodes. The proposed framework was based on symbolizing EEG signals and adapting a previously proposed method from natural language processing (NLP) to break symbols into words. Specifically, we symbolized the multichannel EEG time series and applied a byte-pair encoding (BPE) algorithm to extract a dictionary of the most frequent patterns (tokens) reflecting the variability of EEG waveforms. To demonstrate the performance of our framework, we used newly-reconstructed EEG features to predict patients' biological age with a Random Forest regression model. This age prediction model achieved a mean absolute error of 15.7 years. We also correlated tokens' occurrence frequencies with age. The highest correlations between the frequencies of tokens and age were observed at frontal and occipital EEG channels. Our findings demonstrated the feasibility of applying an NLP-based approach to classifying routine clinical EEG. Notably, the proposed algorithm could be instrumental in classifying clinical EEG with minimal preprocessing and identifying clinically-relevant short events, such as epileptic spikes.

Associations between spontaneous electroencephalogram oscillations and oxygen saturation across normobaric and hypobaric hypoxia.

High-altitude indoctrination (HAI) trains individuals to recognize symptoms of hypoxia by simulating high-altitude conditions using normobaric (NH) or hypobaric (HH) hypoxia. Previous studies suggest that despite equivalent inspired oxygen levels, physiological differences could exist between these conditions. In particular, differences in neurophysiological responses to these conditions are not clear. Our study aimed to investigate correlations between oxygen saturation (SpO2 ) and neural responses in NH and HH. We recorded 5-min of resting-state eyes-open electroencephalogram (EEG) and SpO2 during control, NH, and HH conditions from 13 participants. We applied a multivariate framework to characterize correlations between SpO2 and EEG measures (spectral power and multiscale entropy [MSE]), within each participant and at the group level. Participants were desaturating during the first 150 s of NH versus steadily desaturated in HH. We considered the entire time interval, first and second half intervals, separately. All the conditions were characterized by statistically significant participant-specific patterns of EEG-SpO2 correlations. However, at the group level, the desaturation period expressed a robust pattern of these correlations across frequencies and brain locations. Specifically, the first 150 s of NH during desaturation differed significantly from the other conditions with negative absolute alpha power-SpO2 correlations and positive MSE-SpO2 correlations. Once steadily desaturated, NH and HH had no significant differences in EEG-SpO2 correlations. Our findings indicate that the desaturating phase of hypoxia is a critical period in HAI courses, which would require developing strategies for mitigating the hypoxic stimulus in a real-world situation.

Children with autism spectrum disorder show atypical electroencephalographic response to processing contextual incongruencies.

Children with autism spectrum disorder (ASD) experience difficulties with social communication, making it challenging to interpret contextual information that aids in accurately interpreting language. To investigate how the brain processes the contextual information and how this is different in ASD, we compared event-related potentials (ERPs) in response to processing visual and auditory congruent and incongruent information. Two groups of children participated in the study: 37 typically developing children and 15 children with ASD (age range = 6 to 12). We applied a language task involving auditory sentences describing congruent or incongruent images. We investigated two ERP components associated with language processing: the N400 and P600. Our results showed how children with ASD present significant differences in their neural responses in comparison with the TD group, even when their reaction times and correct trials are not significantly different from the TD group.

Alterations in coordinated EEG activity precede the development of seizures in comatose children.

OBJECTIVE: We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children. METHODS: In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring. RESULTS: Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results. CONCLUSIONS: These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures. SIGNIFICANCE: If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.

Atypical age-related changes in cortical thickness in autism spectrum disorder.

Recent longitudinal neuroimaging and neurophysiological studies have shown that tracking relative age-related changes in neural signals, rather than a static snapshot of a neural measure, could offer higher sensitivity for discriminating typically developing (TD) individuals from those with autism spectrum disorder (ASD). It is not clear, however, which aspects of age-related changes (trajectories) would be optimal for identifying atypical brain development in ASD. Using a large cross-sectional data set (Autism Brain Imaging Data Exchange [ABIDE] repository; releases I and II), we aimed to explore age-related changes in cortical thickness (CT) in TD and ASD populations (age range 6-30 years old). Cortical thickness was estimated from T1-weighted MRI images at three scales of spatial coarseness (three parcellations with different numbers of regions of interest). For each parcellation, three polynomial models of age-related changes in CT were tested. Specifically, to characterize alterations in CT trajectories, we compared the linear slope, curvature, and aberrancy of CT trajectories across experimental groups, which was estimated using linear, quadratic, and cubic polynomial models, respectively. Also, we explored associations between age-related changes with ASD symptomatology quantified as the Autism Diagnostic Observation Schedule (ADOS) scores. While no overall group differences in cortical thickness were observed across the entire age range, ASD and TD populations were different in terms of age-related changes, which were located primarily in frontal and tempo-parietal areas. These atypical age-related changes were also associated with ADOS scores in the ASD group and used to predict ASD from TD development. These results indicate that the curvature is the most reliable feature for localizing brain areas developmentally atypical in ASD with a more pronounced effect with symptomatology and is the most sensitive in predicting ASD development.

Estimating Fugl-Meyer Upper Extremity Motor Score From Functional-Connectivity Measures.

Fugl-Meyer assessment is an accepted method of evaluating motor function for people with stroke. A challenge associated with this assessment is the availability of trained examiners to carry out the evaluation. Neurophysiological biomarkers show promise in addressing the above impediment. Our study investigated the potential of using resting state electroencephalographic (EEG) functional connectivity measures as biomarkers for estimating Fugl-Meyer upper extremity motor score (FMU) in people with chronic stroke. Resting state EEG was recorded from 10 individuals with stroke. Functional connectivity was evaluated through five different processing algorithms and quantified in terms of maximum-coherence between EEG electrodes at 15 frequencies from 1 to 45 Hz. We applied a multi-variate Partial Least Squares (PLS) Correlation analysis to simultaneously identify specific connectivity channels (EEG electrode pairings) and frequencies that robustly correlated with FMU. We then applied PLS-Regression to the identified channels and frequencies to generate a set of coefficients for estimating the FMU. Participants were randomly assigned to a training-set of eight and a test-set of two. Cross-validation with leave-one-out approach on the training-set, using Phase-Lag-Index processing algorithm, resulted in an R2 of 0.97 and a least-square linear fit slope of 1 for predicted versus actual FMU, with a root-mean-square error of 1.9 on FMU scale. Application of regression coefficients to the connectivity measures from the test-set resulted in predicted FMU of 47 and 38 versus actual scores of 46 and 39, respectively. Our results demonstrated that the evaluation of neural correlates of FMU shows promise in addressing the challenges associated with the availability of trained examiners to carry out the assessments.

Sex differences in brain connectivity and male vulnerability in very preterm children.

Evidence indicates better cognitive and behavioral outcomes for females born very preterm (≤32 weeks gestation) compared to males, but the neurophysiology underlying this apparent resiliency of the female brain remains poorly understood. Here we test the hypothesis that very preterm males express more pronounced connectivity alterations as a reflection of higher male vulnerability. Resting state MEG recordings, neonatal and psychometric data were collected from 100 children at age 8 years: very preterm boys (n = 27), very preterm girls (n = 34), full-term boys (n = 15) and full-term girls (n = 24). Neuromagnetic source dynamics were reconstructed from 76 cortical brain regions. Functional connectivity was estimated using inter-regional phase-synchronization. We performed a series of multivariate analyses to test for differences across groups as well as to explore relationships between deviations in functional connectivity and psychometric scores and neonatal factors for very preterm children. Very preterm boys displayed significantly higher (p < .001) absolute deviation from average connectivity of same-sex full-term group, compared to very preterm girls versus full-term girls. In the connectivity comparison between very preterm and full-term groups separately for boys and girls, significant group differences (p < .05) were observed for boys, but not girls. Sex differences in connectivity (p < .01) were observed in very preterm children but not in full-term groups. Our findings indicate that very preterm boys have greater alterations in resting neurophysiological network communication than girls. Such uneven brain communication disruption in very preterm boys and girls suggests that stronger connectivity alterations might contribute to male vulnerability in long-term behavioral and cognitive outcome.

EEG before and after total corpus callosotomy for pharmacoresistant infantile spasms: Fast oscillations and slow-wave connectivity in hypsarrhythmia.

OBJECTIVE: We analyzed the features of fast oscillations (FOs) and connectivity in hypsarrhythmia to identify biomarkers for predicting seizure outcomes after total corpus callosotomy (TCC) in children with pharmacoresistant infantile spasms (IS). We hypothesize that the power of FOs and connectivity of slow waves in hypsarrhythmia would indicate the prognosis of IS. METHOD: We retrospectively identified 42 children with pharmacoresistant IS who underwent TCC from 2009 to 2014 at Nagasaki Medical Center. We collected preoperative hypsarrhythmia for 200 seconds from each child. Children were categorized into three groups with interictal epileptic discharges on EEG at 6 months after TCC: group A, no epileptic discharge; group B, lateralized epileptic discharges; and group C; bilateral epileptic discharges. We analyzed spectral power and phase synchronization in preoperative hypsarrhythmia among the three groups. RESULTS: We found 10 children in group A, 10 children in group B, and 22 children in group C. All group A and 1 in group B achieved seizure freedom after TCC. Six (67%) of 9 group B children who underwent further surgeries achieved seizure freedom. Ten (45%) of group C children had seizure reduction >50% after TCC, and 13 (87%) of 15 children who underwent further surgeries had residual seizures. The clinical profiles of the three groups did not differ significantly. The power of FOs (≥45 Hz) in hypsarrhythmia was significantly stronger in group C at the midline and temporal regions than in groups B and A (P = .014). The connectivity of theta (4-9 Hz) and FOs (29-70 Hz) tended to increase in group C, compared with the increased connectivity of 1-2 Hz in group A (P = .08). SIGNIFICANCE: The increased power and connectivity of FOs in hypsarrhythmia may correlate with pharmacoresistant and surgically resistant seizures in IS. The existence and connectivity of FOs are associated with unilateral/bilateral cortical epileptogenicity in hypsarrhythmia. Prominent slow waves and connectivity without FOs might correlate with seizure freedom after TCC. Modulation of the callosal system with subcortical/cortical epileptic discharges might play a role in generating hypsarrhythmia and IS.

Atypical resting state neuromagnetic connectivity and spectral power in very preterm children.

BACKGROUND: Children born very preterm often display selective cognitive difficulties at school age even in the absence of major brain injury. Alterations in neurophysiological activity underpinning such difficulties, as well as their relation to specific aspects of adverse neonatal experience, remain poorly understood. In the present study, we examined interregional connectivity and spectral power in very preterm children at school age, and their relationship with clinical neonatal variables and long-term outcomes (IQ, executive functions, externalizing/internalizing behavior, visual-motor integration). METHODS: We collected resting state magnetoencephalographic (MEG) and psychometric data from a cohort at the age of 8 years followed prospectively since birth, which included three groups: Extremely Low Gestational Age (ELGA, 24-28 weeks GA n = 24, age 7.7 ± 0.38, 10 girls), Very Low Gestational Age (VLGA, 29-32 weeks GA n = 37, age 7.7 ± 0.39, 24 girls), and full-term children (38-41 weeks GA n = 39, age 7.9 ± 1.02, 24 girls). Interregional phase synchrony and spectral power were tested for group differences, and associations with neonatal and outcome variables were examined using mean-centered and behavioral Partial Least Squares (PLS) analyses, respectively. RESULTS: We found greater connectivity in the theta band in the ELGA group compared to VLGA and full-term groups, primarily involving frontal connections. Spectral power analysis demonstrated overall lower power in the ELGA and VLGA compared to full-term group. PLS indicated strong associations between neurophysiological connectivity at school age, adverse neonatal experience and cognitive performance, and behavior. Resting spectral power was associated only with behavioral scores. CONCLUSIONS: Our findings indicate significant atypicalities of neuromagnetic brain activity and connectivity in very preterm children at school age, with alterations in connectivity mainly observed only in the ELGA group. We demonstrate a significant relationship between connectivity, adverse neonatal experience, and long-term outcome, indicating that the disruption of developing neurophysiological networks may mediate relationships between neonatal events and cognitive and behavioral difficulties at school age.

Dominant Patterns of Information Flow in the Propagation of the Neuromagnetic Somatosensory Steady-State Response.

Methods of functional connectivity are applied ubiquitously in studies involving non-invasive whole-brain signals, but may be not optimal for exploring the propagation of the steady-state responses, which are strong oscillatory patterns of neurodynamics evoked by periodic stimulation. In our study, we explore a functional network underlying the somatosensory steady-state response using methods of effective connectivity. Human magnetoencephalographic (MEG) data were collected in 10 young healthy adults during 23-Hz vibro-tactile stimulation of the right hand index finger. The whole-brain dynamics of MEG source activity was reconstructed with a linearly-constrained minimum variance beamformer. We applied information-theoretic tools to quantify asymmetries in information flows between primary somatosensory area SI and the rest of the brain. Our analysis identified a pattern of coupling, leading from area SI to a source in the secondary somato-sensory area SII, thalamus, and motor cortex all contralateral to stimuli as well as to a source in the cerebellum ipsilateral to the stimuli. Our results support previously reported empirical evidence collected both in in vitro and in vivo, indicating critical areas of activation of the somatosensory system at the level of systems neuroscience.

Developmental changes in neuromagnetic rhythms and network synchrony in autism.

OBJECTIVE: There is gathering consensus that altered connectivity is a hallmark of the autistic brain. This includes atypical neural oscillations and their coordination across brain regions, which are understood to mediate information processing and integration. It remains unclear whether and how connectivity in various neurophysiological frequency ranges develops atypically in autism spectrum disorder (ASD). METHODS: To address this in a cross-sectional sample, we recorded resting-state magnetoencephalography from 134 children and adolescents with and without ASD, and calculated resting spectral power and inter-regional synchrony (functional connectivity). RESULTS: Although no overall group differences were observed, significant alterations in linear and nonlinear age-related changes in resting oscillatory power and network synchrony were found. These differences were frequency- and region-specific and implicated brain systems thought to play a prominent role in ASD, such as the frontal cortex and cerebellum. We also found correlations between Autism Diagnostic Observation Schedule scores and the degree to which connectivity in cerebellar networks is "idiosyncratic" in an individual with autism. INTERPRETATION: We provide the first evidence that it is the curvatures of maturational changes in neurophysiological oscillations and synchrony, rather than disturbances in a particular direction, that characterize the brain function in individuals with ASD. Moreover, the patterns of idiosyncratic distortions of network synchrony relative to the group curve are associated with behavioral symptoms of ASD. Ann Neurol 2017;81:199-211.

Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity.

Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8-12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.

Interplay of brain structure and function in neonatal congenital heart disease.

OBJECTIVE: To evaluate whether structural and microstructural brain abnormalities in neonates with congenital heart disease (CHD) correlate with neuronal network dysfunction measured by analysis of EEG connectivity. METHODS: We studied a prospective cohort of 20 neonates with CHD who underwent continuous EEG monitoring before surgery to assess functional brain maturation and network connectivity, structural magnetic resonance imaging (MRI) to determine the presence of brain injury and structural brain development, and diffusion tensor MRI to assess brain microstructural development. RESULTS: Neonates with MRI brain injury and delayed structural and microstructural brain development demonstrated significantly stronger high-frequency (beta and gamma frequency band) connectivity. Furthermore, neonates with delayed microstructural brain development demonstrated significantly weaker low-frequency (delta, theta, alpha frequency band) connectivity. Neonates with brain injury also displayed delayed functional maturation of EEG background activity, characterized by greater background discontinuity. INTERPRETATION: These data provide new evidence that early structural and microstructural developmental brain abnormalities can have immediate functional consequences that manifest as characteristic alterations of neuronal network connectivity. Such early perturbations of developing neuronal networks, if sustained, may be responsible for the persistent neurocognitive impairment prevalent in adolescent survivors of CHD. These foundational insights into the complex interplay between evolving brain structure and function may have relevance for a wide spectrum of neurological disorders manifesting early developmental brain injury.

Coordinated Information Generation and Mental Flexibility: Large-Scale Network Disruption in Children with Autism.

Autism spectrum disorder (ASD) includes deficits in social cognition, communication, and executive function. Recent neuroimaging studies suggest that ASD disrupts the structural and functional organization of brain networks and, presumably, how they generate information. Here, we relate deficits in an aspect of cognitive control to network-level disturbances in information processing. We recorded magnetoencephalography while children with ASD and typically developing controls performed a set-shifting task designed to test mental flexibility. We used multiscale entropy (MSE) to estimate the rate at which information was generated in a set of sources distributed across the brain. Multivariate partial least-squares analysis revealed 2 distributed networks, operating at fast and slow time scales, that respond completely differently to set shifting in ASD compared with control children, indicating disrupted temporal organization within these networks. Moreover, when typically developing children engaged these networks, they achieved faster reaction times. When children with ASD engaged these networks, there was no improvement in performance, suggesting that the networks were ineffective in children with ASD. Our data demonstrate that the coordination and temporal organization of large-scale neural assemblies during the performance of cognitive control tasks is disrupted in children with ASD, contributing to executive function deficits in this group.

Auditory-prefrontal axonal connectivity in the macaque cortex: quantitative assessment of processing streams.

Primate sensory systems subserve complex neurocomputational functions. Consequently, these systems are organised anatomically in a distributed fashion, commonly linking areas to form specialised processing streams. Each stream is related to a specific function, as evidenced from studies of the visual cortex, which features rather prominent segregation into spatial and non-spatial domains. It has been hypothesised that other sensory systems, including auditory, are organised in a similar way on the cortical level. Recent studies offer rich qualitative evidence for the dual stream hypothesis. Here we provide a new paradigm to quantitatively uncover these patterns in the auditory system, based on an analysis of multiple anatomical studies using multivariate techniques. As a test case, we also apply our assessment techniques to more ubiquitously-explored visual system. Importantly, the introduced framework opens the possibility for these techniques to be applied to other neural systems featuring a dichotomised organisation, such as language or music perception.

Developmental trajectory of face processing revealed by integrative dynamics.

Given their unique connectivity, a primary function of brain networks must be to transfer and integrate information. Therefore, the way in which information is integrated by individual nodes of the network may be an informative aspect of cognitive processing. Here we present a method inspired by telecommunications research that utilizes time-frequency fluctuations of neural activity to infer how information is integrated by individual nodes of the network. We use a queueing theoretical model to interpret empirical data in terms of information processing and integration. In particular, we demonstrate, in participants aged from 6 to 41 years, that the well-known face inversion phenomenon may be explained in terms of information integration. Our model suggests that inverted faces may be associated with shorter and more frequent neural integrative stages, indicating fractured processing and consistent with the notion that inverted faces are perceived by parts. Conversely, our model suggests that upright faces may be associated with a smaller number of sustained episodes of integration, indicating more involved processing, akin to holistic and configural processing. These differences in how upright and inverted faces are processed became more pronounced during development, indicating a gradual specialization for face perception. These effects were robustly expressed in the right fusiform gyrus (all groups), as well as right parahippocampal gyrus (children and adolescents only) and left inferior temporal cortex (adults only).

Exploring age-related changes in dynamical non-stationarity in electroencephalographic signals during early adolescence.

Dynamics of brain signals such as electroencephalogram (EEG) can be characterized as a sequence of quasi-stable patterns. Such patterns in the brain signals can be associated with coordinated neural oscillations, which can be modeled by non-linear systems. Further, these patterns can be quantified through dynamical non-stationarity based on detection of qualitative changes in the state of the systems underlying the observed brain signals. This study explored age-related changes in dynamical non-stationarity of the brain signals recorded at rest, longitudinally with 128-channel EEG during early adolescence (10 to 13 years of age, 56 participants). Dynamical non-stationarity was analyzed based on segmentation of the time series with subsequent grouping of the segments into clusters with similar dynamics. Age-related changes in dynamical non-stationarity were described in terms of the number of stationary states and the duration of the stationary segments. We found that the EEG signal became more non-stationary with age. Specifically, the number of states increased whereas the mean duration of the stationary segment decreased with age. These two effects had global and parieto-occipital distribution, respectively, with the later effect being most dominant in the alpha (around 10 Hz) frequency band.