EEG/ERP as a pragmatic method to expand the reach of infant-toddler neuroimaging in HBCD: Promises and challenges

Co-metabolic degradation and metabolite detection of hexabromocyclododecane by Shewanella oneidensis MR-1

Hexabromocyclododecane (HBCD) is a widely used brominated flame retardant; however, it is a persistent organic pollutant as well as affects the human thyroid hormones and causes cancer. However, the degradation of HBCD has received little attention from researchers. Due to its bioaccumulative and hazardous properties, an appropriate strategy for its remediation is required. In this study, we investigated the biodegradation of HBCD using Shewanella oneidensis MR-1 under optimized conditions. The Box-Behnken design (BBD) was implemented for the optimization of the physical degradation parameters of HBCD. S. oneidensis MR-1 showed the best degradation performance at a temperature of 30 °C, pH 7, and agitation speed of 115 rpm, with an HBCD concentration of 1125 μg/L in mineral salt medium (MSM). The strain tolerated up to 2000 μg/L HBCD. Gas chromatography-mass spectrometry analysis identified three intermediates, including 2-bromo dodecane, 2,7,10-trimethyldodecane, and 4-methyl-1-decene. The results provide an insightful understanding of the biodegradation of HBCD by S. oneidensis MR-1 under optimized conditions and could pave the way for further eco-friendly applications. KEY POINTS: • HBCD biodegradation by Shewanella oneidensis • Optimization of HBCD biodegradation by the Box-Behnken analysis • Identification of useful metabolites from HBCD degradation.

Evaluating a novel high-density EEG sensor net structure for improving inclusivity in infants with curly or tightly coiled hair

Electroencephalography (EEG) is an important tool in the field of developmental cognitive neuroscience for indexing neural activity. However, racial biases persist in EEG research that limit the utility of this tool. One bias comes from the structure of EEG nets/caps that do not facilitate equitable data collection across hair textures and types. Recent efforts have improved EEG net/cap design, but these solutions can be time-intensive, reduce sensor density, and are more difficult to implement in younger populations. The present study focused on testing EEG sensor net designs over infancy. Specifically, we compared EEG data quality and retention between two high-density saline-based EEG sensor net designs from the same company (Magstim EGI, Whitland, UK) within the same infants during a baseline EEG paradigm. We found that within infants, the tall sensor nets resulted in lower impedances during collection, including lower impedances in the key online reference electrode for those with greater hair heights and resulted in a greater number of usable EEG channels and data segments retained during pre-processing. These results suggest that along with other best practices, the modified tall sensor net design is useful for improving data quality and retention in infant participants with curly or tightly-coiled hair.

Getting closer to social interactions using electroencephalography in developmental cognitive neuroscience

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.

Fostering inclusion in EEG measures of pediatric brain activity

The past two decades have seen a rapid increase in neuroscientific evidence being used to characterize how contextual, structural, and societal factors shape cognition and school readiness. Measures of functional brain activity are increasingly viewed as markers of child development and biomarkers that could be employed to track the impact of interventions. While electroencephalography (EEG) provides a promising tool to understand educational inequities, traditional EEG data acquisition is commonly limited in some racial and ethnic groups due to hair types and styles. This ultimately constitutes unintentional systemic racism by disproportionately excluding participants from certain racial and ethnic groups from participation and representation in neuroscience research. Here, we provide a comprehensive review of how cultural considerations surrounding hair density, texture, and styling consistently skew samples to be less representative by disproportionately excluding Black and Latinx participants. We also provide recommendations and materials to promote best practices.

Mismatch Responses to Speech Contrasts in Preschoolers with and without Developmental Language Disorder

This study compared cortical responses to speech in preschoolers with typical language development (TLD) and with Developmental Language Disorder (DLD). We investigated whether top-down language effects modulate speech perception in young children in an adult-like manner. We compared cortical mismatch responses (MMRs) during the passive perception of speech contrasts in three groups of participants: preschoolers with TLD (n = 11), preschoolers with DLD (n = 16), and adults (n = 20). We also measured children's phonological skills and investigated whether they are associated with the cortical discrimination of phonemic changes involving different linguistic complexities. The results indicated top-down language effects in adults, with enhanced cortical discrimination of lexical stimuli but not of non-words. In preschoolers, the TLD and DLD groups did not differ in the MMR measures, and no top-down effects were detected. Moreover, we found no association between MMRs and phonological skills, even though the DLD group's phonological skills were significantly lower. Our findings suggest that top-down language modulations in speech discrimination may not be present during early childhood, and that children with DLD may not exhibit cortical speech perception deficits. The lack of association between phonological and MMR measures indicates that further research is needed to understand the link between language skills and cortical activity in preschoolers.

Maturation of the mismatch response in pre-school children: Systematic literature review and meta-analysis

Event-related potentials (ERPs), specifically the Mismatch Response (MMR), holds promise for investigating auditory maturation in children. It has the potential to predict language development and distinguish between language-impaired and typically developing groups. However, summarizing the MMR's developmental trajectory in typically developing children remains challenging despite numerous studies. This pioneering meta-analysis outlines changes in MMR amplitude among typically developing children, while offering methodological best-practices. Our search identified 51 articles for methodology analysis and 21 for meta-analysis, involving 0-8-year-old participants from 2000 to 2022. Risk of Bias assessment and methodology analysis revealed shortcomings in control condition usage and reporting of study confounders. The meta-analysis results were inconsistent, indicating large effect sizes in some conditions and no effect sizes in others. Subgroup analysis revealed the main effects of age and brain region, as well as an interaction of age and time-window of the MMR. Future research requires a specific protocol, larger samples, and replication studies to deepen the understanding of the auditory discrimination maturation process in children.

Resting brain activity in early childhood predicts IQ at 18 years

Resting brain activity has been widely used as an index of brain development in neuroscience and clinical research. However, it remains unclear whether early differences in resting brain activity have meaningful implications for predicting long-term cognitive outcomes. Using data from the Bucharest Early Intervention Project (Zeanah et al., 2003), we examined the impact of institutional rearing and the consequences of early foster care intervention on 18-year IQ. We found that higher resting theta electroencephalogram (EEG) power, reflecting atypical neurodevelopment, across three assessments from 22 to 42 months predicted lower full-scale IQ at 18 years, providing the first evidence that brain activity in early childhood predicts cognitive outcomes into adulthood. In addition, both institutional rearing and later (vs. earlier) foster care intervention predicted higher resting theta power in early childhood, which in turn predicted lower IQ at 18 years. These findings demonstrate that experientially-induced changes in brain activity early in life have profound impact on long-term cognitive development, highlighting the importance of early intervention for promoting healthy development among children living in disadvantaged environments.

Prerequisites of language acquisition in the newborn brain

Learning to decode and produce speech is one of the most demanding tasks faced by infants. Nevertheless, infants typically utter their first words within a year, and phrases soon follow. Here we review cognitive abilities of newborn infants that promote language acquisition, focusing primarily on studies tapping neural activity. The results of these studies indicate that infants possess core adult auditory abilities already at birth, including statistical learning and rule extraction from variable speech input. Thus, the neonatal brain is ready to categorize sounds, detect word boundaries, learn words, and separate speech streams: in short, to acquire language quickly and efficiently from everyday linguistic input.

A comprehensive wireless neurological and cardiopulmonary monitoring platform for pediatrics

Neurodevelopment in the first 10 years of life is a critical time window during which milestones that define an individual's functional potential are achieved. Comprehensive multimodal neurodevelopmental monitoring is particularly crucial for socioeconomically disadvantaged, marginalized, historically underserved and underrepresented communities as well as medically underserved areas. Solutions designed for use outside the traditional clinical environment represent an opportunity for addressing such health inequalities. In this work, we present an experimental platform, ANNE EEG, which adds 16-channel cerebral activity monitoring to the existing, USA FDA-cleared ANNE wireless monitoring platform which provides continuous electrocardiography, respiratory rate, pulse oximetry, motion, and temperature measurements. The system features low-cost consumables, real-time control and streaming with widely available mobile devices, and fully wearable operation to allow a child to remain in their naturalistic environment. This multi-center pilot study successfully collected ANNE EEG recordings from 91 neonatal and pediatric patients at academic quaternary pediatric care centers and in LMIC settings. We demonstrate the practicality and feasibility to conduct electroencephalography studies with high levels of accuracy, validated via both quantitative and qualitative metrics, compared against gold standard systems. An overwhelming majority of parents surveyed during studies indicated not only an overall preference for the wireless system, but also that its use would improve their children's physical and emotional health. Our findings demonstrate the potential for the ANNE system to perform multimodal monitoring to screen for a variety of neurologic diseases that have the potential to negatively impact neurodevelopment.

Effects of Early Nutrition Factors on Baseline Neurodevelopment during the First 6 Months of Life: An EEG Study

Throughout infancy, the brain undergoes rapid changes in structure and function that are sensitive to environmental influences, such as diet. Breastfed (BF) infants score higher on cognitive tests throughout infancy and into adolescence than formula fed (FF) infants, and these differences in neurocognitive development are reflected in higher concentrations of white and grey matter as measured by MRI. To further explore the effect diet has on cognitive development, electroencephalography (EEG) is used as a direct measure of neuronal activity and to assess specific frequency bands associated with cognitive processes. Task-free baseline EEGs were collected from infants fed with human milk (BF), dairy-based formula (MF), or soy-based formula (SF) at 2, 3, 4, 5, and 6 months of age to explore differences in frequency bands in both sensor and source space. Significant global differences in sensor space were seen in beta and gamma bands between BF and SF groups at ages 2 and 6 months, and these differences were further observed through volumetric modeling in source space. We conclude that BF infants exhibit earlier brain maturation reflected in greater power spectral density in these frequency bands.

Mindfulness Training Improves Attention: Evidence from Behavioral and Event-related Potential Analyses

Mindfulness meditation helps to improve attentional capacity. However, the neural correlates that indicate the mechanism through which mindfulness improves attention are unclear. To address this gap, we aimed to assess the effects of mindfulness training on sustained attentional capacity. Event-related potentials (ERPs) associated with the modified sustained attention response task (mSART) were used in this study. A total of 45 college students were randomly assigned to either the mindfulness group (n = 21) or the control group (n = 24). Participants in the mindfulness group received a three-week mindfulness training. The self-report results showed that the mindfulness group reported higher mindfulness scores (observing and non-judgment of inner experiences) after the training. The mindfulness group also scored lower on the state anxiety than the control group. Behavioral results also showed that self-caught mind wandering in the mindfulness group significantly decreased after the training, and the mindfulness group showed a faster response after the training. The ERP results showed that N2 amplitudes in the post-test were significantly greater than those in the pre-test in the mindfulness group. We did not find any interactions between group and time for P3. The findings suggest that mindfulness training can effectively improve sustained attentional capacity, as indicated by reduced mind wandering and increased N2 responses.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.

Translating RDoC to Real-World Impact in Developmental Psychopathology: A Neurodevelopmental Framework for Application of Mental Health Risk Calculators

The National Institute of Mental Health Research Domain Criteria's (RDoC) has prompted a paradigm shift from categorical psychiatric disorders to considering multiple levels of vulnerability for probabilistic risk of disorder. However, the lack of neurodevelopmentally-based tools for clinical decision-making has limited RDoC's real-world impact. Integration with developmental psychopathology principles and statistical methods actualize the clinical implementation of RDoC to inform neurodevelopmental risk. In this conceptual paper, we introduce the probabilistic mental health risk calculator as an innovation for such translation and lay out a research agenda for generating an RDoC- and developmentally-informed paradigm that could be applied to predict a range of developmental psychopathologies from early childhood to young adulthood. We discuss methods that weigh the incremental utility for prediction based on intensity and burden of assessment, the addition of developmental change patterns, considerations for assessing outcomes, and integrative data approaches. Throughout, we illustrate the risk calculator approach with different neurodevelopmental pathways and phenotypes. Finally, we discuss real-world implementation of these methods for improving early identification and prevention of developmental psychopathology. We propose that mental health risk calculators can build a needed bridge between RDoC's multiple units of analysis and developmental science.