Multilayer network associations between the exposome and childhood brain development.

Growing up in a high poverty neighborhood is associated with elevated risk for academic challenges and health problems. Here, we take a data-driven approach to exploring how measures of children's environments relate to the development of their brain structure and function in a community sample of children between the ages of 4 and 10 years. We constructed exposomes including measures of family socioeconomic status, children's exposure to adversity, and geocoded measures of neighborhood socioeconomic status, crime, and environmental toxins. We connected the exposome to two structural measures (cortical thickness and surface area, n = 170) and two functional measures (participation coefficient and clustering coefficient, n = 130). We found dense connections within exposome and brain layers and sparse connections between exposome and brain layers. Lower family income was associated with thinner visual cortex, consistent with the theory that accelerated development is detectable in early-developing regions. Greater neighborhood incidence of high blood lead levels was associated with greater segregation of the default mode network, consistent with evidence that toxins are deposited into the brain along the midline. Our study demonstrates the utility of multilayer network analysis to bridge environmental and neural explanatory levels to better understand the complexity of child development.

Individual differences in frontoparietal plasticity in humans.

Neuroplasticity, defined as the brain's potential to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here, we investigated the plasticity of the frontoparietal system by asking whether VTA resting-state functional connectivity and myelin map values (T1w/T2w ratios) predicted learning after short-term training on the adaptive n-back (n = 46, ages 18-25). We found that stronger baseline connectivity between VTA and lateral prefrontal cortex predicted greater improvements in accuracy. Lower myelin map values predicted improvements in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.

The functional brain networks that underlie visual working memory in the first two years of life.

Visual working memory (VWM) is a central cognitive system used to compare views of the world and detect changes in the local environment. This system undergoes dramatic development in the first two years; however, we know relatively little about the functional organization of VWM at the level of the brain. Here, we used image-based functional near-infrared spectroscopy (fNIRS) to test four hypotheses about the spatial organization of the VWM network in early development. Four-month-olds, 1-year-olds, and 2-year-olds completed a VWM task while we recorded neural activity from 19 cortical regions-of-interest identified from a meta-analysis of the adult fMRI literature on VWM. Results showed significant task-specific functional activation near 6 of 19 ROIs, revealing spatial consistency in the brain regions activated in our study and brain regions identified to be part of the VWM network in adult fMRI studies. Working memory related activation was centered on bilateral anterior intraparietal sulcus (aIPS), left temporoparietal junction (TPJ), and left ventral occipital complex (VOC), while visual exploratory measures were associated with activation in right dorsolateral prefrontal cortex, left TPJ, and bilateral IPS. Results show that a distributed brain network underlies functional changes in VWM in infancy, revealing new insights into the neural mechanisms that support infants' improved ability to remember visual information and to detect changes in an on-going visual stream.

Early adversity in rural India impacts the brain networks underlying visual working memory.

There is a growing need to understand the global impact of poverty on early brain and behavioural development, particularly with regard to key cognitive processes that emerge in early development. Although the impact of adversity on brain development can trap children in an intergenerational cycle of poverty, the massive potential for brain plasticity is also a source of hope: reliable, accessible, culturally agnostic methods to assess early brain development in low resource settings might be used to measure the impact of early adversity, identify infants for timely intervention and guide the development and monitor the effectiveness of early interventions. Visual working memory (VWM) is an early marker of cognitive capacity that has been assessed reliably in early infancy and is predictive of later academic achievement in Western countries. Here, we localized the functional brain networks that underlie VWM in early development in rural India using a portable neuroimaging system, and we assessed the impact of adversity on these brain networks. We recorded functional brain activity as young children aged 4-48 months performed a VWM task. Brain imaging results revealed localized activation in the frontal cortex, replicating findings from a Midwestern US sample. Critically, children from families with low maternal education and income showed weaker brain activity and poorer distractor suppression in canonical working memory areas in the left frontal cortex. Implications of this work are far-reaching: it is now cost-effective to localize functional brain networks in early development in low-resource settings, paving the way for novel intervention and assessment methods.

Evaluating motion processing algorithms for use with functional near-infrared spectroscopy data from young children.

Motion artifacts are often a significant component of the measured signal in functional near-infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal components analysis (PCA), correlation-based signal improvement (CBSI), wavelet filtering, and spline interpolation. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Brigadoi et al. compared motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Given that fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. This study addresses that problem by evaluating motion correction algorithms implemented in HomER2. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response. Results showed that targeted PCA (tPCA), spline, and CBSI retained a higher number of trials. These techniques also performed well in direct head-to-head comparisons with the other approaches using quantitative metrics. The CBSI method corrected many of the artifacts present in our data; however, this approach produced sometimes unstable HRFs. The targeted PCA and spline methods proved to be the most robust, performing well across all comparison metrics. When compared head to head, tPCA consistently outperformed spline. We conclude, therefore, that tPCA is an effective technique for correcting motion artifacts in fNIRS data from young children.