Using MRI, researchers chart brain growth and development during early childhood

University of North Carolina-led researchers have used brain connectivity charts built from functional MRI data as a tool for tracking early childhood brain development.

Charts mapped the maturation of brain networks from birth to age six and identified key transitions in how regions of the brain interact. Deviations from these developmental patterns were significantly associated with differences in early cognitive ability, involving primary, default, control, and attention networks.

Early childhood marks a critical period in brain growth, during which neural networks undergo rapid, variable changes that shape cognitive development. While physical growth charts are well-established tools for monitoring parameters such as height and weight, comparable standards for assessing the development of brain function, with timing that differs across children, remain elusive.

Structural brain growth curves have revealed correlations between altered development and neuropsychiatric risk. Resting-state functional MRI has emerged as a method to capture functional brain activity without requiring task performance, but its use to chart normative functional development has not been systematically applied.

In the study, “Charting brain functional development from birth to 6 years of age,” published in Nature Human Behaviour, researchers designed a multi-phase neuroimaging analysis to discern the functional differences between sleep and awake states, establish functional developmental charts from birth to early childhood, and determine the potential associations between brain growth charts and cognition.

A total of 501 participants contributed 1,091 resting-state functional MRI scans collected across five pediatric imaging cohorts.

Researchers developed functional growth charts by harmonizing differences between sleep and awake imaging states using elastic net regression, and minimized site-based imaging variability using the ComBat method. Functional connectivity data were extracted using whole-brain parcellation, and cognitive outcomes were assessed through associations with Mullen Scales of Early Learning scores.

Functional connectivity patterns differed significantly between sleep and awake states, with higher overall connectivity observed during wakefulness. To account for these differences, researchers used machine-learning models to estimate how brain connectivity would appear during sleep based on awake scans. This process allowed them to combine and compare imaging data across all ages.

After aligning data from sleep and wake imaging states, researchers produced growth charts for eight canonical brain networks spanning birth to six years.

Visual network connectivity peaked near five months, declined during specialization, then leveled by 48 months. Somatomotor connectivity dropped from birth and settled by 18 months.

Limbic network strength rose to an apex around 10 months before stabilizing. Default network connectivity peaked near 16 months and then plateaued.

Ventral attention connectivity climbed rapidly until about 21 months and remained steady afterward. Dorsal attention connectivity began a gradual ascent around 18 months.

Control network strength increased steadily across the full six-year span. Subcortical connectivity stayed high and stable throughout the period.

Twelve pairs of functional networks also showed transitions between integration, competition, and dissociation over time. These patterns revealed how interactions between brain systems evolve during early development.

Deviations from the normative growth charts were significantly associated with cognitive performance. Functional connectivity predicted scores in expressive and receptive language, fine motor skills, and visual reception, with the strongest contributions to cognitive prediction coming from primary, control, attention, and default mode networks.

Results suggest that tracking deviations from normative brain function patterns may allow for early identification of atypical development, enabling timely intervention. Future studies may benefit from acquiring more high-quality awake-state imaging from infants to further validate and refine the charts.

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