EEG effective connectivity during the first year of life mirrors brain synaptogenesis, myelination, and early right hemisphere predominance

A Systematic Review over the Effect of Early Infant Diet on Neurodevelopment: Insights from Neuroimaging

The optimization of infant neuronal development through nutrition is an increasingly studied area. While human milk consumption during infancy is thought to give a slight cognitive advantage throughout early childhood in comparison to commercial formula, the biological underpinnings of this process are less well-known and debated in the literature. This systematic review seeks to quantitatively analyze whether early diet affects infant neurodevelopment as measured by various neuroimaging modalities and techniques. Results presented suggest that human milk does have a slight positive impact on the structural development of the infant brain-and that this impact is larger in preterm infants. Other diets with distinct macronutrient compositions were also considered, although these had more conflicting results.

The burden of epilepsy on long-term outcome of genetic developmental and epileptic encephalopathies: A single tertiary center longitudinal retrospective cohort study

BACKGROUND

This retrospective cohort analysis highlighted neurodevelopmental outcome predictors of genetic developmental and epileptic encephalopathies (DEE).

PATIENTS AND METHODS

Patients' demographic, clinical and molecular genetics data were collected. All patients underwent clinical, developmental, and neuropsychological assessments.

RESULTS

We recruited 100 participants (53 males, 47 females) with a mean follow-up lasting 10.46 ± 8.37 years. Age at epilepsy-onset was predictive of poor adaptive and cognitive functions (VABS-II score, r = 0.350, p = 0.001; BRIEF control subscale, r = -0.253; p = 0.031). Duration of epilepsy correlated negatively with IQ (r = -0.234, p = 0.019) and VABS-II score (r = -0.367, p = 0.001). Correlations were found between delayed/lacking EEG maturation/organization and IQ (r = 0.587, p = 0.001), VABS-II score (r = 0.658, p = 0.001), BRIEF-MI and BRIEF-GEC scores (r = -0.375, p = 0.001; r = -0.236, p = 0.033), ASEBA anxiety (r = -0.220, p = 0.047) and ADHD (r = -0.233, p = 0.035) scores. The number of antiseizure medications (ASMs) correlated with IQ (r = -0.414, p = 0.001), VABS-II (r = -0.496, p = 0.001), and BRIEF-MI (r = 0.294, p = 0.012) scores; while age at the beginning of therapy with ASEBA anxiety score (r = 0.272, p = 0.013). The occurrence of status epilepticus was associated with worse adaptive performances. The linear regression analysis model showed that delayed/lacking EEG maturation/organization had a significant influence on the IQ (R2 = 0.252, p < 0.001) and the BRIEF-GEC variability (R2 = 0.042, p = 0.036). The delayed/lacking EEG maturation/organization and the duration of epilepsy also had a significant influence on the VABS-II score (R2 = 0.455, p = 0.005).

CONCLUSIONS

Age at seizure-onset, EEG maturation/organization, duration of epilepsy, occurrence of status epilepticus, age at the introduction and number of ASMs used are reliable predictors of long-term outcomes in patients with genetic DEE.

The emergence of the EEG dominant rhythm across the first year of life

The spectral composition of EEG provides important information on the function of the developing brain. For example, the frequency of the dominant rhythm, a salient features of EEG data, increases from infancy to adulthood. Changes of the dominant rhythm during infancy are yet to be fully characterized, in terms of their developmental trajectory and spectral characteristics. In this study, the development of dominant rhythm frequency was examined during a novel sustained attention task across 6-month-old (n = 39), 9-month-old (n = 30), and 12-month-old (n = 28) infants. During this task, computer-generated objects and faces floated down a computer screen for 10 s after a 5-second fixation cross. The peak frequency in the range between 5 and 9 Hz was calculated using center of gravity (CoG) and examined in response to faces and objects. Results indicated that peak frequency increased from 6 to 9 to 12 months of age in face and object conditions. We replicated the same result for the baseline. There was high reliability between the CoGs in the face, object, and baseline conditions across all channels. The developmental increase in CoG was more reliable than measures of mode frequency across different conditions. These findings suggest that CoG is a robust index of brain development across infancy.

Development of EEG connectivity from preschool to school-age children

Introduction

Many studies have collected normative developmental EEG data to better understand brain function in early life and associated changes during both aging and pathology. Higher cognitive functions of the brain do not normally stem from the workings of a single brain region that works but, rather, on the interaction between different brain regions. In this regard studying the connectivity between brain regions is of great importance towards understanding higher cognitive functions and its underlying mechanisms.

Methods

In this study, EEG data of children (N = 253; 3-10 years old; 113 females, 140 males) from pre-school to schoolage was collected, and the weighted phase delay index and directed transfer function method was used to find the electrophysiological indicators of both functional connectivity and effective connectivity. A general linear model was built between the indicators and age, and the change trend of electrophysiological indicators analyzed for age.

Results

The results showed an age trend for the functional and effective connectivity of the brain of children.

Discussion

The results are of importance in understanding normative brain development and in defining those conditions that deviate from typical growth trajectories.

Longitudinal developmental trajectories of functional connectivity reveal regional distribution of distinct age effects in infancy

Prior work has shown that different functional brain networks exhibit different maturation rates, but little is known about whether and how different brain areas may differ in the exact shape of longitudinal functional connectivity growth trajectories during infancy. We used resting-state functional magnetic resonance imaging (fMRI) during natural sleep to characterize developmental trajectories of different regions using a longitudinal cohort of infants at 3 weeks (neonate), 1 year, and 2 years of age (n = 90; all with usable data at three time points). A novel whole brain heatmap analysis was performed with four mixed-effect models to determine the best fit of age-related changes for each functional connection: (i) growth effects: positive-linear-age, (ii) emergent effects: positive-log-age, (iii) pruning effects: negative-quadratic-age, and (iv) transient effects: positive-quadratic-age. Our results revealed that emergent (logarithmic) effects dominated developmental trajectory patterns, but significant pruning and transient effects were also observed, particularly in connections centered on inferior frontal and anterior cingulate areas that support social learning and conflict monitoring. Overall, unique global distribution patterns were observed for each growth model indicating that developmental trajectories for different connections are heterogeneous. All models showed significant effects concentrated in association areas, highlighting the dominance of higher-order social/cognitive development during the first 2 years of life.

Metformin in Gestational Diabetes Mellitus: To Use or Not to Use, That Is the Question

In recent years, there has been a dramatic increase in the number of pregnancies complicated by gestational diabetes mellitus (GDM). GDM occurs when maternal insulin resistance develops and/or progresses during gestation, and it is not compensated by a rise in maternal insulin secretion. If not properly managed, this condition can cause serious short-term and long-term problems for both mother and child. Lifestyle changes are the first line of treatment for GDM, but if ineffective, insulin injections are the recommended pharmacological treatment choice. Some guidance authorities and scientific societies have proposed the use of metformin as an alternative pharmacological option for treating GDM, but there is not yet a unanimous consensus on this. Although the use of metformin appears to be safe for the mother, concerns remain about its long-term metabolic effects on the child that is exposed in utero to the drug, given that metformin, contrary to insulin, crosses the placenta. This review article describes the existing lines of evidence about the use of metformin in pregnancies complicated by GDM, in order to clarify its potential benefits and limits, and to help clinicians make decisions about who could benefit most from this drug treatment.

Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants

Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.

The sleep and wake electroencephalogram over the lifespan

Both sleep and wake encephalograms (EEG) change over the lifespan. While prior studies have characterized age-related changes in the EEG, the datasets span a particular age group, or focused on sleep and wake macrostructure rather than the microstructure. Here, we present sex-stratified data from 3372 community-based or clinic-based otherwise neurologically and psychiatrically healthy participants ranging from 11 days to 80 years of age. We estimate age norms for key sleep and wake EEG parameters including absolute and relative powers in delta, theta, alpha, and sigma bands, as well as sleep spindle density, amplitude, duration, and frequency. To illustrate the potential use of the reference measures developed herein, we compare them to sleep EEG recordings from age-matched participants with Alzheimer's disease, severe sleep apnea, depression, osteoarthritis, and osteoporosis. Although the partially clinical nature of the datasets may bias the findings towards less normal and hence may underestimate pathology in practice, age-based EEG reference values enable objective screening of deviations from healthy aging among individuals with a variety of disorders that affect brain health.

Right brain-to-right brain psychotherapy: recent scientific and clinical advances

This article overviews my recent acceptance of a Lifetime Achievement Award from Sapienza University of Rome, in which I discussed three decades of my work on the right brain in development, psychopathogenesis, and psychotherapy. In the following, I offer current brain laterality and hemispheric asymmetry research indicating that right brain emotional and relational processes operate beneath conscious awareness not only in early human development, but over the lifespan. I discuss recent interdisciplinary studies on the central role of ultrarapid right brain-to-right brain intersubjective communications of face, voice, and gesture and the implicit regulation of emotion in nonverbal attachment dynamics. Special emphasis is on the fundamental psychobiological process of interpersonal synchrony, and on the evolutionary mechanism of attachment, the interactive regulation of biological synchrony within and between organisms. I then present some clinical applications, suggesting that effective therapeutic work with "primitive" nonverbal emotional attachment dynamics focuses not on conscious verbal insight but on the formation of an unconscious emotion-communicating and regulating bond within the therapeutic relationship. Lastly, I review recent hyperscanning research of the patient's and therapist's brains during a face-to-face, emotionally focused psychotherapy session that supports the right brain-to-right brain communication model. I end suggesting that the right brain is dominant in both short-term symptom-reducing and long-term growth-promoting deep psychotherapy.