The intrahemispheric functional properties of the developing sensorimotor cortex are influenced by maturation

Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty

In this study, we aim to develop a machine learning model to predict the level of coordination between two players in tacit coordination games by analyzing the similarity of their spatial EEG features. We present an analysis, demonstrating the model's sensitivity, which was assessed through three conventional measures (precision, recall, and f1 score) based on the EEG patterns. These measures are evaluated in relation to the coordination task difficulty, as determined by the coordination index (CI). Tacit coordination games are games in which two individuals are requested to select the same option out of a closed set without the ability to communicate. This study aims to examine the effect of the difficulty of a semantic coordination task on the ability to predict a successful coordination between two players based on the compatibility between their EEG signals. The difficulty of each of the coordination tasks was estimated based on the degree of dispersion of the different answers given by the players reflected by the CI. The classification of the spatial distance between each pair of individual brain patterns, analyzed using the random walk algorithm, was used to predict whether successful coordination occurred or not. The classification performance was obtained for each game individually, i.e., for each different complexity level, via recall and precision indices. The results showed that the classifier performance depended on the CI, that is, on the level of coordination difficulty. These results, along with possibilities for future research, are discussed.

Early development of the functional brain network in newborns

During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.

Brain electrical microstate features as biomarkers of a stable motor output

Objective.The aim of the present study was to elucidate the brain dynamics underlying the maintenance of a constant force level exerted during a visually guided isometric contraction task by optimizing a predictive multivariate model based on global and spectral brain dynamics features.Approach.Electroencephalography (EEG) was acquired in 18 subjects who were asked to press a bulb and maintain a constant force level, indicated by a bar on a screen. For intervals of 500 ms, we calculated an index of force stability as well as indices of brain dynamics: microstate metrics (duration, occurrence, global explained variance, directional predominance) and EEG spectral amplitudes in the theta, low alpha, high alpha and beta bands. We optimized a multivariate regression model (partial least square (PLS)) where the microstate features and the spectral amplitudes were the input variables and the indexes of force stability were the output variables. The issues related to the collinearity among the input variables and to the generalizability of the model were addressed using PLS in a nested cross-validation approach.Main results.The optimized PLS regression model reached a good generalizability and succeeded to show the predictive value of microstates and spectral features in inferring the stability of the exerted force. Longer duration and higher occurrence of microstates, associated with visual and executive control networks, corresponded to better contraction performances, in agreement with the role played by the visual system and executive control network for visuo-motor integration.Significance.A combination of microstate metrics and brain rhythm amplitudes could be considered as biomarkers of a stable visually guided motor output not only at a group level, but also at an individual level. Our results may play an important role for a better understanding of the motor control in single trials or in real-time applications as well as in the study of motor control.

Evolution of Cortical Functional Networks in Healthy Infants

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

Modulation of Brain Functional Connectivity and Efficiency During an Endurance Cycling Task: A Source-Level EEG and Graph Theory Approach

Various methods have been employed to investigate different aspects of brain activity modulation related to the performance of a cycling task. In our study, we examined how functional connectivity and brain network efficiency varied during an endurance cycling task. For this purpose, we reconstructed EEG signals at source level: we computed current densities in 28 anatomical regions of interest (ROIs) through the eLORETA algorithm, and then we calculated the lagged coherence of the 28 current density signals to define the adjacency matrix. To quantify changes of functional network efficiency during an exhaustive cycling task, we computed three graph theoretical indices: local efficiency (LE), global efficiency (GE), and density (D) in two different frequency bands, Alpha and Beta bands, that indicate alertness processes and motor binding/fatigue, respectively. LE is a measure of functional segregation that quantifies the ability of a network to exchange information locally. GE is a measure of functional integration that quantifies the ability of a network to exchange information globally. D is a global measure of connectivity that describes the extent of connectivity in a network. This analysis was conducted for six different task intervals: pre-cycling; initial, intermediate, and final stages of cycling; and active recovery and passive recovery. Fourteen participants performed an incremental cycling task with simultaneous EEG recording and rated perceived exertion monitoring to detect the participants’ exhaustion. LE remained constant during the endurance cycling task in both bands. Therefore, we speculate that fatigue processes did not affect the segregated neural processing. We observed an increase of GE in the Alpha band only during cycling, which could be due to greater alertness processes and preparedness to stimuli during exercise. Conversely, although D did not change significantly over time in the Alpha band, its general reduction in the Beta bands during cycling could be interpreted within the framework of the neural efficiency hypothesis, which posits a reduced neural activity for expert/automated performances. We argue that the use of graph theoretical indices represents a clear methodological advancement in studying endurance performance.

Factors Associated with Apgar Score among Newborns Delivered by Cesarean Sections at Gandhi Memorial Hospital, Addis Ababa

Background

Newborns can be assessed clinically using the Apgar score test to quickly and summarily assess the health of newborn physical condition immediately after delivery and to determine any immediate need for extra medical or emergency care. This study is aimed at assessing factors associated with Apgar score among newborns delivered by cesarean sections and factors associated with Apgar score.

Method

Institutional-based cohort study design was conducted. All eligible study participants were included. Training was given for data collectors and supervisors. Regular supervision and follow-up was made. Data was entered into Epi Info version 7 computer software by investigators and was transported to SPSS version 20 computer program for analysis. Bivariate and multivariate analysis was used to identify factors associated with Apgar score.

Result

A total 354 newborn babies were included into the study. Majority of baby had low Apgar score at one minute and high Apgar score at five minutes. About 30.2% of newborn baby had Apgar score below seven minutes. On the other hand, about 12.8% of all newborns had low Apgar score at five minutes. It had been found that those neonates who were born when skin incision to delivery time is greater than three minutes were about fourfolds more likely to have low Apgar score than those who were born when skin incision to delivery time is less than three minutes (AOR 3.645) (95% CI (0.116-26.421)).

Conclusion

Newborn babies have a low Apgar score at one minute as compared to five minutes. But low Apgar score at five minutes has long-term sequel. Therefore, it is very important to reduce factors associated with low Apgar score at both minutes.

Magnetic Source Imaging and Infant MEG: Current Trends and Technical Advances

Magnetoencephalography (MEG) is known for its temporal precision and good spatial resolution in cognitive brain research. Nonetheless, it is still rarely used in developmental research, and its role in developmental cognitive neuroscience is not adequately addressed. The current review focuses on the source analysis of MEG measurement and its potential to answer critical questions on neural activation origins and patterns underlying infants’ early cognitive experience. The advantages of MEG source localization are discussed in comparison with functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), two leading imaging tools for studying cognition across age. Challenges of the current MEG experimental protocols are highlighted, including measurement and data processing, which could potentially be resolved by developing and improving both software and hardware. A selection of infant MEG research in auditory, speech, vision, motor, sleep, cross-modality, and clinical application is then summarized and discussed with a focus on the source localization analyses. Based on the literature review and the advancements of the infant MEG systems and source analysis software, typical practices of infant MEG data collection and analysis are summarized as the basis for future developmental cognitive research.

Magnetoencephalography and the infant brain

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that provides whole-head measures of neural activity with millisecond temporal resolution. Over the last three decades, MEG has been used for assessing brain activity, most commonly in adults. MEG has been used less often to examine neural function during early development, in large part due to the fact that infant whole-head MEG systems have only recently been developed. In this review, an overview of infant MEG studies is provided, focusing on the period from birth to three years. The advantages of MEG for measuring neural activity in infants are highlighted (See Box 1), including the ability to assess activity in brain (source) space rather than sensor space, thus allowing direct assessment of neural generator activity. Recent advances in MEG hardware and source analysis are also discussed. As the review indicates, efforts in this area demonstrate that MEG is a promising technology for studying the infant brain. As a noninvasive technology, with emerging hardware providing the necessary sensitivity, an expected deliverable is the capability for longitudinal infant MEG studies evaluating the developmental trajectory (maturation) of neural activity. It is expected that departures from neuro-typical trajectories will offer early detection and prognosis insights in infants and toddlers at-risk for neurodevelopmental disorders, thus paving the way for early targeted interventions.

Focusing Attention on Muscle Exertion Increases EEG Coherence in an Endurance Cycling Task

The aim of this study was to examine EEG coherence before, during, and after time to exhaustion (TTE) trials in an endurance cycling task, as well as the effect of effort level and attentional focus (i.e., functional external, functional internal, and dysfunctional internal associative strategies-leading to Type 1, Type 2, and Type 3 performances) on brain functional connectivity. Eleven college-aged participants performed the TTE test on a cycle-ergometer with simultaneous EEG and rate of perceived exertion (RPE) monitoring. EEG data from 32 electrodes were divided into five effort level periods based on RPE values (Baseline, RPE 0-4, RPE 5-8, RPE 9-MAX, and Recovery). Within subjects RM-ANOVA was conducted to examine time to task completion across Type 1, Type 2, and Type 3 performance trials. RM-ANOVA (3 performance types × 5 effort levels) was also performed to compare the EEG coherence matrices in the alpha and beta bands for 13 pairs of electrodes (F3-F4, F3-P3, F4-P4, T7-T8, T7-P3, C3-C4, C3-P3, C4-P4, T8-P4, P3-P4, P3-O1, P4-O2, O2-O1). Significant differences were observed on TTE performance outcomes between Type 1 and Type 3, and between Type 2 and Type 3 performance states (p < 0.05), whereas Type 1 and Type 2 performance states did not differ. No significant main effects were observed on performance type (p > 0.05) for all frequency bands in any pair of electrodes of the coherence matrices. Higher EEG coherence values were observed at rest (Baseline) than during cycling (RPE 0-4, 5-8, 9-MAX) for all pairs of electrodes and EEG frequency bands irrespective of the type of performance (main effect of effort, p < 0.05). Interestingly, we observed a performance × effort interaction in C3-C4 in beta 3 band [F_{(4, 77)} = 2.62, p = 0.038] during RPE 9-MAX for Type 3 performance as compared to Type 1 and Type 2 performances. These findings may have practical implications in the development of performance optimization strategies in cycling, as we found that focusing attention on a core component of the action could stimulate functional connectivity among specific brain areas and lead to enhanced performance.

Development of brain functional connectivity and its relation to infant sustained attention in the first year of life

The study of brain functional connectivity is crucial to understanding the neural mechanisms underlying the improved behavioral performance and amplified ERP responses observed during infant sustained attention. Previous investigations on the development of functional brain connectivity during infancy are primarily confined to the use of functional and structural MRI techniques. The current study examined the relation between infant sustained attention and brain functional connectivity and their development during infancy with high-density EEG recordings. Fifty-nine infants were tested at 6 (N = 15), 8 (N =14), 10 (N = 17), and 12 (N = 13) months. Infant sustained attention was defined by measuring infant heart rate changes during infants' looking. Functional connectivity was estimated from the electrodes on the scalp and with reconstructed cortical source activities in brain regions. It was found that infant sustained attention was accompanied by attenuated functional connectivity in the dorsal attention and default mode networks in the alpha band. Graph theory analyses showed that there was an increase in path length and a decrease in clustering coefficient during infant sustained attention. The functional connectivity within the visual, somatosensory, dorsal attention, and ventral attention networks and graph theory measures of path length and clustering coefficient were found to increase with age. These findings suggest that infant sustained attention is accompanied by distinct patterns of brain functional connectivity. The current findings also suggest the rapid development of functional connectivity in brain networks during infancy.

Development of cortical motor circuits between childhood and adulthood: A navigated TMS-HdEEG study

Motor functions improve during childhood and adolescence, but little is still known about the development of cortical motor circuits during early life. To elucidate the neurophysiological hallmarks of motor cortex development, we investigated the differences in motor cortical excitability and connectivity between healthy children, adolescents, and adults by means of navigated suprathreshold motor cortex transcranial magnetic stimulation (TMS) combined with high-density electroencephalography (EEG). We demonstrated that with development, the excitability of the motor system increases, the TMS-evoked EEG waveform increases in complexity, the magnitude of induced activation decreases, and signal spreading increases. Furthermore, the phase of the oscillatory response to TMS becomes less consistent with age. These changes parallel an improvement in manual dexterity and may reflect developmental changes in functional connectivity. Hum Brain Mapp 38:2599-2615, 2017. © 2017 Wiley Periodicals, Inc.

Toward Developmental Connectomics of the Human Brain

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders.

Brain asymmetry in the white matter making and globularity

Recent studies from the field of language genetics and evolutionary anthropology have put forward the hypothesis that the emergence of our species-specific brain is to be understood not in terms of size, but in light of developmental changes that gave rise to a more globular braincase configuration after the split from Neanderthals-Denisovans. On the grounds that (i) white matter myelination is delayed relative to other brain structures and, in humans, is protracted compared with other primates and that (ii) neural connectivity is linked genetically to our brain/skull morphology and language-ready brain, I argue that one significant evolutionary change in Homo sapiens' lineage is the interhemispheric connectivity mediated by the Corpus Callosum. The size, myelination and fiber caliber of the Corpus Callosum present an anterior-to-posterior increase, in a way that inter-hemispheric connectivity is more prominent in the sensory motor areas, whereas "high- order" areas are more intra-hemispherically connected. Building on evidence from language-processing studies that account for this asymmetry ('lateralization') in terms of brain rhythms, I present an evo-devo hypothesis according to which the myelination of the Corpus Callosum, Brain Asymmetry, and Globularity are conjectured to make up the angles of a co-evolutionary triangle that gave rise to our language-ready brain.