Left OFC activation in fNIRS during an inhibitory control task in an early years sample: integrating stress responses with cognitive function and brain activation.

INTRODUCTION: Previous functional near-infrared (fNIRS) studies using Go/No-Go (GNG) tasks have focused on brain activation in relation to cognitive processes, particularly inhibitory control (IC). The results of these studies commonly describe right hemispheric engagement of the dorsolateral, ventromedial or inferior frontal regions of the prefrontal cortex (PFC). Considering that typical healthy cognitive development is negatively correlated with higher cortisol levels (which may alter brain development), the overarching aim of the current study was to investigate how elevated stress (due to unforeseeable events such as the pandemic) impacts early cognitive development. METHOD: In this study, we examined fNIRS data collected from a sample of children (aged 2 - 4 years) during a GNG task relative to the response to stressors measured via hair cortisol concentrations. We acquired data in an ecological setting (Early Childhood Education and Care) during the coronavirus pandemic. RESULTS: We found that children with higher stress levels and a less efficient inhibitory control recruited more neural terrain and our group-level analysis indicated activation in the left orbitofrontal area during IC performance. CONCLUSIONS: A contextual stressor may disrupt accuracy in the executive function of inhibitory control early in development. More research efforts are needed to understand better how an orbitofrontal network subserves goal-directed behavior.

Cognitive Effort during Visuospatial Problem Solving in Physical Real World, on Computer Screen, and in Virtual Reality.

Spatial cognition plays a crucial role in academic achievement, particularly in science, technology, engineering, and mathematics (STEM) domains. Immersive virtual environments (VRs) have the growing potential to reduce cognitive load and improve spatial reasoning. However, traditional methods struggle to assess the mental effort required for visuospatial processes due to the difficulty in verbalizing actions and other limitations in self-reported evaluations. In this neuroergonomics study, we aimed to capture the neural activity associated with cognitive workload during visuospatial tasks and evaluate the impact of the visualization medium on visuospatial task performance. We utilized functional near-infrared spectroscopy (fNIRS) wearable neuroimaging to assess cognitive effort during spatial-reasoning-based problem-solving and compared a VR, a computer screen, and a physical real-world task presentation. Our results reveal a higher neural efficiency in the prefrontal cortex (PFC) during 3D geometry puzzles in VR settings compared to the settings in the physical world and on the computer screen. VR appears to reduce the visuospatial task load by facilitating spatial visualization and providing visual cues. This makes it a valuable tool for spatial cognition training, especially for beginners. Additionally, our multimodal approach allows for progressively increasing task complexity, maintaining a challenge throughout training. This study underscores the potential of VR in developing spatial skills and highlights the value of comparing brain data and human interaction across different training settings.

Distinct patterns of metabolic motor cortex activity for phantom and residual limb pain in people with amputations: A functional near-infrared spectroscopy study.

BACKGROUND: Phantom pain limb (PLP) has gained more attention due to the large number of people with amputations around the world and growing knowledge of the pain process, although its mechanisms are not completely understood. OBJECTIVES: The aim of this study was to understand, in patients with amputations, the association between PLP and residual limb pain (RLP), and the brain metabolic response in cortical motor circuits, using functional near-infrared spectroscopy (fNIRS). METHODS: Sixty participants were recruited from the rehabilitation program in São Paulo, Brazil. Included patients were aged over 18 years, with traumatic unilateral lower-limb amputation, with PLP for at least 3 months after full recovery from amputation surgery. PLP and RLP levels were measured using visual analogue scales. fNIRS was performed during motor execution and motor mirror tasks for 20 s. In order to highlight possible variables related to variation in pain measures, univariate linear regression analyses were performed for both experimental conditions, resulting in four fNIRS variables (two hemispheres x two experimental conditions). Later, in order to test the topographic specificity of the models, eight multivariate regression analyses were performed (two pain scales x two experimental conditions x two hemispheres), including the primary motor cortex (PMC) related channel as an independent variable as well as five other channels related to the premotor area, supplementary area, and somatosensory cortex. All models were controlled for age, sex, ethnicity, and education. RESULTS: We found that: i) there is an asymmetric metabolic activation during motor execution and mirror task between hemispheres (with a predominance that is ipsilateral to the amputated limb), ii) increased metabolic response in the PMC ipsilateral to the amputation is associated with increased PLP (during both experimental tasks), while increased metabolic response in the contralateral PMC is associated with increased RLP (during the mirror motor task only); ii) increased metabolic activity of the ipsilateral premotor region is associated with increased PLP during the motor mirror task; iii) RLP was only associated with higher metabolic activity in the contralateral PMC and lower metabolic activity in the ipsilateral inferior frontal region during motor mirror task, but PLP was associated with higher metabolic activity during both tasks. CONCLUSION: These results suggest there is both task and region specificity for the association between the brain metabolic response and the two different types of post-amputation pain. The metabolic predominance that is ipsilateral to the amputated limb during both tasks was associated with higher levels of PLP, suggesting a cortical motor network activity imbalance due to potential interhemispheric compensatory mechanisms. The present work contributes to the understanding of the underlying topographical patterns in the motor-related circuits associated with pain after amputations.

Task-related brain activity in upper limb dystonia revealed by simultaneous fNIRS and EEG.

OBJECTIVE: The aim of this study was to explore differences in brain activity and connectivity using simultaneous electroencephalography and near-infrared spectroscopy in patients with focal dystonia during handwriting and finger-tapping tasks. METHODS: Patients with idiopathic right upper limb focal dystonia and controls were assessed by simultaneous near-infrared spectroscopy and electroencephalography during the writing and finger-tapping tasks in terms of the mu-alpha, mu-beta, beta and low gamma power and effective connectivity, as well as relative changes in oxyhemoglobin (oxy-Hb) and deoxyhemoglobin using a channel-wise approach with a mixed-effect model. RESULTS: Patients exhibited higher oxy-Hb levels in the right and left motor cortex and supplementary motor area during writing, but lower oxy-Hb levels in the left sensorimotor and bilateral somatosensory area during finger-tapping compared to controls. During writing, patients showed increased low gamma power in the bilateral sensorimotor cortex and less mu-beta and beta attenuation compared to controls. Additionally, patients had reduced connectivity between the supplementary motor area and the left sensorimotor cortex during writing. No differences were observed in terms of effective connectivity in either task. Finally, patients failed to attenuate the mu-alpha, mu-beta, and beta rhythms during the finger-tapping task. CONCLUSIONS: Cortical blood flow and EEG spectral power differ between controls and dystonia patients, depending on the task. Writing increased blood flow and altered connectivity in dystonia patients, and it also decreased slow-band attenuation. Finger-tapping decreased blood flow and slow-band attenuation. SIGNIFICANCE: Simultaneous fNIRS and EEG may show relevant information regarding brain dynamics in movement disorders patients in unconstrained environments.

Analyzing teacher-student interactions through graph theory applied to hyperscanning fNIRS data.

Teacher-student relationships have been found consistently important for student school effectiveness in mathematics in the last three decades. Although this observation is generally made from the teacher's perspective, neuroscience can provide new insights by establishing the neurobiological underpinning of social interactions. This paper further develops this line of research by utilizing graph theory to represent interactions between teachers and students at the neural level. Through hyperscanning with functional near-infrared spectroscopy (fNIRS), we collected data from the prefrontal cortex and the temporoparietal junction of 24 dyads composed of a teacher and a student. Each dyad used a board game to perform a programming logic class that consisted of three steps: independent activities (control), presentation of concepts, and interactive exercises. Graph theory provides results regarding the strength of teacher-student interaction and the main channels involved in these interactions. We combined graph modularity and bootstrap to measure pair coactivation, thus establishing the strength of teacher-student interaction. Also, graph centrality detects the main brain channels involved during this interaction. In general, the teacher's most relevant nodes rely on the regions related to language and number processing, spatial cognition, and attention. Also, the students' most relevant nodes rely on the regions related to task management.

Exploring the potential of eye tracking on personalized learning and real-time feedback in modern education.

Eye tracking is one of the techniques used to investigate cognitive mechanisms involved in the school context, such as joint attention and visual perception. Eye tracker has portability, straightforward application, cost-effectiveness, and infant-friendly neuroimaging measures of cognitive processes such as attention, engagement, and learning. Furthermore, the ongoing software enhancements coupled with the implementation of artificial intelligence algorithms have improved the precision of collecting eye movement data and simplified the calibration process. These characteristics make it plausible to consider eye-tracking technology a promising tool to assist the teaching-learning process in school routines. However, eye tracking needs to be explored more as an educational instrument for real-time classroom activities and teachers' feedback. This perspective article briefly presents the fundamentals of the eye-tracking technique and four illustrative examples of employing this method in everyday school life. The first application shows how eye tracker information may contribute to teacher assessment of students' computational thinking in coding classes. In the second and third illustrations, we discuss the additional information provided by the eye-tracker to the teacher assessing the student's strategies to solve fraction problems and chart interpretation. The last illustration demonstrates the potential of eye tracking to provide Real-time feedback on learning difficulties/disabilities. Thus, we highlight the potential of the eye tracker as a complementary tool to promote personalized education and discuss future perspectives. In conclusion, we suggest that an eye-tracking system could be helpful by providing real-time student gaze leading to immediate teacher interventions and metacognition strategies.

Hyperscanning fNIRS data analysis using multiregression dynamic models: an illustration in a violin duo.

Introduction: Interpersonal neural synchronization (INS) demands a greater understanding of a brain's influence on others. Therefore, brain synchronization is an even more complex system than intrasubject brain connectivity and must be investigated. There is a need to develop novel methods for statistical inference in this context. Methods: In this study, motivated by the analysis of fNIRS hyperscanning data, which measure the activity of multiple brains simultaneously, we propose a two-step network estimation: Tabu search local method and global maximization in the selected subgroup [partial conditional directed acyclic graph (DAG) + multiregression dynamic model]. We illustrate this approach in a dataset of two individuals who are playing the violin together. Results: This study contributes new tools to the social neuroscience field, which may provide new perspectives about intersubject interactions. Our proposed approach estimates the best probabilistic network representation, in addition to providing access to the time-varying parameters, which may be helpful in understanding the brain-to-brain association of these two players. Discussion: The illustration of the violin duo highlights the time-evolving changes in the brain activation of an individual influencing the other one through a data-driven analysis. We confirmed that one player was leading the other given the ROI causal relation toward the other player.

A magnetic resonance imaging-based morphometric and structural covariance network study of Brazilian adolescents stratified by depression risk

Objectives: To explore differences in regional cortical morphometric structure between adolescents at risk for depression or with current depression. Methods: We analyzed cross-sectional structural neuroimaging data from a sample of 150 Brazilian adolescents classified as low-risk (LR) (n=50) or high-risk (HR) for depression (n=50) or with current depression (n=50) through a vertex-based approach with measurements of cortical volume (CV), surface area (SA), and cortical thickness (CT). Differences between groups in subcortical volume and in the organization of networks of structural covariance were also explored. Results: No significant differences in brain structure between groups were observed in whole-brain vertex-wise CV, SA, or CT. Also, no significant differences in subcortical volume were observed between risk groups. In relation to the structural covariance network, there was an indication of an increase in the hippocampus betweenness centrality index in the HR group network compared to the LR and current depression group networks. However, this result was only statistically significant when applying false discovery rate correction for nodes within the affective network. Conclusion: In an adolescent sample recruited using an empirically based composite risk score, no major differences in brain structure were detected according to the risk and presence of depression.

A Novel Exploratory Graph-Based Analytical Tool for Functional Near-Infrared Spectroscopy in Naturalistic Experiments: An Illustrative Application in Typically Developing Children.

Naturalistic paradigms are being increasingly applied to investigate human brain function. Compared with resting-state and task-based paradigms in neuroimaging, naturalistic stimuli and situations can be potentially more readily translated to daily-life applications. Among neuroimaging modalities, functional near-infrared spectroscopy (fNIRS) is particularly suitable for naturalistic investigations and applications. However, specific and tailored statistical analysis to interrogate brain function using naturalistic fNIRS is warranted. Here, we describe an exploratory graph-centrality-based approach to investigating participants' spatiotemporal similarities from the fNIRS signal. We illustrate the usefulness of our approach in a sample of typically developing children (10 males and 9 females; mean age of 5.2 years old; sd = 0.78) while they watch the Inscapes movie designed for neuroimaging acquisition. A node in the left dorsal prefrontal cortex presented similar responses across children, and those fNIRS responses were in line with scene transitions in the movie stimulus. Our results suggest the feasibility of applying centrality graph-based measures to investigate brain function in naturalistic fNIRS during development.

An MRI-based morphometric and structural covariance network study of Brazilian adolescents stratified by depression risk.

OBJECTIVE: To explore differences in regional cortical morphometric structure between adolescents at risk for depression or with current depression. METHODS: We analyzed cross-sectional structural neuroimaging data from a sample of 150 Brazilian adolescents classified as low-risk (n=50) or high-risk for depression (n=50) or with current depression (n=50) through a vertex-based approach with measurements of cortical volume, surface area and thickness. Differences between groups in subcortical volumes and in the organization of networks of structural covariance were also explored. RESULTS: No significant differences in brain structure between groups were observed in whole-brain vertex-wise cortical volume, surface area or thickness. Also, no significant differences in subcortical volume were observed between risk groups. In relation to the structural covariance network, there was an indication of an increase in the hippocampus betweenness centrality index in the high-risk group network compared to the low-risk and current depression group networks. However, this result was only statistically significant when applying false discovery rate correction for nodes within the affective network. CONCLUSION: In an adolescent sample recruited using an empirically based composite risk score, no major differences in brain structure were detected according to the risk and presence of depression.

Generalizability of 3D CNN models for age estimation in diverse youth populations using structural MRI.

Recently, several studies have investigated the neurodevelopment of psychiatric disorders using brain data acquired via structural magnetic resonance imaging (sMRI). These analyses have shown the potential of sMRI data to provide a relatively precise characterization of brain structural biomarkers. Despite these advances, a relatively unexplored question is how reliable and consistent a model is when assessing subjects from other independent datasets. In this study, we investigate the performance and generalizability of the same model architecture trained from distinct datasets comprising youths in diverse stages of neurodevelopment and with different mental health conditions. We employed models with the same 3D convolutional neural network (CNN) architecture to assess autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), brain age, and a measure of dimensional psychopathology, the Child Behavior Checklist (CBCL) total score. The investigated datasets include the Autism Brain Imaging Data Exchange II (ABIDE-II, N = 580), Attention Deficit Hyperactivity Disorder (ADHD-200, N = 922), Brazilian High-Risk Cohort Study (BHRCS, N = 737), and Adolescent Brain Cognitive Development (ABCD, N = 11,031). Models' performance and interpretability were assessed within each dataset (for diagnosis tasks) and inter-datasets (for age estimation). Despite the demographic and phenotypic differences of the subjects, all models presented significant estimations for age (p value < 0.001) within and between datasets. In addition, most models showed a moderate to high correlation in age estimation. The results, including the models' brain regions of interest (ROI), were analyzed and discussed in light of the youth neurodevelopmental structural changes. Among other interesting discoveries, we found that less confounded training datasets produce models with higher generalization capacity.

Polygenic risk score for attention-deficit/hyperactivity disorder and brain functional networks segregation in a community-based sample.

Neuroimaging studies suggest that brain development mechanisms might explain at least some behavioural and cognitive attention-deficit/hyperactivity disorder (ADHD) symptoms. However, the putative mechanisms by which genetic susceptibility factors influence clinical features via alterations of brain development remain largely unknown. Here, we set out to integrate genomics and connectomics tools by investigating the associations between an ADHD polygenic risk score (ADHD-PRS) and functional segregation of large-scale brain networks. With this aim, ADHD symptoms score, genetic and rs-fMRI (resting-state functional magnetic resonance image) data obtained in a longitudinal community-based cohort of 227 children and adolescents were analysed. A follow-up was conducted approximately 3 years after the baseline, with rs-fMRI scanning and ADHD likelihood assessment in both stages. We hypothesised a negative correlation between probable ADHD and the segregation of networks involved in executive functions, and a positive correlation with the default-mode network (DMN). Our findings suggest that ADHD-PRS is correlated with ADHD at baseline, but not at follow-up. Despite not surviving for multiple comparison correction, we found significant correlations between ADHD-PRS and segregation of cingulo-opercular networks and DMN at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation. These directions of associations corroborate the proposed counter-balanced role of attentional networks and DMN in attentional processes. However, the association between ADHD-PRS and brain networks functional segregation was not found at follow-up. Our results provide evidence for specific influences of genetic factors on development of attentional networks and DMN. We found significant correlations between polygenic risk score for ADHD (ADHD-PRS) and segregation of cingulo-opercular networks and default-mode network (DMN) at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation.

Análise da Viabilidade de Uso do FNIRS em Atividades Educacionais com Crianças e Jovens com Deficiência Intelectual e Autismo / Analysis of FNIRS Usability in Educational Activities with Children and Young People with Intellectual Disability and Autism

RESUMO: Métodos em neurociência cognitiva podem auxiliar o planejamento educacional de docentes no contexto da Educação Especial, por favorecerem práticas personalizadas que valorizem a velocidade individual de aprendizagem de estudantes com transtorno do espectro do autismo (TEA) e/ou deficiência intelectual (DI). Assim sendo, este estudo objetivou verificar a viabilidade de uso da Espectroscopia Funcional de Infravermelho Próximo (fNIRS) em situação naturalística clínica com crianças e jovens com TEA e/ou DI durante tarefas de ensino. Ademais, o estudo buscou identificar as estratégias de treino para que as crianças e os jovens utilizassem o equipamento durante a realização da atividade. Sete estudantes com diagnóstico de TEA e/ou DI foram treinados com atividades de matemática, leitura e expressividade emocional, de acordo com seus respectivos currículos educacionais prévios. Cada participante foi exposto a duas tarefas em cada atividade, uma na qual já apresentava domínio e outra que necessitava de apoio para emitir uma resposta independente. Os resultados indicaram a viabilidade de uso do fNIRS nesse contexto natural da criança e do jovem e forneceram medidas implícitas para além das medidas observacionais de acerto e erro na tarefa. Esta é uma importante demonstração da viabilidade do uso do fNIRS em experimentos no contexto da Educação Especial.

ABSTRACT: Methods in cognitive neuroscience can assist educational planning of teachers in the context of Special Education, as they favor personalized practices that value individual students with Autism Spectrum Disorder (ASD) and/or Intellectual Deficiency (ID). Therefore, this study aimed to verify the feasibility of using functional near-infrared spectroscopy (fNIRS) in clinical naturalistic situation with children and young people with ASD and/or ID during teaching tasks. In addition, the study sought to identify training strategies so that children and young people use the equipment during the activity. Seven students diagnosed with ASD and/or ID were trained with mathematics, reading and emotional expressiveness, according to their respective previous educational curricula. Each participant was exposed to two tasks in each activity, one in which he/she already had a domain and one that needed support to issue an independent response. The results indicated the feasibility of using fNIRS in this natural context of the child and the young student and provided implicit measures beyond the observational arrangement measures and task error. This is an important demonstration of the feasibility of using fNIRS in experiments in the context of Special Education.

Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms.

Hyperscanning is a promising tool for investigating the neurobiological underpinning of social interactions and affective bonds. Recently, graph theory measures, such as modularity, have been proposed for estimating the global synchronization between brains. This paper proposes the bootstrap modularity test as a way of determining whether a pair of brains is coactivated. This test is illustrated as a screening tool in an application to fNIRS data collected from the prefrontal cortex and temporoparietal junction of five dyads composed of a teacher and a preschooler while performing an interaction task. In this application, graph hub centrality measures identify that the dyad's synchronization is critically explained by the relation between teacher's language and number processing and the child's phonological processing. The analysis of these metrics may provide further insights into the neurobiological underpinnings of interaction, such as in educational contexts.

Optical imaging and spectroscopy for the study of the human brain: status report.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Adolescent depression and resting-state fMRI brain networks: a scoping review of longitudinal studies

The neurobiological factors associated with the emergence of major depressive disorder (MDD) in adolescence are still unclear. Previous cross-sectional studies have documented aberrant connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) networks. However, whether these findings precede MDD onset has not been established. This scoping review mapped key methodological aspects and main findings of longitudinal rs-fMRI studies of MDD in adolescence. Three sets of neuroimaging methods to analyze rs-fMRI data were identified: seed-based analysis, independent component analysis, and network-based approaches. Main findings involved aberrant connectivity within and between the default mode network (DMN), the cognitive control network (CCN), and the salience network (SN). Accordingly, we utilized Menon's (2011) triple-network model for neuropsychiatric disorders to summarize key results. Adolescent MDD was associated with hyperconnectivity within the SN and between DMN and SN, as well as hypoconnectivity within the CCN. These findings suggested that dysfunctional connectivity among the three main large-scale brain networks preceded MDD onset. However, there was high heterogeneity in neuroimaging methods and sampling procedures, which may limit comparisons between studies. Future studies should consider some level of harmonization for clinical instruments and neuroimaging methods.

Adolescent depression and resting-state fMRI brain networks: a scoping review of longitudinal studies.

The neurobiological factors associated with the emergence of major depressive disorder (MDD) in adolescence are still unclear. Previous cross-sectional studies have documented aberrant connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) networks. However, whether these findings precede MDD onset has not been established. This scoping review mapped key methodological aspects and main findings of longitudinal rs-fMRI studies of MDD in adolescence. Three sets of neuroimaging methods to analyze rs-fMRI data were identified: seed-based analysis, independent component analysis, and network-based approaches. Main findings involved aberrant connectivity within and between the default mode network (DMN), the cognitive control network (CCN), and the salience network (SN). Accordingly, we utilized Menon's (2011) triple-network model for neuropsychiatric disorders to summarize key results. Adolescent MDD was associated with hyperconnectivity within the SN and between DMN and SN, as well as hypoconectivity within the CCN. These findings suggested that dysfunctional connectivity among the three main large-scale brain networks preceded MDD onset. However, there was high heterogeneity in neuroimaging methods and sampling procedures, which may limit comparisons between studies. Future studies should consider some level of harmonization for clinical instruments and neuroimaging methods.

Efficacy and safety of HD-tDCS and respiratory rehabilitation for critically ill patients with COVID-19 The HD-RECOVERY randomized clinical trial.

BACKGROUND AND PURPOSE: Acute Respiratory Distress Syndrome (ADRS) due to coronavirus disease 2019 (COVID-19) has been associated with muscle fatigue, corticospinal pathways dysfunction, and mortality. High-Definition transcranial Direct Current Stimulation (HD-tDCS) may be used to attenuate clinical impairment in these patients. The HD-RECOVERY randomized clinical trial was conducted to evaluate the efficacy and safety of HD-tDCS with respiratory rehabilitation in patients with moderate to severe ARDS due to COVID-19. METHODS: Fifty-six critically ill patients were randomized 1:1 to active (n = 28) or sham (n = 28) HD-tDCS (twice a day, 30-min, 3-mA) plus respiratory rehabilitation for up to 10 days or until intensive care unit discharge. The primary outcome was ventilator-free days during the first 28 days, defined as the number of days free from mechanical ventilation. Furthermore, secondary outcomes such as delirium, organ failure, hospital length of stay and adverse effects were investigated. RESULTS: Active HD-tDCS induced more ventilator-free days compared to sham HD-tDCS. Patients in the active group vs in the sham group experienced lower organ dysfunction, delirium, and length of stay rates over time. In addition, positive clinical response was higher in the active vs sham group. There was no significant difference in the prespecified secondary outcomes at 5 days. Adverse events were similar between groups. CONCLUSIONS: Among patients with COVID-19 and moderate to severe ARDS, use of active HD-tDCS compared with sham HD-tDCS plus respiratory rehabilitation resulted in a statistically significant increase in the number of ventilator-free days over 28 days. HD-tDCS combined with concurrent rehabilitation therapy is a safe, feasible, potentially add-on intervention, and further trials should examine HD-tDCS efficacy in a larger sample of patients with COVID-19 and severe hypoxemia.

Associations between Family Functioning and Maternal Behavior on Default Mode Network Connectivity in School-Age Children.

Background: Most early children's experiences will occur in a family context; therefore, the quality of this environment is critical for development outcomes. Not many studies have assessed the correlations between brain functional connectivity (FC) in important areas such as the default mode network (DMN) and the quality of parent-child relationships in school-age children and early adolescence. The quality of family relationships and maternal behavior have been suggested to modulate DMN FC once they act as external regulators of children's affect and behavior. Objective: We aimed to test the associations between the quality of family environment/maternal behavior and FC within the DMN of school-age children. Method: Resting-state, functional magnetic resonance imaging data, were collected from 615 children (6-12 age range) enrolled in the Brazilian High-Risk Cohort (HRC) study. We assessed DMN intra-connectivity between the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and inferior parietal lobule (IPL-bilateral) regions. The family functioning was assessed by levels of family cohesiveness and conflict and by maternal behavior styles such as maternal responsiveness, maternal stimulus to the child's autonomy, and maternal overprotection. The family environment was assessed with the Family Environment Scale (FES), and maternal behavior was assessed by the mother's self-report. Results: We found that the quality of the family environment was correlated with intra-DMN FC. The more conflicting the family environment was, the greater the FC between the mPFC-left IPL (lIPL), while a more cohesive family functioning was negatively correlated with FC between the PCC-lIPL. On the other hand, when moderated by a positive maternal behavior, cohesive family functioning was associated with increased FC in both regions of the DMN (mPFC-lIPL and PCC-lIPL). Conclusions: Our results highlight that the quality of the family environment might be associated with differences in the intrinsic DMN FC.